Project_PDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yttPyRnErRy2"
   },
   "source": [
    "# Project PDA - Drug Consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6KR0PQSGadoD"
   },
   "source": [
    "Hello!\n",
    "\n",
    "Here is the notebook for the project or Python for Data Analysis.\n",
    "For this project we have to analyse, visualize and create a model aroud a given dataset.\n",
    "The dataset we will be working with is \"Drug Consumption\" from 2016 with 1885 respondants and 12 attributes.\n",
    "\n",
    "You can find our dataset here: https://archive.ics.uci.edu/dataset/373/drug+consumption+quantified"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i9gIkRZFrYZE"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MU1zRe81b6dS"
   },
   "source": [
    "We import the libraries we will need for the rest of this notebook :\n",
    "\n",
    "* Pandas to help us manipulate datasets\n",
    "* Matplolib to help us creating plots to visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "jYsSXGaFrJDS"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a_TbfGBl6SQv"
   },
   "source": [
    "We start by importing all the scikit-learn library and all the models we will need for creating ou own machine learning algorithm models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "sKBEAQ0d6RLI"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder, StandardScaler, Binarizer\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xTZLr0-ksM1m"
   },
   "source": [
    "## Load the Dataset and rearranging the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T7HuLcuqcCBS"
   },
   "source": [
    "For this part we are going to pre-process the dataset by encoding, normalizing, and impute all of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P_vdITHEsaqf"
   },
   "source": [
    "### Load the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1e9sXLjXcFO1"
   },
   "source": [
    "The first step is to read the dataset into a dataframe thanks to Pandas library. We are reaching it with a direct link to Racel's Github account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
    },
    "id": "I0B1oAp3sKto",
    "outputId": "a8a891b9-640b-4bf7-c805-4929822b788b"
   },
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>CL0</td>\n",
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       "      <td>-0.31685</td>\n",
       "      <td>-0.67825</td>\n",
       "      <td>1.93886</td>\n",
       "      <td>1.43533</td>\n",
       "      <td>0.76096</td>\n",
       "      <td>...</td>\n",
       "      <td>CL4</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL3</td>\n",
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       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>0.49788</td>\n",
       "      <td>-0.48246</td>\n",
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       "      <td>0.96082</td>\n",
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       "      <td>-0.46725</td>\n",
       "      <td>0.80523</td>\n",
       "      <td>-0.84732</td>\n",
       "      <td>-1.62090</td>\n",
       "      <td>...</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL1</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.95197</td>\n",
       "      <td>0.48246</td>\n",
       "      <td>1.16365</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>-0.14882</td>\n",
       "      <td>-0.80615</td>\n",
       "      <td>-0.01928</td>\n",
       "      <td>0.59042</td>\n",
       "      <td>...</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
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       "      <td>CL2</td>\n",
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       "      <td>1.98437</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>0.73545</td>\n",
       "      <td>-1.63340</td>\n",
       "      <td>-0.45174</td>\n",
       "      <td>-0.30172</td>\n",
       "      <td>...</td>\n",
       "      <td>CL1</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL1</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
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       "<p>5 rows × 32 columns</p>\n",
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      "text/plain": [
       "   0        1        2        3        4        5        6        7        8   \\\n",
       "0   1  0.49788  0.48246 -0.05921  0.96082  0.12600  0.31287 -0.57545 -0.58331   \n",
       "1   2 -0.07854 -0.48246  1.98437  0.96082 -0.31685 -0.67825  1.93886  1.43533   \n",
       "2   3  0.49788 -0.48246 -0.05921  0.96082 -0.31685 -0.46725  0.80523 -0.84732   \n",
       "3   4 -0.95197  0.48246  1.16365  0.96082 -0.31685 -0.14882 -0.80615 -0.01928   \n",
       "4   5  0.49788  0.48246  1.98437  0.96082 -0.31685  0.73545 -1.63340 -0.45174   \n",
       "\n",
       "        9   ...   22   23   24   25   26   27   28   29   30   31  \n",
       "0 -0.91699  ...  CL0  CL0  CL0  CL0  CL0  CL0  CL0  CL2  CL0  CL0  \n",
       "1  0.76096  ...  CL4  CL0  CL2  CL0  CL2  CL3  CL0  CL4  CL0  CL0  \n",
       "2 -1.62090  ...  CL0  CL0  CL0  CL0  CL0  CL0  CL1  CL0  CL0  CL0  \n",
       "3  0.59042  ...  CL0  CL0  CL2  CL0  CL0  CL0  CL0  CL2  CL0  CL0  \n",
       "4 -0.30172  ...  CL1  CL0  CL0  CL1  CL0  CL0  CL2  CL2  CL0  CL0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"drug_consumption.csv\", header=None)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mXCVHPGtcIxC"
   },
   "source": [
    "We can have a first look at the loaded dataframe. We can observe the lack of columns names. We add them manually and set an index name 'ID'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "r0HFrCxOs5UT",
    "outputId": "22e90276-3aaa-4f37-fc12-9c983837ba61"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>Country</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Nscore</th>\n",
       "      <th>Escore</th>\n",
       "      <th>Oscore</th>\n",
       "      <th>Ascore</th>\n",
       "      <th>Cscore</th>\n",
       "      <th>...</th>\n",
       "      <th>Ecstasy</th>\n",
       "      <th>Heroin</th>\n",
       "      <th>Ketamine</th>\n",
       "      <th>Legalh</th>\n",
       "      <th>LSD</th>\n",
       "      <th>Meth</th>\n",
       "      <th>Mushrooms</th>\n",
       "      <th>Nicotine</th>\n",
       "      <th>Semer</th>\n",
       "      <th>VSA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
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       "      <td>0.49788</td>\n",
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       "      <td>-0.00665</td>\n",
       "      <td>...</td>\n",
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       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
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       "    <tr>\n",
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       "      <td>-0.14277</td>\n",
       "      <td>...</td>\n",
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       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL4</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.49788</td>\n",
       "      <td>-0.48246</td>\n",
       "      <td>-0.05921</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>-0.46725</td>\n",
       "      <td>0.80523</td>\n",
       "      <td>-0.84732</td>\n",
       "      <td>-1.62090</td>\n",
       "      <td>-1.01450</td>\n",
       "      <td>...</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL1</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.95197</td>\n",
       "      <td>0.48246</td>\n",
       "      <td>1.16365</td>\n",
       "      <td>0.96082</td>\n",
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       "      <td>-0.14882</td>\n",
       "      <td>-0.80615</td>\n",
       "      <td>-0.01928</td>\n",
       "      <td>0.59042</td>\n",
       "      <td>0.58489</td>\n",
       "      <td>...</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.49788</td>\n",
       "      <td>0.48246</td>\n",
       "      <td>1.98437</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>0.73545</td>\n",
       "      <td>-1.63340</td>\n",
       "      <td>-0.45174</td>\n",
       "      <td>-0.30172</td>\n",
       "      <td>1.30612</td>\n",
       "      <td>...</td>\n",
       "      <td>CL1</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL1</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "        Age   Gender  Education  Country  Ethnicity   Nscore   Escore  \\\n",
       "ID                                                                      \n",
       "1   0.49788  0.48246   -0.05921  0.96082    0.12600  0.31287 -0.57545   \n",
       "2  -0.07854 -0.48246    1.98437  0.96082   -0.31685 -0.67825  1.93886   \n",
       "3   0.49788 -0.48246   -0.05921  0.96082   -0.31685 -0.46725  0.80523   \n",
       "4  -0.95197  0.48246    1.16365  0.96082   -0.31685 -0.14882 -0.80615   \n",
       "5   0.49788  0.48246    1.98437  0.96082   -0.31685  0.73545 -1.63340   \n",
       "\n",
       "     Oscore   Ascore   Cscore  ...  Ecstasy  Heroin Ketamine Legalh  LSD Meth  \\\n",
       "ID                             ...                                              \n",
       "1  -0.58331 -0.91699 -0.00665  ...      CL0     CL0      CL0    CL0  CL0  CL0   \n",
       "2   1.43533  0.76096 -0.14277  ...      CL4     CL0      CL2    CL0  CL2  CL3   \n",
       "3  -0.84732 -1.62090 -1.01450  ...      CL0     CL0      CL0    CL0  CL0  CL0   \n",
       "4  -0.01928  0.59042  0.58489  ...      CL0     CL0      CL2    CL0  CL0  CL0   \n",
       "5  -0.45174 -0.30172  1.30612  ...      CL1     CL0      CL0    CL1  CL0  CL0   \n",
       "\n",
       "   Mushrooms Nicotine Semer  VSA  \n",
       "ID                                \n",
       "1        CL0      CL2   CL0  CL0  \n",
       "2        CL0      CL4   CL0  CL0  \n",
       "3        CL1      CL0   CL0  CL0  \n",
       "4        CL0      CL2   CL0  CL0  \n",
       "5        CL2      CL2   CL0  CL0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provide the accurate column names in a list\n",
    "columns = [\n",
    "    \"ID\",\n",
    "    \"Age\",\n",
    "    \"Gender\",\n",
    "    \"Education\",\n",
    "    \"Country\",\n",
    "    \"Ethnicity\",\n",
    "    \"Nscore\",\n",
    "    \"Escore\",\n",
    "    \"Oscore\",\n",
    "    \"Ascore\",\n",
    "    \"Cscore\",\n",
    "    \"Impulsive\",\n",
    "    \"SS\",\n",
    "    \"Alcohol\",\n",
    "    \"Amphet\",\n",
    "    \"Amyl\",\n",
    "    \"Benzos\",\n",
    "    \"Caff\",\n",
    "    \"Cannabis\",\n",
    "    \"Choc\",\n",
    "    \"Coke\",\n",
    "    \"Crack\",\n",
    "    \"Ecstasy\",\n",
    "    \"Heroin\",\n",
    "    \"Ketamine\",\n",
    "    \"Legalh\",\n",
    "    \"LSD\",\n",
    "    \"Meth\",\n",
    "    \"Mushrooms\",\n",
    "    \"Nicotine\",\n",
    "    \"Semer\",\n",
    "    \"VSA\"\n",
    "]\n",
    "\n",
    "# Assign them to the df\n",
    "df.columns = columns\n",
    "\n",
    "# Set the index to the ID\n",
    "df.set_index('ID', inplace=True)\n",
    "\n",
    "# Display the df with the new columns and index\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QsSb6x1w4cVp"
   },
   "source": [
    "### Remove Overclaimers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KbotuqDu4leR"
   },
   "source": [
    "Semer (Semeron) is interesting because it's a fake drug used only to check whether people are lying on the poll. So we can determine which people are lying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MR7VCISv4gGW",
    "outputId": "23f32aff-3615-4d9a-a538-4f86182d22d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    1885\n",
       "Name: Semer, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the Overclaimers\n",
    "(df['Semer'] != 'Never Used').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "7ByrxfMOOkFR"
   },
   "outputs": [],
   "source": [
    "df = df[df['Semer'] != 'Never Used']\n",
    "df.drop('Semer', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zMZorYq840Fv"
   },
   "source": [
    "No overclaimers so no need to treat this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zQ6Y8HgmL8pB"
   },
   "source": [
    "### Adding Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fIhqV-6ukG0n"
   },
   "source": [
    "Let's add here some colums and parameters to help us analyze and visualize the data better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zSyu4Yl740Vg",
    "outputId": "67e29f39-e0ff-40fd-e4e9-a7cb74c30d79"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alcohol', 'Amphet', 'Amyl', 'Benzos', 'Caff', 'Cannabis', 'Choc',\n",
       "       'Coke', 'Crack', 'Ecstasy', 'Heroin', 'Ketamine', 'Legalh', 'LSD',\n",
       "       'Meth', 'Mushrooms', 'Nicotine', 'VSA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of columns with all the drugs in it\n",
    "drug_names = df.columns[list(df.columns).index('Alcohol'):]\n",
    "drug_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "yMaY54ZzDXDU"
   },
   "outputs": [],
   "source": [
    "def has_used_at_least_once(element):\n",
    "  '''\n",
    "  Determines if a person has used a drug at least once.\n",
    "  Returns:\n",
    "  - bool: True if the person has used the drug at least once, False otherwise.\n",
    "  '''\n",
    "  possible_values = ['Never Used', 'CL0']\n",
    "  return (element not in possible_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "id": "8IX2AMm37Fmf",
    "outputId": "6a9bc6bd-cf32-40f3-bf03-789a18247f2a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Amphet</th>\n",
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       "      <th>1886</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1885 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Alcohol  Amphet   Amyl  Benzos  Caff  Cannabis  Choc   Coke  Crack  \\\n",
       "ID                                                                         \n",
       "1        True    True  False    True  True     False  True  False  False   \n",
       "2        True    True   True   False  True      True  True   True  False   \n",
       "3        True   False  False   False  True      True  True  False  False   \n",
       "4        True   False  False    True  True      True  True   True  False   \n",
       "5        True    True   True   False  True      True  True  False  False   \n",
       "...       ...     ...    ...     ...   ...       ...   ...    ...    ...   \n",
       "1884     True   False  False   False  True      True  True  False  False   \n",
       "1885     True   False  False   False  True      True  True  False  False   \n",
       "1886     True    True   True    True  True      True  True   True  False   \n",
       "1887     True   False  False   False  True      True  True  False  False   \n",
       "1888     True    True  False    True  True      True  True   True  False   \n",
       "\n",
       "      Ecstasy  Heroin  Ketamine  Legalh    LSD   Meth  Mushrooms  Nicotine  \\\n",
       "ID                                                                           \n",
       "1       False   False     False   False  False  False      False      True   \n",
       "2        True   False      True   False   True   True      False      True   \n",
       "3       False   False     False   False  False  False       True     False   \n",
       "4       False   False      True   False  False  False      False      True   \n",
       "5        True   False     False    True  False  False       True      True   \n",
       "...       ...     ...       ...     ...    ...    ...        ...       ...   \n",
       "1884    False   False     False    True   True  False      False     False   \n",
       "1885     True   False     False    True   True   True       True      True   \n",
       "1886     True   False      True   False   True  False       True      True   \n",
       "1887     True   False     False    True   True  False       True      True   \n",
       "1888     True   False     False    True   True  False       True      True   \n",
       "\n",
       "        VSA  \n",
       "ID           \n",
       "1     False  \n",
       "2     False  \n",
       "3     False  \n",
       "4     False  \n",
       "5     False  \n",
       "...     ...  \n",
       "1884   True  \n",
       "1885  False  \n",
       "1886  False  \n",
       "1887  False  \n",
       "1888   True  \n",
       "\n",
       "[1885 rows x 18 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[drug_names].applymap(has_used_at_least_once)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tcBq0eQg9WFu"
   },
   "source": [
    "#### Adding Drugs Tested & Tested More Than Once column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fHFenvzx8ddE",
    "outputId": "696412dc-f230-47a0-bd44-3cd306b62cd2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1        6\n",
       "2       12\n",
       "3        5\n",
       "4        8\n",
       "5       10\n",
       "        ..\n",
       "1884     7\n",
       "1885    10\n",
       "1886    13\n",
       "1887     9\n",
       "1888    13\n",
       "Name: Drugs Tested, Length: 1885, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column with the number of drugs tested for each person\n",
    "df['Drugs Tested'] = df[drug_names].applymap(has_used_at_least_once).sum(axis=1)\n",
    "df['Drugs Tested']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e9c0BB6OudT9",
    "outputId": "3364daa0-773e-4b4d-cfde-ae764bf16849"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean number of drugs used in the dataset is: 8.97\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean number of drugs used\n",
    "mean_num_drugs_used = df['Drugs Tested'].mean()\n",
    "\n",
    "print(f\"The mean number of drugs used in the dataset is: {mean_num_drugs_used:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9Gb01tB1-ijH",
    "outputId": "38d3e4a1-fdb7-4fb1-fa53-cab5bdb0f3f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1884\n",
       "0       1\n",
       "Name: TMTO, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column for the \"Tested More Than Once\" people\n",
    "df['TMTO'] = (df['Drugs Tested'] >= 1).astype(int)\n",
    "df['TMTO'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uhfQ2p9QQrZt"
   },
   "source": [
    "So about everyone in this dataset has tested at least one drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 179
    },
    "id": "9zLLB-fuQw3s",
    "outputId": "113abc16-7c5d-44d5-929e-e7432711233c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>Country</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Nscore</th>\n",
       "      <th>Escore</th>\n",
       "      <th>Oscore</th>\n",
       "      <th>Ascore</th>\n",
       "      <th>Cscore</th>\n",
       "      <th>...</th>\n",
       "      <th>Heroin</th>\n",
       "      <th>Ketamine</th>\n",
       "      <th>Legalh</th>\n",
       "      <th>LSD</th>\n",
       "      <th>Meth</th>\n",
       "      <th>Mushrooms</th>\n",
       "      <th>Nicotine</th>\n",
       "      <th>VSA</th>\n",
       "      <th>Drugs Tested</th>\n",
       "      <th>TMTO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>-0.95197</td>\n",
       "      <td>-0.48246</td>\n",
       "      <td>0.45468</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>-0.50212</td>\n",
       "      <td>0.82562</td>\n",
       "      <td>0.63779</td>\n",
       "      <td>-0.84732</td>\n",
       "      <td>-2.21844</td>\n",
       "      <td>0.93949</td>\n",
       "      <td>...</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age   Gender  Education  Country  Ethnicity   Nscore   Escore  \\\n",
       "ID                                                                        \n",
       "1281 -0.95197 -0.48246    0.45468  0.96082   -0.50212  0.82562  0.63779   \n",
       "\n",
       "       Oscore   Ascore   Cscore  ...  Heroin  Ketamine Legalh  LSD Meth  \\\n",
       "ID                               ...                                      \n",
       "1281 -0.84732 -2.21844  0.93949  ...     CL0       CL0    CL0  CL0  CL0   \n",
       "\n",
       "     Mushrooms Nicotine  VSA Drugs Tested TMTO  \n",
       "ID                                              \n",
       "1281       CL0      CL0  CL0            0    0  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the person who hasn't used drugs\n",
    "df[df['TMTO'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5PNqHzD5TLjz"
   },
   "source": [
    "This means that this dataset doesn't contain data about people who never used drugs. So we cannot predict if someone will have a tendency to use drug according to his/her personnality score, only the amount of drugs and the frequency of people already using drugs. So we might as well delete this entry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "247RUgU6xwpI"
   },
   "source": [
    "#### Adding legal and illegal drugs columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LYu6hY5bVt-r"
   },
   "source": [
    "We can also add columns about the number of legal and illegal drugs each of these persons took"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "4dWgDKmUXERp"
   },
   "outputs": [],
   "source": [
    "legal = ['Alcohol','Choc','Caff', 'Nicotine']\n",
    "illegal = ['Amphet', 'Amyl', 'Benzos', 'Cannabis', 'Coke', 'Crack', 'Ecstasy', 'Heroin', 'Ketamine', 'Legalh', 'LSD', 'Meth', 'Mushrooms']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "RiADttosXAQF"
   },
   "outputs": [],
   "source": [
    "df['Legal Drugs'] = df[legal].applymap(has_used_at_least_once).sum(axis=1)\n",
    "df['Illegal Drugs'] = df[illegal].applymap(has_used_at_least_once).sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nD1tGwW05aWX"
   },
   "source": [
    "### Changing the data for drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "id": "irH-m5mN5jDm",
    "outputId": "bbe66b52-a8b7-49c1-a179-bc0a4423a317"
   },
   "outputs": [
    {
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       "      <th>Alcohol</th>\n",
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       "      <td>CL0</td>\n",
       "      <td>CL6</td>\n",
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       "      <td>CL4</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL1</td>\n",
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       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL5</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL4</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
       "      <td>CL5</td>\n",
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       "      <td>CL0</td>\n",
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       "      <td>CL5</td>\n",
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       "      <td>CL4</td>\n",
       "      <td>CL5</td>\n",
       "      <td>CL0</td>\n",
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       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>CL4</td>\n",
       "      <td>CL6</td>\n",
       "      <td>CL5</td>\n",
       "      <td>CL5</td>\n",
       "      <td>CL6</td>\n",
       "      <td>CL6</td>\n",
       "      <td>CL6</td>\n",
       "      <td>CL4</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL4</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL2</td>\n",
       "      <td>CL6</td>\n",
       "      <td>CL0</td>\n",
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       "      <th>1887</th>\n",
       "      <td>CL5</td>\n",
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       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
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       "      <td>CL0</td>\n",
       "      <td>CL3</td>\n",
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       "      <td>CL0</td>\n",
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       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>CL4</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL6</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL6</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL0</td>\n",
       "      <td>CL3</td>\n",
       "      <td>CL6</td>\n",
       "      <td>CL2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1885 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Alcohol Amphet Amyl Benzos Caff Cannabis Choc Coke Crack Ecstasy Heroin  \\\n",
       "ID                                                                             \n",
       "1        CL5    CL2  CL0    CL2  CL6      CL0  CL5  CL0   CL0     CL0    CL0   \n",
       "2        CL5    CL2  CL2    CL0  CL6      CL4  CL6  CL3   CL0     CL4    CL0   \n",
       "3        CL6    CL0  CL0    CL0  CL6      CL3  CL4  CL0   CL0     CL0    CL0   \n",
       "4        CL4    CL0  CL0    CL3  CL5      CL2  CL4  CL2   CL0     CL0    CL0   \n",
       "5        CL4    CL1  CL1    CL0  CL6      CL3  CL6  CL0   CL0     CL1    CL0   \n",
       "...      ...    ...  ...    ...  ...      ...  ...  ...   ...     ...    ...   \n",
       "1884     CL5    CL0  CL0    CL0  CL4      CL5  CL4  CL0   CL0     CL0    CL0   \n",
       "1885     CL5    CL0  CL0    CL0  CL5      CL3  CL4  CL0   CL0     CL2    CL0   \n",
       "1886     CL4    CL6  CL5    CL5  CL6      CL6  CL6  CL4   CL0     CL4    CL0   \n",
       "1887     CL5    CL0  CL0    CL0  CL6      CL6  CL5  CL0   CL0     CL3    CL0   \n",
       "1888     CL4    CL3  CL0    CL3  CL6      CL3  CL6  CL3   CL0     CL3    CL0   \n",
       "\n",
       "     Ketamine Legalh  LSD Meth Mushrooms Nicotine  VSA  \n",
       "ID                                                      \n",
       "1         CL0    CL0  CL0  CL0       CL0      CL2  CL0  \n",
       "2         CL2    CL0  CL2  CL3       CL0      CL4  CL0  \n",
       "3         CL0    CL0  CL0  CL0       CL1      CL0  CL0  \n",
       "4         CL2    CL0  CL0  CL0       CL0      CL2  CL0  \n",
       "5         CL0    CL1  CL0  CL0       CL2      CL2  CL0  \n",
       "...       ...    ...  ...  ...       ...      ...  ...  \n",
       "1884      CL0    CL3  CL3  CL0       CL0      CL0  CL5  \n",
       "1885      CL0    CL3  CL5  CL4       CL4      CL5  CL0  \n",
       "1886      CL2    CL0  CL2  CL0       CL2      CL6  CL0  \n",
       "1887      CL0    CL3  CL3  CL0       CL3      CL4  CL0  \n",
       "1888      CL0    CL3  CL3  CL0       CL3      CL6  CL2  \n",
       "\n",
       "[1885 rows x 18 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[drug_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G_yJ6pYt5yo4"
   },
   "source": [
    "Theses values aren't real values. Let's change them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Sv9JvLT36BOw",
    "outputId": "eda32778-0d8f-4a72-cd59-f646578aaa18"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'],\n",
       " ['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6']]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugs_categories = ['CL'+str(i) for i in range(0,7)]\n",
    "drugs_categories = [ drugs_categories ] * len(drug_names)\n",
    "drugs_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 95
    },
    "id": "z7QFySFm6Kce",
    "outputId": "3f8842d1-40c6-4136-c387-b96a340d1471"
   },
   "outputs": [
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      ],
      "text/plain": [
       "OrdinalEncoder()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord_enc = OrdinalEncoder()\n",
    "ord_enc.fit(df[drug_names])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "PBA1hvXX6hlS"
   },
   "outputs": [],
   "source": [
    "df[drug_names] = ord_enc.transform(df[drug_names])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
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       "</table>\n",
       "<p>1885 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Alcohol  Amphet  Amyl  Benzos  Caff  Cannabis  Choc  Coke  Crack  \\\n",
       "ID                                                                       \n",
       "1         5.0     2.0   0.0     2.0   6.0       0.0   5.0   0.0    0.0   \n",
       "2         5.0     2.0   2.0     0.0   6.0       4.0   6.0   3.0    0.0   \n",
       "3         6.0     0.0   0.0     0.0   6.0       3.0   4.0   0.0    0.0   \n",
       "4         4.0     0.0   0.0     3.0   5.0       2.0   4.0   2.0    0.0   \n",
       "5         4.0     1.0   1.0     0.0   6.0       3.0   6.0   0.0    0.0   \n",
       "...       ...     ...   ...     ...   ...       ...   ...   ...    ...   \n",
       "1884      5.0     0.0   0.0     0.0   4.0       5.0   4.0   0.0    0.0   \n",
       "1885      5.0     0.0   0.0     0.0   5.0       3.0   4.0   0.0    0.0   \n",
       "1886      4.0     6.0   5.0     5.0   6.0       6.0   6.0   4.0    0.0   \n",
       "1887      5.0     0.0   0.0     0.0   6.0       6.0   5.0   0.0    0.0   \n",
       "1888      4.0     3.0   0.0     3.0   6.0       3.0   6.0   3.0    0.0   \n",
       "\n",
       "      Ecstasy  Heroin  Ketamine  Legalh  LSD  Meth  Mushrooms  Nicotine  VSA  \n",
       "ID                                                                            \n",
       "1         0.0     0.0       0.0     0.0  0.0   0.0        0.0       2.0  0.0  \n",
       "2         4.0     0.0       2.0     0.0  2.0   3.0        0.0       4.0  0.0  \n",
       "3         0.0     0.0       0.0     0.0  0.0   0.0        1.0       0.0  0.0  \n",
       "4         0.0     0.0       2.0     0.0  0.0   0.0        0.0       2.0  0.0  \n",
       "5         1.0     0.0       0.0     1.0  0.0   0.0        2.0       2.0  0.0  \n",
       "...       ...     ...       ...     ...  ...   ...        ...       ...  ...  \n",
       "1884      0.0     0.0       0.0     3.0  3.0   0.0        0.0       0.0  5.0  \n",
       "1885      2.0     0.0       0.0     3.0  5.0   4.0        4.0       5.0  0.0  \n",
       "1886      4.0     0.0       2.0     0.0  2.0   0.0        2.0       6.0  0.0  \n",
       "1887      3.0     0.0       0.0     3.0  3.0   0.0        3.0       4.0  0.0  \n",
       "1888      3.0     0.0       0.0     3.0  3.0   0.0        3.0       6.0  2.0  \n",
       "\n",
       "[1885 rows x 18 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[drug_names]"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "That's way better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YZDzvfLBAbxy",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Data visualisation and analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dFp1z1UVBmYH"
   },
   "source": [
    "### Setup for data visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "BU35yw4OQeoR"
   },
   "outputs": [],
   "source": [
    "# Creating a copy of the df to make data visualisation\n",
    "df_vis = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FffAsBGSQ7-G",
    "outputId": "5e79e342-0d0e-4235-b426-b4bfaacc48a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1884\n",
       "0       1\n",
       "Name: TMTO, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vis['TMTO'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uGrfszyaUGn7"
   },
   "source": [
    "We know that every person in this dataset has used at least a drug so that column is useless."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "65ROG4i_UDBI",
    "outputId": "5585230a-8d11-42bf-d9b0-38f6738b61aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Education', 'Country', 'Ethnicity', 'Nscore',\n",
       "       'Escore', 'Oscore', 'Ascore', 'Cscore', 'Impulsive', 'SS', 'Alcohol',\n",
       "       'Amphet', 'Amyl', 'Benzos', 'Caff', 'Cannabis', 'Choc', 'Coke', 'Crack',\n",
       "       'Ecstasy', 'Heroin', 'Ketamine', 'Legalh', 'LSD', 'Meth', 'Mushrooms',\n",
       "       'Nicotine', 'VSA', 'Drugs Tested', 'Legal Drugs', 'Illegal Drugs'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vis.drop('TMTO', inplace = True, axis = 1)\n",
    "df_vis.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vhdDRyQbW8FH"
   },
   "source": [
    "We could split the data into \"soft\" and \"hard\" or \"legal\" and \"illegal\" psychoactive drugs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "REOc3VVs03LQ"
   },
   "source": [
    "### Remaping the values in the original df  \n",
    "The dataframe contains numbers these numbers represent categories. For example 0.49788 in Age means that person is between 18-24 years old. So we remap them for in the original df to strings.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YKXwEnA1zlJl",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nG_yh6ZWc12S"
   },
   "source": [
    "We first change the age category for it to be clearer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-GwdprTvzPNu",
    "outputId": "4ef519f3-c442-4ed9-f92f-e7934cc1883e"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Age values\n",
    "age_mapping = {\n",
    "    -0.95197: '18-24',\n",
    "    -0.07854: '25-34',\n",
    "    0.49788: '35-44',\n",
    "    1.09449: '45-54',\n",
    "    1.82213: '55-64',\n",
    "    2.59171: '65+'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Age\" column with their meanings\n",
    "df_vis['Age'] = df_vis['Age'].replace(age_mapping)\n",
    "\n",
    "# Display the Age column\n",
    "df_vis['Age'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "id": "gQYnlekQzSFk",
    "outputId": "60ef414c-7a88-432b-f835-63dff0433e36"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique age category\n",
    "age_counts = df_vis['Age'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "age_fractions = (age_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "age_info_df = pd.DataFrame({'Age Category': age_counts.index, 'Cases': age_counts.values, 'Fraction (%)': age_fractions.values})\n",
    "\n",
    "# Display the Dataframe\n",
    "age_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IIeI5q2idAlb"
   },
   "source": [
    "We can already observe that the biggest category represented is the 18-24 years old category\n",
    "\n",
    "The Cases and the Fractions are accurate, let's continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xWvIpFdIz-uQ",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JTMsYw-Oc6O-"
   },
   "source": [
    "We do the same for the Gender attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4JlhDB7jzzlx",
    "outputId": "38468e8a-85a4-4c1f-fd24-b38330814d2c"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Gender values\n",
    "gender_mapping = {\n",
    "    0.48246: 'Female',\n",
    "    -0.48246: 'Male'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Gender\" column with their meanings\n",
    "df_vis['Gender'] = df_vis['Gender'].replace(gender_mapping)\n",
    "\n",
    "# Display the Gender Column\n",
    "df_vis['Gender'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 149
    },
    "id": "jR8N-evH1fu5",
    "outputId": "73f7513e-9a70-4fa2-9d62-d93d96507427"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique gender category\n",
    "gender_counts = df_vis['Gender'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "gender_fractions = (gender_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "gender_info_df = pd.DataFrame({'Gender': gender_counts.index, 'Cases': gender_counts.values, 'Fraction (%)': gender_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "gender_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bRYDWzsd1mqP",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r8bySMln1q-Y",
    "outputId": "b95a90f0-97ef-4af8-b36c-5ead60494632"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Education values\n",
    "education_mapping = {\n",
    "    -2.43591: 'Left school before 16 years',\n",
    "    -1.73790: 'Left school at 16 years',\n",
    "    -1.43719: 'Left school at 17 years',\n",
    "    -1.22751: 'Left school at 18 years',\n",
    "    -0.61113: 'Some college or university, no certificate or degree',\n",
    "    -0.05921: 'Professional certificate/diploma',\n",
    "    0.45468: 'University degree',\n",
    "    1.16365: 'Masters degree',\n",
    "    1.98437: 'Doctorate degree'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Education\" column with their meanings\n",
    "df_vis['Education'] = df_vis['Education'].replace(education_mapping)\n",
    "\n",
    "# Display the Education column\n",
    "df_vis['Education'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "id": "mc6Ng7JT12KQ",
    "outputId": "09d74a09-1dc0-470b-9df1-99c925d35520"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique education category\n",
    "education_counts = df_vis['Education'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "education_fractions = (education_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "education_info_df = pd.DataFrame({'Education Level': education_counts.index, 'Cases': education_counts.values, 'Fraction (%)': education_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "education_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iVHqyIgcdGPY"
   },
   "source": [
    "We can already observe that the most represented category is \"University, college degrees\" and the least one is \"left scool before 16\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PR7K7azP1_YH",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RjxxSgts16lN",
    "outputId": "e53b1ba0-213a-452a-c321-0da0ee45e2ad"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Country values\n",
    "country_mapping = {\n",
    "    -0.09765: 'Australia',\n",
    "    0.24923: 'Canada',\n",
    "    -0.46841: 'New Zealand',\n",
    "    -0.28519: 'Other',\n",
    "    0.21128: 'Republic of Ireland',\n",
    "    0.96082: 'UK',\n",
    "    -0.57009: 'USA'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Country\" column with their meanings\n",
    "df_vis['Country'] = df_vis['Country'].replace(country_mapping)\n",
    "\n",
    "# Display the Country column\n",
    "df_vis['Country'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 358
    },
    "id": "AELZ-3gT2VOK",
    "outputId": "0043fa26-12b0-4155-d58a-9b79581b5263"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique country category\n",
    "country_counts = df_vis['Country'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "country_fractions = (country_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "country_info_df = pd.DataFrame({'Country': country_counts.index, 'Cases': country_counts.values, 'Fraction (%)': country_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "country_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kmLxafNcdJn5"
   },
   "source": [
    "This survey took place mostly in the UK and US"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3i98e6OQ2g3t",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nwt2zzaM2Xd4",
    "outputId": "f077b638-7ca6-418b-ab74-693e4417212c"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Ethnicity values\n",
    "ethnicity_mapping = {\n",
    "    -0.50212: 'Asian',\n",
    "    -1.10702: 'Black',\n",
    "    1.90725: 'Mixed-Black/Asian',\n",
    "    0.12600: 'Mixed-White/Asian',\n",
    "    -0.22166: 'Mixed-White/Black',\n",
    "    0.11440: 'Other',\n",
    "    -0.31685: 'White'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Ethnicity\" column with their meanings\n",
    "df_vis['Ethnicity'] = df_vis['Ethnicity'].replace(ethnicity_mapping)\n",
    "\n",
    "# Display the Ethnicity Column\n",
    "df_vis['Ethnicity'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 358
    },
    "id": "Rdkaqr-o2paC",
    "outputId": "d35f27ab-63d5-494c-9ee4-58ecc86e5796"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique ethnicity category\n",
    "ethnicity_counts = df_vis['Ethnicity'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "ethnicity_fractions = (ethnicity_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "ethnicity_info_df = pd.DataFrame({'Ethnicity': ethnicity_counts.index, 'Cases': ethnicity_counts.values, 'Fraction (%)': ethnicity_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "ethnicity_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q_akKE_F3nl-",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Nscore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3UdUgRCudMMm"
   },
   "source": [
    "The Nscore references to NEO-FFI-R Neuroticism test that helps showing one's specific personality traits such as anxiety, angry hostily, depression, self-conciousness, impulsiveness and vulnerability. The more the score is high, the more those traits are important in the individual tested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BwWuEdvP2r8b",
    "outputId": "f6105449-6622-4010-b23d-583f3368a38e"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Nscore values\n",
    "nscore_mapping = {\n",
    "    -3.46436: '12',\n",
    "    -3.15735: '13',\n",
    "    -2.75696: '14',\n",
    "    -2.52197: '15',\n",
    "    -2.42317: '16',\n",
    "    -2.34360: '17',\n",
    "    -2.21844: '18',\n",
    "    -2.05048: '19',\n",
    "    -1.86962: '20',\n",
    "    -1.69163: '21',\n",
    "    -1.55078: '22',\n",
    "    -1.43907: '23',\n",
    "    -1.32828: '24',\n",
    "    -1.19430: '25',\n",
    "    -1.05308: '26',\n",
    "    -0.92104: '27',\n",
    "    -0.79151: '28',\n",
    "    -0.67825: '29',\n",
    "    -0.58016: '30',\n",
    "    -0.46725: '31',\n",
    "    -0.34799: '32',\n",
    "    -0.24649: '33',\n",
    "    -0.14882: '34',\n",
    "    -0.05188: '35',\n",
    "    0.04257: '36',\n",
    "    0.13606: '37',\n",
    "    0.22393: '38',\n",
    "    0.31287: '39',\n",
    "    0.41667: '40',\n",
    "    0.52135: '41',\n",
    "    0.62967: '42',\n",
    "    0.73545: '43',\n",
    "    0.82562: '44',\n",
    "    0.91093: '45',\n",
    "    1.02119: '46',\n",
    "    1.13281: '47',\n",
    "    1.23461: '48',\n",
    "    1.37297: '49',\n",
    "    1.49158: '50',\n",
    "    1.60383: '51',\n",
    "    1.72012: '52',\n",
    "    1.83990: '53',\n",
    "    1.98437: '54',\n",
    "    2.12700: '55',\n",
    "    2.28554: '56',\n",
    "    2.46262: '57',\n",
    "    2.61139: '58',\n",
    "    2.82196: '59',\n",
    "    3.27393: '60'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Nscore\" column with their meanings\n",
    "df_vis['Nscore'] = df_vis['Nscore'].replace(nscore_mapping)\n",
    "\n",
    "# Display the Nscore column\n",
    "df_vis['Nscore'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "id": "IgLQ0hoM3OGS",
    "outputId": "7bd2e10c-952b-4d9e-dffc-e8842125510a"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique Nscore category\n",
    "nscore_counts = df_vis['Nscore'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "nscore_fractions = (nscore_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "nscore_info_df = pd.DataFrame({'Nscore Value': nscore_counts.index, 'Cases': nscore_counts.values, 'Fraction (%)': nscore_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "nscore_info_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eR2QPmQi4kap",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Escore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uaD5sCqLdq8G"
   },
   "source": [
    " Escore (Real) is NEO-FFI-R Extraversion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JrtSSJ8p3r4W",
    "outputId": "c63b87a6-f8d3-4d49-efd3-4b91226714b8"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Escore values\n",
    "escore_mapping = {\n",
    "    -3.27393: '16',\n",
    "    -3.00537: '18',\n",
    "    -2.72827: '19',\n",
    "    -2.53830: '20',\n",
    "    -2.44904: '21',\n",
    "    -2.32338: '22',\n",
    "    -2.21069: '23',\n",
    "    -2.11437: '24',\n",
    "    -2.03972: '25',\n",
    "    -1.92173: '26',\n",
    "    -1.76250: '27',\n",
    "    -1.63340: '28',\n",
    "    -1.50796: '29',\n",
    "    -1.37639: '30',\n",
    "    -1.23177: '31',\n",
    "    -1.09207: '32',\n",
    "    -0.94779: '33',\n",
    "    -0.80615: '34',\n",
    "    -0.69509: '35',\n",
    "    -0.57545: '36',\n",
    "    -0.43999: '37',\n",
    "    -0.30033: '38',\n",
    "    -0.15487: '39',\n",
    "    0.00332: '40',\n",
    "    0.16767: '41',\n",
    "    0.32197: '42',\n",
    "    0.47617: '43',\n",
    "    0.63779: '44',\n",
    "    0.80523: '45',\n",
    "    0.96248: '46',\n",
    "    1.11406: '47',\n",
    "    1.28610: '48',\n",
    "    1.45421: '49',\n",
    "    1.58487: '50',\n",
    "    1.74091: '51',\n",
    "    1.93886: '52',\n",
    "    2.12700: '53',\n",
    "    2.32338: '54',\n",
    "    2.57309: '55',\n",
    "    2.85950: '56',\n",
    "    3.00537: '58',\n",
    "    3.27393: '59'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Escore\" column with their meanings\n",
    "df_vis['Escore'] = df_vis['Escore'].replace(escore_mapping)\n",
    "\n",
    "# Display the Escore column\n",
    "df_vis['Escore'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244
    },
    "id": "vn52nmZi4uY4",
    "outputId": "0d790a01-0267-4e80-cb5f-5b8024e3586d"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique Escore category\n",
    "escore_counts = df_vis['Escore'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "escore_fractions = (escore_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "escore_info_df = pd.DataFrame({'Escore Value': escore_counts.index, 'Cases': escore_counts.values, 'Fraction (%)': escore_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "escore_info_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "umQvpE6-48BQ",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Oscore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xx-pcSspdzMD"
   },
   "source": [
    "Oscore also refers to the NEO-FFI-R test but is about the Openness to experience personality trait."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nxMukEt047gU",
    "outputId": "c596657c-5e62-47ee-f740-4d6ac2b7ba7a"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Oscore values\n",
    "oscore_mapping = {\n",
    "    -3.27393: '24',\n",
    "    -2.85950: '26',\n",
    "    -2.63199: '28',\n",
    "    -2.39883: '29',\n",
    "    -2.21069: '30',\n",
    "    -2.09015: '31',\n",
    "    -1.97495: '32',\n",
    "    -1.82919: '33',\n",
    "    -1.68062: '34',\n",
    "    -1.55521: '35',\n",
    "    -1.42424: '36',\n",
    "    -1.27553: '37',\n",
    "    -1.11902: '38',\n",
    "    -0.97631: '39',\n",
    "    -0.84732: '40',\n",
    "    -0.71727: '41',\n",
    "    -0.58331: '42',\n",
    "    -0.45174: '43',\n",
    "    -0.31776: '44',\n",
    "    -0.17779: '45',\n",
    "    -0.01928: '46',\n",
    "    0.14143: '47',\n",
    "    0.29338: '48',\n",
    "    0.44585: '49',\n",
    "    0.58331: '50',\n",
    "    0.72330: '51',\n",
    "    0.88309: '52',\n",
    "    1.06238: '53',\n",
    "    1.24033: '54',\n",
    "    1.43533: '55',\n",
    "    1.65653: '56',\n",
    "    1.88511: '57',\n",
    "    2.15324: '58',\n",
    "    2.44904: '59',\n",
    "    2.90161: '60'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Oscore\" column with their meanings\n",
    "df_vis['Oscore'] = df_vis['Oscore'].replace(oscore_mapping)\n",
    "\n",
    "# Display the Oscore column\n",
    "df_vis['Oscore'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244
    },
    "id": "TDyMATDI5FKw",
    "outputId": "ef8073fd-174d-4687-f91a-10c3648cd6c7"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique Oscore category\n",
    "oscore_counts = df_vis['Oscore'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "oscore_fractions = (oscore_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "oscore_info_df = pd.DataFrame({'Oscore Value': oscore_counts.index, 'Cases': oscore_counts.values, 'Fraction (%)': oscore_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "oscore_info_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "442ba_MI5krl",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Ascore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KL2a6DrUeGdv"
   },
   "source": [
    "A score corresponds to the Agreeableness personality trait."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f9rfuMev5nbk",
    "outputId": "fb3b99b7-8616-4c07-89bc-cc23bc3d78d6"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Ascore values\n",
    "ascore_mapping = {\n",
    "    -3.46436: '12',\n",
    "    -3.15735: '16',\n",
    "    -3.00537: '18',\n",
    "    -2.90161: '23',\n",
    "    -2.78793: '24',\n",
    "    -2.70172: '25',\n",
    "    -2.53830: '26',\n",
    "    -2.35413: '27',\n",
    "    -2.21844: '28',\n",
    "    -2.07848: '29',\n",
    "    -1.92595: '30',\n",
    "    -1.77200: '31',\n",
    "    -1.62090: '32',\n",
    "    -1.47955: '33',\n",
    "    -1.34289: '34',\n",
    "    -1.21213: '35',\n",
    "    -1.07533: '36',\n",
    "    -0.91699: '37',\n",
    "    -0.76096: '38',\n",
    "    -0.60633: '39',\n",
    "    -0.45321: '40',\n",
    "    -0.30172: '41',\n",
    "    -0.15487: '42',\n",
    "    -0.01729: '43',\n",
    "    0.13136: '44',\n",
    "    0.28783: '45',\n",
    "    0.43852: '46',\n",
    "    0.59042: '47',\n",
    "    0.76096: '48',\n",
    "    0.94156: '49',\n",
    "    1.11406: '50',\n",
    "    1.28610: '51',\n",
    "    1.45039: '52',\n",
    "    1.61108: '53',\n",
    "    1.81866: '54',\n",
    "    2.03972: '55',\n",
    "    2.23427: '56',\n",
    "    2.46262: '57',\n",
    "    2.75696: '58',\n",
    "    3.15735: '59',\n",
    "    3.46436: '60'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Ascore\" column with their meanings\n",
    "df_vis['Ascore'] = df_vis['Ascore'].replace(ascore_mapping)\n",
    "\n",
    "# Display the Ascore column\n",
    "df_vis['Ascore'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244
    },
    "id": "J0agySLZ7pFA",
    "outputId": "021c3671-84b6-4bf4-fc0e-18a7cd77d745"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique Ascore category\n",
    "ascore_counts = df_vis['Ascore'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "ascore_fractions = (ascore_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "ascore_info_df = pd.DataFrame({'Ascore Value': ascore_counts.index, 'Cases': ascore_counts.values, 'Fraction (%)': ascore_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "ascore_info_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l3nt1JP075v5",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Cscore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MIBpYfnSeMCq"
   },
   "source": [
    "Cscore coresponds to Conscientiousness personality trait."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZN9Y5lWY7ukO",
    "outputId": "08d3a43d-2567-44a9-daa7-9efad69e28e3"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Cscore values\n",
    "cscore_mapping = {\n",
    "    -3.46436: '17',\n",
    "    -3.15735: '19',\n",
    "    -2.90161: '20',\n",
    "    -2.72827: '21',\n",
    "    -2.57309: '22',\n",
    "    -2.42317: '23',\n",
    "    -2.30408: '24',\n",
    "    -2.18109: '25',\n",
    "    -2.04506: '26',\n",
    "    -1.92173: '27',\n",
    "    -1.78169: '28',\n",
    "    -1.64101: '29',\n",
    "    -1.51840: '30',\n",
    "    -1.38502: '31',\n",
    "    -1.25773: '32',\n",
    "    -1.13788: '33',\n",
    "    -1.01450: '34',\n",
    "    -0.89891: '35',\n",
    "    -0.78155: '36',\n",
    "    -0.65253: '37',\n",
    "    -0.52745: '38',\n",
    "    -0.40581: '39',\n",
    "    -0.27607: '40',\n",
    "    -0.14277: '41',\n",
    "    -0.00665: '42',\n",
    "    0.12331: '43',\n",
    "    0.25953: '44',\n",
    "    0.41594: '45',\n",
    "    0.58489: '46',\n",
    "    0.75830: '47',\n",
    "    0.93949: '48',\n",
    "    1.13407: '49',\n",
    "    1.30612: '50',\n",
    "    1.46191: '51',\n",
    "    1.63088: '52',\n",
    "    1.81175: '53',\n",
    "    2.04506: '54',\n",
    "    2.33337: '55',\n",
    "    2.63199: '56',\n",
    "    3.00537: '57',\n",
    "    3.46436: '59'\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Cscore\" column with their meanings\n",
    "df_vis['Cscore'] = df_vis['Cscore'].replace(cscore_mapping)\n",
    "\n",
    "# Display the Cscore column\n",
    "df_vis['Cscore'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244
    },
    "id": "VPGG1Zzf8DTW",
    "outputId": "b9763feb-489e-433a-f4f9-57e7f5cab567"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique Cscore category\n",
    "cscore_counts = df_vis['Cscore'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "cscore_fractions = (cscore_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "cscore_info_df = pd.DataFrame({'Cscore Value': cscore_counts.index, 'Cases': cscore_counts.values, 'Fraction (%)': cscore_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "cscore_info_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W-Rp6N478HiE",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Impulsive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iWTUNSwd8GLG",
    "outputId": "eea383d3-8d49-4321-d2ed-b615c1b3161e"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for Impulsive values\n",
    "impulsive_mapping = {\n",
    "    -2.55524: 'Very Low',\n",
    "    -1.37983: 'Low',\n",
    "    -0.71126: 'Moderate',\n",
    "    -0.21712: 'Slightly Low',\n",
    "    0.19268: 'Slightly High',\n",
    "    0.52975: 'High',\n",
    "    0.88113: 'Very High',\n",
    "    1.29221: 'Extremely High',\n",
    "    1.86203: 'Much Higher',\n",
    "    2.90161: 'Most High',\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Impulsive\" column with their meanings\n",
    "df_vis['Impulsive'] = df_vis['Impulsive'].replace(impulsive_mapping)\n",
    "\n",
    "# Display the Impulsive column\n",
    "df_vis['Impulsive'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 470
    },
    "id": "eIkmGCF48Uyo",
    "outputId": "7c1e49d4-8c40-4a4c-e391-5357ef310819"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique Impulsive category\n",
    "impulsive_counts = df_vis['Impulsive'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "impulsive_fractions = (impulsive_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "impulsive_info_df = pd.DataFrame({'Impulsive Value': impulsive_counts.index, 'Cases': impulsive_counts.values, 'Fraction (%)': impulsive_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "impulsive_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "56Z-D-iE9qhi",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### SS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wZKIvGz8eRt5"
   },
   "source": [
    "SS reprensents the \"sensation seeing\" attribute, we can see by categories, how much the individual felt after the consumption of the drug."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ipe3XLgy8XpP",
    "outputId": "da1ff70a-fc8c-47f2-b188-37b1bd814feb"
   },
   "outputs": [],
   "source": [
    "# Define a mapping dictionary for SS values\n",
    "ss_mapping = {\n",
    "    -2.07848: 'Very Low',\n",
    "    -1.54858: 'Low',\n",
    "    -1.18084: 'Moderate Low',\n",
    "    -0.84637: 'Moderate',\n",
    "    -0.52593: 'Moderate High',\n",
    "    -0.21575: 'High',\n",
    "    0.07987: 'Very High',\n",
    "    0.40148: 'Much Higher',\n",
    "    0.76540: 'Most High',\n",
    "    1.22470: 'Almost Highest',\n",
    "    1.92173: 'Highest',\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"SS\" column with their meanings\n",
    "df_vis['SS'] = df_vis['SS'].replace(ss_mapping)\n",
    "\n",
    "# Display the SS column\n",
    "df_vis['SS'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 519
    },
    "id": "nSolFEps9_Ab",
    "outputId": "5eca4b6c-e148-474c-d380-e09be882cbaa"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique SS category\n",
    "ss_counts = df_vis['SS'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "ss_fractions = (ss_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "ss_info_df = pd.DataFrame({'SS Value': ss_counts.index, 'Cases': ss_counts.values, 'Fraction (%)': ss_fractions.values})\n",
    "\n",
    "# Display the DataFrame\n",
    "ss_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lFsGIRpPefGP"
   },
   "source": [
    "Now we are going to see the mapping of the different drugs from never used to used in last day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZcJKdmLa-uKo",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Alcohol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2ZD9sCeqex5I"
   },
   "source": [
    "Alcohol is class of alcohol consumption. It is output attribute with following distribution of classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FKRQWSbE-Cgu",
    "outputId": "4ccd7433-82c3-48a5-d6d8-883ecfc906bb"
   },
   "outputs": [],
   "source": [
    "# Define mapping dictionary for all the CL classes\n",
    "CL_mapping = {\n",
    "    '0.0': 'Never Used',\n",
    "    '1.0': 'Used over a Decade Ago',\n",
    "    '2.0': 'Used in Last Decade',\n",
    "    '3.0': 'Used in Last Year',\n",
    "    '4.0': 'Used in Last Month',\n",
    "    '5.0': 'Used in Last Week',\n",
    "    '6.0': 'Used in Last Day',\n",
    "}\n",
    "\n",
    "# Replace the numeric values in the \"Alcohol\" column with their meanings\n",
    "df_vis['Alcohol'] = df_vis['Alcohol'].replace(CL_mapping)\n",
    "\n",
    "# Display the Alcohol column\n",
    "df_vis['Alcohol'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 324
    },
    "id": "DynfBAqK_bRm",
    "outputId": "8fff5157-4a44-4d11-cefb-37b4dc17542b"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique class in \"Alcohol\"\n",
    "alcohol_counts = df_vis['Alcohol'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "alcohol_fractions = (alcohol_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results for \"Alcohol\"\n",
    "alcohol_info_df = pd.DataFrame({'Alcohol Class': alcohol_counts.index, 'Alcohol Cases': alcohol_counts.values, 'Alcohol Fraction (%)': alcohol_fractions.values})\n",
    "\n",
    "# Display the DataFrame for \"Alcohol\"\n",
    "alcohol_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zi9MUmwX_6rR",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Amphet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W3zN4BoGeu3r"
   },
   "source": [
    "Amphet is class of amphetamines consumption. It is output attribute with following distribution of classes. Amphet can be prescribed for medical purposes, such as treating attention deficit hyperactivity disorder (ADHD)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qjQPfL9J_qsX",
    "outputId": "1bd1e8f3-3088-4f6c-ac24-033c7152cbc2"
   },
   "outputs": [],
   "source": [
    "# Replace the numeric values in the \"Amphet\" column with their meanings\n",
    "df_vis['Amphet'] = df_vis['Amphet'].replace(CL_mapping)\n",
    "\n",
    "# Display the Amphet column\n",
    "df_vis['Amphet'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 324
    },
    "id": "uJu38_bkANmB",
    "outputId": "96caf1e7-e7d0-41d9-85fe-3eb4cba83c19"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique class in \"Amphet\"\n",
    "amphet_counts = df_vis['Amphet'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "amphet_fractions = (amphet_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results for \"Amphet\"\n",
    "amphet_info_df = pd.DataFrame({'Amphet Class': amphet_counts.index, 'Amphet Cases': amphet_counts.values, 'Amphet Fraction (%)': amphet_fractions.values})\n",
    "\n",
    "# Display the DataFrame for \"Amphet\"\n",
    "amphet_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WiYAymVVAaZf"
   },
   "source": [
    "C'est intéressant de voir qu'il y a beaucoup de personne qui ont pris des amphets il y a plus de 10 ans, peut être des gens adultes qui en prennaient quand ils étaient jeunes, paralèlle à faire avec l'age c'est SUR\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GzPbQFG9BFm9",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Amyl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TxK7LCdFe0bY"
   },
   "source": [
    "Amyl is class of amyl nitrite consumption. It is output attribute with following distribution of classes. One example of Amyl is poppers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jQgYsbJPARPw",
    "outputId": "b233055a-c5d7-4b4c-b39f-19892cc95bee"
   },
   "outputs": [],
   "source": [
    "# Replace the numeric values in the \"Amyl\" column with their meanings\n",
    "df_vis['Amyl'] = df_vis['Amyl'].replace(CL_mapping)\n",
    "\n",
    "# Display the Amyl column\n",
    "df_vis['Amyl'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 324
    },
    "id": "KAc5W9XsBQGi",
    "outputId": "ca12f126-f438-4fda-afb1-91553c6c8fca"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique class in \"Amyl\"\n",
    "amyl_counts = df_vis['Amyl'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "amyl_fractions = (amyl_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results for \"Amyl\"\n",
    "amyl_info_df = pd.DataFrame({'Amyl Class': amyl_counts.index, 'Amyl Cases': amyl_counts.values, 'Amyl Fraction (%)': amyl_fractions.values})\n",
    "\n",
    "# Display the DataFrame for \"Amyl\"\n",
    "amyl_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5CuO4v95BdW7",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Benzos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LGC_p_qle3eP"
   },
   "source": [
    "Benzos is class of benzodiazepine consumption. It is output attribute with following distribution of classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fvxQpTQKBYzt",
    "outputId": "e336883e-d6ed-4fdf-9d73-b51b33e6d835"
   },
   "outputs": [],
   "source": [
    "# Replace the numeric values in the \"Benzos\" column with their meanings\n",
    "df_vis['Benzos'] = df_vis['Benzos'].replace(CL_mapping)\n",
    "\n",
    "# Display the Benzos column\n",
    "df_vis['Benzos'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 324
    },
    "id": "2g2dPR7RBm2E",
    "outputId": "ed9e8875-689f-468b-a3e4-674d5cc16807"
   },
   "outputs": [],
   "source": [
    "# Calculate the fractions and cases for each unique class in \"Benzos\"\n",
    "benzos_counts = df_vis['Benzos'].value_counts()\n",
    "total_cases = len(df_vis)\n",
    "benzos_fractions = (benzos_counts / total_cases) * 100\n",
    "\n",
    "# Create a DataFrame to display the results for \"Benzos\"\n",
    "benzos_info_df = pd.DataFrame({'Benzos Class': benzos_counts.index, 'Benzos Cases': benzos_counts.values, 'Benzos Fraction (%)': benzos_fractions.values})\n",
    "\n",
    "# Display the DataFrame for \"Benzos\"\n",
    "benzos_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SLFk0Yi_B0-N",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Caff, Cannabis, Choc and Coke"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZyZSKf9-e90F"
   },
   "source": [
    "Here are grouped what we consider \"soft\" drugs :\n",
    "\n",
    "* Caff is Caffeine\n",
    "* Cannabis is cannabis (obviously)\n",
    "* Choc is Chocolate\n",
    "* Coke is cocaine\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yYtucZJ-CjgY",
    "outputId": "d0c31f10-8c23-4150-9cfd-21e4199a96ea"
   },
   "outputs": [],
   "source": [
    "# Replace the numeric values in the columns with their meanings\n",
    "df_vis['Caff'] = df_vis['Caff'].replace(CL_mapping)\n",
    "df_vis['Cannabis'] = df_vis['Cannabis'].replace(CL_mapping)\n",
    "df_vis['Choc'] = df_vis['Choc'].replace(CL_mapping)\n",
    "df_vis['Coke'] = df_vis['Coke'].replace(CL_mapping)\n",
    "\n",
    "# Calculate the fractions and cases for each unique class in each column\n",
    "caff_counts = df_vis['Caff'].value_counts()\n",
    "cannabis_counts = df_vis['Cannabis'].value_counts()\n",
    "choc_counts = df_vis['Choc'].value_counts()\n",
    "coke_counts = df_vis['Coke'].value_counts()\n",
    "\n",
    "total_cases = len(df)\n",
    "\n",
    "caff_fractions = (caff_counts / total_cases) * 100\n",
    "cannabis_fractions = (cannabis_counts / total_cases) * 100\n",
    "choc_fractions = (choc_counts / total_cases) * 100\n",
    "coke_fractions = (coke_counts / total_cases) * 100\n",
    "\n",
    "# Create DataFrames to display the results for each column\n",
    "caff_info_df = pd.DataFrame({'Caff Class': caff_counts.index, 'Caff Cases': caff_counts.values, 'Caff Fraction (%)': caff_fractions.values})\n",
    "cannabis_info_df = pd.DataFrame({'Cannabis Class': cannabis_counts.index, 'Cannabis Cases': cannabis_counts.values, 'Cannabis Fraction (%)': cannabis_fractions.values})\n",
    "choc_info_df = pd.DataFrame({'Choc Class': choc_counts.index, 'Choc Cases': choc_counts.values, 'Choc Fraction (%)': choc_fractions.values})\n",
    "coke_info_df = pd.DataFrame({'Coke Class': coke_counts.index, 'Coke Cases': coke_counts.values, 'Coke Fraction (%)': coke_fractions.values})\n",
    "\n",
    "# Display the DataFrames for each column\n",
    "print(\"Caff:\")\n",
    "print(caff_info_df)\n",
    "\n",
    "print(\"\\nCannabis:\")\n",
    "print(cannabis_info_df)\n",
    "\n",
    "print(\"\\nChoc:\")\n",
    "print(choc_info_df)\n",
    "\n",
    "print(\"\\nCoke:\")\n",
    "print(coke_info_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aHcWLx69DFat",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Crack, Ecstasy, Heroin and Ketamine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wrSKgUs-fig0"
   },
   "source": [
    "Here are grouped what we considered \"hard\" drugs:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VtTID_TbCkAK",
    "outputId": "e30985c8-9706-40a2-db14-d5f7c44014f0"
   },
   "outputs": [],
   "source": [
    "# Replace the numeric values in the columns with their meanings\n",
    "df_vis['Crack'] = df_vis['Crack'].replace(CL_mapping)\n",
    "df_vis['Ecstasy'] = df_vis['Ecstasy'].replace(CL_mapping)\n",
    "df_vis['Heroin'] = df_vis['Heroin'].replace(CL_mapping)\n",
    "df_vis['Ketamine'] = df_vis['Ketamine'].replace(CL_mapping)\n",
    "\n",
    "# Calculate the fractions and cases for each unique class in each column\n",
    "crack_counts = df_vis['Crack'].value_counts()\n",
    "ecstasy_counts = df_vis['Ecstasy'].value_counts()\n",
    "heroin_counts = df_vis['Heroin'].value_counts()\n",
    "ketamine_counts = df_vis['Ketamine'].value_counts()\n",
    "\n",
    "total_cases = len(df)\n",
    "\n",
    "crack_fractions = (crack_counts / total_cases) * 100\n",
    "ecstasy_fractions = (ecstasy_counts / total_cases) * 100\n",
    "heroin_fractions = (heroin_counts / total_cases) * 100\n",
    "ketamine_fractions = (ketamine_counts / total_cases) * 100\n",
    "\n",
    "# Create DataFrames to display the results for each column\n",
    "crack_info_df = pd.DataFrame({'Crack Class': crack_counts.index, 'Crack Cases': crack_counts.values, 'Crack Fraction (%)': crack_fractions.values})\n",
    "ecstasy_info_df = pd.DataFrame({'Ecstasy Class': ecstasy_counts.index, 'Ecstasy Cases': ecstasy_counts.values, 'Ecstasy Fraction (%)': ecstasy_fractions.values})\n",
    "heroin_info_df = pd.DataFrame({'Heroin Class': heroin_counts.index, 'Heroin Cases': heroin_counts.values, 'Heroin Fraction (%)': heroin_fractions.values})\n",
    "ketamine_info_df = pd.DataFrame({'Ketamine Class': ketamine_counts.index, 'Ketamine Cases': ketamine_counts.values, 'Ketamine Fraction (%)': ketamine_fractions.values})\n",
    "\n",
    "# Display the DataFrames for each column\n",
    "print(\"Crack:\")\n",
    "print(crack_info_df)\n",
    "\n",
    "print(\"\\nEcstasy:\")\n",
    "print(ecstasy_info_df)\n",
    "\n",
    "print(\"\\nHeroin:\")\n",
    "print(heroin_info_df)\n",
    "\n",
    "print(\"\\nKetamine:\")\n",
    "print(ketamine_info_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vxFD1RfRDVxv",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Legalh, LSD, Meth and Mushrooms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_IbnLk_Yfq_c"
   },
   "source": [
    "Legalh is legalize highs that are found in a pharmaceutical context.Here are grouped the hallucinogenic effect drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "asV97gFBDO9H",
    "outputId": "dfcb9693-fc79-4110-e97f-3d7c56b00525"
   },
   "outputs": [],
   "source": [
    "# Replace the numeric values in the columns with their meanings\n",
    "df_vis['Legalh'] = df_vis['Legalh'].replace(CL_mapping)\n",
    "df_vis['LSD'] = df_vis['LSD'].replace(CL_mapping)\n",
    "df_vis['Meth'] = df_vis['Meth'].replace(CL_mapping)\n",
    "df_vis['Mushrooms'] = df_vis['Mushrooms'].replace(CL_mapping)\n",
    "\n",
    "# Calculate the fractions and cases for each unique class in each column\n",
    "legalh_counts = df_vis['Legalh'].value_counts()\n",
    "lsd_counts = df_vis['LSD'].value_counts()\n",
    "meth_counts = df_vis['Meth'].value_counts()\n",
    "mushrooms_counts = df_vis['Mushrooms'].value_counts()\n",
    "\n",
    "total_cases = len(df_vis)\n",
    "\n",
    "legalh_fractions = (legalh_counts / total_cases) * 100\n",
    "lsd_fractions = (lsd_counts / total_cases) * 100\n",
    "meth_fractions = (meth_counts / total_cases) * 100\n",
    "mushrooms_fractions = (mushrooms_counts / total_cases) * 100\n",
    "\n",
    "# Create DataFrames to display the results for each column\n",
    "legalh_info_df = pd.DataFrame({'Legalh Class': legalh_counts.index, 'Legalh Cases': legalh_counts.values, 'Legalh Fraction (%)': legalh_fractions.values})\n",
    "lsd_info_df = pd.DataFrame({'LSD Class': lsd_counts.index, 'LSD Cases': lsd_counts.values, 'LSD Fraction (%)': lsd_fractions.values})\n",
    "meth_info_df = pd.DataFrame({'Meth Class': meth_counts.index, 'Meth Cases': meth_counts.values, 'Meth Fraction (%)': meth_fractions.values})\n",
    "mushrooms_info_df = pd.DataFrame({'Mushrooms Class': mushrooms_counts.index, 'Mushrooms Cases': mushrooms_counts.values, 'Mushrooms Fraction (%)': mushrooms_fractions.values})\n",
    "\n",
    "# Display the DataFrames for each column\n",
    "print(\"Legalh:\")\n",
    "print(legalh_info_df)\n",
    "\n",
    "print(\"\\nLSD:\")\n",
    "print(lsd_info_df)\n",
    "\n",
    "print(\"\\nMeth:\")\n",
    "print(meth_info_df)\n",
    "\n",
    "print(\"\\nMushrooms:\")\n",
    "print(mushrooms_info_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5voaBlhEbSv",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Nicotine, Semer and VSA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GVhOfgM__GZ-"
   },
   "source": [
    "VSA are versatile substance abuse such as solvants like glue, gasoline... Semer is class of fictitious drug Semeron consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1UlAiBMFEUiC",
    "outputId": "a89f7fa8-6cf0-4f6b-9e21-043a0496377e"
   },
   "outputs": [],
   "source": [
    "# Replace the numeric values in the columns with their meanings\n",
    "df_vis['Nicotine'] = df_vis['Nicotine'].replace(CL_mapping)\n",
    "df_vis['VSA'] = df_vis['VSA'].replace(CL_mapping)\n",
    "\n",
    "# Calculate the fractions and cases for each unique class in each column\n",
    "nicotine_counts = df['Nicotine'].value_counts()\n",
    "vsa_counts = df['VSA'].value_counts()\n",
    "\n",
    "total_cases = len(df)\n",
    "\n",
    "nicotine_fractions = (nicotine_counts / total_cases) * 100\n",
    "vsa_fractions = (vsa_counts / total_cases) * 100\n",
    "\n",
    "# Create DataFrames to display the results for each column\n",
    "nicotine_info_df = pd.DataFrame({'Nicotine Class': nicotine_counts.index, 'Nicotine Cases': nicotine_counts.values, 'Nicotine Fraction (%)': nicotine_fractions.values})\n",
    "vsa_info_df = pd.DataFrame({'VSA Class': vsa_counts.index, 'VSA Cases': vsa_counts.values, 'VSA Fraction (%)': vsa_fractions.values})\n",
    "\n",
    "# Display the DataFrames for each column\n",
    "print(\"Nicotine:\")\n",
    "print(nicotine_info_df)\n",
    "\n",
    "print(\"\\nVSA:\")\n",
    "print(vsa_info_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RzUi8EupEmDE"
   },
   "source": [
    "#### Display the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1MffZTsNgQWg"
   },
   "source": [
    "After remapping and renaming the attributes, we show our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 666
    },
    "id": "Vk3h3f8vEjrw",
    "outputId": "3052c439-fb45-4218-9e99-893c1cdeb74e"
   },
   "outputs": [],
   "source": [
    "df_vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lxzo5GVogVdt"
   },
   "source": [
    "Here are our columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7hohBXBuaagk",
    "outputId": "78a9e339-25f9-419d-c4f5-a3e31b1c53ca"
   },
   "outputs": [],
   "source": [
    "df_vis.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AuCGX1xgBncp",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Consumption by Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7iCo5VomB0HR"
   },
   "source": [
    "We want to see in a first place simple graphics about the basic parameters: here are diagram fo age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "jZQJH8d9C31H",
    "outputId": "5f9bfa7b-0e66-419c-fa78-ffdc24365268"
   },
   "outputs": [],
   "source": [
    "age_categories = age_info_df['Age Category']\n",
    "fractions = age_info_df['Fraction (%)']\n",
    "\n",
    "# Defining colors for the bars\n",
    "colors = plt.cm.Set3(np.linspace(0, 1, len(age_categories)))\n",
    "\n",
    "# Créer the bar diagram\n",
    "plt.bar(age_categories, fractions, color=colors)\n",
    "plt.xlabel('Age category')\n",
    "plt.ylabel('Percentage (%)')\n",
    "plt.title('Proportion of consumers by age')\n",
    "\n",
    "# Getting the legend rotated\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Show the final plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l5Xk-bWwCFOH"
   },
   "source": [
    "For the age we can see that the biggest Age category represented in this dataset is the 18-24 years old. We notice that the consumer percentage tend to decrease when the age category increase. Which could mean that the older people get the less likely they are to consume drugs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0MrafL6zB1uu",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Number of people who have never used drugs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e9jwfM5og1_z"
   },
   "source": [
    "Among the different drugs we want to show the number of people that never consume them. We seperate the legal drugs and illegal ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "v5HiPcHczJ9J",
    "outputId": "243b077c-2dc3-478c-9cf6-d023caa86a71"
   },
   "outputs": [],
   "source": [
    "# Count of 'Never Used' for legal drugs\n",
    "never_used_legal = (df_vis[legal] == 0).sum(axis=0)\n",
    "\n",
    "# Count of 'Never Used' for illegal drugs\n",
    "never_used_illegal = (df_vis[illegal] == 0).sum(axis=0)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Plot for legal drugs\n",
    "axes[0].bar(never_used_legal.index, never_used_legal, color=colors)\n",
    "axes[0].set_title('Number of People who have Never Used Legal Drugs')\n",
    "axes[0].set_xlabel('Drugs')\n",
    "axes[0].set_ylabel('Number of People')\n",
    "axes[0].set_ylim(0, df_vis.shape[0])\n",
    "\n",
    "# Plot for illegal drugs\n",
    "axes[1].bar(never_used_illegal.index, never_used_illegal, color=colors)\n",
    "axes[1].set_title('Number of People who have Never Used Illegal Drugs')\n",
    "axes[1].set_xlabel('Drugs')\n",
    "axes[1].set_ylabel('Number of People')\n",
    "axes[1].set_ylim(0, df_vis.shape[0])\n",
    "axes[1].tick_params(axis='x', rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The higher the bar, the lesser someone is likely to have tried that drug."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rQe_JJe6hA_s"
   },
   "source": [
    "Nicotine is the only legal drug to be less likely to be consumed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the illegal drugs, Crack, Heroin, Ketamine and Meth are the less likely to have been taken by someone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GqrGeS_E0u8f",
    "outputId": "b2b4542c-2c91-46d1-d298-bb46183b1eb7"
   },
   "outputs": [],
   "source": [
    "# Display the count of 'Never Used' for legal drugs\n",
    "never_used_legal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QRRMDz4H0dXU"
   },
   "source": [
    "We see that legal drugs were used more than illegal ones. We also note that the most widely used legal drug is caffeine (there are only 27 people who have never used caffeine). The most widely used illegal drug is cannabis by far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "11ByHwyyB9uN",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Link between education level and drug consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jADtTc8niH5Z"
   },
   "source": [
    "We are now going to use the education parameter to see a link between education level and drug consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "50yQa4YC9e05",
    "outputId": "b53d8a12-0654-4b87-89a3-c9530905d62c"
   },
   "outputs": [],
   "source": [
    "# Group by 'Education' and calculate the mean of drug consumption, illegal drugs tested, and legal drugs tested\n",
    "education_means = df_vis.groupby('Education')['Drugs Tested'].mean().sort_values(ascending=True)\n",
    "illegal_education_means = df_vis.groupby('Education')['Illegal Drugs'].mean().sort_values(ascending=True)\n",
    "legal_education_means = df_vis.groupby('Education')['Legal Drugs'].mean().sort_values(ascending=True)\n",
    "\n",
    "# Plotting all three on the same plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Define positions for each category and define the width of each bar\n",
    "x = np.arange(len(education_means))\n",
    "width = 0.25\n",
    "\n",
    "# Plot the mean drugs consumption and the nb of illegal/legal drugs tested\n",
    "plt.bar(x - width, education_means, width=width, color='#6FB1A6', label='Mean Drug Consumption')\n",
    "plt.bar(x, illegal_education_means, width=width, color='#ABC8A4', label='Mean Illegal Drugs Tested')\n",
    "plt.bar(x + width, legal_education_means, width=width, color='#9D99B8', label='Mean Legal Drugs Tested')\n",
    "\n",
    "# Set the x axis ticks and labels then finally plot the final plot\n",
    "plt.xticks(x, education_means.index, rotation=45, ha='right')\n",
    "plt.title('Mean Drug Consumption and Mean Illegal/Legal Drugs Tested by Education Level')\n",
    "plt.xlabel('Education Level')\n",
    "plt.ylabel('Mean')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mZOKV2bZiurU"
   },
   "source": [
    "Here we can see several things:\n",
    "\n",
    "1.   On average, regardless of education level, people have consumed the same number of legal drugs. This is probably due to the fact that legality makes consumption easier.\n",
    "2.   It follows that the ranking will be between those who have consumed the most illegal drugs.\n",
    "3.   We can see that the category with the smallest mean drug consumption is Master degree. Whereas the one with biggest mean drug consumption is \"Some college or univeristy, no certification or degree\". We can therefore try to create 2 categories: 'Graduate' and 'Non-graduate' to try to find correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "4qFBbBRHOY6n",
    "outputId": "ca6e07cb-a6da-46ac-ca69-1f889c72ad50"
   },
   "outputs": [],
   "source": [
    "# Define categories for 'Graduate' and 'Non-graduate'\n",
    "graduate_categories = ['Doctorate degree', 'Masters degree', 'University degree']\n",
    "non_graduate_categories = [\n",
    "    'Left school at 16 years',\n",
    "    'Left school at 17 years',\n",
    "    'Left school at 18 years',\n",
    "    'Left school before 16 years',\n",
    "    'Professional certificate/diploma',\n",
    "    'Some college or university, no certificate or degree'\n",
    "]\n",
    "\n",
    "# Create a new column 'Education Category' based on the defined categories\n",
    "df_vis['Education Category'] = df_vis['Education'].apply(lambda x: 'Graduate' if x in graduate_categories else 'Non-graduate')\n",
    "df_vis['Education Category'] = df_vis['Education Category'].map({'Graduate': 1, 'Non-graduate': 0})\n",
    "\n",
    "# Group by 'Education Category' and calculate the mean of illegal drugs consumption\n",
    "education_category_means = df_vis.groupby('Education Category')['Illegal Drugs'].mean()\n",
    "\n",
    "# Plotting the mean number of illegal drugs consumed by 'Graduate' and 'Non-graduate'\n",
    "plt.figure(figsize=(8, 6))\n",
    "education_category_means.plot(kind='bar', color=colors)\n",
    "plt.title('Mean Illegal Drugs Consumption by Education Category')\n",
    "plt.xlabel('Education Category')\n",
    "plt.ylabel('Mean Illegal Drugs Consumption')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oE5zevE7SFDz",
    "outputId": "32c038cf-0a78-426c-b366-479bd441a3af"
   },
   "outputs": [],
   "source": [
    "education_category_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L8A499vbR37i"
   },
   "source": [
    "We can see that, on average, graduate people test 1.4 drugs less than non-graduate people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "id": "85ZBp27Q_raT",
    "outputId": "dbd29c57-ac88-4d75-9a9a-78f470e7f6c4",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "correlation_columns = df_vis[['Education', 'Education Category', 'Illegal Drugs','Legal Drugs', 'Drugs Tested']]\n",
    "\n",
    "# Calculate the matrix\n",
    "correlation_matrix = correlation_columns.corr()\n",
    "\n",
    "# Create the heatmap\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='Oranges', linewidths=.5)\n",
    "\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZyA49oazGlHw"
   },
   "source": [
    "Thanks to this correlation matrix, we can see many things:\n",
    "1. The 'obvious' correlations such as education category and education and the nb of illegal drugs tested and the nb of drugs tested.\n",
    "2. However, the nb of **legal** drugs tested is not as correlated with the nb of drugs tested as it is with **illegal** drugs. This is always due to the fact that everyone has already tested legal drugs, and that the number of drugs tested is due to the number of illegal drugs tested.\n",
    "3. We can see that there is no correlation between Education and the number of drugs tested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Jk479hSUqW0"
   },
   "source": [
    "Conclusions: Legal drugs have been trivialized. Graduates test less than non-graduates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q2ual-S8Uw6I",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Big Five Personality and Drug consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gL7y_x8HVebB"
   },
   "source": [
    "There are 5 personality traits, a widely recognized model in psychology that describes human personality using five broad dimensions:\n",
    "1. Openness to Experience (Oscore): Reflects a person's imagination, creativity, willingness to try new things, and appreciation for art and adventure.\n",
    "\n",
    "2. Conscientiousness (Nscore): Refers to how organized, responsible, dependable, and goal-oriented an individual is.\n",
    "\n",
    "3. Extraversion (Escore): Represents the degree of sociability, assertiveness, talkativeness, and comfort in social settings.\n",
    "\n",
    "4. Agreeableness (Ascore): Relates to how cooperative, compassionate, empathetic, and considerate a person is in social interactions.\n",
    "\n",
    "5. Neuroticism (Cscore): Reflects emotional stability or instability, including tendencies toward anxiety, moodiness, irritability, and stress susceptibility.\n",
    "\n",
    "There is also other personality traits:\n",
    "6. Impulsiveness (Impulsive Rate): Reflects a tendency to act on immediate desires without considering consequences.\n",
    "\n",
    "7. Sensation Seeking (SS): Measures the desire for varied, novel, and intense experiences. Higher scores suggest a preference for excitement, risk-taking, and novelty-seeking behaviors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WFR0nQQ90iaS"
   },
   "source": [
    "1. **Sensation Seeking and Personality Traits:**\n",
    "\n",
    "High sensation seekers (SS) tend to be more open-minded (Oscore) and might explore various experiences.\n",
    "Individuals open to experience might show a greater inclination to test a variety of drugs (Narcotics), signifying a relationship between their exploratory nature and drug testing behavior.\n",
    "\n",
    "2. **Impulsiveness and Sensation Seeking:**\n",
    "\n",
    "The strong correlation between sensation seeking and impulsiveness indicates that those seeking sensations may act impulsively in pursuing new experiences, potentially leading to drug testing.\n",
    "Individuals with higher sensation seeking tendencies might impulsively explore diverse substances, aligning with their thrill-seeking nature.\n",
    "\n",
    "3. **Personality Traits - Extraversion and Conscientiousness:**\n",
    "\n",
    "An inverse relationship exists between extraversion (Escore) and neuroticism (Nscore), suggesting that more outgoing individuals (high E) might experience fewer negative emotions (low N).\n",
    "Similarly, the negative correlation between conscientiousness (Cscore) and neuroticism (Nscore) implies that responsible and organized individuals (high C) might experience fewer mood swings or negative emotions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "id": "76LBm0WVVYg3",
    "outputId": "05146efc-508f-4dc8-de1d-d69d975c3de0",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bigv = ['Nscore','Escore', 'Oscore', 'Ascore', 'Cscore']\n",
    "drugs = [\n",
    "    'Alcohol','Amphet', 'Amyl', 'Benzos', 'Caff', 'Cannabis', 'Choc', 'Coke', 'Crack',\n",
    "    'Ecstasy', 'Heroin', 'Ketamine', 'Legalh', 'LSD', 'Meth', 'Mushrooms',\n",
    "    'Nicotine', 'VSA'\n",
    "]\n",
    "\n",
    "subset_df = df_vis[bigv+drugs].astype(float).corr()\n",
    "#subset_df = pd.pivot_table(subset_df, index=bigv, values=drugs)\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "#correlation_matrix = subset_df.corr()\n",
    "\n",
    "# Create the heatmap\n",
    "plt.figure(figsize=(16, 13))\n",
    "sns.heatmap(subset_df, annot=True, cmap='Oranges', linewidths=.5, fmt=\".1f\")\n",
    "\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "id": "AUNErBeN1X5R",
    "outputId": "06cac9fe-ce69-4fda-a80d-8899ae24f7d0",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "columns_of_interest = ['Amphet', 'Amyl', 'Benzos', 'Cannabis', 'Coke',\n",
    "                       'Crack', 'Ecstasy', 'Heroin', 'Ketamine', 'Legalh', 'LSD', 'Meth', 'Mushrooms',\n",
    "                       'VSA', 'Nscore', 'Escore', 'Oscore', 'Ascore', 'Cscore', 'Impulsive', 'SS']\n",
    "\n",
    "# Create a subset DataFrame with selected columns\n",
    "subset_df = df_vis[columns_of_interest]\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "correlation_matrix = subset_df.corr()\n",
    "\n",
    "# Create a heatmap using seaborn\n",
    "plt.figure(figsize=(15, 12))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='YlOrRd', fmt='.2f')\n",
    "plt.title('Correlation Heatmap of Selected Columns')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jjFOwEjlAtNv"
   },
   "source": [
    "We can clearly see that Oscore, Impulsiveness and Sensation Seekness are more correlated with the illegal drugs consumption than Nscore,Escore, Ascore and Cscore."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "chUoiY0CDVFq"
   },
   "source": [
    "Personnality:\n",
    "1. Cannabis and SS/Oscore: Cannabis use often appeals to individuals seeking altered sensory experiences or relaxation. Those with higher SS scores might be more inclined towards the sensory effects or experiences related to cannabis use.\n",
    "\n",
    "2. Legal Highs (Legalh) and SS/Oscore: Legal highs might offer unconventional or legal alternatives to illicit drugs, attracting individuals with higher sensation-seeking traits who seek novel experiences but within legal boundaries.\n",
    "\n",
    "3. Ecstasy (Extasy) and SS/Oscore: Ecstasy, known for its euphoric effects and sensory enhancement, is commonly linked to social settings and heightened sensations. Individuals with higher SS scores might be drawn to its effects on sensory experiences and sociability.\n",
    "\n",
    "4. Mushrooms and SS/Oscore: Psychedelic mushrooms can induce hallucinations and altered perceptions. Those with higher SS scores might be intrigued by the intense sensory and perceptual changes associated with their use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MmANrIwwQ1qZ"
   },
   "source": [
    "Drugs:\n",
    "1. Correlation of Highly Addictive Drugs: Crack and heroin exhibit a notable correlation between each other and certain other substances like cannabis and meth. These substances possess high addiction potential, leading to focused consumption once tried due to intense cravings and dependence.\n",
    "\n",
    "2. Influence of Addiction Potential: Substances with high addictive qualities, such as crack and heroin, may prompt individuals to prioritize and focus primarily on these drugs after initial use. This focused consumption pattern might lead to weaker correlations with other substances as users may be less inclined to explore or use different drugs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qbCYKFPxeitZ",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Consumer vs Non-Consumer/Tester"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eBlUB9O-a1Rz",
    "outputId": "21b736e2-181b-4c86-fda1-20c3ae04eb38"
   },
   "outputs": [],
   "source": [
    "# For the Alcoholo column\n",
    "df_vis['Consumer_Type'] = df_vis['Alcohol'].apply(lambda x: 2 if x == 4.0 or x == 5.0 or x == 6.0 else (1 if x == 3.0 or x == 2.0 or x == 1.0 else 0))\n",
    "df_vis['Consumer_Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "zTH4f_WEjOgg",
    "outputId": "de40e8dd-c761-4f19-907e-d56804d5a88a"
   },
   "outputs": [],
   "source": [
    "# Grouping by 'Consumer_Type' and 'Alcohol' columns and counting occurrences\n",
    "grouped = df_vis.groupby(['Consumer_Type', 'Alcohol']).size().unstack()\n",
    "\n",
    "# Plotting the bar plot with specific colors\n",
    "grouped.plot(kind='bar', stacked=True, figsize=(10, 6), color=colors)\n",
    "plt.title('Consumer Type on Alcohol Usage')\n",
    "plt.xlabel('Consumer Type')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(title='Alcohol')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HmrpVdI5j4PO"
   },
   "source": [
    "Many people drinks \"often\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "yxO3CeZ7ngsE",
    "outputId": "d62ca651-3b08-43f2-e110-8a3b6fe13900"
   },
   "outputs": [],
   "source": [
    "drug_columns = ['Amphet', 'Amyl', 'Benzos', 'Cannabis', 'Coke', 'Crack', 'Ecstasy', 'Heroin', 'Ketamine', 'Legalh', 'LSD', 'Meth', 'Mushrooms', 'VSA']\n",
    "\n",
    "# Just to make a beautiful plot:\n",
    "# Calculate the number of rows and columns for subplots\n",
    "n_rows = 3  # Number of rows\n",
    "n_cols = len(drug_columns) // n_rows + (len(drug_columns) % n_rows > 0)  # Number of columns\n",
    "\n",
    "# Create subplots for each drug in a grid\n",
    "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=(15, 20))\n",
    "\n",
    "# Plot Consumer Types for each drug\n",
    "for i, drug in enumerate(drug_columns):\n",
    "    row = i // n_cols\n",
    "    col = i % n_cols\n",
    "    df_vis[f'{drug}_Consumer_Type'] = df_vis[drug].apply(lambda x: 2 if x == 4.0 or x == 5.0 or x == 6.0 else (1 if x == 3.0 or x == 2.0 or x == 1.0 else 0))\n",
    "    grouped = df_vis.groupby([f'{drug}_Consumer_Type', drug]).size().unstack()\n",
    "    grouped.plot(kind='bar', stacked=True, ax=axes[row, col], color=colors)\n",
    "    axes[row, col].set_title(f'Consumer Type on {drug} Usage')\n",
    "    axes[row, col].set_xlabel('Consumer Type')\n",
    "    axes[row, col].set_ylabel('Count')\n",
    "    axes[row, col].legend(title=drug)\n",
    "\n",
    "# Hide empty subplots\n",
    "for i in range(len(drug_columns), n_rows * n_cols):\n",
    "    axes.flatten()[i].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lZzxpqydxq9M"
   },
   "source": [
    "We found on different sources talking about the drugs that are the hardest to find and to quit/most dangerous.\n",
    "\n",
    "Firstly here is an approximative list in order to the lass accessible drug to the to the most.\n",
    "\n",
    "(Sources : The organisation of American addiction center, Oxford Treatment center, National Institute of drug abuse)\n",
    "\n",
    "\n",
    "* Heroin: A highly addictive and illicit drug, often associated with high prices on the black market.\n",
    "\n",
    "* Cocaine (Coke): A stimulant derived from the coca plant, known for its stimulating effects and relatively expensive price.\n",
    "\n",
    "* Ketamine: A dissociative anesthetic that can induce hallucinogenic effects, considered relatively costly.\n",
    "\n",
    "* LSD: A potent hallucinogenic drug often sold as small squares of paper (\"tabs\"), generally less expensive than heroin or cocaine.\n",
    "\n",
    "* Methamphetamine (Meth): A highly addictive stimulant, relatively cheaper compared to heroin or cocaine.\n",
    "\n",
    "* Ecstasy: A synthetic drug with both stimulant and hallucinogenic effects, generally less expensive than some other drugs mentioned.\n",
    "\n",
    "* Cannabis: A psychoactive drug whose price can vary depending on factors such as quality, legality, and location.\n",
    "\n",
    "* Benzos: Prescription drugs used to treat anxiety and insomnia, generally less expensive compared to illicit drugs.\n",
    "\n",
    "* Mushrooms: Psychedelic mushrooms containing psilocybin, generally considered not too expensive.\n",
    "\n",
    "* Legalh: Those drugs are the legalized highs. They depends on the legislation of the country.\n",
    "\n",
    "* Amphetamines (Amphet): Stimulant drugs sometimes prescribed for medical purposes, generally less expensive compared to illicit drugs.\n",
    "\n",
    "* Amyl (Poppers): Volatile liquids used recreationally, typically considered less expensive compared to many other drugs and easy to find.\n",
    "\n",
    "* VSA (Versatile Substance Abuse): Not a specific substance or drug, this is a class of solvant drugs such as glue. It is easy to find.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CCA8T6gLChE5"
   },
   "source": [
    "We can see a direct link between the accessibility of the drugs and the consumers usage of the drugs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J55xnV6DAPua"
   },
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ffHAdFY7Ozxj"
   },
   "source": [
    "For this section we will try to predict the number of drugs someone would have taken in his/her life according to his/her personnality scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_nXm3LN8xMXZ"
   },
   "source": [
    "### Encoding the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "44ggSy7XY6wP"
   },
   "source": [
    "First we ordinally encode the data about drugs and last moment of consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "GRZQKXb7xW-0"
   },
   "outputs": [],
   "source": [
    "df_ml = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "Fq2RrYD85y4U",
    "outputId": "24320242-9c38-4cac-98bc-b1e3d35b8a8a"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>Country</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Nscore</th>\n",
       "      <th>Escore</th>\n",
       "      <th>Oscore</th>\n",
       "      <th>Ascore</th>\n",
       "      <th>Cscore</th>\n",
       "      <th>...</th>\n",
       "      <th>Legalh</th>\n",
       "      <th>LSD</th>\n",
       "      <th>Meth</th>\n",
       "      <th>Mushrooms</th>\n",
       "      <th>Nicotine</th>\n",
       "      <th>VSA</th>\n",
       "      <th>Drugs Tested</th>\n",
       "      <th>TMTO</th>\n",
       "      <th>Legal Drugs</th>\n",
       "      <th>Illegal Drugs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.49788</td>\n",
       "      <td>0.48246</td>\n",
       "      <td>-0.05921</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>0.12600</td>\n",
       "      <td>0.31287</td>\n",
       "      <td>-0.57545</td>\n",
       "      <td>-0.58331</td>\n",
       "      <td>-0.91699</td>\n",
       "      <td>-0.00665</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.07854</td>\n",
       "      <td>-0.48246</td>\n",
       "      <td>1.98437</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>-0.67825</td>\n",
       "      <td>1.93886</td>\n",
       "      <td>1.43533</td>\n",
       "      <td>0.76096</td>\n",
       "      <td>-0.14277</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.49788</td>\n",
       "      <td>-0.48246</td>\n",
       "      <td>-0.05921</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>-0.46725</td>\n",
       "      <td>0.80523</td>\n",
       "      <td>-0.84732</td>\n",
       "      <td>-1.62090</td>\n",
       "      <td>-1.01450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.95197</td>\n",
       "      <td>0.48246</td>\n",
       "      <td>1.16365</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>-0.14882</td>\n",
       "      <td>-0.80615</td>\n",
       "      <td>-0.01928</td>\n",
       "      <td>0.59042</td>\n",
       "      <td>0.58489</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.49788</td>\n",
       "      <td>0.48246</td>\n",
       "      <td>1.98437</td>\n",
       "      <td>0.96082</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>0.73545</td>\n",
       "      <td>-1.63340</td>\n",
       "      <td>-0.45174</td>\n",
       "      <td>-0.30172</td>\n",
       "      <td>1.30612</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>-0.95197</td>\n",
       "      <td>0.48246</td>\n",
       "      <td>-0.61113</td>\n",
       "      <td>-0.57009</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>-1.19430</td>\n",
       "      <td>1.74091</td>\n",
       "      <td>1.88511</td>\n",
       "      <td>0.76096</td>\n",
       "      <td>-1.13788</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>-0.95197</td>\n",
       "      <td>-0.48246</td>\n",
       "      <td>-0.61113</td>\n",
       "      <td>-0.57009</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>-0.24649</td>\n",
       "      <td>1.74091</td>\n",
       "      <td>0.58331</td>\n",
       "      <td>0.76096</td>\n",
       "      <td>-1.51840</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>-0.07854</td>\n",
       "      <td>0.48246</td>\n",
       "      <td>0.45468</td>\n",
       "      <td>-0.57009</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>1.13281</td>\n",
       "      <td>-1.37639</td>\n",
       "      <td>-1.27553</td>\n",
       "      <td>-1.77200</td>\n",
       "      <td>-1.38502</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>-0.95197</td>\n",
       "      <td>0.48246</td>\n",
       "      <td>-0.61113</td>\n",
       "      <td>-0.57009</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>0.91093</td>\n",
       "      <td>-1.92173</td>\n",
       "      <td>0.29338</td>\n",
       "      <td>-1.62090</td>\n",
       "      <td>-2.57309</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>-0.95197</td>\n",
       "      <td>-0.48246</td>\n",
       "      <td>-0.61113</td>\n",
       "      <td>0.21128</td>\n",
       "      <td>-0.31685</td>\n",
       "      <td>-0.46725</td>\n",
       "      <td>2.12700</td>\n",
       "      <td>1.65653</td>\n",
       "      <td>1.11406</td>\n",
       "      <td>0.41594</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1885 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age   Gender  Education  Country  Ethnicity   Nscore   Escore  \\\n",
       "ID                                                                        \n",
       "1     0.49788  0.48246   -0.05921  0.96082    0.12600  0.31287 -0.57545   \n",
       "2    -0.07854 -0.48246    1.98437  0.96082   -0.31685 -0.67825  1.93886   \n",
       "3     0.49788 -0.48246   -0.05921  0.96082   -0.31685 -0.46725  0.80523   \n",
       "4    -0.95197  0.48246    1.16365  0.96082   -0.31685 -0.14882 -0.80615   \n",
       "5     0.49788  0.48246    1.98437  0.96082   -0.31685  0.73545 -1.63340   \n",
       "...       ...      ...        ...      ...        ...      ...      ...   \n",
       "1884 -0.95197  0.48246   -0.61113 -0.57009   -0.31685 -1.19430  1.74091   \n",
       "1885 -0.95197 -0.48246   -0.61113 -0.57009   -0.31685 -0.24649  1.74091   \n",
       "1886 -0.07854  0.48246    0.45468 -0.57009   -0.31685  1.13281 -1.37639   \n",
       "1887 -0.95197  0.48246   -0.61113 -0.57009   -0.31685  0.91093 -1.92173   \n",
       "1888 -0.95197 -0.48246   -0.61113  0.21128   -0.31685 -0.46725  2.12700   \n",
       "\n",
       "       Oscore   Ascore   Cscore  ...  Legalh  LSD  Meth  Mushrooms  Nicotine  \\\n",
       "ID                               ...                                           \n",
       "1    -0.58331 -0.91699 -0.00665  ...     0.0  0.0   0.0        0.0       2.0   \n",
       "2     1.43533  0.76096 -0.14277  ...     0.0  2.0   3.0        0.0       4.0   \n",
       "3    -0.84732 -1.62090 -1.01450  ...     0.0  0.0   0.0        1.0       0.0   \n",
       "4    -0.01928  0.59042  0.58489  ...     0.0  0.0   0.0        0.0       2.0   \n",
       "5    -0.45174 -0.30172  1.30612  ...     1.0  0.0   0.0        2.0       2.0   \n",
       "...       ...      ...      ...  ...     ...  ...   ...        ...       ...   \n",
       "1884  1.88511  0.76096 -1.13788  ...     3.0  3.0   0.0        0.0       0.0   \n",
       "1885  0.58331  0.76096 -1.51840  ...     3.0  5.0   4.0        4.0       5.0   \n",
       "1886 -1.27553 -1.77200 -1.38502  ...     0.0  2.0   0.0        2.0       6.0   \n",
       "1887  0.29338 -1.62090 -2.57309  ...     3.0  3.0   0.0        3.0       4.0   \n",
       "1888  1.65653  1.11406  0.41594  ...     3.0  3.0   0.0        3.0       6.0   \n",
       "\n",
       "      VSA  Drugs Tested  TMTO  Legal Drugs  Illegal Drugs  \n",
       "ID                                                         \n",
       "1     0.0             6     1            4              2  \n",
       "2     0.0            12     1            4              8  \n",
       "3     0.0             5     1            3              2  \n",
       "4     0.0             8     1            4              4  \n",
       "5     0.0            10     1            4              6  \n",
       "...   ...           ...   ...          ...            ...  \n",
       "1884  5.0             7     1            3              3  \n",
       "1885  0.0            10     1            4              6  \n",
       "1886  0.0            13     1            4              9  \n",
       "1887  0.0             9     1            4              5  \n",
       "1888  2.0            13     1            4              8  \n",
       "\n",
       "[1885 rows x 34 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9W25Bqd2py3v"
   },
   "source": [
    "We binarize binary features (gender in our case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2dsrZdM-qDSu",
    "outputId": "1e41dd08-68f2-40a0-b8d8-152a2968f766"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.48246    943\n",
       " 0.48246    942\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MSn1gkkQPSAf"
   },
   "source": [
    "We also need to ordinally encode columns education and age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bK0y3COwPqhJ",
    "outputId": "c1f53065-ab88-4e44-fe93-310c4d7d6e2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.95197    643\n",
       "-0.07854    481\n",
       " 0.49788    356\n",
       " 1.09449    294\n",
       " 1.82213     93\n",
       " 2.59171     18\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4WyMoPthPkWT",
    "outputId": "c61cab5b-3c45-4267-d927-d2aeddb7c268"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([-0.95197, -0.07854, 0.49788, 1.09449, 1.82213, 2.59171], dtype='float64')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_categories = df_ml['Age'].value_counts().index.sort_values()\n",
    "age_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "wfClVwD2WxnQ"
   },
   "outputs": [],
   "source": [
    "ord_enc = OrdinalEncoder(categories=[age_categories])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RKTggkReQLSu",
    "outputId": "0a672c4d-6edf-44e9-f2db-d67a98a3248c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1       2.0\n",
       "2       1.0\n",
       "3       2.0\n",
       "4       0.0\n",
       "5       2.0\n",
       "       ... \n",
       "1884    0.0\n",
       "1885    0.0\n",
       "1886    1.0\n",
       "1887    0.0\n",
       "1888    0.0\n",
       "Name: Age, Length: 1885, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml['Age'] = ord_enc.fit_transform(df_ml['Age'].to_numpy().reshape(-1,1))\n",
    "df_ml['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "obnLgqd3cjuV",
    "outputId": "65537850-3818-41ec-cc88-6629fcbcedaf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([-2.43591,  -1.7379, -1.43719, -1.22751, -0.61113, -0.05921,\n",
       "               0.45468,  1.16365,  1.98437],\n",
       "             dtype='float64')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_categories = df_ml['Education'].value_counts().index.sort_values()\n",
    "edu_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "7gfaSPneah-T"
   },
   "outputs": [],
   "source": [
    "ord_enc = OrdinalEncoder(categories=[edu_categories])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tUe9u1D-dYaW",
    "outputId": "83cac09f-3e94-42b4-fbab-b8cd1db24dd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1       5.0\n",
       "2       8.0\n",
       "3       5.0\n",
       "4       7.0\n",
       "5       8.0\n",
       "       ... \n",
       "1884    4.0\n",
       "1885    4.0\n",
       "1886    6.0\n",
       "1887    4.0\n",
       "1888    4.0\n",
       "Name: Education, Length: 1885, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml['Education'] = ord_enc.fit_transform(df_ml['Education'].to_numpy().reshape(-1,1))\n",
    "df_ml['Education']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop the columns of ethnicity and country of origin. Besides being racist, making predictions based on people's ethnic/nation origin lacks relevance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "b5PT9LdurI7j"
   },
   "outputs": [],
   "source": [
    "#Decide what to do with ethnicity and country\n",
    "df_ml.drop(['Ethnicity','Country'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fZcCqv9UPOnB"
   },
   "source": [
    "We scale our data, using standard scaler. Scaling the data is an important step in machine learning because it helps to ensure that features are on a similar scale and have a comparable range of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "5GQA9Fpfl4Zs",
    "outputId": "07f4a09e-5446-4101-fbfb-790b38c42238"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(df_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I8Kp1Q-ImcgW",
    "outputId": "e5f43de3-5f4d-4436-bf6b-48f8e5a98d0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    943\n",
       "1.0    942\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binarizer = Binarizer(threshold=0.0)\n",
    "df_ml['Gender'] = binarizer.fit_transform([df['Gender']]).reshape(-1,1)\n",
    "df_ml['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_36L20Cq0lLF"
   },
   "source": [
    "We can check how much of these people has never tested any drug."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GDQ1r_1L0kRO",
    "outputId": "d613faf6-6bd7-4ddf-cdcd-992f4c51da50"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1884\n",
       "0       1\n",
       "Name: TMTO, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml['TMTO'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "id": "1EWezXpp0s-D",
    "outputId": "f2c954e4-d93f-4f66-dd4d-ba3052084ee8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>Nscore</th>\n",
       "      <th>Escore</th>\n",
       "      <th>Oscore</th>\n",
       "      <th>Ascore</th>\n",
       "      <th>Cscore</th>\n",
       "      <th>Impulsive</th>\n",
       "      <th>SS</th>\n",
       "      <th>...</th>\n",
       "      <th>Legalh</th>\n",
       "      <th>LSD</th>\n",
       "      <th>Meth</th>\n",
       "      <th>Mushrooms</th>\n",
       "      <th>Nicotine</th>\n",
       "      <th>VSA</th>\n",
       "      <th>Drugs Tested</th>\n",
       "      <th>TMTO</th>\n",
       "      <th>Legal Drugs</th>\n",
       "      <th>Illegal Drugs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
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       "      <th>1281</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.63779</td>\n",
       "      <td>-0.84732</td>\n",
       "      <td>-2.21844</td>\n",
       "      <td>0.93949</td>\n",
       "      <td>1.86203</td>\n",
       "      <td>1.92173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender  Education   Nscore   Escore   Oscore   Ascore   Cscore  \\\n",
       "ID                                                                          \n",
       "1281  0.0     0.0        6.0  0.82562  0.63779 -0.84732 -2.21844  0.93949   \n",
       "\n",
       "      Impulsive       SS  ...  Legalh  LSD  Meth  Mushrooms  Nicotine  VSA  \\\n",
       "ID                        ...                                                \n",
       "1281    1.86203  1.92173  ...     0.0  0.0   0.0        0.0       0.0  0.0   \n",
       "\n",
       "      Drugs Tested  TMTO  Legal Drugs  Illegal Drugs  \n",
       "ID                                                    \n",
       "1281             0     0            0              0  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml[df_ml['TMTO'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J4TKHejh00Tz"
   },
   "source": [
    "We now know that each of these individuals has tested at least one drug (which is not surprising considering chocolate is a column in this df). Because the TMTO has little variance we can drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "SzdCJ-RZ4Wu1"
   },
   "outputs": [],
   "source": [
    "df_ml.drop('TMTO', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GiAwn3-uqItC"
   },
   "source": [
    "For this part we make sure to work whith floats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wJbjyEhivDzU",
    "outputId": "9a052f66-2e09-4783-9a36-e5040c5a87ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age              float64\n",
       "Gender           float64\n",
       "Education        float64\n",
       "Nscore           float64\n",
       "Escore           float64\n",
       "Oscore           float64\n",
       "Ascore           float64\n",
       "Cscore           float64\n",
       "Impulsive        float64\n",
       "SS               float64\n",
       "Alcohol          float64\n",
       "Amphet           float64\n",
       "Amyl             float64\n",
       "Benzos           float64\n",
       "Caff             float64\n",
       "Cannabis         float64\n",
       "Choc             float64\n",
       "Coke             float64\n",
       "Crack            float64\n",
       "Ecstasy          float64\n",
       "Heroin           float64\n",
       "Ketamine         float64\n",
       "Legalh           float64\n",
       "LSD              float64\n",
       "Meth             float64\n",
       "Mushrooms        float64\n",
       "Nicotine         float64\n",
       "VSA              float64\n",
       "Drugs Tested     float64\n",
       "Legal Drugs      float64\n",
       "Illegal Drugs    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml = df_ml.astype('float64')\n",
    "df_ml.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R2hlNXm1z3nT"
   },
   "source": [
    "Making a df without the drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "wYNTAoATrMAb",
    "outputId": "2ac51590-bc16-41cf-c260-d178de56392e"
   },
   "outputs": [
    {
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       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.13281</td>\n",
       "      <td>-1.37639</td>\n",
       "      <td>-1.27553</td>\n",
       "      <td>-1.77200</td>\n",
       "      <td>-1.38502</td>\n",
       "      <td>0.52975</td>\n",
       "      <td>-0.52593</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.91093</td>\n",
       "      <td>-1.92173</td>\n",
       "      <td>0.29338</td>\n",
       "      <td>-1.62090</td>\n",
       "      <td>-2.57309</td>\n",
       "      <td>1.29221</td>\n",
       "      <td>1.22470</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.46725</td>\n",
       "      <td>2.12700</td>\n",
       "      <td>1.65653</td>\n",
       "      <td>1.11406</td>\n",
       "      <td>0.41594</td>\n",
       "      <td>0.88113</td>\n",
       "      <td>1.22470</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1885 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender  Education   Nscore   Escore   Oscore   Ascore   Cscore  \\\n",
       "ID                                                                          \n",
       "1     2.0     1.0        5.0  0.31287 -0.57545 -0.58331 -0.91699 -0.00665   \n",
       "2     1.0     0.0        8.0 -0.67825  1.93886  1.43533  0.76096 -0.14277   \n",
       "3     2.0     0.0        5.0 -0.46725  0.80523 -0.84732 -1.62090 -1.01450   \n",
       "4     0.0     1.0        7.0 -0.14882 -0.80615 -0.01928  0.59042  0.58489   \n",
       "5     2.0     1.0        8.0  0.73545 -1.63340 -0.45174 -0.30172  1.30612   \n",
       "...   ...     ...        ...      ...      ...      ...      ...      ...   \n",
       "1884  0.0     1.0        4.0 -1.19430  1.74091  1.88511  0.76096 -1.13788   \n",
       "1885  0.0     0.0        4.0 -0.24649  1.74091  0.58331  0.76096 -1.51840   \n",
       "1886  1.0     1.0        6.0  1.13281 -1.37639 -1.27553 -1.77200 -1.38502   \n",
       "1887  0.0     1.0        4.0  0.91093 -1.92173  0.29338 -1.62090 -2.57309   \n",
       "1888  0.0     0.0        4.0 -0.46725  2.12700  1.65653  1.11406  0.41594   \n",
       "\n",
       "      Impulsive       SS  Drugs Tested  Legal Drugs  Illegal Drugs  \n",
       "ID                                                                  \n",
       "1      -0.21712 -1.18084           6.0          4.0            2.0  \n",
       "2      -0.71126 -0.21575          12.0          4.0            8.0  \n",
       "3      -1.37983  0.40148           5.0          3.0            2.0  \n",
       "4      -1.37983 -1.18084           8.0          4.0            4.0  \n",
       "5      -0.21712 -0.21575          10.0          4.0            6.0  \n",
       "...         ...      ...           ...          ...            ...  \n",
       "1884    0.88113  1.92173           7.0          3.0            3.0  \n",
       "1885    0.88113  0.76540          10.0          4.0            6.0  \n",
       "1886    0.52975 -0.52593          13.0          4.0            9.0  \n",
       "1887    1.29221  1.22470           9.0          4.0            5.0  \n",
       "1888    0.88113  1.22470          13.0          4.0            8.0  \n",
       "\n",
       "[1885 rows x 13 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml_without_drugs = df_ml.drop(drug_names, axis=1)\n",
    "df_ml_without_drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "AEsrGGlM4hG6"
   },
   "outputs": [],
   "source": [
    "df_ml_without_drugs.to_csv('pda_api/kNN.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DvnItP0H4Hhf"
   },
   "source": [
    "### Making the test and train sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "GQL0fErpEeAp",
    "outputId": "1dc8dcec-7216-4019-85b6-0a084d0a6692"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>Nscore</th>\n",
       "      <th>Escore</th>\n",
       "      <th>Oscore</th>\n",
       "      <th>Ascore</th>\n",
       "      <th>Cscore</th>\n",
       "      <th>Impulsive</th>\n",
       "      <th>SS</th>\n",
       "      <th>Drugs Tested</th>\n",
       "      <th>Legal Drugs</th>\n",
       "      <th>Illegal Drugs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>-0.58331</td>\n",
       "      <td>-0.91699</td>\n",
       "      <td>-0.00665</td>\n",
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       "      <td>-1.18084</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-0.67825</td>\n",
       "      <td>1.93886</td>\n",
       "      <td>1.43533</td>\n",
       "      <td>0.76096</td>\n",
       "      <td>-0.14277</td>\n",
       "      <td>-0.71126</td>\n",
       "      <td>-0.21575</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.46725</td>\n",
       "      <td>0.80523</td>\n",
       "      <td>-0.84732</td>\n",
       "      <td>-1.62090</td>\n",
       "      <td>-1.01450</td>\n",
       "      <td>-1.37983</td>\n",
       "      <td>0.40148</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.14882</td>\n",
       "      <td>-0.80615</td>\n",
       "      <td>-0.01928</td>\n",
       "      <td>0.59042</td>\n",
       "      <td>0.58489</td>\n",
       "      <td>-1.37983</td>\n",
       "      <td>-1.18084</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.73545</td>\n",
       "      <td>-1.63340</td>\n",
       "      <td>-0.45174</td>\n",
       "      <td>-0.30172</td>\n",
       "      <td>1.30612</td>\n",
       "      <td>-0.21712</td>\n",
       "      <td>-0.21575</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.19430</td>\n",
       "      <td>1.74091</td>\n",
       "      <td>1.88511</td>\n",
       "      <td>0.76096</td>\n",
       "      <td>-1.13788</td>\n",
       "      <td>0.88113</td>\n",
       "      <td>1.92173</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.24649</td>\n",
       "      <td>1.74091</td>\n",
       "      <td>0.58331</td>\n",
       "      <td>0.76096</td>\n",
       "      <td>-1.51840</td>\n",
       "      <td>0.88113</td>\n",
       "      <td>0.76540</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.13281</td>\n",
       "      <td>-1.37639</td>\n",
       "      <td>-1.27553</td>\n",
       "      <td>-1.77200</td>\n",
       "      <td>-1.38502</td>\n",
       "      <td>0.52975</td>\n",
       "      <td>-0.52593</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.91093</td>\n",
       "      <td>-1.92173</td>\n",
       "      <td>0.29338</td>\n",
       "      <td>-1.62090</td>\n",
       "      <td>-2.57309</td>\n",
       "      <td>1.29221</td>\n",
       "      <td>1.22470</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.46725</td>\n",
       "      <td>2.12700</td>\n",
       "      <td>1.65653</td>\n",
       "      <td>1.11406</td>\n",
       "      <td>0.41594</td>\n",
       "      <td>0.88113</td>\n",
       "      <td>1.22470</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1885 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender  Education   Nscore   Escore   Oscore   Ascore   Cscore  \\\n",
       "ID                                                                          \n",
       "1     2.0     1.0        5.0  0.31287 -0.57545 -0.58331 -0.91699 -0.00665   \n",
       "2     1.0     0.0        8.0 -0.67825  1.93886  1.43533  0.76096 -0.14277   \n",
       "3     2.0     0.0        5.0 -0.46725  0.80523 -0.84732 -1.62090 -1.01450   \n",
       "4     0.0     1.0        7.0 -0.14882 -0.80615 -0.01928  0.59042  0.58489   \n",
       "5     2.0     1.0        8.0  0.73545 -1.63340 -0.45174 -0.30172  1.30612   \n",
       "...   ...     ...        ...      ...      ...      ...      ...      ...   \n",
       "1884  0.0     1.0        4.0 -1.19430  1.74091  1.88511  0.76096 -1.13788   \n",
       "1885  0.0     0.0        4.0 -0.24649  1.74091  0.58331  0.76096 -1.51840   \n",
       "1886  1.0     1.0        6.0  1.13281 -1.37639 -1.27553 -1.77200 -1.38502   \n",
       "1887  0.0     1.0        4.0  0.91093 -1.92173  0.29338 -1.62090 -2.57309   \n",
       "1888  0.0     0.0        4.0 -0.46725  2.12700  1.65653  1.11406  0.41594   \n",
       "\n",
       "      Impulsive       SS  Drugs Tested  Legal Drugs  Illegal Drugs  \n",
       "ID                                                                  \n",
       "1      -0.21712 -1.18084           6.0          4.0            2.0  \n",
       "2      -0.71126 -0.21575          12.0          4.0            8.0  \n",
       "3      -1.37983  0.40148           5.0          3.0            2.0  \n",
       "4      -1.37983 -1.18084           8.0          4.0            4.0  \n",
       "5      -0.21712 -0.21575          10.0          4.0            6.0  \n",
       "...         ...      ...           ...          ...            ...  \n",
       "1884    0.88113  1.92173           7.0          3.0            3.0  \n",
       "1885    0.88113  0.76540          10.0          4.0            6.0  \n",
       "1886    0.52975 -0.52593          13.0          4.0            9.0  \n",
       "1887    1.29221  1.22470           9.0          4.0            5.0  \n",
       "1888    0.88113  1.22470          13.0          4.0            8.0  \n",
       "\n",
       "[1885 rows x 13 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml_without_drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "YzYmeF9sz080"
   },
   "outputs": [],
   "source": [
    "x = df_ml_without_drugs.drop(['Drugs Tested','Legal Drugs','Illegal Drugs'], axis=1)\n",
    "y = df_ml_without_drugs['Illegal Drugs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "qQOtOD8R3hpg"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y0-PhLRz4OBX"
   },
   "source": [
    "### kNN predicting number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zlK8xdEGpyH6"
   },
   "source": [
    "We are going to use the Knn algotithm for our model. The K-nearest neighbors (KNN) algorithm is a supervised machine learning algorithm that classifies a new data point based on the majority class of its K nearest neighbors in the feature space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "ow23nrA24R5k"
   },
   "outputs": [],
   "source": [
    "model = KNeighborsRegressor(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "n05hhUKe5Exy",
    "outputId": "13a180c4-01cc-46f1-a576-afa145b68c37"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "8ML21luq5JUp"
   },
   "outputs": [],
   "source": [
    "pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aPBdPP2q5S47",
    "outputId": "82005086-f625-4e25-f276-d62aa3dafcf6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.69406779661017"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kDnifXlU5zwl"
   },
   "source": [
    "We try to select the best value for k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "cmzG_a1IKUi6"
   },
   "outputs": [],
   "source": [
    "params = {'weights':['uniform','distance'],'n_neighbors':[50,100,150]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WARNING : The sklearn_evaluation package has a compatibility bug that makes it read deprecated keys of the cv_results_ dict. To solve it you need to go into the source code of the package at the specified file in the error and change the \n",
    "- ```cv_results_[\"std_test_score\"] and cv_results_[\"mean_test_score\"]```  \n",
    "to  \n",
    "- ```cv_results_[\"std_test_r2\"] and cv_results_[\"mean_test_r2\"]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn_evaluation import plot as plt_eval\n",
    "\n",
    "def grid_search_report(model, params, change, figsize=(10,10), scoring=None):\n",
    "    if not scoring:\n",
    "        scoring = ['r2']\n",
    "        \n",
    "    grid_search = GridSearchCV(model, params, scoring=scoring, refit=scoring[0], n_jobs=-1, return_train_score=True)\n",
    "    grid_search.fit(x_train,y_train)\n",
    "    print(grid_search.best_params_)\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    plt_eval.grid_search(grid_search.cv_results_, change=change,ax=ax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 50, 'weights': 'distance'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_search_report(model, params, params.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.725169413343941"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsRegressor(n_neighbors=5, weights='distance')\n",
    "model.fit(x_train, y_train)\n",
    "pred = model.predict(x_test)\n",
    "mean_squared_error(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uwCS5tW_9_l-"
   },
   "source": [
    "### Gaussian naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "btTbK2yy-Ncd"
   },
   "source": [
    "We can suppose the personnality of people follow a gaussian distribution, like IQ or height. Let's try to build better models with gaussian naive Bayes models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "id": "qw1041ek-YQU"
   },
   "outputs": [],
   "source": [
    "model = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "kP2y4TwC-MVt",
    "outputId": "e06c8582-a9e1-414b-b992-f2b3e01c7ce0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "id": "cMfuR8YM99kT"
   },
   "outputs": [],
   "source": [
    "pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FWQ4ZEAF_PyW",
    "outputId": "81a06856-1788-4355-8725-8815d0311780"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.61228813559322"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jbRnA3p4_c6w"
   },
   "source": [
    "Poor performance of the gaussian naive Bayes model, compared to kNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OsbK20cxBWjU"
   },
   "source": [
    "### Random forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RuRXIwmnBYW4",
    "outputId": "3be151f8-064e-4d7f-c4db-db40a9a83ced"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.090613347457628"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(random_state=0)\n",
    "model.fit(x_train, y_train)\n",
    "pred = model.predict(x_test)\n",
    "mean_squared_error(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QP86d8QGDbgz"
   },
   "source": [
    "Now we'll search for the best value of the estimators of the ensemble of the decision trees in the random forest. This can take a few minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WBnGXhebDn9w"
   },
   "source": [
    "Because of the random nature of the random forest regressor the model's error never really stabilizes but on average it stops decreasing at around 60, which is near the optimal value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Ds0kVrOEjUv"
   },
   "source": [
    "The random forest model seems to have nearly the same performance as the kNN one. What we could do the improve that difference and discard one of the two is through cross validation. But that also adds time to model selection phase. The main advantage of kNN is that it doesn't need training, in comparison to the random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"criterion\":[\"squared_error\",\"absolute_error\",\"friedman_mse\",\"poisson\"], \"n_estimators\":[50,100,150]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'absolute_error', 'n_estimators': 150}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_search_report(model, params, change=('criterion','n_estimators'), figsize=(7,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like the best model is the one with 100 estimators (100 random forests) with the absolute error as a criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.283036723163844"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(criterion='absolute_error', n_estimators=150, random_state=0)\n",
    "model.fit(x_train, y_train)\n",
    "pred = model.predict(x_test)\n",
    "mean_squared_error(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random forest model has less MSE than kNN, but takes more time to train."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2HZg2-7FTUHF"
   },
   "source": [
    "## Deep learning (bonus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6D9mJKUjF0mr"
   },
   "source": [
    "Using the keras library from tensorflow we can build a deep learning model. For this kind of regression with (supposedly) unrelated data, we can simply use dense layers. If we had an image we would have used a convolutionnal layers and if we had time-related data we would have used a recurrent layers or LSTMs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is to disable tensorflow for using the GPU. If you are launching that notebook from your PATH you can comment out that cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '-1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "id": "voXe0_trXrB6"
   },
   "outputs": [],
   "source": [
    "from keras import Sequential, Model\n",
    "from keras.layers import Dense, Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "id": "s4u8Vo2avLgS"
   },
   "outputs": [],
   "source": [
    "def create_dense_model(n_hidden_layers, n_inputs):\n",
    "  model = Sequential()\n",
    "  model.add(Input(n_inputs))\n",
    "  for _ in range(0,n_hidden_layers):\n",
    "    model.add(Dense(n_inputs, activation='relu'))\n",
    "\n",
    "  model.add(Dense(1, activation='relu'))\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "id": "wy17aakLg1mG"
   },
   "outputs": [],
   "source": [
    "n_inputs = len(x.columns)\n",
    "\n",
    "model = create_dense_model(3, n_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "id": "s3Ub2Iamiwb3"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_absolute_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "id": "8f0tHOnVirPJ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "45/45 [==============================] - 1s 914us/step - loss: 40.6581 - mean_absolute_error: 5.0557\n",
      "Epoch 2/10\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 38.5030 - mean_absolute_error: 4.9037\n",
      "Epoch 3/10\n",
      "45/45 [==============================] - 0s 990us/step - loss: 28.8050 - mean_absolute_error: 4.2374\n",
      "Epoch 4/10\n",
      "45/45 [==============================] - 0s 968us/step - loss: 17.3844 - mean_absolute_error: 3.5307\n",
      "Epoch 5/10\n",
      "45/45 [==============================] - 0s 964us/step - loss: 14.5074 - mean_absolute_error: 3.2530\n",
      "Epoch 6/10\n",
      "45/45 [==============================] - 0s 996us/step - loss: 13.4235 - mean_absolute_error: 3.0826\n",
      "Epoch 7/10\n",
      "45/45 [==============================] - 0s 997us/step - loss: 12.8382 - mean_absolute_error: 2.9841\n",
      "Epoch 8/10\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 12.4341 - mean_absolute_error: 2.9130\n",
      "Epoch 9/10\n",
      "45/45 [==============================] - 0s 994us/step - loss: 12.1161 - mean_absolute_error: 2.8798\n",
      "Epoch 10/10\n",
      "45/45 [==============================] - 0s 978us/step - loss: 11.8660 - mean_absolute_error: 2.8496\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "id": "mtnCmKgdjJT9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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064q8gKZB8M4VsLUPjIoz/6ifp0HXlXD9alinkCMiUisEepsjKFYsgVVzg+1zeHt7s3TpUhYtWkSrVq3497//TfPmzdm9ezcA77zzDqtWreKqq67iv//9L5dffjmrV6+unuKKuEW4Wbt2La+//jrt2rVzWX///ffz6aefMnfuXJKTkzl48CBDhw61qMqLaxYMM66ArX3h9qKQ81kadFkJN3wP69OtrlBERKqSzWaeGrJiqaxpLS1btmTVqlUYhlGy7ttvvyUkJIQGDRoUvU8b3bt357HHHuOHH37Az8+P+fPnl2zfsWNHJk6cyHfffUebNm2YPXt25RRXRpaHm+zsbG699VbefPNN6tT5baJKRkYGb7/9Ni+88AJ9+vShU6dOvPPOO3z33XfVngDL67JgeLco5PyxgflH/jQVOifDjd/DhnSrKxQRETG/azdu3Oiy3H333aSkpDBu3Di2bt3KggULmDRpEhMmTMDLy4vvv/+ep59+mnXr1rFv3z4+/vhjjhw5QsuWLdm9ezcTJ05k1apV7N27ly+++IIdO3ZU+7wbyycUJyYmMnjwYPr168eTTz5Zsn79+vUUFBTQr1+/knUtWrSgYcOGrFq1iiuvvNKKcsvlsmCY2Qn+1Rye2Aaz98PCVHO5MdqceNwxzOoqRUSktlqxYgUdO3Z0WXfHHXfwv//9jwcffJD27dsTHh7OHXfcwb/+9S8AHA4HK1eu5MUXXyQzM5P4+Hief/55Bg0aRFpaGlu3buXdd9/l2LFjxMTEkJiYyD333FOt78vScDNnzhw2bNjA2rVrz3ktNTUVPz8/wsLCXNZHRUWRmpp6wWPm5eWRl5dX8jwzM7PS6q2oy4PhvU7wr8vhie3wwX5YkGouQ6JhUgvoEGp1lSIiUpvMmDGDGTNmXPD1NWvWnHd9y5YtWbx48Xlfi4qKcjk9VRnO/h632+3Y7fZS97HstFRKSgr33Xcfs2bNwt/fv9KOm5SURGhoaMkSFxdXace+VM1D4P1OsKUPjGwANuCTVOi4AoaugR8zrK5QRETEvcTFxbl8ryclJV10H8vCzfr16zl8+DBXXHEFPj4++Pj4kJyczMsvv4yPjw9RUVHk5+eTnp7usl9aWhrR0dEXPO7EiRPJyMgoWVJSUqr4nZRfixCYVRRybqlvhpz5h6DDChi2Bn5SyBEREQHMwZAzv9cnTpx40X0sCzd9+/Zl06ZNLpOYOnfuzK233lry2NfXl2XLlpXss23bNvbt20dCQsIFj2u323E4HC6Lu2oZArM7myHn5qKQ8/EhaL8ChivkiIiInPOdfrFTUmDhnJuQkBDatGnjsi4oKIiIiIiS9XfccQcTJkwgPDwch8PBuHHjSEhIqBGTicujZQh80BkeuRwe3w4fHoCPDpnL8Fh4tDm0dd+MJiIi4lYsvxS8NFOnTuW6665j2LBh9OjRg+joaD7++GOry6oyrRwwpzNs6g0jYs2RnHkHod1XMGItbLZ+brSIiJzHmT1hpOyq6u9mMzz8E8nMzCQ0NJSMjAy3PkV1Ppsz4fFtMPeg+dwG/L5oJKd1zXorIiIeqaCggF9//ZXY2FhCQ3XZa3kdO3aMw4cPc/nll+Pt7dpi+VK+vy3vcyMX1sYBH3aBTUUhZ95B+PCgGXZG1IdHLzdHe0RExBo+Pj4EBgZy5MgRfH198fJy6xMibsMwDHJzczl8+DBhYWHnBJtLpZGbGmRTJjy21ZyLA+ZIzh/qm3N1FHJERKyRn5/P7t27cTqdVpdS44SFhREdHe1yk85il/L9rXBTA/2UAY9tM6+sAjPk3FwfHmluTk4WEZHq5XQ6yc/Pt7qMGsXX17fUERuFm1J4Yrgp9mNRyJl/Rsi5pYE5ktNCIUdERGqwS/n+1snBGqx9KHzcFX7oZd7GwcC8f1Xr5XDbetiWZXWFIiIi1U/hxgN0CIX53WBDT/OGnE5g1n5otRz+uB62Z1tdoYiISPVRuPEgHcPgk26wvifcUBRy3t8PLZfB7Qo5IiJSSyjceKArwmBBN1jXE64vCjnvFYWcURtgh0KOiIh4MIUbD9YpDBZ2g7U94LooM+TMTIGWy2H0BvhVIUdERDyQwk0t0LkOfHolrOkBg6Og0IB3U6DFchizAXbmWF2hiIhI5VG4qUW61IHProTve8C1RSFnRgo0X2berFNERMQTKNzUQl3rwOdXwurfQd+6Zsh56Gfzp4iISE2ncFOLdQs35+SE+8LuXFhwyOqKRERELp3CTS0X6AP3NjIfT91paSkiIiKVQuFGSGwMvjb45jisPWF1NSIiIpdG4UaIDTDvLg4avRERkZpP4UYAuL+p+XPuQdh/0tpaRERELoXCjQBmV+OeEXDagGm7rK5GRESk4hRupETx6M3reyH7tLW1iIiIVJTCjZS4LhqaBUF6Aby7z+pqREREKkbhRkp42+C+JubjF3eBU039RESkBlK4ERejG0KYL/yaA5+lWl2NiIhI+SnciItgH7g73nw8VROLRUSkBlK4kXOMbWKeolpxFH5It7oaERGR8lG4kXPEBcDvY83HauonIiI1jcKNnFfxZeFzDsChU9bWIiIiUh4KN3JeXetA93AoMOCV3VZXIyIiUnYKN3JBxaM303dDrpr6iYhIDaFwIxc0JAYaB8LxAnhvv9XViIiIlI3CjVyQtw3+WtzUb6ea+omISM2gcCOl+lNDcPjA1mxYfNjqakRERC5O4UZK5fCFO4ub+umycBERqQEUbuSixjUx/6F8eQQ2ZVpdjYiISOkUbuSiGgXCMDX1ExGRGkLhRsqk+LLwWfshTU39RETEjSncSJkkhEO3OpDvhOl7rK5GRETkwhRupMwmFI3evLobThVaW4uIiMiFKNxImQ2NgYYBcCTfPD0lIiLijhRupMx8vMwrp8CcWGyoqZ+IiLghhRsplzvjIdgbtmTB0iNWVyMiInIuhRsplzBf+JOa+omIiBtTuJFy+2sTsGHejuGXLKurERERcaVwI+XWNMi8YziYN9QUERFxJwo3UiHFTf1mpsDRPGtrEREROZPCjVTI1eHQKRROOeG1PVZXIyIi8huFG6kQmw0mNDMfv7Ib8tTUT0RE3ITCjVTY72Ohvj+k5sGcA1ZXIyIiYlK4kQrz9YKxjc3HauonIiLuwtJwM336dNq1a4fD4cDhcJCQkMCiRYtKXu/Vqxc2m81luffeey2sWM52dyMI9IYfM2HFUaurERERsTjcNGjQgGeeeYb169ezbt06+vTpw4033siWLVtKtrnrrrs4dOhQyTJlyhQLK5azhfvB6Djz8Qu6LFxERNyAj5W//Prrr3d5/tRTTzF9+nRWr15N69atAQgMDCQ6OtqK8qSM7msKr+6Bz9JgezZcHmx1RSIiUpu5zZybwsJC5syZQ05ODgkJCSXrZ82aRd26dWnTpg0TJ04kNze31OPk5eWRmZnpskjVujwYri/Kny9p9EZERCrR2d/peXkXb65mebjZtGkTwcHB2O127r33XubPn0+rVq0AGDlyJO+//z5fffUVEydO5L333uO2224r9XhJSUmEhoaWLHFxcdXxNmq9+4vuFj4jBY7nW1uLiIh4jri4OJfv9aSkpIvuYzMMa69xyc/PZ9++fWRkZDBv3jzeeustkpOTSwLOmZYvX07fvn359ddfadq06XmPl5eX55LqMjMziYuLIyMjA4fDUWXvo7YzDOi4wpxYnNQSHrrc6opERKQmy8zMJDQ0lJSUFJfvb7vdjt1uL3Vfy8PN2fr160fTpk15/fXXz3ktJyeH4OBgFi9ezIABA8p0vOI/jsJN1Zu5D0b9ALH+sPsa8LN8XFBERGqqS/n+druvH6fTecHzaRs3bgQgJiamGiuSsrq5AUTb4eApmKumfiIiYhFLr5aaOHEigwYNomHDhmRlZTF79mxWrFjBkiVL2LlzJ7Nnz+baa68lIiKCn376ifvvv58ePXrQrl07K8uWC/DzgsTG8MhWs6nfyAbmbRpERESqk6UjN4cPH+b222+nefPm9O3bl7Vr17JkyRKuueYa/Pz8+PLLL+nfvz8tWrTggQceYNiwYXz66adWliwXcW8j8PeC9RnwzXGrqxERkdrI7ebcVDbNual+92yEN/bCkGiY383qakREpCbyqDk3UvONL7qQbUEq7MyxthYREal9FG6k0rUMgUGRYAAv77K6GhERqW0UbqRK3F80evP2XkgvsLYWERGpXRRupEr0qwdtQiCnEN7aa3U1IiJSmyjcSJWw2X4bvXl5F5x2WluPiIjUHgo3UmVGNoBIO6SchI8OWV2NiIjUFgo3UmX8veHPjczHU3W3cBERqSYKN1Kl/twI7F7w/QlYpaZ+IiJSDRRupEpF+cOtDczHL2j0RkREqoHCjVS54qZ+Hx+EPbnW1iIiIp5P4UaqXFsHXFMPnMC/1dRPRESqmMKNVIviy8Lf3AuZauonIiJVSOFGqsWASGgRDFmn4T/7rK5GREQ8mcKNVAuvM5r6vbQLCj36XvQiImIlhRupNn+Mgwg/c1LxJ2rqJyIiVUThRqpNgDfc28h8rKZ+IiJSVRRupFolNgZfG3x7HNacsLoaERHxRAo3Uq1i/OGWoqZ+Gr0REZGqoHAj1a54YvHcg+ZNNUVERCqTwo1Uuw6h0LuuecXUNDX1ExGRSqZwI5YoHr15fQ9kn7a0FBER8TAKN2KJwVFwWRBknIYZauonIiKVSOFGLOFl++2GmmrqJyIilUnhRiwzKg7q+MKvOfBZqtXViIiIp1C4EcsE+cDd8eZjXRYuIiKVReFGLDW2CfjYIPkYbEi3uhoREfEECjdiqQYBMKK++VijNyIiUhkUbsRy9zcxf845AAfV1E9ERC6Rwo1YrnMd+F0EnDbgld1WVyMiIjWdwo24heLRm9f2QK6a+omIyCVQuBG3cEMMNAmE4wUwM8XqakREpCZTuBG34G2D+4pGb17cBU419RMRkQpSuBG3MaYhOHxgWzYsSrO6GhERqakUbsRthPjCXWrqJyIil0jhRtzKuCbmKaplR+GnDKurERGRmkjhRtxKfCAMizEfa/RGREQqQuFG3M79RXcLn30AUk9ZW4uIiNQ8Cjfidq4Mh4Q6kO+E6XusrkZERGoahRtxS8WjN9N3w8lCa2sREZGaReFG3NJNMRAfAEfyYdZ+q6sREZGaROFG3JKPF/y1uKnfTjDU1E9ERMpI4Ubc1h3xEOwNW7Jg6RGrqxERkZpC4UbcVqivGXAAXtBl4SIiUkYKN+LW/trE/Ee65DD8nGl1NSIiUhMo3IhbaxIEQ4qa+r24y9paRESkZlC4EbdXfFn4zBQ4kmdtLSIi4v4UbsTtdQ+HLmGQ54TX9lhdjYiIuDtLw8306dNp164dDocDh8NBQkICixYtKnn91KlTJCYmEhERQXBwMMOGDSMtLc3CisUKNttvozev7IY8NfUTEZFSWBpuGjRowDPPPMP69etZt24dffr04cYbb2TLli0A3H///Xz66afMnTuX5ORkDh48yNChQ60sWSwyPBYa+ENaHnxwwOpqRETEndkMw73ao4WHh/Pcc88xfPhw6tWrx+zZsxk+fDgAW7dupWXLlqxatYorr7yyTMfLzMwkNDSUjIwMHA5HVZYuVWzKDvjHz9DOARt7mSM6IiLimS7l+9tt5twUFhYyZ84ccnJySEhIYP369RQUFNCvX7+SbVq0aEHDhg1ZtWrVBY+Tl5dHZmamyyKe4a54CPSGnzLhq6NWVyMiItXh7O/0vLyLX1liebjZtGkTwcHB2O127r33XubPn0+rVq1ITU3Fz8+PsLAwl+2joqJITU294PGSkpIIDQ0tWeLi4qr4HUh1qeMHf2poPlZTPxGR2iEuLs7lez0pKemi+/hUQ12lat68ORs3biQjI4N58+YxatQokpOTK3y8iRMnMmHChJLnmZmZCjge5L4m5qTiz9NgWxY0D7G6IhERqUopKSkup6XsdvtF97E83Pj5+dGsWTMAOnXqxNq1a3nppZf4wx/+QH5+Punp6S6jN2lpaURHR1/weHa7vUxvXGqmZsFwfTQsTIWXdsGr7a2uSEREqlLxFdXlYflpqbM5nU7y8vLo1KkTvr6+LFu2rOS1bdu2sW/fPhISEiysUKw2oeiy8BkpcCzf2lpERMT9WDpyM3HiRAYNGkTDhg3Jyspi9uzZrFixgiVLlhAaGsodd9zBhAkTCA8Px+FwMG7cOBISEsp8pZR4ph4R0DEUfsiAN/bAxMutrkhERNyJpeHm8OHD3H777Rw6dIjQ0FDatWvHkiVLuOaaawCYOnUqXl5eDBs2jLy8PAYMGMCrr75qZcniBoqb+t2+AabthgeagZ/bjUGKiIhV3K7PTWVTnxvPlO+ERl/AoTx47wq4TXPGRUQ8ikf0uREpDz8vGNvEfDx1J3h2RBcRkfJQuJEa655GEOANGzLg62NWVyMiIu5C4UZqrAg/GFV0OkpN/UREpJjCjdRo44tOTS1MhV+zra1FRETcg8KN1GjNQ+DaKDCAl3dZXY2IiLgDhRup8Yqb+v1nH6QXWFuLiIhYT+FGarw+daGdA3IK4c09VlcjIiJWU7iRGs9mM2+oCfDGXl0WLiJS2ynciEf4Q30I9oZfc+C741ZXIyIiVlK4EY8Q5AO/r28+fmeftbWIiIi1FG7EY4xpaP788CDknLa2FhERsY7CjXiMq8OhSSBknYb5h6yuRkRErKJwIx7DZoPRRaM3M3RqSkSk1lK4EY9ye9HtGJYfhb251tYiIiLWULgRjxIfaPa9MYCZKVZXIyIiVlC4EY8z5oxTU+p5IyJS+yjciMe5KQZCfGBXLnyjnjciIrWOwo14nCAfGBFrPtbEYhGR2kfhRjxS8VVTHx5QzxsRkdpG4UY8UvdwaBYE2YXw0UGrqxERkeqkcCMeyWaD0UWXhc/QVVMiIrWKwo14rD/GgQ346ijsUc8bEZFaQ+FGPFbDQOhbz3z8riYWi4jUGgo34tGKe968mwJO9bwREakVFG7Eow2JBocP7M6Fr49ZXY2IiFSHCoWblJQU9u/fX/J8zZo1jB8/njfeeKPSChOpDIE+8If65mP1vBERqR0qFG5GjhzJV199BUBqairXXHMNa9as4eGHH+bxxx+v1AJFLlVxz5u5ByFbPW9ERDxehcLN5s2b6dq1KwAffvghbdq04bvvvmPWrFnMmDGjMusTuWQJdeDyIMgphHnqeSMi4vEqFG4KCgqw2+0AfPnll9xwww0AtGjRgkOHDlVedSKVwGb7bfRGp6ZERDxfhcJN69atee211/j6669ZunQpAwcOBODgwYNERERUaoEilaG4503yMdiVY3U1IiJSlSoUbp599llef/11evXqxS233EL79u0BWLhwYcnpKhF30iAArinueaOOxSIiHs1mGEaFun8UFhaSmZlJnTp1Stbt2bOHwMBAIiMjK63AS5WZmUloaCgZGRk4HA6ryxELfbAfRq6H+ADYdQ142ayuSERELuRSvr8rNHJz8uRJ8vLySoLN3r17efHFF9m2bZtbBRuRMw2JgVAf2HsSko9aXY2IiFSVCoWbG2+8kZkzZwKQnp5Ot27deP755xkyZAjTp0+v1AJFKkuAN9zcwHysm2mKiHiuCoWbDRs28Lvf/Q6AefPmERUVxd69e5k5cyYvv/xypRYoUpmK7xQ+7yBkFVhbi4iIVI0KhZvc3FxCQkIA+OKLLxg6dCheXl5ceeWV7N27t1ILFKlM3epA82DILTSb+omIiOepULhp1qwZn3zyCSkpKSxZsoT+/fsDcPjwYU3aFbdms/12M02dmhIR8UwVCjePPvoof/vb32jUqBFdu3YlISEBMEdxOnbsWKkFilS22xqY//C/Pga/ZltdjYiIVLYKhZvhw4ezb98+1q1bx5IlS0rW9+3bl6lTp1ZacSJVoX4A9C+6qG+mRm9ERDxOhcINQHR0NB07duTgwYMldwjv2rUrLVq0qLTiRKpK8e0Y3k0BZ4U6PYmIiLuqULhxOp08/vjjhIaGEh8fT3x8PGFhYTzxxBM4nc7KrlGk0t0YDWG+sO8kfKWeNyIiHsWnIjs9/PDDvP322zzzzDN0794dgG+++YbJkydz6tQpnnrqqUotUqSy+XvDLfVh+h7zZpp961ldkYiIVJYK3X4hNjaW1157reRu4MUWLFjAX/7yFw4cOFBpBV4q3X5BLmTNCei20mzulzoAHL5WVyQiIsWq/fYLx48fP+/cmhYtWnD8+PGKHFKk2nUJg5bBcLIQPlTPGxERj1GhcNO+fXumTZt2zvpp06bRrl27Sy5KpDrYbL9NLJ6xz9paRESk8lRozs2UKVMYPHgwX375ZUmPm1WrVpGSksL//ve/Si1QpCr9MQ4m/gzfHocd2XBZsNUViYjIparQyE3Pnj3Zvn07N910E+np6aSnpzN06FC2bNnCe++9V9k1ilSZGH8YGGU+flc9b0REPEKF+9zExsby1FNP8dFHH/HRRx/x5JNPcuLECd5+++0yHyMpKYkuXboQEhJCZGQkQ4YMYdu2bS7b9OrVC5vN5rLce++9FS1b5BzFN9N8dx8UqueNiEiNV+FwUxmSk5NJTExk9erVLF26lIKCAvr3709OTo7LdnfddReHDh0qWaZMmWJRxeKJro+GOr6w/xQsP2J1NSIicqkqNOemsixevNjl+YwZM4iMjGT9+vX06NGjZH1gYCDR0dHVXZ7UEv7eMLIBvLLbvJnmNZFWVyQiIpfC0pGbs2VkZAAQHh7usn7WrFnUrVuXNm3aMHHiRHJzc60oTzxY8ampjw9CRoG1tYiIyKUp18jN0KFDS309PT29woU4nU7Gjx9P9+7dadOmTcn6kSNHEh8fT2xsLD/99BP/+Mc/2LZtGx9//PF5j5OXl0deXl7J88zMzArXJLVHpzBoHQJbsuC/B+DuRlZXJCIicO73uN1ux263l7pPucJNaGjoRV+//fbby3PIEomJiWzevJlvvvnGZf3dd99d8rht27bExMTQt29fdu7cSdOmTc85TlJSEo899liFapDaq7jnzYNbzJ43CjciIu4hLi7O5fmkSZOYPHlyqftU6PYLlW3s2LEsWLCAlStX0rhx41K3zcnJITg4mMWLFzNgwIBzXj/fyE1cXJxuvyAXlXoKGnxhXjG1tQ80D7G6IhGR2qv49gspKSku399lGbmxdM6NYRiMHTuW+fPns3z58osGG4CNGzcCEBMTc97X7XY7DofDZREpi2h/GFQ0mVg9b0RE3MPZ3+kXCzZgcbhJTEzk/fffZ/bs2YSEhJCamkpqaionT54EYOfOnTzxxBOsX7+ePXv2sHDhQm6//XZ69Oih2zxIlSi+HcPMFPW8ERGpqSw9LWWz2c67/p133mH06NGkpKRw2223sXnzZnJycoiLi+Omm27iX//6V5lHZHRXcCmPvEKIXQLHC2BxAgzQZeEiIpa4lO9vS/vcXCxXxcXFkZycXE3ViIC9qOfNtN3mxGKFGxGRmset+tyIuIMxRaem5h+CdPW8ERGpcRRuRM7SMRTaOiDPafa8ERGRmkXhRuQsNttvHYvf2WdtLSIiUn4KNyLncWsD8LbB9yfglyyrqxERkfJQuBE5jyh/GBxlPn5XozciIjWKwo3IBRSfmnpvv3reiIjUJAo3IhcwOBoi/ODgKfjisNXViIhIWSnciFyAn5c59wbMnjciIlIzKNyIlKK4580nqXAi39paRESkbBRuRErRIRTaOyDfCXPU80ZEpEZQuBG5iOKbaarnjYhIzaBwI3IRtzYAHxusTYctmVZXIyIiF6NwI3IR9exwXXHPmxRraxERkYtTuBEpg+JTU++lwGmntbWIiEjpFG5EyuDaKKjnB6l5sEQ9b0RE3JrCjUgZ+J7Z80anpkRE3JrCjUgZFZ+aWpgKx9TzRkTEbSnciJRR+1DoGFrU82a/1dWIiMiFKNyIlEPxzTR1akpExH0p3IiUw8gG4GuDdemwWT1vRETcksKNSDnUtcN10eZj3UxTRMQ9KdyIlFPxzTTf3w8F6nkjIuJ2FG5EymlgJETaIU09b0RE3JLCjUg5+XrBbUU9b3QzTRER96NwI1IBxT1vPk2Fo3nW1iIiIq4UbkQqoK0DOoVCgQEfHLC6GhEROZPCjUgFFY/e6KopERH3onAjUkG3FPW82ZABP2VYXY2IiBRTuBGpoAg/uKG45406FouIuA2FG5FLUHxq6v0U9bwREXEXCjcil2BgJETZ4Ug+LEqzuhoREQGFG5FL4uMFfyy6maZ63oiIuAeFG5FLNKoo3HyWBkfU80ZExHIKNyKXqI0DOofBaQNm77e6GhERUbgRqQTFN9PUVVMiItZTuBGpBDfXBz8v2JhhLiIiYh2FG5FKEO4HNxb3vNHEYhERSynciFSS4p43s/ZDvnreiIhYRuFGpJL0rwcxdjiaD/9TzxsREcso3IhUEvW8ERFxDwo3IpWouOfN52mQdsraWkREaiuFG5FK1MoBXcOg0IDZB6yuRkSkdlK4EalkxROL39kHhmFtLSIitZHCjUglu7k+2L1gU6Z63oiIWEHhRqSS1fGDITHmY00sFhGpfgo3IlVgdNHE4ln7Ia/Q2lpERGobhRuRKnBNJMT6w/EC88opERGpPgo3IlXA2wa3F43e6GaaIiLVy9Jwk5SURJcuXQgJCSEyMpIhQ4awbds2l21OnTpFYmIiERERBAcHM2zYMNLS9J/C4v6Ke978Lw1S1fNGRKTaWBpukpOTSUxMZPXq1SxdupSCggL69+9PTk5OyTb3338/n376KXPnziU5OZmDBw8ydOhQC6sWKZsWIXBlHbPnzaz9VlcjIlJ72AzDfTpxHDlyhMjISJKTk+nRowcZGRnUq1eP2bNnM3z4cAC2bt1Ky5YtWbVqFVdeeeVFj5mZmUloaCgZGRk4HI6qfgsiLl7fA/f+CG1C4KfeYLNZXZGISM1wKd/fbjXnJiPDbAoSHh4OwPr16ykoKKBfv34l27Ro0YKGDRuyatWq8x4jLy+PzMxMl0XEKn+oD/5esDkLNqjnjYhIuZ39nZ6Xl3fRfdwm3DidTsaPH0/37t1p06YNAKmpqfj5+REWFuaybVRUFKmpqec9TlJSEqGhoSVLXFxcVZcuckFhvnCTet6IiFRYXFycy/d6UlLSRfdxm3CTmJjI5s2bmTNnziUdZ+LEiWRkZJQsKSm6VEWsVXw7htnqeSMiUm4pKSku3+sTJ0686D4+1VDXRY0dO5bPPvuMlStX0qBBg5L10dHR5Ofnk56e7jJ6k5aWRnR09HmPZbfbsdvtVV2ySJn1rQcN/GH/Kfg0DYbHWl2RiEjN4XA4atacG8MwGDt2LPPnz2f58uU0btzY5fVOnTrh6+vLsmXLStZt27aNffv2kZCQUN3lilSIS88bnZoSEalylo7cJCYmMnv2bBYsWEBISEjJPJrQ0FACAgIIDQ3ljjvuYMKECYSHh+NwOBg3bhwJCQllulJKxF2MaghP74BFaXDoFMT4W12RiIjnsnTkZvr06WRkZNCrVy9iYmJKlv/+978l20ydOpXrrruOYcOG0aNHD6Kjo/n4448trFqk/C4PhqvCwQm8r2lgIiJVyq363FQF9bkRd/HmHrj7R2gVApvV80ZEpFQe0+dGxJONqA8B3vBzFqxLt7oaERHPpXAjUk1CfWGoet6IiFQ5hRuRajS66KqpDw7AKfW8ERGpEgo3ItWodz2IC4D0Alh4/ibbIiJyiRRuRKqRet6IiFQ9hRuRalZ8amrJYTh40tpaREQ8kcKNSDVrFgxXF/W8eW+/1dWIiHgehRsRCxTfTHPGPvDsTlMiItVP4UbEAr+PNXvebM2GNSesrkZExLMo3IhYwOELw4t63szQ7RhERCqVwo2IRYpPTX2wH06q542ISKVRuBGxSK+60DAAMk7DgkNWVyMi4jkUbkQs4mWDUcU9b3RqSkSk0ijciFhoVNGpqaWH4YB63oiIVAqFGxELNQ2CHhFmz5uZGr0REakUCjciFht9xu0Y1PNGROTSKdyIWGx4LAR6w/YcWK2eNyIil0zhRsRiIb5mwAHdTFNEpDIo3Ii4gTFFE4vnHIDc09bWIiJS0ynciLiBHhHQKBAyT8MnqVZXIyJSsynciLgBl543OjUlInJJFG5E3ERxuPnyCKSo542ISIUp3Ii4icZB5i0ZDOA/e62uRkSk5lK4EXEjxT1vHtsG92/S5GIRkYpQuBFxI7fFwd3x5ujNi7ugwwr49pjVVYmI1CwKNyJuxNsGr3eARVdCfX/YkQO/+wYe2AwnC62uTkSkZlC4EXFDA6Ngcx+z/40BvLATOnwFq45bXZmIiPtTuBFxU2G+8J+O8PmVEOtv3p7h6q/h71vglEZxREQuSOFGxM1dGwWbe8Ptcebdw5/7FTqugO81iiMicl4KNyI1QB0/ePcKWNgNou2wNRuu+hom/gx5GsUREXGhcCNSg1wfDVv6wG0NzFGcZ3bAFcmwVncTFxEpoXAjUsOE+8F7neCTrhBlh5+zIOFreFijOCIigMKNSI11Y4w5inNLfSg04Okd0DkZ1qdbXZmIiLUUbkRqsAg/mN0ZPuoC9fxgcxZ0WwmP/AL5TqurExGxhsKNiAcYGmuO4oyINUdxntwOXZLhh3SrKxMRqX4KNyIeop4d/tsFPuwMdf3gp0zouhImb9UojojULgo3Ih7m9/XNUZxhMXDaMG/C2W0l/JhhdWUiItVD4UbEA0XaYW4XmNPZnJezMcOcbPzENijQKI6IeDiFGxEPZbPBH+rDlt5wU9EozqNb4cqVsCnT6upERKqOwo2Ih4vyN6+mmtUJ6vjChgzotAKe2ganNYojIh5I4UakFrDZYGQDcy7ODdFQYMC/tprN/7ZoFEdEPIzCjUgtEuNvdjZ+7wrzruPr0s3bNzyzXaM4IuI5FG5EahmbDW6LM+fiXBdlXiY+8Rfo/jX8kmV1dSIil07hRqSWig0w7zI+oyOE+sCadOi4Ap7bYTYCFBGpqRRuRGoxmw1GNTTn4gyKhDwn/P1nuPpr2KZRHBGpoRRuRIT6AfD5lfB2B3D4wOoT0GEFPP+rRnFEpOZRuBERwBzF+VM8bO4D/evBKSf8bQv0+Aa2Z1tdnYhI2VkablauXMn1119PbGwsNpuNTz75xOX10aNHY7PZXJaBAwdaU6xILREXAIsT4M0OEOID3x2H9l/BizvBqVEcEakBLA03OTk5tG/fnldeeeWC2wwcOJBDhw6VLB988EE1VihSO9lscGc8bO4N/YpGce7fDL2+hV81iiMibs7Hyl8+aNAgBg0aVOo2drud6OjoaqpIRM7UMBC+SIA39sLfNsPXx6DdCni2FSQ2Bi+b1RWKiJzL7efcrFixgsjISJo3b86f//xnjh07Vur2eXl5ZGZmuiwiUnE2G9zTCDb1gT514WQh/HUT9PkWduVYXZ2IeLqzv9Pz8vIuuo9bh5uBAwcyc+ZMli1bxrPPPktycjKDBg2isLDwgvskJSURGhpassTFxVVjxSKeq1EgLL0KXmkHQd6QfAzafQWv7NJcHBGpOnFxcS7f60lJSRfdx2YYhlv835LNZmP+/PkMGTLkgtvs2rWLpk2b8uWXX9K3b9/zbpOXl+eS6jIzM4mLiyMjIwOHw1HZZYvUSrty4E8/mAEHoHdd+E9HMwCJiFSGzMxMQkNDSUlJcfn+ttvt2O32Uvd165GbszVp0oS6devy66+/XnAbu92Ow+FwWUSkcjUJguXd4d9tIdAbvjoKbZfDa7vBPf5zSUQ8xdnf6RcLNlDDws3+/fs5duwYMTExVpciUut52WBsE/ipN/wuArIL4c8/Qf9VsDfX6upEpDazNNxkZ2ezceNGNm7cCMDu3bvZuHEj+/btIzs7mwcffJDVq1ezZ88eli1bxo033kizZs0YMGCAlWWLyBmaBsGK7vBiGwjwhi+PQNuv4M09GsUREWtYOudmxYoV9O7d+5z1o0aNYvr06QwZMoQffviB9PR0YmNj6d+/P0888QRRUVFl/h3F5+w050ak6u3IhjE/wLfHzeetQsw+Ob3rQo8ICPeztj4RqTku5fvbbSYUVxWFG5HqVWjASzvh4V/M5n/FbED7UDPo9IqAHnUhzNeyMkXEzSnclELhRsQaR/PMicbFy9azOht7AR1DoVddM/D8LgIcCjsiUkThphQKNyLuIfUUrCgKOiuOwvazGgB6AZ3CikZ26sLV4RCisCNSaynclELhRsQ9HThp9skpHtnZeVbY8bZBl7DfRna6h0OQpTeMEZHqpHBTCoUbkZoh5aTryM7usy4n97FB1zpm0OldFxLqQKDCjojHUrgphcKNSM20J9cMOcWBZ99J19f9vKBbnd8mKCeEg7+3NbWKSOVTuCmFwo1IzWcY5kjOijMmKB845bqN3csMOL0izMDTrQ7YFXZEaiyFm1Io3Ih4HsMw5+h8dRRWHIOvjsChs24U7O8FV4X/NkG5ax1ztEdEagaFm1Io3Ih4PsOA7dlFQadoZOfwWWEn0NuclFw8QblzGPgq7Ii4LYWbUijciNQ+hmH21fnqjAnKR/NdtwnyhqsjfpugfEUo+CjsiLgNhZtSKNyIiNOAn7N+CzorjsLxAtdtQnzMRoLFE5Q7hpmXo4uINRRuSqFwIyJncxqwKfO3CcrJxyD9rLDj8DHvh9W7aL5O0yCItoNNgUekWijclELhRkQuptCAnzJ+O4218hhknj53u0BvaBJoBp3ipfh5o0DN4RGpTAo3pVC4EZHyKjTgh3RzgvKKo7AlC/blgrOUfbyAhoHQ9KzwUxyAdN8skfJRuCmFwo2IVIZ8J+zNNS9B35kDu854vDMXThaWvn9dvzMCz1kBSKe7RM51Kd/fal4uIlIGfl5wWbC5nM0wIDXvjLBTFHh25sCuHDiSb16tdTQfvj9x7v4BZ5/uOuNxfKD684iUl8KNiMglstkgxt9cro449/XMgrNGes4IQPuKRn22ZJnL2YpPd50916c4AOl0l8i5FG5ERKqYwxc6hJrL2fKdZsDZeZ7wsysXcgvN+2ztyYXlR8/d/8zTXWeP/sT463SX1E4KNyIiFvLzgmbB5nK2s093nT36U97TXfEBEO0PMXbzZ7Td7O+jACSeRuFGRMRNVfR0165cc/Jzaae7igV4myEn+ozAc3YAivaHKLvm/kjNoXAjIlJDlXa6q6D46q4zws/+k+ZIUOop80ajWafNALQ711wuJty3lAB0xuNwP/DSaJBYSOFGRMQD+ZZyuqtYzmlIy/st8Jz589BZz08b5i0rjheYt7IojY/NHOkpCUHnCUDFP4P0LSRVQP+sRERqqSAfaOIDTYJK385pwImCiweg1Dw4lm8GoQOnzIWM0o8d7H3xABRth0i7bmwqZadwIyIipfKyQYSfubS+yLb5Tjh8xqmvswPRmafFThZCdiH8mmMupbEB9c4IOhF+EOH7W13nW0I1WbrWUrgREZFK4+cFDQLMpTSGAdmnSw9AxT/T8sxbXxzOM5ey8raZ84TODDxnP3dZil6ze1/Sn0DcgMKNiIhUO5sNQnzN5fJS5gWBea+vo3m/BZ/DRae/zlkKfnucW2judyTfXMojyPvCwUejRDWDwo2IiLg1bxtE+ZtL+zLuc6rwwsHn+HnWFa93AjmFkHMS9p0sX40XHBUqWh+uUaJqo3AjIiIex98b6geYS1k5DcgoOH8gqqpRIrsXhPqCw8cc/Qn1LVou8Ph82wV4a9TobAo3IiIimBOn6/iZS7Ny7HfmKNHxCwWiC4wS5TnLP5fobD62soWgcx77mL2SQn0g2MNOqynciIiIXIKKjhJlnjZHiop/ZhRAxvken7Uu84yfTsxL74tDU0V58VvQOV8IKglMFwhL9ezmCJK7ULgRERGpZl42CPM1l4oqvuLsQiHofIHofK8VGmZISi8wF8ox16jYy21hXJOKv5fKpnAjIiJSA515xdnFLr2/EMMw5wydPVKUeZGwdPa6UDdLE25WjoiIiFQXm83sVB3kA7GXcBynUWklVQo1sxYREZFL4m43SlW4EREREY+icCMiIiIeReFGREREPIrCjYiIiHgUhRsRERHxKAo3IiIi4lEUbkRERMSjKNyIiIiIR1G4EREREY+icCMiIiIeReFGREREPIrCjYiIiHgUhRsRERHxKD5WF1DVDMO8D3tmZqbFlYiIiEhZFX9vF3+Pl4fHh5usrCwA4uLiLK5EREREyisrK4vQ0NBy7WMzKhKJahCn08nBgwcJCQnBZrNV2nEzMzOJi4sjJSUFh8NRaceVitNn4l70ebgXfR7uRZ/HxRmGQVZWFrGxsXh5lW8WjceP3Hh5edGgQYMqO77D4dA/TDejz8S96PNwL/o83Is+j9KVd8SmmCYUi4iIiEdRuBERERGPonBTQXa7nUmTJmG3260uRYroM3Ev+jzciz4P96LPo2p5/IRiERERqV00ciMiIiIeReFGREREPIrCjYiIiHgUhRsRERHxKAo3FfTKK6/QqFEj/P396datG2vWrLG6pFopKSmJLl26EBISQmRkJEOGDGHbtm1WlyVFnnnmGWw2G+PHj7e6lFrtwIED3HbbbURERBAQEEDbtm1Zt26d1WXVSoWFhTzyyCM0btyYgIAAmjZtyhNPPFGh+yfJhSncVMB///tfJkyYwKRJk9iwYQPt27dnwIABHD582OrSap3k5GQSExNZvXo1S5cupaCggP79+5OTk2N1abXe2rVref3112nXrp3VpdRqJ06coHv37vj6+rJo0SJ+/vlnnn/+eerUqWN1abXSs88+y/Tp05k2bRq//PILzz77LFOmTOHf//631aV5FF0KXgHdunWjS5cuTJs2DTDvXxUXF8e4ceN46KGHLK6udjty5AiRkZEkJyfTo0cPq8uptbKzs7niiit49dVXefLJJ+nQoQMvvvii1WXVSg899BDffvstX3/9tdWlCHDdddcRFRXF22+/XbJu2LBhBAQE8P7771tYmWfRyE055efns379evr161eyzsvLi379+rFq1SoLKxOAjIwMAMLDwy2upHZLTExk8ODBLv87EWssXLiQzp078/vf/57IyEg6duzIm2++aXVZtdZVV13FsmXL2L59OwA//vgj33zzDYMGDbK4Ms/i8TfOrGxHjx6lsLCQqKgol/VRUVFs3brVoqoEzBG08ePH0717d9q0aWN1ObXWnDlz2LBhA2vXrrW6FAF27drF9OnTmTBhAv/85z9Zu3Ytf/3rX/Hz82PUqFFWl1frPPTQQ2RmZtKiRQu8vb0pLCzkqaee4tZbb7W6NI+icCMeIzExkc2bN/PNN99YXUqtlZKSwn333cfSpUvx9/e3uhzBDP2dO3fm6aefBqBjx45s3ryZ1157TeHGAh9++CGzZs1i9uzZtG7dmo0bNzJ+/HhiY2P1eVQihZtyqlu3Lt7e3qSlpbmsT0tLIzo62qKqZOzYsXz22WesXLmSBg0aWF1OrbV+/XoOHz7MFVdcUbKusLCQlStXMm3aNPLy8vD29rawwtonJiaGVq1auaxr2bIlH330kUUV1W4PPvggDz30EDfffDMAbdu2Ze/evSQlJSncVCLNuSknPz8/OnXqxLJly0rWOZ1Oli1bRkJCgoWV1U6GYTB27Fjmz5/P8uXLady4sdUl1Wp9+/Zl06ZNbNy4sWTp3Lkzt956Kxs3blSwsUD37t3PaY+wfft24uPjLaqodsvNzcXLy/Wr19vbG6fTaVFFnkkjNxUwYcIERo0aRefOnenatSsvvvgiOTk5jBkzxurSap3ExERmz57NggULCAkJITU1FYDQ0FACAgIsrq72CQkJOWe+U1BQEBEREZoHZZH777+fq666iqeffpoRI0awZs0a3njjDd544w2rS6uVrr/+ep566ikaNmxI69at+eGHH3jhhRf405/+ZHVpHkWXglfQtGnTeO6550hNTaVDhw68/PLLdOvWzeqyah2bzXbe9e+88w6jR4+u3mLkvHr16qVLwS322WefMXHiRHbs2EHjxo2ZMGECd911l9Vl1UpZWVk88sgjzJ8/n8OHDxMbG8stt9zCo48+ip+fn9XleQyFGxEREfEomnMjIiIiHkXhRkRERDyKwo2IiIh4FIUbERER8SgKNyIiIuJRFG5ERETEoyjciIiIiEdRuBGRWsdms/HJJ59YXYaIVBGFGxGpVqNHj8Zms52zDBw40OrSRMRD6N5SIlLtBg4cyDvvvOOyzm63W1SNiHgajdyISLWz2+1ER0e7LHXq1AHMU0bTp09n0KBBBAQE0KRJE+bNm+ey/6ZNm+jTpw8BAQFERERw9913k52d7bLNf/7zH1q3bo3dbicmJoaxY8e6vH706FFuuukmAgMDueyyy1i4cGHVvmkRqTYKNyLidh555BGGDRvGjz/+yK233srNN9/ML7/8AkBOTg4DBgygTp06rF27lrlz5/Lll1+6hJfp06eTmJjI3XffzaZNm1i4cCHNmjVz+R2PPfYYI0aM4KeffuLaa6/l1ltv5fjx49X6PkWkihgiItVo1KhRhre3txEUFOSyPPXUU4ZhGAZg3HvvvS77dOvWzfjzn/9sGIZhvPHGG0adOnWM7Ozsktc///xzw8vLy0hNTTUMwzBiY2ONhx9++II1AMa//vWvkufZ2dkGYCxatKjS3qeIWEdzbkSk2vXu3Zvp06e7rAsPDy95nJCQ4PJaQkICGzduBOCXX36hffv2BAUFlbzevXt3nE4n27Ztw2azcfDgQfr27VtqDe3atSt5HBQUhMPh4PDhwxV9SyLiRhRuRKTaBQUFnXOaqLIEBASUaTtfX1+X5zabDafTWRUliUg105wbEXE7q1evPud5y5YtAWjZsiU//vgjOTk5Ja9/++23eHl50bx5c0JCQmjUqBHLli2r1ppFxH1o5EZEql1eXh6pqaku63x8fKhbty4Ac+fOpXPnzlx99dXMmjWLNWvW8PbbbwNw6623MmnSJEaNGsXkyZM5cuQI48aN449//CNRUVEATJ48mXvvvZfIyEgGDRpEVlYW3377LePGjaveNyoillC4EZFqt3jxYmJiYlzWNW/enK1btwLmlUxz5szhL3/5CzExMXzwwQe0atUKgMDAQJYsWcJ9991Hly5dCAwMZNiwYbzwwgslxxo1ahSnTp1i6tSp/O1vf6Nu3boMHz68+t6giFjKZhiGYXURIiLFbDYb8+fPZ8iQIVaXIiI1lObciIiIiEdRuBERERGPojk3IuJWdKZcRC6VRm5ERETEoyjciIiIiEdRuBERERGPonAjIiIiHkXhRkRERDyKwo2IiIh4FIUbERER8SgKNyIiIuJRFG5ERETEo/w/MhrWrHgLBKQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_hist = history.history['loss']\n",
    "mae_hist = history.history['mean_absolute_error']\n",
    "plt.plot(history.epoch, loss_hist, label='Loss')\n",
    "#plt.plot(history.epoch, mae_hist, label='MAE')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss per epoch')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "id": "eQ7at7lIsDOo"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 860us/step\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "id": "gVHNkpr_sUdf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.878561076723923, 12.231545665403281)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(mean_absolute_error(pred, y_test), mean_squared_error(pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N3S7kjlEHAMk"
   },
   "source": [
    "We obtained a model with around the same performance as the previous machine learning models. We could improve this model and add more layers, but that would increase training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "id": "e_DSf_CXG8nx"
   },
   "outputs": [],
   "source": [
    "model = create_dense_model(12, n_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "id": "VO5pd1EAHUpZ"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_absolute_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "id": "PrykEJb7HXY7",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "45/45 [==============================] - 1s 1ms/step - loss: 33.7182 - mean_absolute_error: 4.5501\n",
      "Epoch 2/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 13.9065 - mean_absolute_error: 3.1200\n",
      "Epoch 3/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 12.5160 - mean_absolute_error: 2.9685\n",
      "Epoch 4/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 12.1780 - mean_absolute_error: 2.9163\n",
      "Epoch 5/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 11.7814 - mean_absolute_error: 2.8530\n",
      "Epoch 6/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 11.5110 - mean_absolute_error: 2.8180\n",
      "Epoch 7/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 11.2498 - mean_absolute_error: 2.7840\n",
      "Epoch 8/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 11.1069 - mean_absolute_error: 2.7565\n",
      "Epoch 9/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.8989 - mean_absolute_error: 2.7277\n",
      "Epoch 10/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.7956 - mean_absolute_error: 2.7066\n",
      "Epoch 11/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.5886 - mean_absolute_error: 2.6608\n",
      "Epoch 12/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.5298 - mean_absolute_error: 2.6751\n",
      "Epoch 13/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.4608 - mean_absolute_error: 2.6622\n",
      "Epoch 14/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.4312 - mean_absolute_error: 2.6446\n",
      "Epoch 15/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.3370 - mean_absolute_error: 2.6247\n",
      "Epoch 16/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.2419 - mean_absolute_error: 2.6268\n",
      "Epoch 17/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.1930 - mean_absolute_error: 2.6096\n",
      "Epoch 18/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.0964 - mean_absolute_error: 2.5866\n",
      "Epoch 19/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.0285 - mean_absolute_error: 2.6038\n",
      "Epoch 20/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.9176 - mean_absolute_error: 2.5880\n",
      "Epoch 21/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 10.0283 - mean_absolute_error: 2.5706\n",
      "Epoch 22/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.8359 - mean_absolute_error: 2.5643\n",
      "Epoch 23/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 9.7732 - mean_absolute_error: 2.5555\n",
      "Epoch 24/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.8007 - mean_absolute_error: 2.5588\n",
      "Epoch 25/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.6946 - mean_absolute_error: 2.5317\n",
      "Epoch 26/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.8020 - mean_absolute_error: 2.5394\n",
      "Epoch 27/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.6420 - mean_absolute_error: 2.5369\n",
      "Epoch 28/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.5755 - mean_absolute_error: 2.5182\n",
      "Epoch 29/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.5706 - mean_absolute_error: 2.5014\n",
      "Epoch 30/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.5326 - mean_absolute_error: 2.5112\n",
      "Epoch 31/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.5355 - mean_absolute_error: 2.5007\n",
      "Epoch 32/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.4462 - mean_absolute_error: 2.4920\n",
      "Epoch 33/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.4383 - mean_absolute_error: 2.4874\n",
      "Epoch 34/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 9.3603 - mean_absolute_error: 2.4743\n",
      "Epoch 35/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.3691 - mean_absolute_error: 2.4723\n",
      "Epoch 36/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.3382 - mean_absolute_error: 2.4677\n",
      "Epoch 37/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.2988 - mean_absolute_error: 2.4468\n",
      "Epoch 38/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 9.2630 - mean_absolute_error: 2.4586\n",
      "Epoch 39/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.1985 - mean_absolute_error: 2.4525\n",
      "Epoch 40/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 9.1556 - mean_absolute_error: 2.4335\n",
      "Epoch 41/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.1760 - mean_absolute_error: 2.4361\n",
      "Epoch 42/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 9.2470 - mean_absolute_error: 2.4508\n",
      "Epoch 43/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.3133 - mean_absolute_error: 2.4482\n",
      "Epoch 44/50\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 9.1514 - mean_absolute_error: 2.4408\n",
      "Epoch 45/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.1078 - mean_absolute_error: 2.4311\n",
      "Epoch 46/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.1492 - mean_absolute_error: 2.4182\n",
      "Epoch 47/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.1590 - mean_absolute_error: 2.4403\n",
      "Epoch 48/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.0667 - mean_absolute_error: 2.4285\n",
      "Epoch 49/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.3555 - mean_absolute_error: 2.4870\n",
      "Epoch 50/50\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.1969 - mean_absolute_error: 2.4373\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HL0zq1YAIAS9"
   },
   "source": [
    "Because the dataset we have is not that big, the training takes little time. We have improved the metric of our model, but the model could be overfitting for too many epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "id": "50Z4iCZGINit"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 976us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2.594036429867906, 10.254967851634392)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(x_test)\n",
    "(mean_absolute_error(pred, y_test), mean_squared_error(pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BmheYzqBIdzi"
   },
   "source": [
    "The model we have now is not overfitting. It is now comparable to the Random Forest one. Let's see what could happen if we set the epoch to, say, 200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "id": "9-9uK2tDIdah",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.0661 - mean_absolute_error: 2.4227\n",
      "Epoch 2/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 9.0210 - mean_absolute_error: 2.4121\n",
      "Epoch 3/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.9879 - mean_absolute_error: 2.3979\n",
      "Epoch 4/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.9265 - mean_absolute_error: 2.3970\n",
      "Epoch 5/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.8850 - mean_absolute_error: 2.3713\n",
      "Epoch 6/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.9914 - mean_absolute_error: 2.4094\n",
      "Epoch 7/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 9.0128 - mean_absolute_error: 2.4015\n",
      "Epoch 8/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.9624 - mean_absolute_error: 2.4174\n",
      "Epoch 9/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.8993 - mean_absolute_error: 2.3941\n",
      "Epoch 10/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.9683 - mean_absolute_error: 2.4038\n",
      "Epoch 11/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.9181 - mean_absolute_error: 2.3980\n",
      "Epoch 12/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.8284 - mean_absolute_error: 2.3836\n",
      "Epoch 13/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.9264 - mean_absolute_error: 2.3997\n",
      "Epoch 14/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.8517 - mean_absolute_error: 2.3864\n",
      "Epoch 15/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.8637 - mean_absolute_error: 2.3956\n",
      "Epoch 16/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.9363 - mean_absolute_error: 2.3925\n",
      "Epoch 17/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.7918 - mean_absolute_error: 2.3780\n",
      "Epoch 18/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.7857 - mean_absolute_error: 2.3615\n",
      "Epoch 19/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.7007 - mean_absolute_error: 2.3629\n",
      "Epoch 20/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.7883 - mean_absolute_error: 2.3728\n",
      "Epoch 21/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6105 - mean_absolute_error: 2.3528\n",
      "Epoch 22/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.9031 - mean_absolute_error: 2.3872\n",
      "Epoch 23/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6303 - mean_absolute_error: 2.3489\n",
      "Epoch 24/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6632 - mean_absolute_error: 2.3472\n",
      "Epoch 25/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.7485 - mean_absolute_error: 2.3407\n",
      "Epoch 26/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6635 - mean_absolute_error: 2.3562\n",
      "Epoch 27/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6966 - mean_absolute_error: 2.3394\n",
      "Epoch 28/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.6180 - mean_absolute_error: 2.3425\n",
      "Epoch 29/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6255 - mean_absolute_error: 2.3481\n",
      "Epoch 30/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.5466 - mean_absolute_error: 2.3314\n",
      "Epoch 31/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6098 - mean_absolute_error: 2.3429\n",
      "Epoch 32/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.5603 - mean_absolute_error: 2.3308\n",
      "Epoch 33/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6301 - mean_absolute_error: 2.3250\n",
      "Epoch 34/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.5196 - mean_absolute_error: 2.3230\n",
      "Epoch 35/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.5479 - mean_absolute_error: 2.3379\n",
      "Epoch 36/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.5084 - mean_absolute_error: 2.3213\n",
      "Epoch 37/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.5736 - mean_absolute_error: 2.3374\n",
      "Epoch 38/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6631 - mean_absolute_error: 2.3352\n",
      "Epoch 39/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6558 - mean_absolute_error: 2.3531\n",
      "Epoch 40/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.5555 - mean_absolute_error: 2.3216\n",
      "Epoch 41/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.6698 - mean_absolute_error: 2.3336\n",
      "Epoch 42/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.4369 - mean_absolute_error: 2.2997\n",
      "Epoch 43/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.4802 - mean_absolute_error: 2.3176\n",
      "Epoch 44/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.4963 - mean_absolute_error: 2.3137\n",
      "Epoch 45/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3987 - mean_absolute_error: 2.2972\n",
      "Epoch 46/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.3658 - mean_absolute_error: 2.3013\n",
      "Epoch 47/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3277 - mean_absolute_error: 2.2779\n",
      "Epoch 48/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3694 - mean_absolute_error: 2.2895\n",
      "Epoch 49/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.3542 - mean_absolute_error: 2.2862\n",
      "Epoch 50/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.4675 - mean_absolute_error: 2.2977\n",
      "Epoch 51/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3800 - mean_absolute_error: 2.2904\n",
      "Epoch 52/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3223 - mean_absolute_error: 2.2737\n",
      "Epoch 53/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3602 - mean_absolute_error: 2.2855\n",
      "Epoch 54/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.4308 - mean_absolute_error: 2.3137\n",
      "Epoch 55/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.2790 - mean_absolute_error: 2.2879\n",
      "Epoch 56/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.2717 - mean_absolute_error: 2.2614\n",
      "Epoch 57/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.2560 - mean_absolute_error: 2.2727\n",
      "Epoch 58/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.3347 - mean_absolute_error: 2.2822\n",
      "Epoch 59/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.2238 - mean_absolute_error: 2.2539\n",
      "Epoch 60/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3246 - mean_absolute_error: 2.2885\n",
      "Epoch 61/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.2601 - mean_absolute_error: 2.2710\n",
      "Epoch 62/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3685 - mean_absolute_error: 2.2716\n",
      "Epoch 63/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.2161 - mean_absolute_error: 2.2647\n",
      "Epoch 64/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3464 - mean_absolute_error: 2.2780\n",
      "Epoch 65/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.2677 - mean_absolute_error: 2.2810\n",
      "Epoch 66/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3240 - mean_absolute_error: 2.2970\n",
      "Epoch 67/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3205 - mean_absolute_error: 2.2882\n",
      "Epoch 68/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3274 - mean_absolute_error: 2.2759\n",
      "Epoch 69/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1233 - mean_absolute_error: 2.2446\n",
      "Epoch 70/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1694 - mean_absolute_error: 2.2523\n",
      "Epoch 71/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1533 - mean_absolute_error: 2.2545\n",
      "Epoch 72/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1219 - mean_absolute_error: 2.2503\n",
      "Epoch 73/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.1984 - mean_absolute_error: 2.2565\n",
      "Epoch 74/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.1742 - mean_absolute_error: 2.2541\n",
      "Epoch 75/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1885 - mean_absolute_error: 2.2505\n",
      "Epoch 76/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1399 - mean_absolute_error: 2.2399\n",
      "Epoch 77/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1994 - mean_absolute_error: 2.2756\n",
      "Epoch 78/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3122 - mean_absolute_error: 2.2713\n",
      "Epoch 79/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1376 - mean_absolute_error: 2.2496\n",
      "Epoch 80/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1544 - mean_absolute_error: 2.2619\n",
      "Epoch 81/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0932 - mean_absolute_error: 2.2341\n",
      "Epoch 82/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1815 - mean_absolute_error: 2.2528\n",
      "Epoch 83/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.0339 - mean_absolute_error: 2.2365\n",
      "Epoch 84/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0716 - mean_absolute_error: 2.2479\n",
      "Epoch 85/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0089 - mean_absolute_error: 2.2180\n",
      "Epoch 86/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.3244 - mean_absolute_error: 2.2842\n",
      "Epoch 87/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1556 - mean_absolute_error: 2.2679\n",
      "Epoch 88/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1548 - mean_absolute_error: 2.2624\n",
      "Epoch 89/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 8.2103 - mean_absolute_error: 2.2505\n",
      "Epoch 90/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1543 - mean_absolute_error: 2.2626\n",
      "Epoch 91/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.2380 - mean_absolute_error: 2.2806\n",
      "Epoch 92/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1198 - mean_absolute_error: 2.2532\n",
      "Epoch 93/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1844 - mean_absolute_error: 2.2477\n",
      "Epoch 94/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0864 - mean_absolute_error: 2.2511\n",
      "Epoch 95/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9829 - mean_absolute_error: 2.2283\n",
      "Epoch 96/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0354 - mean_absolute_error: 2.2398\n",
      "Epoch 97/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0368 - mean_absolute_error: 2.2231\n",
      "Epoch 98/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9532 - mean_absolute_error: 2.2270\n",
      "Epoch 99/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.9839 - mean_absolute_error: 2.2200\n",
      "Epoch 100/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9981 - mean_absolute_error: 2.2331\n",
      "Epoch 101/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.1014 - mean_absolute_error: 2.2527\n",
      "Epoch 102/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9414 - mean_absolute_error: 2.2376\n",
      "Epoch 103/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8899 - mean_absolute_error: 2.2105\n",
      "Epoch 104/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9703 - mean_absolute_error: 2.2161\n",
      "Epoch 105/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9485 - mean_absolute_error: 2.2265\n",
      "Epoch 106/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9455 - mean_absolute_error: 2.2215\n",
      "Epoch 107/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9717 - mean_absolute_error: 2.2359\n",
      "Epoch 108/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.9070 - mean_absolute_error: 2.2209\n",
      "Epoch 109/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9254 - mean_absolute_error: 2.2274\n",
      "Epoch 110/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8659 - mean_absolute_error: 2.2084\n",
      "Epoch 111/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9054 - mean_absolute_error: 2.2199\n",
      "Epoch 112/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8160 - mean_absolute_error: 2.1934\n",
      "Epoch 113/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0698 - mean_absolute_error: 2.2511\n",
      "Epoch 114/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9209 - mean_absolute_error: 2.2214\n",
      "Epoch 115/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9882 - mean_absolute_error: 2.2275\n",
      "Epoch 116/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8600 - mean_absolute_error: 2.2130\n",
      "Epoch 117/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8997 - mean_absolute_error: 2.2070\n",
      "Epoch 118/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.9619 - mean_absolute_error: 2.2333\n",
      "Epoch 119/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9806 - mean_absolute_error: 2.2258\n",
      "Epoch 120/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0017 - mean_absolute_error: 2.2434\n",
      "Epoch 121/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9101 - mean_absolute_error: 2.2262\n",
      "Epoch 122/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8614 - mean_absolute_error: 2.2133\n",
      "Epoch 123/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.8294 - mean_absolute_error: 2.2109\n",
      "Epoch 124/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7847 - mean_absolute_error: 2.2067\n",
      "Epoch 125/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7974 - mean_absolute_error: 2.2136\n",
      "Epoch 126/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.2391 - mean_absolute_error: 2.2437\n",
      "Epoch 127/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8689 - mean_absolute_error: 2.2265\n",
      "Epoch 128/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8596 - mean_absolute_error: 2.2240\n",
      "Epoch 129/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7224 - mean_absolute_error: 2.1915\n",
      "Epoch 130/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8527 - mean_absolute_error: 2.2099\n",
      "Epoch 131/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8287 - mean_absolute_error: 2.2081\n",
      "Epoch 132/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0774 - mean_absolute_error: 2.2411\n",
      "Epoch 133/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.7759 - mean_absolute_error: 2.2099\n",
      "Epoch 134/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.7737 - mean_absolute_error: 2.2082\n",
      "Epoch 135/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7437 - mean_absolute_error: 2.1965\n",
      "Epoch 136/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.7721 - mean_absolute_error: 2.1930\n",
      "Epoch 137/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9416 - mean_absolute_error: 2.2397\n",
      "Epoch 138/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7958 - mean_absolute_error: 2.1966\n",
      "Epoch 139/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7025 - mean_absolute_error: 2.1905\n",
      "Epoch 140/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.6426 - mean_absolute_error: 2.1815\n",
      "Epoch 141/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6191 - mean_absolute_error: 2.1782\n",
      "Epoch 142/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6496 - mean_absolute_error: 2.1802\n",
      "Epoch 143/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9899 - mean_absolute_error: 2.2221\n",
      "Epoch 144/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8384 - mean_absolute_error: 2.2097\n",
      "Epoch 145/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6994 - mean_absolute_error: 2.1943\n",
      "Epoch 146/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7205 - mean_absolute_error: 2.1996\n",
      "Epoch 147/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6664 - mean_absolute_error: 2.1812\n",
      "Epoch 148/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6543 - mean_absolute_error: 2.1723\n",
      "Epoch 149/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 8.0176 - mean_absolute_error: 2.2504\n",
      "Epoch 150/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6580 - mean_absolute_error: 2.1921\n",
      "Epoch 151/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7842 - mean_absolute_error: 2.2022\n",
      "Epoch 152/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.6713 - mean_absolute_error: 2.1986\n",
      "Epoch 153/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.7295 - mean_absolute_error: 2.2026\n",
      "Epoch 154/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6548 - mean_absolute_error: 2.1836\n",
      "Epoch 155/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7142 - mean_absolute_error: 2.1862\n",
      "Epoch 156/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6467 - mean_absolute_error: 2.1863\n",
      "Epoch 157/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5715 - mean_absolute_error: 2.1625\n",
      "Epoch 158/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6101 - mean_absolute_error: 2.1842\n",
      "Epoch 159/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8499 - mean_absolute_error: 2.2154\n",
      "Epoch 160/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7282 - mean_absolute_error: 2.1939\n",
      "Epoch 161/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4696 - mean_absolute_error: 2.1538\n",
      "Epoch 162/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5094 - mean_absolute_error: 2.1493\n",
      "Epoch 163/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5415 - mean_absolute_error: 2.1713\n",
      "Epoch 164/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.5305 - mean_absolute_error: 2.1556\n",
      "Epoch 165/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6588 - mean_absolute_error: 2.1881\n",
      "Epoch 166/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8166 - mean_absolute_error: 2.2003\n",
      "Epoch 167/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5862 - mean_absolute_error: 2.1764\n",
      "Epoch 168/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.8182 - mean_absolute_error: 2.2020\n",
      "Epoch 169/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.9198 - mean_absolute_error: 2.2226\n",
      "Epoch 170/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7401 - mean_absolute_error: 2.1904\n",
      "Epoch 171/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5381 - mean_absolute_error: 2.1592\n",
      "Epoch 172/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5832 - mean_absolute_error: 2.1566\n",
      "Epoch 173/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5044 - mean_absolute_error: 2.1491\n",
      "Epoch 174/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4309 - mean_absolute_error: 2.1375\n",
      "Epoch 175/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.5944 - mean_absolute_error: 2.1701\n",
      "Epoch 176/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7712 - mean_absolute_error: 2.1915\n",
      "Epoch 177/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5307 - mean_absolute_error: 2.1611\n",
      "Epoch 178/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4251 - mean_absolute_error: 2.1436\n",
      "Epoch 179/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5325 - mean_absolute_error: 2.1810\n",
      "Epoch 180/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4413 - mean_absolute_error: 2.1539\n",
      "Epoch 181/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5701 - mean_absolute_error: 2.1604\n",
      "Epoch 182/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4598 - mean_absolute_error: 2.1424\n",
      "Epoch 183/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5842 - mean_absolute_error: 2.1638\n",
      "Epoch 184/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4653 - mean_absolute_error: 2.1492\n",
      "Epoch 185/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5288 - mean_absolute_error: 2.1587\n",
      "Epoch 186/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2436 - mean_absolute_error: 2.1095\n",
      "Epoch 187/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3959 - mean_absolute_error: 2.1300\n",
      "Epoch 188/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4964 - mean_absolute_error: 2.1410\n",
      "Epoch 189/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4059 - mean_absolute_error: 2.1310\n",
      "Epoch 190/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3794 - mean_absolute_error: 2.1392\n",
      "Epoch 191/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5019 - mean_absolute_error: 2.1464\n",
      "Epoch 192/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3986 - mean_absolute_error: 2.1301\n",
      "Epoch 193/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4661 - mean_absolute_error: 2.1329\n",
      "Epoch 194/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.4943 - mean_absolute_error: 2.1455\n",
      "Epoch 195/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3884 - mean_absolute_error: 2.1365\n",
      "Epoch 196/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.6002 - mean_absolute_error: 2.1639\n",
      "Epoch 197/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3867 - mean_absolute_error: 2.1394\n",
      "Epoch 198/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3870 - mean_absolute_error: 2.1243\n",
      "Epoch 199/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3901 - mean_absolute_error: 2.1172\n",
      "Epoch 200/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4938 - mean_absolute_error: 2.1550\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "id": "9wX2ikmgIq1Z"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_hist = history.history['loss']\n",
    "mae_hist = history.history['mean_absolute_error']\n",
    "plt.plot(history.epoch, loss_hist, label='Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss per epoch')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "id": "jL8u8vEJJHkL",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.4781 - mean_absolute_error: 2.1326\n",
      "Epoch 2/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.8391 - mean_absolute_error: 2.1977\n",
      "Epoch 3/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5147 - mean_absolute_error: 2.1200\n",
      "Epoch 4/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.7171 - mean_absolute_error: 2.1666\n",
      "Epoch 5/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.4223 - mean_absolute_error: 2.1399\n",
      "Epoch 6/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.5771 - mean_absolute_error: 2.1473\n",
      "Epoch 7/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3640 - mean_absolute_error: 2.1124\n",
      "Epoch 8/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3397 - mean_absolute_error: 2.1107\n",
      "Epoch 9/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4328 - mean_absolute_error: 2.1229\n",
      "Epoch 10/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4046 - mean_absolute_error: 2.1164\n",
      "Epoch 11/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7409 - mean_absolute_error: 2.1909\n",
      "Epoch 12/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4502 - mean_absolute_error: 2.1350\n",
      "Epoch 13/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4172 - mean_absolute_error: 2.1153\n",
      "Epoch 14/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4089 - mean_absolute_error: 2.1357\n",
      "Epoch 15/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3482 - mean_absolute_error: 2.1051\n",
      "Epoch 16/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3054 - mean_absolute_error: 2.1051\n",
      "Epoch 17/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3402 - mean_absolute_error: 2.1027\n",
      "Epoch 18/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3205 - mean_absolute_error: 2.1039\n",
      "Epoch 19/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.7634 - mean_absolute_error: 2.1978\n",
      "Epoch 20/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4789 - mean_absolute_error: 2.1310\n",
      "Epoch 21/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4020 - mean_absolute_error: 2.1238\n",
      "Epoch 22/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3633 - mean_absolute_error: 2.1091\n",
      "Epoch 23/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3711 - mean_absolute_error: 2.1142\n",
      "Epoch 24/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3895 - mean_absolute_error: 2.1026\n",
      "Epoch 25/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3236 - mean_absolute_error: 2.0963\n",
      "Epoch 26/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.4945 - mean_absolute_error: 2.1359\n",
      "Epoch 27/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3537 - mean_absolute_error: 2.1118\n",
      "Epoch 28/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.4840 - mean_absolute_error: 2.1421\n",
      "Epoch 29/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.6213 - mean_absolute_error: 2.1494\n",
      "Epoch 30/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3286 - mean_absolute_error: 2.1087\n",
      "Epoch 31/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.2908 - mean_absolute_error: 2.0901\n",
      "Epoch 32/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3203 - mean_absolute_error: 2.1024\n",
      "Epoch 33/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5119 - mean_absolute_error: 2.1508\n",
      "Epoch 34/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4869 - mean_absolute_error: 2.1491\n",
      "Epoch 35/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.7009 - mean_absolute_error: 2.1680\n",
      "Epoch 36/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3204 - mean_absolute_error: 2.1178\n",
      "Epoch 37/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.4779 - mean_absolute_error: 2.1330\n",
      "Epoch 38/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3629 - mean_absolute_error: 2.1080\n",
      "Epoch 39/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3299 - mean_absolute_error: 2.0895\n",
      "Epoch 40/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3470 - mean_absolute_error: 2.1191\n",
      "Epoch 41/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3481 - mean_absolute_error: 2.1138\n",
      "Epoch 42/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.2525 - mean_absolute_error: 2.1057\n",
      "Epoch 43/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1887 - mean_absolute_error: 2.0734\n",
      "Epoch 44/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2545 - mean_absolute_error: 2.0952\n",
      "Epoch 45/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5764 - mean_absolute_error: 2.1409\n",
      "Epoch 46/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3928 - mean_absolute_error: 2.1164\n",
      "Epoch 47/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3921 - mean_absolute_error: 2.1230\n",
      "Epoch 48/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4472 - mean_absolute_error: 2.1203\n",
      "Epoch 49/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4632 - mean_absolute_error: 2.1321\n",
      "Epoch 50/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2766 - mean_absolute_error: 2.0956\n",
      "Epoch 51/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.2935 - mean_absolute_error: 2.1001\n",
      "Epoch 52/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3411 - mean_absolute_error: 2.1015\n",
      "Epoch 53/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2663 - mean_absolute_error: 2.0906\n",
      "Epoch 54/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5517 - mean_absolute_error: 2.1507\n",
      "Epoch 55/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3949 - mean_absolute_error: 2.1175\n",
      "Epoch 56/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3748 - mean_absolute_error: 2.1149\n",
      "Epoch 57/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.2303 - mean_absolute_error: 2.0840\n",
      "Epoch 58/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1608 - mean_absolute_error: 2.0851\n",
      "Epoch 59/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1375 - mean_absolute_error: 2.0861\n",
      "Epoch 60/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1566 - mean_absolute_error: 2.0735\n",
      "Epoch 61/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3447 - mean_absolute_error: 2.1180\n",
      "Epoch 62/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1851 - mean_absolute_error: 2.0884\n",
      "Epoch 63/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.2213 - mean_absolute_error: 2.0813\n",
      "Epoch 64/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.8204 - mean_absolute_error: 2.2133\n",
      "Epoch 65/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.5311 - mean_absolute_error: 2.1286\n",
      "Epoch 66/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.7365 - mean_absolute_error: 2.2074\n",
      "Epoch 67/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.6069 - mean_absolute_error: 2.1793\n",
      "Epoch 68/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3930 - mean_absolute_error: 2.1371\n",
      "Epoch 69/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.4207 - mean_absolute_error: 2.1306\n",
      "Epoch 70/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3199 - mean_absolute_error: 2.1276\n",
      "Epoch 71/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3158 - mean_absolute_error: 2.1352\n",
      "Epoch 72/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3874 - mean_absolute_error: 2.1320\n",
      "Epoch 73/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3531 - mean_absolute_error: 2.1201\n",
      "Epoch 74/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.3140 - mean_absolute_error: 2.1196\n",
      "Epoch 75/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3114 - mean_absolute_error: 2.1278\n",
      "Epoch 76/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2349 - mean_absolute_error: 2.0995\n",
      "Epoch 77/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1744 - mean_absolute_error: 2.0893\n",
      "Epoch 78/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2594 - mean_absolute_error: 2.1099\n",
      "Epoch 79/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.2928 - mean_absolute_error: 2.1090\n",
      "Epoch 80/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1589 - mean_absolute_error: 2.0898\n",
      "Epoch 81/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3640 - mean_absolute_error: 2.1166\n",
      "Epoch 82/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.2570 - mean_absolute_error: 2.1037\n",
      "Epoch 83/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3860 - mean_absolute_error: 2.1228\n",
      "Epoch 84/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2027 - mean_absolute_error: 2.1196\n",
      "Epoch 85/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.2099 - mean_absolute_error: 2.0932\n",
      "Epoch 86/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1582 - mean_absolute_error: 2.0873\n",
      "Epoch 87/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4178 - mean_absolute_error: 2.1372\n",
      "Epoch 88/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.4230 - mean_absolute_error: 2.1317\n",
      "Epoch 89/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1582 - mean_absolute_error: 2.0837\n",
      "Epoch 90/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1005 - mean_absolute_error: 2.0736\n",
      "Epoch 91/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1086 - mean_absolute_error: 2.0820\n",
      "Epoch 92/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1311 - mean_absolute_error: 2.0842\n",
      "Epoch 93/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2841 - mean_absolute_error: 2.1071\n",
      "Epoch 94/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1070 - mean_absolute_error: 2.0746\n",
      "Epoch 95/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1045 - mean_absolute_error: 2.0659\n",
      "Epoch 96/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1915 - mean_absolute_error: 2.1042\n",
      "Epoch 97/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2090 - mean_absolute_error: 2.1168\n",
      "Epoch 98/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3647 - mean_absolute_error: 2.1050\n",
      "Epoch 99/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1373 - mean_absolute_error: 2.0967\n",
      "Epoch 100/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.2422 - mean_absolute_error: 2.1047\n",
      "Epoch 101/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3262 - mean_absolute_error: 2.1161\n",
      "Epoch 102/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1877 - mean_absolute_error: 2.0984\n",
      "Epoch 103/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1357 - mean_absolute_error: 2.0981\n",
      "Epoch 104/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2147 - mean_absolute_error: 2.1118\n",
      "Epoch 105/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.1259 - mean_absolute_error: 2.0840\n",
      "Epoch 106/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9981 - mean_absolute_error: 2.0637\n",
      "Epoch 107/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1392 - mean_absolute_error: 2.0870\n",
      "Epoch 108/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1177 - mean_absolute_error: 2.0841\n",
      "Epoch 109/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1048 - mean_absolute_error: 2.0742\n",
      "Epoch 110/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9041 - mean_absolute_error: 2.0378\n",
      "Epoch 111/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0360 - mean_absolute_error: 2.0629\n",
      "Epoch 112/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2356 - mean_absolute_error: 2.1044\n",
      "Epoch 113/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0831 - mean_absolute_error: 2.0646\n",
      "Epoch 114/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9692 - mean_absolute_error: 2.0607\n",
      "Epoch 115/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9712 - mean_absolute_error: 2.0613\n",
      "Epoch 116/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1637 - mean_absolute_error: 2.0927\n",
      "Epoch 117/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0317 - mean_absolute_error: 2.0634\n",
      "Epoch 118/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1325 - mean_absolute_error: 2.0841\n",
      "Epoch 119/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9981 - mean_absolute_error: 2.0496\n",
      "Epoch 120/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9931 - mean_absolute_error: 2.0661\n",
      "Epoch 121/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9005 - mean_absolute_error: 2.0453\n",
      "Epoch 122/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9726 - mean_absolute_error: 2.0621\n",
      "Epoch 123/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.3853 - mean_absolute_error: 2.1193\n",
      "Epoch 124/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0914 - mean_absolute_error: 2.0846\n",
      "Epoch 125/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1308 - mean_absolute_error: 2.0957\n",
      "Epoch 126/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9387 - mean_absolute_error: 2.0390\n",
      "Epoch 127/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0148 - mean_absolute_error: 2.0683\n",
      "Epoch 128/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0142 - mean_absolute_error: 2.0629\n",
      "Epoch 129/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0769 - mean_absolute_error: 2.0756\n",
      "Epoch 130/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 6.9932 - mean_absolute_error: 2.0600\n",
      "Epoch 131/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9300 - mean_absolute_error: 2.0481\n",
      "Epoch 132/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8858 - mean_absolute_error: 2.0439\n",
      "Epoch 133/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9983 - mean_absolute_error: 2.0526\n",
      "Epoch 134/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8362 - mean_absolute_error: 2.0317\n",
      "Epoch 135/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0104 - mean_absolute_error: 2.0560\n",
      "Epoch 136/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1722 - mean_absolute_error: 2.0857\n",
      "Epoch 137/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2676 - mean_absolute_error: 2.1122\n",
      "Epoch 138/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1046 - mean_absolute_error: 2.0855\n",
      "Epoch 139/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1193 - mean_absolute_error: 2.0821\n",
      "Epoch 140/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0929 - mean_absolute_error: 2.0848\n",
      "Epoch 141/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9966 - mean_absolute_error: 2.0752\n",
      "Epoch 142/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8731 - mean_absolute_error: 2.0324\n",
      "Epoch 143/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1976 - mean_absolute_error: 2.0992\n",
      "Epoch 144/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9182 - mean_absolute_error: 2.0626\n",
      "Epoch 145/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9297 - mean_absolute_error: 2.0561\n",
      "Epoch 146/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 7.0470 - mean_absolute_error: 2.0691\n",
      "Epoch 147/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8748 - mean_absolute_error: 2.0351\n",
      "Epoch 148/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9704 - mean_absolute_error: 2.0423\n",
      "Epoch 149/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8351 - mean_absolute_error: 2.0341\n",
      "Epoch 150/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8065 - mean_absolute_error: 2.0282\n",
      "Epoch 151/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8385 - mean_absolute_error: 2.0397\n",
      "Epoch 152/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8742 - mean_absolute_error: 2.0537\n",
      "Epoch 153/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0522 - mean_absolute_error: 2.0588\n",
      "Epoch 154/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7939 - mean_absolute_error: 2.0209\n",
      "Epoch 155/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9311 - mean_absolute_error: 2.0507\n",
      "Epoch 156/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9237 - mean_absolute_error: 2.0561\n",
      "Epoch 157/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7580 - mean_absolute_error: 2.0261\n",
      "Epoch 158/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2915 - mean_absolute_error: 2.1040\n",
      "Epoch 159/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0703 - mean_absolute_error: 2.0767\n",
      "Epoch 160/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8268 - mean_absolute_error: 2.0224\n",
      "Epoch 161/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 6.7455 - mean_absolute_error: 2.0085\n",
      "Epoch 162/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1718 - mean_absolute_error: 2.1027\n",
      "Epoch 163/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9016 - mean_absolute_error: 2.0490\n",
      "Epoch 164/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9059 - mean_absolute_error: 2.0432\n",
      "Epoch 165/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8389 - mean_absolute_error: 2.0324\n",
      "Epoch 166/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7721 - mean_absolute_error: 2.0196\n",
      "Epoch 167/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8269 - mean_absolute_error: 2.0266\n",
      "Epoch 168/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9096 - mean_absolute_error: 2.0430\n",
      "Epoch 169/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.1254 - mean_absolute_error: 2.0791\n",
      "Epoch 170/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9515 - mean_absolute_error: 2.0672\n",
      "Epoch 171/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8310 - mean_absolute_error: 2.0293\n",
      "Epoch 172/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7837 - mean_absolute_error: 2.0280\n",
      "Epoch 173/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 6.7638 - mean_absolute_error: 2.0173\n",
      "Epoch 174/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.6716 - mean_absolute_error: 1.9997\n",
      "Epoch 175/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8223 - mean_absolute_error: 2.0298\n",
      "Epoch 176/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9176 - mean_absolute_error: 2.0536\n",
      "Epoch 177/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 6.9727 - mean_absolute_error: 2.0468\n",
      "Epoch 178/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7437 - mean_absolute_error: 2.0301\n",
      "Epoch 179/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9339 - mean_absolute_error: 2.0525\n",
      "Epoch 180/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.0919 - mean_absolute_error: 2.0864\n",
      "Epoch 181/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 7.2073 - mean_absolute_error: 2.0801\n",
      "Epoch 182/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8145 - mean_absolute_error: 2.0327\n",
      "Epoch 183/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8049 - mean_absolute_error: 2.0156\n",
      "Epoch 184/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8805 - mean_absolute_error: 2.0278\n",
      "Epoch 185/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8649 - mean_absolute_error: 2.0433\n",
      "Epoch 186/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8146 - mean_absolute_error: 2.0270\n",
      "Epoch 187/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8658 - mean_absolute_error: 2.0451\n",
      "Epoch 188/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7240 - mean_absolute_error: 2.0108\n",
      "Epoch 189/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7652 - mean_absolute_error: 2.0106\n",
      "Epoch 190/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7932 - mean_absolute_error: 2.0149\n",
      "Epoch 191/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.9439 - mean_absolute_error: 2.0561\n",
      "Epoch 192/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 6.7678 - mean_absolute_error: 2.0241\n",
      "Epoch 193/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7545 - mean_absolute_error: 2.0262\n",
      "Epoch 194/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.6701 - mean_absolute_error: 1.9967\n",
      "Epoch 195/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.6899 - mean_absolute_error: 2.0125\n",
      "Epoch 196/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7184 - mean_absolute_error: 2.0127\n",
      "Epoch 197/200\n",
      "45/45 [==============================] - 0s 2ms/step - loss: 6.8506 - mean_absolute_error: 2.0236\n",
      "Epoch 198/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.8459 - mean_absolute_error: 2.0371\n",
      "Epoch 199/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7224 - mean_absolute_error: 2.0272\n",
      "Epoch 200/200\n",
      "45/45 [==============================] - 0s 1ms/step - loss: 6.7713 - mean_absolute_error: 2.0154\n"
     ]
    }
   ],
   "source": [
    "# rerun this cell multiple times if necessary\n",
    "\n",
    "history = model.fit(x_train, y_train, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "id": "uZ0hMA0KJK0B"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 778us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2.7460096354706813, 11.760190840568812)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(x_test)\n",
    "(mean_absolute_error(pred, y_test), mean_squared_error(pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nMbvOIcVJa2X"
   },
   "source": [
    "Now we see that the mean absolute error is increasing, this is due to the fact that the model is overfitting to the dataset. It cannot properly predict values from features values outside this dataset.  \n",
    "Solution to this problem at that point is to either increase the number of features per sample, or increase the number of samples in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ufZFb0JKz8XQ"
   },
   "source": [
    "The limits of this model is that first it is not cross validated. Second, it is ²really energy inefficient, we had to wait some time and build the whole model. It's also memory intensive, and uses power. All that to obtain a performance that is in 5% of the kNN model performance. So while the neural network is more precise, it can also be less convenient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l9stPvTIztOW"
   },
   "source": [
    "For our dataset, there is no point in training a whole network to have the same performance as a kNN, that is less greedy in training, time and energy. So for now we will use the kNN as the model for the API."
   ]
  }
 ],
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