RossmannSalesPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='img/logo.png' width=200>\n",
    "<center>\n",
    "    <br/>\n",
    "    Otkrivanje zakonitosti u podacima 2020-2021\n",
    "    <br/>\n",
    "    Fakultet Organizacionih Nauka, 2021\n",
    "    <br/>\n",
    "    <h1>Projektni zadatak</h1>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zahtevi projektnog zadatka\n",
    "- Opis i razumevanje problema - 4 poena\n",
    "- Opis i razumevanje podataka - 4 poena\n",
    "- Priprema podataka - 7 poena\n",
    "- Podešavanje parametara algoritma i selekcija/otežavanje atributa - 7 poena\n",
    "- Poređenje performansi algoritma - 6 poena\n",
    "- Evaluacija rešenja - 6 poena\n",
    "- Primena modela - 3 poena\n",
    "- Zaključak i budući pravci - 3 poena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ucitavanje standardnih biblioteka\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "import datetime as dt\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ucitavanje training data set-a\n",
    "\n",
    "train_data = pd.read_csv('data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek        Date  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "0      1          5  2015-07-31   5263        555     1      1            0   \n",
       "1      2          5  2015-07-31   6064        625     1      1            0   \n",
       "2      3          5  2015-07-31   8314        821     1      1            0   \n",
       "3      4          5  2015-07-31  13995       1498     1      1            0   \n",
       "4      5          5  2015-07-31   4822        559     1      1            0   \n",
       "\n",
       "   SchoolHoliday  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prikaz training data set-a\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store            0\n",
       "DayOfWeek        0\n",
       "Date             0\n",
       "Sales            0\n",
       "Customers        0\n",
       "Open             0\n",
       "Promo            0\n",
       "StateHoliday     0\n",
       "SchoolHoliday    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provera da li postoje nedostajuce vrednosti u training data set-u\n",
    "\n",
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1017209 entries, 0 to 1017208\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count    Dtype \n",
      "---  ------         --------------    ----- \n",
      " 0   Store          1017209 non-null  int64 \n",
      " 1   DayOfWeek      1017209 non-null  int64 \n",
      " 2   Date           1017209 non-null  object\n",
      " 3   Sales          1017209 non-null  int64 \n",
      " 4   Customers      1017209 non-null  int64 \n",
      " 5   Open           1017209 non-null  int64 \n",
      " 6   Promo          1017209 non-null  int64 \n",
      " 7   StateHoliday   1017209 non-null  object\n",
      " 8   SchoolHoliday  1017209 non-null  int64 \n",
      "dtypes: int64(7), object(2)\n",
      "memory usage: 69.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Informacije o podacima\n",
    "\n",
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Atribut Date je tipa object zbog toga su neophodne modifikacije ovog atributa\n",
    "# Pretvaranje u datetime tip podataka, a zatim kreiranje novih kolona koje oznacavaju dan, mesec i godinu\n",
    "\n",
    "train_data['Date'] = pd.to_datetime(train_data['Date'])\n",
    "\n",
    "train_data['Day'] = train_data['Date'].dt.day\n",
    "train_data['Month'] = train_data['Date'].dt.month\n",
    "train_data['Year'] = train_data['Date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(['Date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    855087\n",
       "0    131072\n",
       "a     20260\n",
       "b      6690\n",
       "c      4100\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Atribut StateHoliday je tipa object pa je potrebno proveriti njegove moguce vrednosti i svesti ih na jedinstvene\n",
    "\n",
    "train_data['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['StateHoliday'] = train_data['StateHoliday'].apply(lambda x:'0' if x == 0 or x == '0' else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    986159\n",
       "a     20260\n",
       "b      6690\n",
       "c      4100\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Razumevanje podataka - Vizualizacije"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='StateHoliday', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz broja opservacija za moguce kategorije atributa StateHoliday \n",
    "\n",
    "sns.countplot(train_data['StateHoliday'], palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='StateHoliday', ylabel='Sales'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz prosecnog Sales-a po mogucim kategorijama StateHoliday\n",
    "\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
    "\n",
    "# Prikaz prosecnog Sales-a po svim mogucim kategorijama StateHoliday\n",
    "sns.barplot(x='StateHoliday', y='Sales', data=train_data, ax=axis1, palette=\"husl\")\n",
    "\n",
    "# Prikaz pozitivnih vrednosti prosecnog Sales-a kada je StateHoliday a, b i c to jest kada je praznik \n",
    "sns.barplot(x='StateHoliday', y='Sales', data=train_data[(train_data[\"StateHoliday\"] != \"0\") & (train_data[\"Sales\"] > 0)], ax=axis2, palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SchoolHoliday', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dwEEaP+a80fbI45lvALYCs4AHygvgLuAeSQM0jlzWlbGGJd0C7CvtNpZQjIiIDploSPxH4DuSdtJYy/gIsGmif8T2w8DDZftHwIpx2m1qNa7tfuDyFvUXKQHVYt8WYMtE5xgREZNror/k3y6pn8YNLgV82PbBqjOLiIjXtAmf5iqBklCJiIgJOavb9UdERJxOAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiimoBI+mNkvZK+hNJByT9+1KfK6lP0uHyPqepz82SBiQdkrSqqX6VpP1l3+3l0cmUxyvfV+p7JC1q6tNT/sZhST1ERERH1TyCOQG83/a7gCuA1ZKWAzcBu20vAXaXz0haSuORx5cBq4E7JM0oY90J9AJLymt1qa8Hjtu+BLgNuLWMNRfYAFwNLAM2NAdZRETUVy1g3PCT8vH15WVgDbCt1LcB15TtNcC9tk/YfgoYAJZJmgfMtv2IbQPbx/QZGWsnsKIc3awC+mwP2z4O9HEqlCIiogOqrsFImiHpMeAYjX/h7wEusn0UoLxfWJrPB55p6j5YavPL9tj6qD62TwLPA+e3GWvs/Hol9UvqHxoaegXfNCIixqoaMLZfsn0FsIDG0cjlbZqr1RBt6mfbp3l+m2132+7u6upqM7WIiDhTHbmKzPZfAA/TOE31bDntRXk/VpoNAgubui0AjpT6ghb1UX0kzQTOA4bbjBURER1S8yqyLklvK9uzgA8ATwK7gJGrunqA+8v2LmBduTJsMY3F/L3lNNoLkpaX9ZXrx/QZGWst8FBZp3kQWClpTlncX1lqERHRITMrjj0P2FauBHsdsMP21yU9AuyQtB54GrgWwPYBSTuAg8BJ4EbbL5WxbgC2ArOAB8oL4C7gHkkDNI5c1pWxhiXdAuwr7TbaHq74XSMiYoxqAWP7+8CVLeo/AlaM02cTsKlFvR942fqN7RcpAdVi3xZgy5nNOiIiJkt+yR8REVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKii5iOTF0r6lqQnJB2Q9KlSnyupT9Lh8j6nqc/NkgYkHZK0qql+laT9Zd/t5dHJlMcr31fqeyQtaurTU/7GYUk9RERER9U8gjkJ/CvbfxdYDtwoaSlwE7Db9hJgd/lM2bcOuAxYDdxRHrcMcCfQCywpr9Wlvh44bvsS4Dbg1jLWXGADcDWwDNjQHGQREVFftYCxfdT2d8v2C8ATwHxgDbCtNNsGXFO21wD32j5h+ylgAFgmaR4w2/Yjtg1sH9NnZKydwIpydLMK6LM9bPs40MepUIqIiA7oyBpMOXV1JbAHuMj2UWiEEHBhaTYfeKap22CpzS/bY+uj+tg+CTwPnN9mrLHz6pXUL6l/aGjoFXzDiIgYq3rASHoL8AfAp23/uF3TFjW3qZ9tn1MFe7PtbtvdXV1dbaYWERFnqmrASHo9jXD5su0/LOVny2kvyvuxUh8EFjZ1XwAcKfUFLeqj+kiaCZwHDLcZKyIiOqTmVWQC7gKesP2fmnbtAkau6uoB7m+qrytXhi2msZi/t5xGe0HS8jLm9WP6jIy1FniorNM8CKyUNKcs7q8stYiI6JCZFcd+L/BrwH5Jj5XavwM+B+yQtB54GrgWwPYBSTuAgzSuQLvR9kul3w3AVmAW8EB5QSPA7pE0QOPIZV0Za1jSLcC+0m6j7eFK3zMiIlqoFjC2v03rtRCAFeP02QRsalHvBy5vUX+RElAt9m0Btkx0vhHT3cq7/8tUTyFehb758X9ebez8kj8iIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFzUcmb5F0TNLjTbW5kvokHS7vc5r23SxpQNIhSaua6ldJ2l/23V4em0x5tPJ9pb5H0qKmPj3lbxyWNPJI5YiI6KCaRzBbgdVjajcBu20vAXaXz0haSuNxx5eVPndImlH63An0AkvKa2TM9cBx25cAtwG3lrHmAhuAq4FlwIbmIIuIiM6oFjC2/xgYHlNeA2wr29uAa5rq99o+YfspYABYJmkeMNv2I7YNbB/TZ2SsncCKcnSzCuizPWz7ONDHy4MuIiIq6/QazEW2jwKU9wtLfT7wTFO7wVKbX7bH1kf1sX0SeB44v81YLyOpV1K/pP6hoaFX8LUiImKsV8siv1rU3KZ+tn1GF+3Ntrttd3d1dU1oohERMTGdDphny2kvyvuxUh8EFja1WwAcKfUFLeqj+kiaCZxH45TceGNFREQHdTpgdgEjV3X1APc31deVK8MW01jM31tOo70gaXlZX7l+TJ+RsdYCD5V1mgeBlZLmlMX9laUWEREdNLPWwJK+AvwScIGkQRpXdn0O2CFpPfA0cC2A7QOSdgAHgZPAjbZfKkPdQOOKtFnAA+UFcBdwj6QBGkcu68pYw5JuAfaVdhttj73YICIiKqsWMLavG2fXinHabwI2taj3A5e3qL9ICagW+7YAWyY82YiImHSvlkX+iIiYZhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVDGtA0bSakmHJA1Iummq5xMRcS6ZtgEjaQbwu8AvA0uB6yQtndpZRUScO6ZtwADLgAHbP7D918C9wJopnlNExDlDtqd6DlVIWgustv3PyudfA662/cmmNr1Ab/l4KXCo4xOdvi4AnpvqSUSMI/98Tp632+5qtWNmp2fSQWpRG5WmtjcDmzsznXOLpH7b3VM9j4hW8s9nZ0znU2SDwMKmzwuAI1M0l4iIc850Dph9wBJJiyW9AVgH7JriOUVEnDOm7Sky2yclfRJ4EJgBbLF9YIqndS7Jqcd4Ncs/nx0wbRf5IyJiak3nU2QRETGFEjAREVFFAiYmXW7RE69GkrZIOibp8amey7kiAROTKrfoiVexrcDqqZ7EuSQBE5Mtt+iJVyXbfwwMT/U8ziUJmJhs84Fnmj4PllpEnGMSMDHZTnuLnog4NyRgYrLlFj0RASRgYvLlFj0RASRgYpLZPgmM3KLnCWBHbtETrwaSvgI8AlwqaVDS+qme03SXW8VEREQVOYKJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBE9GGpN+SdEDS9yU9Junqcdp9TNIXJ+lv/lDSBWX7J2f6dyR9VtK/LtsbJX2gRZtfkvT1yZhvxHim7SOTI14pSe8BPgj8vO0T5V/6b5jiaZ0R25+Z6jnEuStHMBHjmwc8Z/sEgO3nbB+R9G5J35H0J5L2SnpraX+xpG9IOizpt0cGkXSdpP2SHpd06+nqEyXp7ZJ2l6Or3ZL+Vos2WyWtLdurJT0p6dvAh5vaLCvf53vl/dJS/1+Srmhq978lvfNM5xnnrgRMxPi+CSyU9H8k3SHpF8vtb+4DPmX7XcAHgL8q7a8AfhX4e8CvSloo6WLgVuD9Zf+7JV0zXr3FHGaVU3OPSXoM2Ni074vAdtvvBL4M3D7eF5H0RuC/Ar8C/APgZ5t2Pwm8z/aVwGeA/1DqXwI+Vvr/HeBnbH9//P+5IkZLwESMw/ZPgKuAXmCIRrB8Ajhqe19p8+NyexyA3baft/0icBB4O/Bu4GHbQ6Xdl4H3tamP9Ve2rxh50QiAEe8Bfr9s3wP8/TZf5x3AU7YPu3H7jt9r2nce8NXypMfbgMtK/avAByW9Hvh1Gg/sipiwrMFEtGH7JeBh4GFJ+4EbGf/xAyeatl+i8f+vVo8voE39lTjdfZ/G238L8C3b/0TSIhrfF9s/ldRH44FxHwG6J2mecY7IEUzEOCRdKmlJU+kKGjfwvFjSu0ubt0pq9x9qe4BflHRBeZz0dcAftamfie/QuFs1wD8Fvt2m7ZPAYkk/Vz5f17TvPODPyvbHxvT7Eo1Tb/ts52mQcUZyBBMxvrcAX5D0NuAkMEDjdNndpT6LxvrLyy4DHmH7qKSbgW/ROGr5n7bvBxivfgZ+E9gi6d/QOIX38TbzeFFSL/A/JD1HI4wuL7t/G9gm6V8CD43p96ikH5fvHHFGcjfliBhXuRjhYeAdtv9miqcTrzE5RRYRLUm6nsapvN9KuMTZyBFMRERUkSOYiIioIgETERFVJGAiIqKKBExERFSRgImIiCr+PwGcpEUtteAOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz broja opservacija za moguce kategorije atributa SchoolHoliday \n",
    "\n",
    "sns.countplot(train_data['SchoolHoliday'], palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SchoolHoliday', ylabel='Sales'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz prosecnog Sales-a po mogucim kategorijama SchoolHoliday\n",
    "\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
    "\n",
    "# Prikaz prosecnog Sales-a po svim mogucim kategorijama SchoolHoliday\n",
    "sns.barplot(x='SchoolHoliday', y='Sales', data=train_data, ax=axis1, palette=\"husl\")\n",
    "\n",
    "# Prikaz pozitivnih vrednosti prosecnog Sales-a kada je SchoolHoliday 1 to jest kada je skolski praznik = kada skole nisu radile\n",
    "sns.barplot(x='SchoolHoliday', y='Sales', data=train_data[(train_data[\"SchoolHoliday\"] != 0) & (train_data[\"Sales\"] > 0)], ax=axis2, palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Open', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz broja opservacija u zavisnosti od toga da li je prodavnica otvorena ili zatvorena po danima u nedelji\n",
    "\n",
    "fig, (axis1) = plt.subplots(1,1,figsize=(15,4))\n",
    "sns.countplot(x='Open',hue='DayOfWeek', data=train_data, ax=axis1, palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Customers'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz prosecne vrednosti Sales-a po godinama u slucaju kada je prodavnica otvorena i kada je bilo prodaje\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
    "sns.barplot(x='Year', y='Sales', data=train_data[(train_data[\"Open\"] != 0) & (train_data[\"Sales\"] > 0)], ax=axis1, palette=\"husl\")\n",
    "\n",
    "# Prikaz prosecnog broja Customers-a po godinama u slucaju kada je prodavnica otvorena i kada je bilo prodaje\n",
    "sns.barplot(x='Year', y='Customers', data=train_data[(train_data[\"Open\"] != 0) & (train_data[\"Sales\"] > 0)], ax=axis2, palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DayOfWeek', ylabel='Customers'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz prosecne vrednosti Sales-a po danima u nedelji\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
    "sns.barplot(x='DayOfWeek', y='Sales', data=train_data[(train_data[\"Open\"] != 0) & (train_data[\"Sales\"] > 0)], order=[1,2,3,4,5,6,7], ax=axis1, palette=\"husl\")\n",
    "\n",
    "# Prikaz prosecne vrednosti Customers-a po danima u nedelji\n",
    "sns.barplot(x='DayOfWeek', y='Customers', data=train_data[(train_data[\"Open\"] != 0) & (train_data[\"Sales\"] > 0)],order=[1,2,3,4,5,6,7], ax=axis2, palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prosečan broj kupaca i prodaja po mesecima\n",
    "\n",
    "ax = train_data.groupby('Month')[['Sales', 'Customers']].mean().plot(secondary_y=['Customers'], figsize=(15,5), marker='o')\n",
    "ax.set_ylabel('€')\n",
    "ax.right_ax.set_ylabel('Count')\n",
    "ax.set_xticks(range(1,13))\n",
    "plt.title('Prosečan broj kupaca i prodaja po mesecima.');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prosecna prodaja po kupcu po mesecima.\n",
    "\n",
    "train_data_graf=train_data\n",
    "train_data_graf['SalesPerCustomer'] = train_data_graf.Sales / train_data_graf.Customers\n",
    "ax = train_data_graf.groupby('Month')[['SalesPerCustomer']].mean().plot(figsize=(15,5), marker='o')\n",
    "ax.set_ylabel('€')\n",
    "ax.set_xticks(range(1,13))\n",
    "plt.title('Prosečna prodaja po kupcu po mesecima.');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ucitavanje podataka o prodavnicama\n",
    "\n",
    "store_data = pd.read_csv('data/store.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "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>Store</th>\n",
       "      <th>StoreType</th>\n",
       "      <th>Assortment</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>PromoInterval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>570.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>620.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>29910.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store StoreType Assortment  CompetitionDistance  CompetitionOpenSinceMonth  \\\n",
       "0      1         c          a               1270.0                        9.0   \n",
       "1      2         a          a                570.0                       11.0   \n",
       "2      3         a          a              14130.0                       12.0   \n",
       "3      4         c          c                620.0                        9.0   \n",
       "4      5         a          a              29910.0                        4.0   \n",
       "\n",
       "   CompetitionOpenSinceYear  Promo2  Promo2SinceWeek  Promo2SinceYear  \\\n",
       "0                    2008.0       0              NaN              NaN   \n",
       "1                    2007.0       1             13.0           2010.0   \n",
       "2                    2006.0       1             14.0           2011.0   \n",
       "3                    2009.0       0              NaN              NaN   \n",
       "4                    2015.0       0              NaN              NaN   \n",
       "\n",
       "     PromoInterval  \n",
       "0              NaN  \n",
       "1  Jan,Apr,Jul,Oct  \n",
       "2  Jan,Apr,Jul,Oct  \n",
       "3              NaN  \n",
       "4              NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prikaz podataka o prodavnicama\n",
    "\n",
    "store_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1115 entries, 0 to 1114\n",
      "Data columns (total 10 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Store                      1115 non-null   int64  \n",
      " 1   StoreType                  1115 non-null   object \n",
      " 2   Assortment                 1115 non-null   object \n",
      " 3   CompetitionDistance        1112 non-null   float64\n",
      " 4   CompetitionOpenSinceMonth  761 non-null    float64\n",
      " 5   CompetitionOpenSinceYear   761 non-null    float64\n",
      " 6   Promo2                     1115 non-null   int64  \n",
      " 7   Promo2SinceWeek            571 non-null    float64\n",
      " 8   Promo2SinceYear            571 non-null    float64\n",
      " 9   PromoInterval              571 non-null    object \n",
      "dtypes: float64(5), int64(2), object(3)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Informacije o podacima\n",
    "\n",
    "store_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Razumevanje podataka - Vizualizacije"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='StoreType', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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krwLeAywA3lpVO8dc1tkwp4a75gCv59nl9Tx75vy1nFUTzZKk8Zptw0eSpDEyFCRJjaGgOSPJsiT3jbsO6VSS/HKSnxl3HafDUJAkNYZCz5L8ZZJ7kuzsnsbWmTk/yeYkH01yW5IvGXdBc1WSl3XX8SNJ/njc9cx1SV7bvczzbxm8bWFOMhT694qqehYwCfxkki8fd0Fz3NOATVX1DcCngZ8Ycz1zUpIrgNcC11TVM4DXjLmkOS3Jsxg8V/VM4MXAs8db0ekzFPr3k0k+AnyQwdPay8dcz1z3cFX9Y7f+J8C3jLOYOewa4LaqehSgqj415nrmum8F3llVn6uqTzOHH7qdVQ+vnWuSPB94IXB1VX0uyV3AReOs6Rxw7IM1PmhzeoLX7mw7J66ndwr9+jLgP7pAeDrwTeMu6Bzw1CRXd+s3AO8fZzFz2Dbg+44MZyZZOOZ65rq/B743yeOTPBH47nEXdLoMhX69m8HE6EeBX2MwhKQzcz+wtrumC4HfH3M9c1L3+piNwPu64c3Xj7mkOa2qPgz8OXAv8A7gH8Za0BnwNReSpMY7BUlSYyhIkhpDQZLUGAqSpMZQkCQ1PrymeS3Ja4EfAA4DnwdeCVzN4FUanzsL578buJDB12cfD/xbt2t1VX3yTM8vnW1+JVXzVvcQ3OuB51fVwSSLgMcBHwAmj7wCYsRzLaiqwyfZ/8PdOV91hmVLvXL4SPPZYuDRqjoI0IXAS4CnAHcmuRMgyQ1JPpbkviS/fuTgJJ9N8qvd3cDVSV6aZHuSe5P8QZIFx35gkvOSPJBkYmh7T5JFSd6W5M1J/iHJvyT5rq7PgiS/keRD3VtNX9n7ldG8ZShoPnsvsLT7BXxzkudV1e8C+4AXVNULkjwF+HUGL5C7Enh2ktXd8U8A7quq5wCPAd8PPLeqrmQwHPWDx35gVX2ewYv8jux7IfCRobuSZcDzgO8E3pzkIuBG4L+q6tkM3r75o0kuP3uXQfoCQ0HzVlV9FngWsA6YAv68G+YZ9mzgrqqaqqpDwNuBb+v2HWbwSgOAld25PpTk3m77q07w0W8FXtatvwK4ZWjflqr6fFU9AHwCeDrwIuBl3XnvBr4c37arnjjRrHmtmwe4C7gryceAtcd0yUkO/5+heYQAm6tqwwif+XCSR5JcAzyHo+8opnsLbIBXV9V7TnVu6Ux5p6B5K8nTkgz/j/tK4F+BzwBP7NruBp7XjfkvYPBm1vdNc7ptwEuSXNKde2GSrzzJx7+FwTDSlmMmqK/v5hm+msGdxm7gPcCPJ7mgO/fXJnnCF/njSiPxTkHz2ZcCb0zyZOAQsIfBUNINwN8k2d/NK2wA7mTwP/a/rqrbjz1RVe1K8gvAe5OcB/wfcBODkJnOVgbDRrcc076bQehcCvxYVf1PkrcwmGv4cJIwGOpafdo/tXQSfiVVGoMkk8BvV9W3DrW9DXhXVd02tsI073mnIM2wJOuBH2eabydJ4+adgiSpcaJZktQYCpKkxlCQJDWGgiSpMRQkSc3/A0UszyuJzvaNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz broja opservacija za moguce kategorije atributa StoreType \n",
    "\n",
    "sns.countplot(x='StoreType', data=store_data, order=['a','b','c', 'd'], palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Assortment', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz broja opservacija za moguce kategorije atributa Assortment \n",
    "\n",
    "sns.countplot(x='Assortment', data=store_data, order=['a','b','c'], palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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>Store</th>\n",
       "      <th>AvgSales</th>\n",
       "      <th>AvgCustomers</th>\n",
       "      <th>StoreType</th>\n",
       "      <th>Assortment</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>PromoInterval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4122.991507</td>\n",
       "      <td>486.045648</td>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5741.253715</td>\n",
       "      <td>620.286624</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>570.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8021.769639</td>\n",
       "      <td>1100.057325</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3867.110403</td>\n",
       "      <td>444.360934</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>620.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4562.375796</td>\n",
       "      <td>525.990446</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>29910.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store     AvgSales  AvgCustomers StoreType Assortment  CompetitionDistance  \\\n",
       "0      1  4122.991507    486.045648         c          a               1270.0   \n",
       "1      2  5741.253715    620.286624         a          a                570.0   \n",
       "2      3  8021.769639   1100.057325         a          a              14130.0   \n",
       "3      4  3867.110403    444.360934         c          c                620.0   \n",
       "4      5  4562.375796    525.990446         a          a              29910.0   \n",
       "\n",
       "   CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
       "0                        9.0                    2008.0       0   \n",
       "1                       11.0                    2007.0       1   \n",
       "2                       12.0                    2006.0       1   \n",
       "3                        9.0                    2009.0       0   \n",
       "4                        4.0                    2015.0       0   \n",
       "\n",
       "   Promo2SinceWeek  Promo2SinceYear    PromoInterval  \n",
       "0              NaN              NaN              NaN  \n",
       "1             13.0           2010.0  Jan,Apr,Jul,Oct  \n",
       "2             14.0           2011.0  Jan,Apr,Jul,Oct  \n",
       "3              NaN              NaN              NaN  \n",
       "4              NaN              NaN              NaN  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_sales_customers = train_data.groupby('Store')[[\"Sales\", \"Customers\"]].mean().reset_index()\n",
    "df_sales_customers = pd.DataFrame({'Store':average_sales_customers.index,\n",
    "                      'AvgSales':average_sales_customers[\"Sales\"], 'AvgCustomers': average_sales_customers[\"Customers\"]}, \n",
    "                      columns=['Store', 'AvgSales', 'AvgCustomers'])\n",
    "df_store_avg_sales_cust = pd.merge(df_sales_customers, store_data, on='Store')\n",
    "df_store_avg_sales_cust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Store Type'), Text(0, 0.5, 'Average No. of Customers')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz prosecnog Sales-a i prosecnog broja Customers-a po StoreType-u\n",
    "\n",
    "fig, (axis1, axis2) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "ax1 = sns.barplot(x=\"StoreType\", y=\"AvgSales\", data=df_store_avg_sales_cust, order=[\"a\", \"b\", \"c\", \"d\"], ax=axis1, ci=None, palette=\"husl\")\n",
    "ax2 = sns.barplot(x=\"StoreType\", y=\"AvgCustomers\", data=df_store_avg_sales_cust, order=[\"a\", \"b\", \"c\", \"d\"], ax=axis2, ci=None, palette=\"husl\")\n",
    "ax1.set(xlabel=\"Store Type\", ylabel=\"Average Sales\")\n",
    "ax2.set(xlabel=\"Store Type\", ylabel=\"Average No. of Customers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'No. of Stores')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz broja prodavnica po mogucim vrednostima Assortment-a\n",
    "\n",
    "fig, (axis1) = plt.subplots(1, 1, figsize=(8, 4))\n",
    "ax = sns.countplot(x=\"Assortment\", data=df_store_avg_sales_cust, order=[\"a\", \"b\", \"c\"], palette=\"husl\")\n",
    "ax.set(ylabel=\"No. of Stores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'No. of Stores')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz broja prodavnica u zavisnosti od toga da li ucestvuju u Promo2\n",
    "\n",
    "fig, (axis1) = plt.subplots(1, 1, figsize=(8,4))\n",
    "ax = sns.countplot(x=\"Promo2\", data=df_store_avg_sales_cust, palette=\"husl\")\n",
    "ax.set(ylabel=\"No. of Stores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                          0\n",
       "StoreType                      0\n",
       "Assortment                     0\n",
       "CompetitionDistance            3\n",
       "CompetitionOpenSinceMonth    354\n",
       "CompetitionOpenSinceYear     354\n",
       "Promo2                         0\n",
       "Promo2SinceWeek              544\n",
       "Promo2SinceYear              544\n",
       "PromoInterval                544\n",
       "dtype: int64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provera da li postoje nedostajuce vrednosti u store data set-u\n",
    "\n",
    "store_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: missingno in c:\\users\\ivana\\anaconda3\\lib\\site-packages (0.4.2)\n",
      "Requirement already satisfied: scipy in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from missingno) (1.5.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from missingno) (1.19.2)\n",
      "Requirement already satisfied: seaborn in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from missingno) (0.11.0)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from missingno) (3.3.2)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from seaborn->missingno) (1.1.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (8.0.1)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (2020.6.20)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn->missingno) (2020.1)\n",
      "Requirement already satisfied: six in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib->missingno) (1.15.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualizacija nedostajucih vrednosti\n",
    "\n",
    "!pip install missingno\n",
    "import missingno as msno\n",
    "msno.matrix(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Popunjavanje nedostajucih vrednosti\n",
    "# Vrednosti koje nedostaju atributu CompetitionDistance su popunjene srednjom vrednoscu tog atributa buduci da postoje\n",
    "# samo tri nedostajuce vrednosti pa ovakav nacin popunjavanja istih nece uticati na rezultate.\n",
    "\n",
    "store_data['CompetitionDistance'] = store_data['CompetitionDistance'].fillna(store_data['CompetitionDistance'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "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>Store</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>544.000000</td>\n",
       "      <td>544.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>544.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>555.531250</td>\n",
       "      <td>6545.312136</td>\n",
       "      <td>7.260101</td>\n",
       "      <td>2009.123737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>317.491536</td>\n",
       "      <td>9357.298227</td>\n",
       "      <td>3.176739</td>\n",
       "      <td>4.326819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>280.250000</td>\n",
       "      <td>997.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>559.000000</td>\n",
       "      <td>2640.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>827.250000</td>\n",
       "      <td>8095.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1114.000000</td>\n",
       "      <td>75860.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Store  CompetitionDistance  CompetitionOpenSinceMonth  \\\n",
       "count   544.000000           544.000000                 396.000000   \n",
       "mean    555.531250          6545.312136                   7.260101   \n",
       "std     317.491536          9357.298227                   3.176739   \n",
       "min       1.000000            30.000000                   1.000000   \n",
       "25%     280.250000           997.500000                   4.000000   \n",
       "50%     559.000000          2640.000000                   8.000000   \n",
       "75%     827.250000          8095.000000                  10.000000   \n",
       "max    1114.000000         75860.000000                  12.000000   \n",
       "\n",
       "       CompetitionOpenSinceYear  Promo2  Promo2SinceWeek  Promo2SinceYear  \n",
       "count                396.000000   544.0              0.0              0.0  \n",
       "mean                2009.123737     0.0              NaN              NaN  \n",
       "std                    4.326819     0.0              NaN              NaN  \n",
       "min                 1990.000000     0.0              NaN              NaN  \n",
       "25%                 2006.000000     0.0              NaN              NaN  \n",
       "50%                 2010.000000     0.0              NaN              NaN  \n",
       "75%                 2013.000000     0.0              NaN              NaN  \n",
       "max                 2015.000000     0.0              NaN              NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nedostajuce vrednosti atributa Promo2SinceWeek, Promo2SinceYear i PromoInterval su popunjene 0 buduci da opservacije koje ih \n",
    "# sadrze imaju vrednost atributa Promo2 jednaku 0.\n",
    "\n",
    "store_data[pd.isnull(store_data['PromoInterval']) & (store_data['Promo2']== 0)].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data['Promo2SinceWeek'] = store_data['Promo2SinceWeek'].fillna(0)\n",
    "store_data['Promo2SinceYear'] = store_data['Promo2SinceYear'].fillna(0)\n",
    "store_data['PromoInterval'] = store_data['PromoInterval'].fillna('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Popunjavanje nedostajucih vrednosti na osnovu KNN principa\n",
    "# Prvo se pronalazi zadati broj \"suseda\" to jest uzoraka koji su slicni po svim atributima sa onim uzorkom\n",
    "# gde postoji nedostajuca vrednost. Zatim se racuna prosecna vrednost susednih uzoraka i ona se upisuje \n",
    "# u trenutnu nedostajucu vrednost.\n",
    "# Proces se ponavlja dok se sve nedostajuce vrednosti ne popune.\n",
    "\n",
    "imputer = KNNImputer(n_neighbors = 3, weights = 'uniform')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data_num = store_data[['CompetitionDistance','CompetitionOpenSinceMonth','CompetitionOpenSinceYear','Promo2','Promo2SinceWeek','Promo2SinceYear']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sa metodom fit se uci model, ali se metodom transform podaci transofrmisu. \n",
    "# Kada se zeli primeniti istovremeno i ucenje i primena postoji funkcija fit_transform.\n",
    "\n",
    "store_data_num_t = imputer.fit_transform(store_data_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data_num_t = pd.DataFrame(store_data_num_t, index = store_data_num.index , columns = store_data_num.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "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>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1115.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5404.901079</td>\n",
       "      <td>7.242152</td>\n",
       "      <td>2008.687892</td>\n",
       "      <td>0.512108</td>\n",
       "      <td>12.083408</td>\n",
       "      <td>1030.239462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7652.849306</td>\n",
       "      <td>2.828666</td>\n",
       "      <td>5.341411</td>\n",
       "      <td>0.500078</td>\n",
       "      <td>15.542241</td>\n",
       "      <td>1006.038782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>720.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2006.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2330.000000</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6875.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>75860.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CompetitionDistance  CompetitionOpenSinceMonth  \\\n",
       "count          1115.000000                1115.000000   \n",
       "mean           5404.901079                   7.242152   \n",
       "std            7652.849306                   2.828666   \n",
       "min              20.000000                   1.000000   \n",
       "25%             720.000000                   5.000000   \n",
       "50%            2330.000000                   7.333333   \n",
       "75%            6875.000000                   9.000000   \n",
       "max           75860.000000                  12.000000   \n",
       "\n",
       "       CompetitionOpenSinceYear       Promo2  Promo2SinceWeek  Promo2SinceYear  \n",
       "count               1115.000000  1115.000000      1115.000000      1115.000000  \n",
       "mean                2008.687892     0.512108        12.083408      1030.239462  \n",
       "std                    5.341411     0.500078        15.542241      1006.038782  \n",
       "min                 1900.000000     0.000000         0.000000         0.000000  \n",
       "25%                 2006.333333     0.000000         0.000000         0.000000  \n",
       "50%                 2009.000000     1.000000         1.000000      2009.000000  \n",
       "75%                 2012.000000     1.000000        22.000000      2012.000000  \n",
       "max                 2015.000000     1.000000        50.000000      2015.000000  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data_num_t.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CompetitionDistance          0\n",
       "CompetitionOpenSinceMonth    0\n",
       "CompetitionOpenSinceYear     0\n",
       "Promo2                       0\n",
       "Promo2SinceWeek              0\n",
       "Promo2SinceYear              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data_num_t.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data['CompetitionOpenSinceYear'] = store_data_num_t['CompetitionOpenSinceYear']\n",
    "store_data['CompetitionOpenSinceMonth'] = store_data_num_t['CompetitionOpenSinceMonth']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ucitavanje test data set-a\n",
    "\n",
    "test_data = pd.read_csv('data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "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>Id</th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Store  DayOfWeek        Date  Open  Promo StateHoliday  SchoolHoliday\n",
       "0   1      1          4  2015-09-17   1.0      1            0              0\n",
       "1   2      3          4  2015-09-17   1.0      1            0              0\n",
       "2   3      7          4  2015-09-17   1.0      1            0              0\n",
       "3   4      8          4  2015-09-17   1.0      1            0              0\n",
       "4   5      9          4  2015-09-17   1.0      1            0              0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prikaz test data set-a\n",
    "\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41088 entries, 0 to 41087\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             41088 non-null  int64  \n",
      " 1   Store          41088 non-null  int64  \n",
      " 2   DayOfWeek      41088 non-null  int64  \n",
      " 3   Date           41088 non-null  object \n",
      " 4   Open           41077 non-null  float64\n",
      " 5   Promo          41088 non-null  int64  \n",
      " 6   StateHoliday   41088 non-null  object \n",
      " 7   SchoolHoliday  41088 non-null  int64  \n",
      "dtypes: float64(1), int64(5), object(2)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Informacije o podacima\n",
    "\n",
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modifikacija atributa Date\n",
    "\n",
    "test_data['Date'] = pd.to_datetime(test_data['Date'])\n",
    "\n",
    "test_data['Day'] = test_data['Date'].dt.day\n",
    "test_data['Month'] = test_data['Date'].dt.month\n",
    "test_data['Year'] = test_data['Date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    40908\n",
       "a      180\n",
       "Name: StateHoliday, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['StateHoliday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                0\n",
       "Store             0\n",
       "DayOfWeek         0\n",
       "Open             11\n",
       "Promo             0\n",
       "StateHoliday      0\n",
       "SchoolHoliday     0\n",
       "Day               0\n",
       "Month             0\n",
       "Year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provera da li postoje nedostajuce vrednosti\n",
    "\n",
    "test_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Medijana atributa Open\n",
    "\n",
    "test_data['Open'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Popunjavanje nedostajucih vrednosti atributa Open medijanom = 1 = prodavnica je u vecem broju slucajeva bila otvorena\n",
    "\n",
    "test_data['Open'] = test_data['Open'].fillna(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spajanje podataka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spajanje train data set-a sa podacima o prodavnicama\n",
    "\n",
    "df_train_store = train_data.merge(store_data, left_on='Store', right_on='Store', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Promo</th>\n",
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       "      <th>Day</th>\n",
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       "      <th>Year</th>\n",
       "      <th>StoreType</th>\n",
       "      <th>Assortment</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>PromoInterval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
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       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>570.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
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       "      <th>2</th>\n",
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       "      <td>7</td>\n",
       "      <td>2015</td>\n",
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       "      <td>a</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>620.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>29910.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "0      1          5   5263        555     1      1            0   \n",
       "1      2          5   6064        625     1      1            0   \n",
       "2      3          5   8314        821     1      1            0   \n",
       "3      4          5  13995       1498     1      1            0   \n",
       "4      5          5   4822        559     1      1            0   \n",
       "\n",
       "   SchoolHoliday  Day  Month  Year StoreType Assortment  CompetitionDistance  \\\n",
       "0              1   31      7  2015         c          a               1270.0   \n",
       "1              1   31      7  2015         a          a                570.0   \n",
       "2              1   31      7  2015         a          a              14130.0   \n",
       "3              1   31      7  2015         c          c                620.0   \n",
       "4              1   31      7  2015         a          a              29910.0   \n",
       "\n",
       "   CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
       "0                        9.0                    2008.0       0   \n",
       "1                       11.0                    2007.0       1   \n",
       "2                       12.0                    2006.0       1   \n",
       "3                        9.0                    2009.0       0   \n",
       "4                        4.0                    2015.0       0   \n",
       "\n",
       "   Promo2SinceWeek  Promo2SinceYear    PromoInterval  \n",
       "0              0.0              0.0                0  \n",
       "1             13.0           2010.0  Jan,Apr,Jul,Oct  \n",
       "2             14.0           2011.0  Jan,Apr,Jul,Oct  \n",
       "3              0.0              0.0                0  \n",
       "4              0.0              0.0                0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1017209 entries, 0 to 1017208\n",
      "Data columns (total 20 columns):\n",
      " #   Column                     Non-Null Count    Dtype  \n",
      "---  ------                     --------------    -----  \n",
      " 0   Store                      1017209 non-null  int64  \n",
      " 1   DayOfWeek                  1017209 non-null  int64  \n",
      " 2   Sales                      1017209 non-null  int64  \n",
      " 3   Customers                  1017209 non-null  int64  \n",
      " 4   Open                       1017209 non-null  int64  \n",
      " 5   Promo                      1017209 non-null  int64  \n",
      " 6   StateHoliday               1017209 non-null  object \n",
      " 7   SchoolHoliday              1017209 non-null  int64  \n",
      " 8   Day                        1017209 non-null  int64  \n",
      " 9   Month                      1017209 non-null  int64  \n",
      " 10  Year                       1017209 non-null  int64  \n",
      " 11  StoreType                  1017209 non-null  object \n",
      " 12  Assortment                 1017209 non-null  object \n",
      " 13  CompetitionDistance        1017209 non-null  float64\n",
      " 14  CompetitionOpenSinceMonth  693861 non-null   float64\n",
      " 15  CompetitionOpenSinceYear   693861 non-null   float64\n",
      " 16  Promo2                     1017209 non-null  int64  \n",
      " 17  Promo2SinceWeek            1017209 non-null  float64\n",
      " 18  Promo2SinceYear            1017209 non-null  float64\n",
      " 19  PromoInterval              1017209 non-null  object \n",
      "dtypes: float64(5), int64(11), object(4)\n",
      "memory usage: 163.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Prikaz informacija o tipovima podataka\n",
    "\n",
    "df_train_store.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spajanje test data set-a sa podacima o prodavnicama\n",
    "\n",
    "df_test_store = test_data.merge(store_data, left_on='Store', right_on='Store', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>DayOfWeek</th>\n",
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       "      <th>Promo</th>\n",
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       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>PromoInterval</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1270.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>c</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>7520.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Store  DayOfWeek  Open  Promo StateHoliday  SchoolHoliday  Day  Month  \\\n",
       "0   1      1          4   1.0      1            0              0   17      9   \n",
       "1   2      3          4   1.0      1            0              0   17      9   \n",
       "2   3      7          4   1.0      1            0              0   17      9   \n",
       "3   4      8          4   1.0      1            0              0   17      9   \n",
       "4   5      9          4   1.0      1            0              0   17      9   \n",
       "\n",
       "   Year StoreType Assortment  CompetitionDistance  CompetitionOpenSinceMonth  \\\n",
       "0  2015         c          a               1270.0                        9.0   \n",
       "1  2015         a          a              14130.0                       12.0   \n",
       "2  2015         a          c              24000.0                        4.0   \n",
       "3  2015         a          a               7520.0                       10.0   \n",
       "4  2015         a          c               2030.0                        8.0   \n",
       "\n",
       "   CompetitionOpenSinceYear  Promo2  Promo2SinceWeek  Promo2SinceYear  \\\n",
       "0                    2008.0       0              0.0              0.0   \n",
       "1                    2006.0       1             14.0           2011.0   \n",
       "2                    2013.0       0              0.0              0.0   \n",
       "3                    2014.0       0              0.0              0.0   \n",
       "4                    2000.0       0              0.0              0.0   \n",
       "\n",
       "     PromoInterval  \n",
       "0                0  \n",
       "1  Jan,Apr,Jul,Oct  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41088 entries, 0 to 41087\n",
      "Data columns (total 19 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Id                         41088 non-null  int64  \n",
      " 1   Store                      41088 non-null  int64  \n",
      " 2   DayOfWeek                  41088 non-null  int64  \n",
      " 3   Open                       41088 non-null  float64\n",
      " 4   Promo                      41088 non-null  int64  \n",
      " 5   StateHoliday               41088 non-null  object \n",
      " 6   SchoolHoliday              41088 non-null  int64  \n",
      " 7   Day                        41088 non-null  int64  \n",
      " 8   Month                      41088 non-null  int64  \n",
      " 9   Year                       41088 non-null  int64  \n",
      " 10  StoreType                  41088 non-null  object \n",
      " 11  Assortment                 41088 non-null  object \n",
      " 12  CompetitionDistance        41088 non-null  float64\n",
      " 13  CompetitionOpenSinceMonth  25872 non-null  float64\n",
      " 14  CompetitionOpenSinceYear   25872 non-null  float64\n",
      " 15  Promo2                     41088 non-null  int64  \n",
      " 16  Promo2SinceWeek            41088 non-null  float64\n",
      " 17  Promo2SinceYear            41088 non-null  float64\n",
      " 18  PromoInterval              41088 non-null  object \n",
      "dtypes: float64(6), int64(9), object(4)\n",
      "memory usage: 6.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# Prikaz informacija o tipovima podataka\n",
    "\n",
    "df_test_store.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nece biti razmatrani slucajevi kada prodavnica nije radila Open = 0 i kada nije bilo prodaje Sales = 0. Zbog toga potrebno je ukloniti te slucajeve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_store = df_train_store[(df_train_store[\"Open\"] != 0) & (df_train_store['Sales'] > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Izbacivanje kolone Open\n",
    "\n",
    "df_train_store = df_train_store.drop([\"Open\"], axis = 1)\n",
    "df_test_store = df_test_store.drop([\"Open\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matrica medjusobne povezanosti to jest korelacije atributa\n",
    "# Plava boja oznacava da je korelacija izmedju atributa veca\n",
    "\n",
    "f, ax = plt.subplots(figsize = (15 ,10))\n",
    "sns.heatmap(df_train_store.corr(), ax = ax, annot=True, cmap=sns.diverging_palette(10, 240, as_cmap=True), linewidths=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Atributi Promo2SinceWeek i Promo2SinceYear su u velikoj korelaciji sa atributom Promo2 pa ih zbog toga uklanjamo iz data seta\n",
    "\n",
    "df_train_store = df_train_store.drop([\"Promo2SinceYear\", \"Promo2SinceWeek\"], axis = 1)\n",
    "df_test_store = df_test_store.drop([\"Promo2SinceYear\", \"Promo2SinceWeek\"], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pretvaranje kategorickih atributa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 844338 entries, 0 to 1017190\n",
      "Data columns (total 17 columns):\n",
      " #   Column                     Non-Null Count   Dtype  \n",
      "---  ------                     --------------   -----  \n",
      " 0   Store                      844338 non-null  int64  \n",
      " 1   DayOfWeek                  844338 non-null  int64  \n",
      " 2   Sales                      844338 non-null  int64  \n",
      " 3   Customers                  844338 non-null  int64  \n",
      " 4   Promo                      844338 non-null  int64  \n",
      " 5   StateHoliday               844338 non-null  object \n",
      " 6   SchoolHoliday              844338 non-null  int64  \n",
      " 7   Day                        844338 non-null  int64  \n",
      " 8   Month                      844338 non-null  int64  \n",
      " 9   Year                       844338 non-null  int64  \n",
      " 10  StoreType                  844338 non-null  object \n",
      " 11  Assortment                 844338 non-null  object \n",
      " 12  CompetitionDistance        844338 non-null  float64\n",
      " 13  CompetitionOpenSinceMonth  575738 non-null  float64\n",
      " 14  CompetitionOpenSinceYear   575738 non-null  float64\n",
      " 15  Promo2                     844338 non-null  int64  \n",
      " 16  PromoInterval              844338 non-null  object \n",
      "dtypes: float64(3), int64(10), object(4)\n",
      "memory usage: 116.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Informacije o podacima\n",
    "\n",
    "df_train_store.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kolone koje su tipa object pretvaramo u numericke u train set-u \n",
    "# Od starog atributa napravice se novi atributi i njih ce biti onoliko koliko ima mogucih vrednosti starog atributa\n",
    "# Novi atributi ce biti binarnog tipa - 1 i 0.\n",
    "\n",
    "df_train_store_dummies = pd.get_dummies(df_train_store, columns=['StateHoliday', 'StoreType', 'Assortment', 'PromoInterval'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Promo</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>...</th>\n",
       "      <th>StoreType_b</th>\n",
       "      <th>StoreType_c</th>\n",
       "      <th>StoreType_d</th>\n",
       "      <th>Assortment_a</th>\n",
       "      <th>Assortment_b</th>\n",
       "      <th>Assortment_c</th>\n",
       "      <th>PromoInterval_0</th>\n",
       "      <th>PromoInterval_Feb,May,Aug,Nov</th>\n",
       "      <th>PromoInterval_Jan,Apr,Jul,Oct</th>\n",
       "      <th>PromoInterval_Mar,Jun,Sept,Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>570.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>620.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>29910.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek  Sales  Customers  Promo  SchoolHoliday  Day  Month  Year  \\\n",
       "0      1          5   5263        555      1              1   31      7  2015   \n",
       "1      2          5   6064        625      1              1   31      7  2015   \n",
       "2      3          5   8314        821      1              1   31      7  2015   \n",
       "3      4          5  13995       1498      1              1   31      7  2015   \n",
       "4      5          5   4822        559      1              1   31      7  2015   \n",
       "\n",
       "   CompetitionDistance  ...  StoreType_b  StoreType_c  StoreType_d  \\\n",
       "0               1270.0  ...            0            1            0   \n",
       "1                570.0  ...            0            0            0   \n",
       "2              14130.0  ...            0            0            0   \n",
       "3                620.0  ...            0            1            0   \n",
       "4              29910.0  ...            0            0            0   \n",
       "\n",
       "   Assortment_a  Assortment_b  Assortment_c  PromoInterval_0  \\\n",
       "0             1             0             0                1   \n",
       "1             1             0             0                0   \n",
       "2             1             0             0                0   \n",
       "3             0             0             1                1   \n",
       "4             1             0             0                1   \n",
       "\n",
       "   PromoInterval_Feb,May,Aug,Nov  PromoInterval_Jan,Apr,Jul,Oct  \\\n",
       "0                              0                              0   \n",
       "1                              0                              1   \n",
       "2                              0                              1   \n",
       "3                              0                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   PromoInterval_Mar,Jun,Sept,Dec  \n",
       "0                               0  \n",
       "1                               0  \n",
       "2                               0  \n",
       "3                               0  \n",
       "4                               0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Isti postupak se ponavlja sa test setom\n",
    "\n",
    "df_test_store_dummies = pd.get_dummies(df_test_store, columns=['StateHoliday', 'StoreType', 'Assortment', 'PromoInterval'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PromoInterval_0</th>\n",
       "      <th>PromoInterval_Feb,May,Aug,Nov</th>\n",
       "      <th>PromoInterval_Jan,Apr,Jul,Oct</th>\n",
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       "      <td>24000.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>7520.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Store  DayOfWeek  Promo  SchoolHoliday  Day  Month  Year  \\\n",
       "0   1      1          4      1              0   17      9  2015   \n",
       "1   2      3          4      1              0   17      9  2015   \n",
       "2   3      7          4      1              0   17      9  2015   \n",
       "3   4      8          4      1              0   17      9  2015   \n",
       "4   5      9          4      1              0   17      9  2015   \n",
       "\n",
       "   CompetitionDistance  CompetitionOpenSinceMonth  ...  StoreType_b  \\\n",
       "0               1270.0                        9.0  ...            0   \n",
       "1              14130.0                       12.0  ...            0   \n",
       "2              24000.0                        4.0  ...            0   \n",
       "3               7520.0                       10.0  ...            0   \n",
       "4               2030.0                        8.0  ...            0   \n",
       "\n",
       "   StoreType_c  StoreType_d  Assortment_a  Assortment_b  Assortment_c  \\\n",
       "0            1            0             1             0             0   \n",
       "1            0            0             1             0             0   \n",
       "2            0            0             0             0             1   \n",
       "3            0            0             1             0             0   \n",
       "4            0            0             0             0             1   \n",
       "\n",
       "   PromoInterval_0  PromoInterval_Feb,May,Aug,Nov  \\\n",
       "0                1                              0   \n",
       "1                0                              0   \n",
       "2                1                              0   \n",
       "3                1                              0   \n",
       "4                1                              0   \n",
       "\n",
       "   PromoInterval_Jan,Apr,Jul,Oct  PromoInterval_Mar,Jun,Sept,Dec  \n",
       "0                              0                               0  \n",
       "1                              1                               0  \n",
       "2                              0                               0  \n",
       "3                              0                               0  \n",
       "4                              0                               0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_store_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Izlazni atribut - Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sales Histogram'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Provera raspodele izlaznog atributa Sales\n",
    "\n",
    "df_train_store_dummies['Sales'].plot(kind = \"hist\", title = \"Sales Histogram\", bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uvidjamo da je raspodela pomerena u levo, zbog toga je potrebno logaritmovati ovaj atribut\n",
    "# Nakon toga, podaci ce biti podeljeni na X i y, gde y uzima samo izlazni atribut Sales, a X sve kolone osim Sales i Customers \n",
    "# (ovaj atribut ne postoji u test setu)\n",
    "\n",
    "X=df_train_store_dummies.drop(['Sales','Customers'], 1)\n",
    "y=np.log1p(df_train_store_dummies['Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sales Histogram'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/e9FMyW1pXxERMYQ6DZifSfo8sIukdwHfA/5pa9+sXFPp9QGa258B5gPTyp1hhwATgDttrwTWSzqmXF85DbiuZUzvHWKnALeU6zQ3AidI2qdMwZ1QahERMYQ6vYtsJs1dW/cCH6e5m+vb/Q2QdBVwHDBa0nKaO7uOk3QkzdnPsrIvbC+SNBe4H9gAnNn742bAGTR3pO1Cc/fYDaV+KXCFpKU0Zy7Tyr7WSPoKzY0IAF+23enNBhERMUiUP85vTJw40T09Pd1uI2JEGT/z+m63sNWWnXNSt1sYViQttD2x3bpOv4vsIdpcc7H9qhfZW0REjFBb811kvXYGTgX2Hfx2IiJipOj0Dy1/0/J4zPbXgXfWbS0iIoazTqfIjmp5+RKaM5o9qnQUEREjQqdTZF9rWd5AcwfYfxj0biIiYsTo9CeTj6/dSEREjCydTpH95/7W2z5vcNqJiIiRYmvuInszzV/PA7wPuJVNv1QyIiLi9zoNmNHAUbbXQ/PDYcD3bP+nWo1FRMTw1ul3kb0CeK7l9XPA+EHvJiIiRoxOz2CuAO6UdC3NX/R/gM1/qTIiIuL3Or2LbJakG4A/LqW/sP2Lem1FRMRw1+kUGcCuwDrbFwDLy9fqR0REtNXpTyafDfwNcFYpvRT4Tq2mIiJi+Ov0DOYDwPuB3wLYXkG+KiYiIvrRacA8V34t0gCSdqvXUkREjASdBsxcSd8C9pb0MeCnwCX12oqIiOFuwLvIJAm4BngdsA54LfC3thdU7i0iIoaxAQPGtiX90PbRQEIlIiI60ukU2e2S3ly1k4iIGFE6/Uv+44G/lLSM5k4y0ZzcvKFWYxERMbz1GzCSXmH7EeDdQ9RPRESMEAOdwfyQ5luUH5b0fdv/fgh6ioiIEWCgazBqWX5VzUYiImJkGShgvIXliIiIfg00RfZGSetozmR2Kcvwh4v8e1btLiIihq1+A8b2TkPVSEREjCxb83X9ERERHUvAREREFdUCRtJsSask3ddS21fSAklLyvM+LevOkrRU0oOSTmypHy3p3rLuwvLdaEh6uaRrSv0OSeNbxkwv77FE0vRaxxgREVtW8wzmMmBKn9pM4GbbE4Cby2skHQZMAw4vY74pqff6z0XADGBCefTu83Rgre1DgfOBc8u+9gXOBt4CTALObg2yiIgYGtUCxvatwJo+5anAnLI8Bzi5pX617WdtPwQsBSZJOhDY0/Zt5fdoLu8zpndf84DJ5ezmRGCB7TW219J8QWffoIuIiMqG+hrMAbZXApTn/Ut9LPBoy3bLS21sWe5b32SM7Q3Ak8B+/exrM5JmSOqR1LN69eoXcVgREdHX9nKRX21q7qe+rWM2LdoX255oe+KYMWM6ajQiIjoz1AHzeJn2ojyvKvXlwMEt240DVpT6uDb1TcZIGgXsRTMlt6V9RUTEEBrqgJkP9N7VNR24rqU+rdwZdgjNxfw7yzTaeknHlOsrp/UZ07uvU4BbynWaG4ETJO1TLu6fUGoRETGEOv09mK0m6SrgOGC0pOU0d3adA8yVdDrwCHAqgO1FkuYC9wMbgDNtbyy7OoPmjrRdgBvKA+BS4ApJS2nOXKaVfa2R9BXgrrLdl233vdkgIiIqqxYwtj+0hVWTt7D9LGBWm3oPcESb+jOUgGqzbjYwu+NmIyJi0G0vF/kjImKEScBEREQVCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRbUfHIuI4W/8zOu73UIMYzmDiYiIKnIGExHR4sWctS0756RB7GT4yxlMRERUkYCJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERU0ZWAkbRM0r2S7pHUU2r7SlogaUl53qdl+7MkLZX0oKQTW+pHl/0slXShJJX6yyVdU+p3SBo/5AcZEbGD6+YZzPG2j7Q9sbyeCdxsewJwc3mNpMOAacDhwBTgm5J2KmMuAmYAE8pjSqmfDqy1fShwPnDuEBxPRES02J6myKYCc8ryHODklvrVtp+1/RCwFJgk6UBgT9u32TZweZ8xvfuaB0zuPbuJiIih0a2AMXCTpIWSZpTaAbZXApTn/Ut9LPBoy9jlpTa2LPetbzLG9gbgSWC/vk1ImiGpR1LP6tWrB+XAIiKi0a1vUz7W9gpJ+wMLJD3Qz7btzjzcT72/MZsW7IuBiwEmTpy42fqIiNh2XTmDsb2iPK8CrgUmAY+XaS/K86qy+XLg4Jbh44AVpT6uTX2TMZJGAXsBa2ocS0REtDfkASNpN0l79C4DJwD3AfOB6WWz6cB1ZXk+MK3cGXYIzcX8O8s02npJx5TrK6f1GdO7r1OAW8p1moiIGCLdmCI7ALi2XHMfBXzX9k8k3QXMlXQ68AhwKoDtRZLmAvcDG4AzbW8s+zoDuAzYBbihPAAuBa6QtJTmzGXaUBxYRET8wZAHjO1fAW9sU/8NMHkLY2YBs9rUe4Aj2tSfoQRURER0x/Z0m3JERIwgCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKhiyH8yOSKG1viZ13e7hdhBJWAiIgbJiw3zZeecNEidbB8yRRYREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVWM6L/klzQFuADYCfi27XO63FLENsnXvcRwNGIDRtJOwD8A7wKWA3dJmm/7/u52FhHR3ov5H4nt8WtmRmzAAJOApbZ/BSDpamAqkICJrshZSOxoRnLAjAUebXm9HHhL6waSZgAzysunJD1YoY/RwK8r7Lem9Dw00vPQ2CF61rmVOhnYK7e0YiQHjNrUvMkL+2Lg4qpNSD22J9Z8j8GWnodGeh4a6bl7RvJdZMuBg1tejwNWdKmXiIgdzkgOmLuACZIOkfQyYBowv8s9RUTsMEbsFJntDZI+CdxIc5vybNuLutBK1Sm4StLz0EjPQyM9d4lsD7xVRETEVhrJU2QREdFFCZiIiKgiAVOZpJ0k/ULSj7rdSyckLZN0r6R7JPV0u59OSNpb0jxJD0haLOmt3e6pP5JeWz7f3sc6SZ/pdl/9kfRZSYsk3SfpKkk7d7ungUj6dOl30fb6+UqaLWmVpPtaavtKWiBpSXnep5s9vhgJmPo+DSzudhNb6XjbRw6j+/AvAH5i+3XAG9nOP2/bD5bP90jgaOB3wLXd7WrLJI0F/gqYaPsImptmpnW3q/5JOgL4GM03erwReK+kCd3tqq3LgCl9ajOBm21PAG4ur4elBExFksYBJwHf7nYvI5WkPYF3AJcC2H7O9hNdbWrrTAb+1fbD3W5kAKOAXSSNAnZl+/+bstcDt9v+ne0NwM+AD3S5p83YvhVY06c8FZhTlucAJw9lT4MpAVPX14G/Bl7och9bw8BNkhaWr9LZ3r0KWA38rzIV+W1Ju3W7qa0wDbiq2030x/ZjwFeBR4CVwJO2b+puVwO6D3iHpP0k7Qq8h03/8Hp7doDtlQDlef8u97PNEjCVSHovsMr2wm73spWOtX0U8G7gTEnv6HZDAxgFHAVcZPtNwG8ZJlMK5Q+A3w98r9u99KdcA5gKHAIcBOwm6SPd7ap/thcD5wILgJ8A/xfY0NWmdkAJmHqOBd4vaRlwNfBOSd/pbksDs72iPK+iuS4wqbsdDWg5sNz2HeX1PJrAGQ7eDdxt+/FuNzKAPwUesr3a9vPAD4C3dbmnAdm+1PZRtt9BMw21pNs9dehxSQcClOdVXe5nmyVgKrF9lu1xtsfTTIPcYnu7/r8+SbtJ2qN3GTiBZqphu2X734BHJb22lCYzfH6S4UNs59NjxSPAMZJ2lSSaz3i7vpECQNL+5fkVwJ8xPD5raL7SanpZng5c18VeXpQR+1UxsU0OAK5t/g1hFPBd2z/pbksd+RRwZZly+hXwF13uZ0DlusC7gI93u5eB2L5D0jzgbppppl8wPL7K5PuS9gOeB860vbbbDfUl6SrgOGC0pOXA2cA5wFxJp9OE+6nd6/DFyVfFREREFZkii4iIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioor/DxKiIO39gyxBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prikaz histograma logaritmovane kolone Sales\n",
    "\n",
    "y.plot(kind = \"hist\", title = \"Sales Histogram\", bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Podela na train i test\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluacija\n",
    "## Mera evaluacije : RMSPE - Root Mean Square Percentage Error \n",
    "## (Koren srednje kvadratne procentualne greške)\n",
    "\n",
    "<img src='img/RMSPE.png' width=280>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RMSPE je bolja mera evaluacije od RMSE buduci da je izrazen procentualno to jest u samoj formuli imamo deljenje sa \n",
    "# stvarnom vrednoscu sto doprinosi mnogo boljoj evaluaciji modela jer ako imamo na primer da je tacna vrednost bila 100 000\n",
    "# a greska je 1000 to je onda mala greska, a ako je tacna vrednost bila 1100, a greska je 1000 to je velika greska i onda\n",
    "# uz pomoc RMSPE-a mi zapravo trazimo udeo u stvarnoj vrednosti odnosnu tu procentualnu gresku. \n",
    "# Potrebno je da vrednost RMSP-a bude sto manja.\n",
    "\n",
    "def ToWeight(y):\n",
    "    w = np.zeros(y.shape, dtype=float)\n",
    "    ind = y != 0\n",
    "    w[ind] = 1./(y[ind]**2)\n",
    "    return w\n",
    "\n",
    "def rmspe(y, yhat):\n",
    "    w = ToWeight(y)\n",
    "    rmspe = np.sqrt(np.mean(w * (y - yhat)**2))\n",
    "    return rmspe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_errors(y_tr, y_te, y_pred_tr, y_pred_te):\n",
    "    print('RMSPE (Train):', rmspe(y_tr, y_pred_tr))\n",
    "    print('RMSPE (Test):', rmspe(y_te, y_pred_te))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-69-3b7e588281f6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel_lr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 505\u001b[1;33m         X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0m\u001b[0;32m    506\u001b[0m                                    y_numeric=True, multi_output=True)\n\u001b[0;32m    507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    793\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    796\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    642\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 644\u001b[1;33m             _assert_all_finite(array,\n\u001b[0m\u001b[0;32m    645\u001b[0m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     94\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[0;32m     95\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'infinity'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'NaN, infinity'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "model_lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr_train = model_lr.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr_test = model_lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PROBA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ToWeight(y):\n",
    "    w = np.zeros(y.shape, dtype=float)\n",
    "    ind = y != 0\n",
    "    w[ind] = 1./(y[ind]**2)\n",
    "    return w\n",
    "\n",
    "def rmspe_score(y, yhat):\n",
    "    w = ToWeight(y)\n",
    "    rmspe = np.sqrt(np.mean(w * (y - yhat)**2))\n",
    "    return rmspe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import cross_validate\n",
    "rmspe = make_scorer(rmspe_score, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': (0.5597068786621093, 0.026883099305451803),\n",
       " 'score_time': (0.029126977920532225, 0.003240839587992137),\n",
       " 'test_RMSPE': (-0.0432720139921502, 0.00015948204163773668)}"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def error_stats(cv_dict):\n",
    "    stats = {}\n",
    "    for k,v in cv_dict.items(): \n",
    "        stats.update({k:(v.mean(), v.std())})\n",
    "    return(stats)\n",
    "\n",
    "error_stats(cv_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': (0.5503288269042969, 0.019395172634484863),\n",
       " 'score_time': (0.03052692413330078, 0.0038078572653155486),\n",
       " 'test_RMSPE': (-0.0432720139921502, 0.00015948204163773668),\n",
       " 'train_RMSPE': (-0.04326981618043779, 3.940592914015463e-05)}"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probati ostale mere\n",
    "# koristiti cross validaciju\n",
    "# ne trebaju nam predikcije y_pred_lr_test i y_pred_lr_train\n",
    "cv_dict = cross_validate(model_lr, X_train, y_train, cv = 5, scoring={'RMSPE':rmspe}, return_train_score=True)\n",
    "error_stats(cv_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# proba optimizacija parametara"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nema ioptimizacije paramatera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 752.7273812748853\n",
      "RMSPE (Test): 752.8715873108105\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.04327009043690369\n",
      "RMSPE (Test): 0.04320947165115374\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_lr_train)-1, np.exp(y_pred_lr_test)-1) # realne vrednosti\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_lr_train, y_pred_lr_test) # logaritmovane vrednosti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.40205934e-07, -2.16758593e-02,  3.23993193e-01,  1.52109613e-02,\n",
       "       -5.13551107e-04,  1.03138715e-02,  3.20509265e-02, -3.40526178e-06,\n",
       "       -4.20985737e-03, -1.17672788e-04, -5.06218265e-02,  6.60317958e-02,\n",
       "       -3.73859226e-02, -6.17309455e-02,  3.30850723e-02, -1.40313370e-01,\n",
       "        4.01655044e-01, -1.30116136e-01, -1.31225538e-01,  4.31789295e-02,\n",
       "       -2.07467137e-01,  1.64288207e-01,  5.06218265e-02, -9.75550847e-03,\n",
       "        2.72486569e-02, -6.81149750e-02])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Koeficijenti su rezultat ucenja modela.\n",
    "# Vece vrednosti koeficijenata znaci da oni vise uticu na izlazni atribut, \n",
    "# dok oni koji imaju male vrednosti imaju mali uticaj, atributi koji imaju vrednost 0 ne uticu na izlazni atribut.\n",
    "\n",
    "model_lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='att'>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualizacija koeficijenata\n",
    "\n",
    "coefs_linear = pd.DataFrame({'att':X.columns, 'coef':model_lr.coef_}).sort_values(by='coef',ascending=True).replace(0,np.nan).dropna()\n",
    "coefs_linear.replace(0,np.nan).dropna().plot.barh(x='att', y='coef', figsize=(20, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vece vrednosti koeficijenata znaci da oni vise uticu na izlazni atribut, \n",
    "# dok oni koji imaju male vrednosti imaju mali uticaj, atributi koji imaju vrednost 0 ne uticu na izlazni atribut\n",
    "# Zbog toga biramo atribute koji imaju velike vrednosti to jest koji su sto dalji od nule\n",
    "\n",
    "X_train_lr = X_train[['StoreType_b', 'Promo', 'Assortment_c', 'StateHoliday_0', 'PromoInterval_0','Assortment_a', 'StateHoliday_c', 'Year', 'PromoInterval_Jan,Apr,Jul,Oct', 'Assortment_b', 'StoreType_a', 'StoreType_d','StoreType_c','PromoInterval_Mar,Jun,Sept,Dec', 'StateHoliday_a', 'Promo2', 'StateHoliday_b', 'DayOfWeek']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lr_2 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr_2.fit(X_train_lr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_lr = X_test[['StoreType_b', 'Promo', 'Assortment_c', 'StateHoliday_0', 'PromoInterval_0','Assortment_a', 'StateHoliday_c', 'Year', 'PromoInterval_Jan,Apr,Jul,Oct', 'Assortment_b', 'StoreType_a', 'StoreType_d','StoreType_c','PromoInterval_Mar,Jun,Sept,Dec', 'StateHoliday_a', 'Promo2', 'StateHoliday_b', 'DayOfWeek']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr2_train = model_lr_2.predict(X_train_lr)\n",
    "y_pred_lr2_test = model_lr_2.predict(X_test_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 751.7156139432298\n",
      "RMSPE (Test): 751.8745423138245\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.04357084412224941\n",
      "RMSPE (Test): 0.043489104314609894\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_lr2_train)-1, np.exp(y_pred_lr2_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_lr2_train, y_pred_lr2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## proba!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ridge_LR  = Ridge(normalize= True, alpha = 3) # alpha = lambda\n",
    "Lasso_LR = Lasso(normalize= True, alpha = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=5, normalize=True)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lasso_LR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr_train = Lasso_LR.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr_test = Lasso_LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 728.0824169268918\n",
      "RMSPE (Test): 728.2331884291139\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.04953548101789771\n",
      "RMSPE (Test): 0.04938085426142559\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_lr_train)-1, np.exp(y_pred_lr_test)-1) # realne vrednosti\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_lr_train, y_pred_lr_test) # logaritmovane vrednosti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dt = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-72-b636d92ecf4e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel_dt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1240\u001b[0m         \"\"\"\n\u001b[0;32m   1241\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1242\u001b[1;33m         super().fit(\n\u001b[0m\u001b[0;32m   1243\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1244\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    154\u001b[0m             \u001b[0mcheck_X_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m             \u001b[0mcheck_y_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 156\u001b[1;33m             X, y = self._validate_data(X, y,\n\u001b[0m\u001b[0;32m    157\u001b[0m                                        validate_separately=(check_X_params,\n\u001b[0;32m    158\u001b[0m                                                             check_y_params))\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    427\u001b[0m                 \u001b[1;31m# :(\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m                 \u001b[0mcheck_X_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_y_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalidate_separately\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 429\u001b[1;33m                 \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_X_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    642\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 644\u001b[1;33m             _assert_all_finite(array,\n\u001b[0m\u001b[0;32m    645\u001b[0m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     94\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[0;32m     95\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'infinity'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'NaN, infinity'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
     ]
    }
   ],
   "source": [
    "model_dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotFittedError",
     "evalue": "This DecisionTreeRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-73-29fc99286da7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred_dt_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_dt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    424\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mpredict\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    425\u001b[0m         \"\"\"\n\u001b[1;32m--> 426\u001b[1;33m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    427\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m         \u001b[0mproba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[1;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[0;32m   1017\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1018\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1019\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1020\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1021\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotFittedError\u001b[0m: This DecisionTreeRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
     ]
    }
   ],
   "source": [
    "y_pred_dt_train = model_dt.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt_test = model_dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_dt_train)-1, np.exp(y_pred_dt_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_dt_train, y_pred_dt_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best param:  {'max_depth': 15, 'max_features': 4, 'min_samples_leaf': 3}\n"
     ]
    }
   ],
   "source": [
    "# Optimizacija parametara algoritma\n",
    "# max_features = maksimalan broj atributa koji se uzimaju u obzir prilikom grananja; \n",
    "# min_samples_leaf = minimalan broj slucajeva da bi cvor postao list; \n",
    "# max_depth = maksimalna dubina stabla\n",
    "\n",
    "decision_tree_params = {'max_features': [1, 2, 4], 'min_samples_leaf': [3, 5, 7],'max_depth': [5, 10, 15]}\n",
    "grid_dt = GridSearchCV(DecisionTreeRegressor(), decision_tree_params, cv=10, scoring = 'neg_root_mean_squared_error')\n",
    "grid_dt.fit(X,y)\n",
    "\n",
    "print('Best param: ', grid_dt.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## proba nova optimizacija"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best param:  {'max_depth': 15, 'max_features': 4, 'min_samples_leaf': 5, 'min_samples_split': 6}\n"
     ]
    }
   ],
   "source": [
    "decision_tree_params = {'max_features': [1, 2, 4], 'min_samples_leaf': [3, 5, 7],'max_depth': [5, 10, 15], 'min_samples_split': [4, 6, 8]}\n",
    "grid_dt = GridSearchCV(DecisionTreeRegressor(), decision_tree_params, cv=10, scoring = 'neg_root_mean_squared_error')\n",
    "grid_dt.fit(X,y)\n",
    "\n",
    "print('Best param: ', grid_dt.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimizovan model\n",
    "\n",
    "model_dt = DecisionTreeRegressor(max_depth=15, max_features=4, min_samples_leaf=5, criterion = \"gini\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'gini'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-299-b636d92ecf4e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel_dt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1240\u001b[0m         \"\"\"\n\u001b[0;32m   1241\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1242\u001b[1;33m         super().fit(\n\u001b[0m\u001b[0;32m   1243\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1244\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    334\u001b[0m                                                          self.n_classes_)\n\u001b[0;32m    335\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 336\u001b[1;33m                 criterion = CRITERIA_REG[self.criterion](self.n_outputs_,\n\u001b[0m\u001b[0;32m    337\u001b[0m                                                          n_samples)\n\u001b[0;32m    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'gini'"
     ]
    }
   ],
   "source": [
    "model_dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DecisionTreeRegressor' object has no attribute 'tree_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-284-29fc99286da7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred_dt_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_dt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    426\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    427\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 428\u001b[1;33m         \u001b[0mproba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    429\u001b[0m         \u001b[0mn_samples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DecisionTreeRegressor' object has no attribute 'tree_'"
     ]
    }
   ],
   "source": [
    "y_pred_dt_train = model_dt.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt_test = model_dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 786.7755391370877\n",
      "RMSPE (Test): 786.1430544332294\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.03427368128316955\n",
      "RMSPE (Test): 0.034822496150572854\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_dt_train)-1, np.exp(y_pred_dt_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_dt_train, y_pred_dt_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feat importance = [8.22765253e-02 9.82299995e-02 2.79927311e-01 3.02558822e-03\n",
      " 3.02972030e-02 3.22119313e-02 8.64937170e-03 1.34478687e-01\n",
      " 7.27068912e-02 8.54608239e-02 2.49440168e-02 1.82025410e-04\n",
      " 5.32501064e-04 1.53657583e-04 5.57693772e-05 3.20120289e-02\n",
      " 1.87961167e-02 9.72973828e-03 9.71503226e-03 2.48222637e-02\n",
      " 1.46273575e-03 2.98315750e-02 1.03873019e-03 5.64341530e-03\n",
      " 5.33027988e-03 8.48578097e-03]\n"
     ]
    }
   ],
   "source": [
    "# Selekcija atributa\n",
    "\n",
    "feat_importance_tree = model_dt.tree_.compute_feature_importances()\n",
    "print(\"feat importance = \" + str(feat_importance_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='att'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coefs_tree = pd.DataFrame({'att':X.columns, 'coef':model_dt.feature_importances_}).sort_values(by='coef',ascending=True).replace(0,np.nan).dropna()\n",
    "coefs_tree.plot.barh(x='att', y='coef', figsize=(20, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train data set sa selektovanim atributima\n",
    "\n",
    "X_train_dt = X_train[['Promo', 'CompetitionDistance', 'Assortment_a', 'DayOfWeek', 'CompetitionOpenSinceYear', 'Store', 'StoreType_b', 'CompetitionOpenSinceMonth', 'PromoInterval_0']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Novi model sa selektovanim atributima\n",
    "\n",
    "model_dt_2 = DecisionTreeRegressor(max_depth=15, max_features=4, min_samples_leaf=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=15, max_features=4, min_samples_leaf=5)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dt_2.fit(X_train_dt, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_dt = X_test[['Promo', 'CompetitionDistance', 'Assortment_a', 'DayOfWeek', 'CompetitionOpenSinceYear', 'Store', 'StoreType_b', 'CompetitionOpenSinceMonth', 'PromoInterval_0']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt2_train = model_dt_2.predict(X_train_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt2_test = model_dt_2.predict(X_test_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 797.1658341812648\n",
      "RMSPE (Test): 795.4726135302012\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.029656176820285738\n",
      "RMSPE (Test): 0.02967658548009376\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_dt2_train)-1, np.exp(y_pred_dt2_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_dt2_train, y_pred_dt2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_knn = KNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn_train = model_knn.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn_test = model_knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 812.8565808150663\n",
      "RMSPE (Test): 808.8860989311509\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.019359768083598624\n",
      "RMSPE (Test): 0.02415739059411452\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_knn_train)-1, np.exp(y_pred_knn_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_knn_train, y_pred_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best param:  {'n_neighbors': 6}\n"
     ]
    }
   ],
   "source": [
    "# Optimizacija parametara algoritma\n",
    "\n",
    "knn_params = {'n_neighbors': range(3,11)}\n",
    "grid_knn = GridSearchCV(KNeighborsRegressor(), knn_params, cv=10, scoring = 'neg_root_mean_squared_error')\n",
    "grid_knn.fit(X,y)\n",
    "\n",
    "print('Best param: ', grid_knn.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_knn = KNeighborsRegressor(n_neighbors=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=6)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn_train = model_knn.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn_test = model_knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 810.0897646905232\n",
      "RMSPE (Test): 806.7006983026357\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.020238977026873113\n",
      "RMSPE (Test): 0.024272906542545977\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_knn_train)-1, np.exp(y_pred_knn_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_knn_train, y_pred_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  48 out of  48 | elapsed:  8.2min finished\n"
     ]
    }
   ],
   "source": [
    "knn_params= {\n",
    "    'n_neighbors' : [3, 5, 11, 19],\n",
    "    'weights' : ['uniform', 'distance'],\n",
    "    'metric' : ['euclidean', 'manhattan']\n",
    "}\n",
    "\n",
    "gs = GridSearchCV (\n",
    "     KNeighborsRegressor(),\n",
    "     knn_params,\n",
    "     verbose = 1,\n",
    "     cv = 3,\n",
    "     n_jobs = -1\n",
    ")\n",
    "\n",
    "model_knn_2 = gs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'metric': 'manhattan', 'n_neighbors': 5, 'weights': 'distance'}"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn_2.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn2_train = model_knn_2.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn2_test = model_knn_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 837.4121630504216\n",
      "RMSPE (Test): 810.5419361978795\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.0\n",
      "RMSPE (Test): 0.020275194435255123\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_knn2_train)-1, np.exp(y_pred_knn2_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_knn2_train, y_pred_knn2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf_train = model_rf.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf_test = model_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 830.0524563580431\n",
      "RMSPE (Test): 823.3224965579099\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.005381387100475257\n",
      "RMSPE (Test): 0.014291682934531879\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_rf_train)-1, np.exp(y_pred_rf_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_rf_train, y_pred_rf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best param:  {'n_estimators': 30}\n"
     ]
    }
   ],
   "source": [
    "# Optimizacija parametara algoritma\n",
    "# n_estimators = broj stabala\n",
    "\n",
    "random_forest_params = {'n_estimators': [20, 25, 30]}\n",
    "grid_rf = GridSearchCV(RandomForestRegressor(), random_forest_params, cv=5, scoring='neg_root_mean_squared_error')\n",
    "grid_rf.fit(X,y)\n",
    "print('Best param: ', grid_rf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimizovan model\n",
    "\n",
    "model_rf = RandomForestRegressor(n_estimators = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=30)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf_train = model_rf.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf_test = model_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 830.1929213310317\n",
      "RMSPE (Test): 823.5546172119936\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.005655197921595682\n",
      "RMSPE (Test): 0.014499642340465661\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_rf_train)-1, np.exp(y_pred_rf_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_rf_train, y_pred_rf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.40147240e-01, 6.90190049e-02, 1.63956588e-01, 5.35059398e-03,\n",
       "       5.19580245e-02, 4.68660209e-02, 1.76693499e-02, 2.07968568e-01,\n",
       "       8.55712107e-02, 9.91239686e-02, 1.41430151e-02, 1.25429978e-03,\n",
       "       2.36102573e-04, 2.36795982e-05, 2.75558019e-05, 1.72274244e-02,\n",
       "       1.56073142e-02, 1.01997074e-02, 1.03283194e-02, 1.79383875e-02,\n",
       "       6.57608197e-04, 4.45232672e-03, 3.71424384e-03, 4.33242493e-03,\n",
       "       4.05555669e-03, 8.17146331e-03])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='att'>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coefs_rf = pd.DataFrame({'att':X.columns, 'coef':model_rf.feature_importances_}).sort_values(by='coef',ascending=True).replace(0,np.nan).dropna()\n",
    "coefs_rf.plot.barh(x='att', y='coef', figsize=(20, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rf = X_train[['CompetitionDistance', 'Promo', 'Store', 'CompetitionOpenSinceYear', 'CompetitionOpenSinceMonth', 'DayOfWeek', 'Day', 'Month', 'Year', 'StoreType_a', 'Assortment_a', 'StoreType_b']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rf_2 = RandomForestRegressor(n_estimators = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=30)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf_2.fit(X_train_rf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_rf = X_test[['CompetitionDistance', 'Promo', 'Store', 'CompetitionOpenSinceYear', 'CompetitionOpenSinceMonth', 'DayOfWeek', 'Day', 'Month', 'Year', 'StoreType_a', 'Assortment_a', 'StoreType_b']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf2_train = model_rf_2.predict(X_train_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf2_test = model_rf_2.predict(X_test_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 829.3117478546537\n",
      "RMSPE (Test): 821.9437634361465\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.005940871518695329\n",
      "RMSPE (Test): 0.015070643579641222\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_rf2_train)-1, np.exp(y_pred_rf2_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_rf2_train, y_pred_rf2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Using cached xgboost-1.4.1-py3-none-win_amd64.whl (97.8 MB)\n",
      "Requirement already satisfied: numpy in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from xgboost) (1.19.2)\n",
      "Requirement already satisfied: scipy in c:\\users\\ivana\\anaconda3\\lib\\site-packages (from xgboost) (1.5.2)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.4.1\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgb = xgb.XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb_train = model_xgb.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb_test = model_xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 809.1764547566504\n",
      "RMSPE (Test): 807.7492891842866\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.01785556377050872\n",
      "RMSPE (Test): 0.017812849503496393\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_xgb_train)-1, np.exp(y_pred_xgb_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_xgb_train, y_pred_xgb_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-77-308b3d6c4d98>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mxgb_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'learning_rate'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0.05\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.15\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.25\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.30\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'min_split_loss'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.2\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m0.3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'max_depth'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mgrid_xgb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mXGBRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxgb_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_root_mean_squared_error'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mgrid_xgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Best param: '\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid_xgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    734\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 736\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    737\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1186\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1187\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1188\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1190\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    706\u001b[0m                               n_splits, n_candidates, n_candidates * n_splits))\n\u001b[0;32m    707\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 708\u001b[1;33m                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),\n\u001b[0m\u001b[0;32m    709\u001b[0m                                                        \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    710\u001b[0m                                                        \u001b[0mtrain\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1049\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1050\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1051\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1052\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    864\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    865\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 866\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    867\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    868\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    782\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    783\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 784\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    785\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    786\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    529\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    431\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 433\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)\u001b[0m\n\u001b[0;32m    734\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    735\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_configure_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxgb_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meval_metric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 736\u001b[1;33m         self._Booster = train(\n\u001b[0m\u001b[0;32m    737\u001b[0m             \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m             \u001b[0mtrain_dmatrix\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\training.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks)\u001b[0m\n\u001b[0;32m    187\u001b[0m     \u001b[0mBooster\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0ma\u001b[0m \u001b[0mtrained\u001b[0m \u001b[0mbooster\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m     \"\"\"\n\u001b[1;32m--> 189\u001b[1;33m     bst = _train_internal(params, dtrain,\n\u001b[0m\u001b[0;32m    190\u001b[0m                           \u001b[0mnum_boost_round\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_boost_round\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    191\u001b[0m                           \u001b[0mevals\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevals\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\training.py\u001b[0m in \u001b[0;36m_train_internal\u001b[1;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks, evals_result, maximize, verbose_eval, early_stopping_rounds)\u001b[0m\n\u001b[0;32m     79\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbefore_iteration\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 81\u001b[1;33m         \u001b[0mbst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     82\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mafter_iteration\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[0;32m   1494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1495\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1496\u001b[1;33m             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,\n\u001b[0m\u001b[0;32m   1497\u001b[0m                                                     \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1498\u001b[0m                                                     dtrain.handle))\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Optimizacija parametara\n",
    " \n",
    "# min_split_loss = parametar koji sprecava overfitting; \n",
    "# max_depth = maksimalna dubina\n",
    "# learning_rate = znacajnost smanjenja greske svake iteracije;\n",
    "\n",
    "xgb_params = {'learning_rate': [0.05, 0.10, 0.15, 0.20, 0.25, 0.30], 'min_split_loss': [0.0, 0.1, 0.2 , 0.3, 0.4], 'max_depth': [4, 6, 8, 10, 12]}\n",
    "grid_xgb = GridSearchCV(xgb.XGBRegressor(), xgb_params, cv=5, scoring='neg_root_mean_squared_error')\n",
    "grid_xgb.fit(X,y)\n",
    "print('Best param: ', grid_xgb.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgb = xgb.XGBRegressor(learning_rate = 0.1, min_split_loss = 0.1, max_depth = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0.100000001,\n",
       "             gpu_id=-1, importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.1, max_delta_step=0, max_depth=10,\n",
       "             min_child_weight=1, min_split_loss=0.1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "             num_parallel_tree=1, random_state=0, reg_alpha=0, reg_lambda=1,\n",
       "             scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb_train = model_xgb.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb_test = model_xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 804.4755743221585\n",
      "RMSPE (Test): 802.1826116972675\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.016588057466464065\n",
      "RMSPE (Test): 0.0168806399947866\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_xgb_train)-1, np.exp(y_pred_xgb_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_xgb_train, y_pred_xgb_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03872637, 0.02252656, 0.233311  , 0.00914325, 0.02396898,\n",
       "       0.01689182, 0.00686537, 0.05040087, 0.04462585, 0.04747954,\n",
       "       0.07119693, 0.01011016, 0.00375945, 0.00325789, 0.00227366,\n",
       "       0.05744628, 0.0869842 , 0.03285516, 0.04668083, 0.07156749,\n",
       "       0.0045605 , 0.01533559, 0.        , 0.02612473, 0.02830127,\n",
       "       0.04560626], dtype=float32)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='att'>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coefs_xgb = pd.DataFrame({'att':X.columns, 'coef':model_xgb.feature_importances_}).sort_values(by='coef',ascending=True).replace(0,np.nan).dropna()\n",
    "coefs_xgb.plot.barh(x='att',y='coef', figsize=(20, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_xgb = X_train[['Store', 'DayOfWeek', 'Promo', 'Day', 'Month',\n",
    "       'CompetitionDistance', 'CompetitionOpenSinceMonth',\n",
    "       'CompetitionOpenSinceYear', 'Promo2', 'StateHoliday_0',\n",
    "       'StoreType_a','StoreType_b', 'StoreType_c', 'StoreType_d', 'Assortment_a',\n",
    "        'Assortment_c', 'PromoInterval_0',\n",
    "       'PromoInterval_Feb,May,Aug,Nov', 'PromoInterval_Jan,Apr,Jul,Oct',\n",
    "       'PromoInterval_Mar,Jun,Sept,Dec', 'Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Store', 'DayOfWeek', 'Promo', 'SchoolHoliday', 'Day', 'Month', 'Year',\n",
       "       'CompetitionDistance', 'CompetitionOpenSinceMonth',\n",
       "       'CompetitionOpenSinceYear', 'Promo2', 'StateHoliday_0',\n",
       "       'StateHoliday_a', 'StateHoliday_b', 'StateHoliday_c', 'StoreType_a',\n",
       "       'StoreType_b', 'StoreType_c', 'StoreType_d', 'Assortment_a',\n",
       "       'Assortment_b', 'Assortment_c', 'PromoInterval_0',\n",
       "       'PromoInterval_Feb,May,Aug,Nov', 'PromoInterval_Jan,Apr,Jul,Oct',\n",
       "       'PromoInterval_Mar,Jun,Sept,Dec'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgb_2 = xgb.XGBRegressor(learning_rate = 0.1, min_split_loss = 0.1, max_depth = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0.100000001,\n",
       "             gpu_id=-1, importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.1, max_delta_step=0, max_depth=10,\n",
       "             min_child_weight=1, min_split_loss=0.1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "             num_parallel_tree=1, random_state=0, reg_alpha=0, reg_lambda=1,\n",
       "             scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb_2.fit(X_train_xgb, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_xgb = X_test[['Store', 'DayOfWeek', 'Promo', 'Day', 'Month',\n",
    "       'CompetitionDistance', 'CompetitionOpenSinceMonth',\n",
    "       'CompetitionOpenSinceYear', 'Promo2', 'StateHoliday_0',\n",
    "       'StoreType_a','StoreType_b', 'StoreType_c', 'StoreType_d', 'Assortment_a',\n",
    "        'Assortment_c', 'PromoInterval_0',\n",
    "       'PromoInterval_Feb,May,Aug,Nov', 'PromoInterval_Jan,Apr,Jul,Oct',\n",
    "       'PromoInterval_Mar,Jun,Sept,Dec', 'Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb2_train = model_xgb_2.predict(X_train_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb2_test = model_xgb_2.predict(X_test_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Realna vrednost:\n",
      "RMSPE (Train): 805.3261117401148\n",
      "RMSPE (Test): 803.2335358772376\n",
      "\n",
      "Procentalna vrednost:\n",
      "RMSPE (Train): 0.01621531116898604\n",
      "RMSPE (Test): 0.016499985614113605\n"
     ]
    }
   ],
   "source": [
    "print(\"Realna vrednost:\")\n",
    "print_errors(y_train, y_test, np.exp(y_pred_xgb2_train)-1, np.exp(y_pred_xgb2_test)-1)\n",
    "\n",
    "print(\"\\nProcentalna vrednost:\")\n",
    "print_errors(y_train, y_test, y_pred_xgb2_train, y_pred_xgb2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poredjenje performansi algoritama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REDUCED LINEAR REGRESSION\n",
      "\n",
      "RMSPE (Train): 0.04357084412224941\n",
      "RMSPE (Test): 0.043489104314609894\n"
     ]
    }
   ],
   "source": [
    "print('REDUCED LINEAR REGRESSION\\n')\n",
    "print_errors(y_train, y_test, y_pred_lr2_train, y_pred_lr2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DECISION TREE\n",
      "\n",
      "RMSPE (Train): 0.03427368128316955\n",
      "RMSPE (Test): 0.034822496150572854\n"
     ]
    }
   ],
   "source": [
    "print('DECISION TREE\\n')\n",
    "print_errors(y_train, y_test, y_pred_dt_train, y_pred_dt_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REDUCED DECISION TREE\n",
      "\n",
      "RMSPE (Train): 0.029656176820285738\n",
      "RMSPE (Test): 0.02967658548009376\n"
     ]
    }
   ],
   "source": [
    "print('REDUCED DECISION TREE\\n')\n",
    "print_errors(y_train, y_test, y_pred_dt2_train, y_pred_dt2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "\n",
      "RMSPE (Train): 0.020238977026873113\n",
      "RMSPE (Test): 0.024272906542545977\n"
     ]
    }
   ],
   "source": [
    "# print('REDUCED KNN\\n')\n",
    "# print_errors(y_train, y_test, y_pred_knn_train, y_pred_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REDUCED KNN\n",
      "\n",
      "RMSPE (Train): 0.0\n",
      "RMSPE (Test): 0.020275194435255123\n"
     ]
    }
   ],
   "source": [
    "print('REDUCED KNN\\n')\n",
    "print_errors(y_train, y_test, y_pred_knn2_train, y_pred_knn2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RANDOM FOREST\n",
      "\n",
      "RMSPE (Train): 0.005687098749990414\n",
      "RMSPE (Test): 0.01447635514134407\n"
     ]
    }
   ],
   "source": [
    "print('RANDOM FOREST\\n')\n",
    "print_errors(y_train, y_test, y_pred_rf_train, y_pred_rf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REDUCED RANDOM FOREST\n",
      "\n",
      "RMSPE (Train): 0.005915161384831391\n",
      "RMSPE (Test): 0.015058565875463684\n"
     ]
    }
   ],
   "source": [
    "print('REDUCED RANDOM FOREST\\n')\n",
    "print_errors(y_train, y_test, y_pred_rf2_train, y_pred_rf2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBOOST\n",
      "\n",
      "RMSPE (Train): 0.016588057466464065\n",
      "RMSPE (Test): 0.0168806399947866\n"
     ]
    }
   ],
   "source": [
    "print('XGBOOST\\n')\n",
    "print_errors(y_train, y_test, y_pred_xgb_train, y_pred_xgb_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REDUCED XGBOOST\n",
      "\n",
      "RMSPE (Train): 0.01621531116898604\n",
      "RMSPE (Test): 0.016499985614113605\n"
     ]
    }
   ],
   "source": [
    "print('REDUCED XGBOOST\\n')\n",
    "print_errors(y_train, y_test, y_pred_xgb2_train, y_pred_xgb2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Primena modela"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Store', 'DayOfWeek', 'Sales', 'Customers', 'Promo', 'SchoolHoliday',\n",
       "       'Day', 'Month', 'Year', 'CompetitionDistance',\n",
       "       'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2',\n",
       "       'StateHoliday_0', 'StateHoliday_a', 'StateHoliday_b', 'StateHoliday_c',\n",
       "       'StoreType_a', 'StoreType_b', 'StoreType_c', 'StoreType_d',\n",
       "       'Assortment_a', 'Assortment_b', 'Assortment_c', 'PromoInterval_0',\n",
       "       'PromoInterval_Feb,May,Aug,Nov', 'PromoInterval_Jan,Apr,Jul,Oct',\n",
       "       'PromoInterval_Mar,Jun,Sept,Dec'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_store_dummies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'Store', 'DayOfWeek', 'Promo', 'SchoolHoliday', 'Day', 'Month',\n",
       "       'Year', 'CompetitionDistance', 'CompetitionOpenSinceMonth',\n",
       "       'CompetitionOpenSinceYear', 'Promo2', 'StateHoliday_0',\n",
       "       'StateHoliday_a', 'StoreType_a', 'StoreType_b', 'StoreType_c',\n",
       "       'StoreType_d', 'Assortment_a', 'Assortment_b', 'Assortment_c',\n",
       "       'PromoInterval_0', 'PromoInterval_Feb,May,Aug,Nov',\n",
       "       'PromoInterval_Jan,Apr,Jul,Oct', 'PromoInterval_Mar,Jun,Sept,Dec'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_store_dummies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uklanjanje kolone Id iz Test skupa podataka (jer ne postoji u Train skupu)\n",
    "\n",
    "df_test_store_dummies.drop(['Id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dodavanje kolona StateHoliday_b i StateHoliday_c koje ne postoje u Test setu, a nalaze se u Train setu\n",
    "\n",
    "df_test_store_dummies['StateHoliday_b'] = 0\n",
    "df_test_store_dummies['StateHoliday_c'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eksplicitno zadavanje redosleda kolona\n",
    "\n",
    "df_test_store_dummies = df_test_store_dummies[['Store', 'DayOfWeek', 'Promo', 'SchoolHoliday',\n",
    "       'Day', 'Year', 'Month', 'CompetitionDistance',\n",
    "       'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2',\n",
    "       'StateHoliday_0', 'StateHoliday_a', 'StateHoliday_b', 'StateHoliday_c',\n",
    "       'StoreType_a', 'StoreType_b', 'StoreType_c', 'StoreType_d',\n",
    "       'Assortment_a', 'Assortment_b', 'Assortment_c', 'PromoInterval_0',\n",
    "       'PromoInterval_Feb,May,Aug,Nov', 'PromoInterval_Jan,Apr,Jul,Oct',\n",
    "       'PromoInterval_Mar,Jun,Sept,Dec']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Store', 'DayOfWeek', 'Promo', 'SchoolHoliday', 'Day', 'Year', 'Month',\n",
       "       'CompetitionDistance', 'CompetitionOpenSinceMonth',\n",
       "       'CompetitionOpenSinceYear', 'Promo2', 'StateHoliday_0',\n",
       "       'StateHoliday_a', 'StateHoliday_b', 'StateHoliday_c', 'StoreType_a',\n",
       "       'StoreType_b', 'StoreType_c', 'StoreType_d', 'Assortment_a',\n",
       "       'Assortment_b', 'Assortment_c', 'PromoInterval_0',\n",
       "       'PromoInterval_Feb,May,Aug,Nov', 'PromoInterval_Jan,Apr,Jul,Oct',\n",
       "       'PromoInterval_Mar,Jun,Sept,Dec'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_store_dummies.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_store_lr2 = df_test_store_dummies[['StoreType_b', 'Promo', 'Assortment_c', 'StateHoliday_0', 'PromoInterval_0','Assortment_a', 'StateHoliday_c', 'Year', 'PromoInterval_Jan,Apr,Jul,Oct', 'Assortment_b', 'StoreType_a', 'StoreType_d','StoreType_c','PromoInterval_Mar,Jun,Sept,Dec', 'StateHoliday_a', 'Promo2', 'StateHoliday_b', 'DayOfWeek']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lr2_test = model_lr_2.predict(df_test_store_lr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lr2_test_exp=np.exp(pred_lr2_test)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Sales'] = pred_lr2_test_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_lr2 = test_data[['Id', 'Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7709.073893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7084.695288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8505.289382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7568.287709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8505.289382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sales\n",
       "Id             \n",
       "1   7709.073893\n",
       "2   7084.695288\n",
       "3   8505.289382\n",
       "4   7568.287709\n",
       "5   8505.289382"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_lr2.set_index(results_lr2['Id'], inplace=True)\n",
    "results_lr2.drop(['Id'], axis = 1, inplace = True)\n",
    "results_lr2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_lr2.to_csv('results_lr2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Sales'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_dt_test = model_dt.predict(df_test_store_dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_dt_test_exp=np.exp(pred_dt_test)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Sales'] = pred_dt_test_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dt = test_data[['Id', 'Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8677.721637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11458.490737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15150.924457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9923.686374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9013.050929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sales\n",
       "Id              \n",
       "1    8677.721637\n",
       "2   11458.490737\n",
       "3   15150.924457\n",
       "4    9923.686374\n",
       "5    9013.050929"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dt.set_index(results_dt['Id'], inplace=True)\n",
    "results_dt.drop(['Id'], axis = 1, inplace = True)\n",
    "results_dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dt.to_csv('results_dt.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Sales'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_store_dt2 = df_test_store_dummies[['Promo', 'CompetitionDistance', 'Assortment_a', 'DayOfWeek', 'CompetitionOpenSinceYear', 'Store', 'StoreType_b', 'Month', 'CompetitionOpenSinceMonth']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_dt2_test = model_dt_2.predict(df_test_store_dt2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_dt2_test_exp=np.exp(pred_dt2_test)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Sales'] = pred_dt2_test_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dt2 = test_data[['Id', 'Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6142.592928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8128.797470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6853.763767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6951.585455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7909.677240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sales\n",
       "Id             \n",
       "1   6142.592928\n",
       "2   8128.797470\n",
       "3   6853.763767\n",
       "4   6951.585455\n",
       "5   7909.677240"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dt2.set_index(results_dt2['Id'], inplace=True)\n",
    "results_dt2.drop(['Id'], axis = 1, inplace = True)\n",
    "results_dt2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dt2.to_csv('results_dt2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Sales'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_knn_test = model_knn_2.predict(df_test_store_dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_knn_test_exp=np.exp(pred_knn_test)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Sales'] = pred_knn_test_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_knn = test_data[['Id', 'Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7394.130972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12218.427313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12765.188664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8824.193855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8970.740067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sales\n",
       "Id              \n",
       "1    7394.130972\n",
       "2   12218.427313\n",
       "3   12765.188664\n",
       "4    8824.193855\n",
       "5    8970.740067"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_knn.set_index(results_knn['Id'], inplace=True)\n",
    "results_knn.drop(['Id'], axis = 1, inplace=True)\n",
    "results_knn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_knn.to_csv('results_knn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Sales'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_rf_test = model_rf.predict(df_test_store_dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_rf_test_exp=np.exp(pred_rf_test)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Sales'] = pred_rf_test_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_rf = test_data[['Id', 'Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7210.643159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11394.619703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12334.793467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8714.858865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9048.013761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sales\n",
       "Id              \n",
       "1    7210.643159\n",
       "2   11394.619703\n",
       "3   12334.793467\n",
       "4    8714.858865\n",
       "5    9048.013761"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_rf.set_index(results_rf['Id'], inplace=True)\n",
    "results_rf.drop(['Id'], axis = 1, inplace = True)\n",
    "results_rf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_rf.to_csv('results_rf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Sales'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_store_rf2 = df_test_store_dummies[['CompetitionDistance', 'Promo', 'Store', 'CompetitionOpenSinceYear', 'CompetitionOpenSinceMonth', 'DayOfWeek', 'Day', 'Month', 'Year', 'StoreType_a', 'Assortment_a', 'StoreType_b']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_rf2_test = model_rf_2.predict(df_test_store_rf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_rf2_test_exp=np.exp(pred_rf2_test)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Sales'] = pred_rf2_test_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_rf2 = test_data[['Id', 'Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4412.477088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7459.182757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9122.565862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7286.982623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7174.471003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sales\n",
       "Id             \n",
       "1   4412.477088\n",
       "2   7459.182757\n",
       "3   9122.565862\n",
       "4   7286.982623\n",
       "5   7174.471003"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_rf2.set_index(results_rf2['Id'], inplace=True)\n",
    "results_rf2.drop(['Id'], axis = 1, inplace = True)\n",
    "results_rf2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_rf2.to_csv('results_rf2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Sales'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_xgb_test = model_xgb.predict(df_test_store_dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_xgb_test_exp=np.exp(pred_xgb_test)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Sales'] = pred_xgb_test_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_xgb = test_data[['Id', 'Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7500.020508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11038.463867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13181.528320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8690.590820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8825.536133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sales\n",
       "Id              \n",
       "1    7500.020508\n",
       "2   11038.463867\n",
       "3   13181.528320\n",
       "4    8690.590820\n",
       "5    8825.536133"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_xgb.set_index(results_xgb['Id'], inplace=True)\n",
    "results_xgb.drop(['Id'], axis = 1, inplace = True)\n",
    "results_xgb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_xgb.to_csv('results_xgb.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Sales'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_store_xgb2 = df_test_store_dummies[['Store', 'DayOfWeek', 'Promo', 'Day', 'Month', 'Year',\n",
    "       'CompetitionDistance', 'CompetitionOpenSinceMonth',\n",
    "       'CompetitionOpenSinceYear', 'Promo2', 'StateHoliday_0',\n",
    "       'StoreType_a','StoreType_b', 'StoreType_c', 'StoreType_d', 'Assortment_a',\n",
    "        'Assortment_c', 'PromoInterval_0',\n",
    "       'PromoInterval_Feb,May,Aug,Nov', 'PromoInterval_Jan,Apr,Jul,Oct',\n",
    "       'PromoInterval_Mar,Jun,Sept,Dec']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_xgb2_test = model_xgb_2.predict(df_test_store_xgb2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_xgb2_test_exp=np.exp(pred_xgb2_test)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Sales'] = pred_xgb2_test_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_xgb2 = test_data[['Id', 'Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6718.903320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7183.461426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7564.434082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7746.037109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7564.434082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sales\n",
       "Id             \n",
       "1   6718.903320\n",
       "2   7183.461426\n",
       "3   7564.434082\n",
       "4   7746.037109\n",
       "5   7564.434082"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_xgb2.set_index(results_xgb2['Id'], inplace=True)\n",
    "results_xgb2.drop(['Id'], axis = 1, inplace = True)\n",
    "results_xgb2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_xgb2.to_csv('results_xgb2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Sales'], axis = 1, inplace=True)"
   ]
  }
 ],
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