machine_learning_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------этап подготовки данных--------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "money = pd.read_csv('C:\\\\Users\\PycharmProjects\\linear_regression\\ML Model\\\\USD_CURRENCY_HISTORY.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "409"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "past = 7 * 4\n",
    "future = 7\n",
    "salary = [10000, 20000, 30000, 40000, 50000]\n",
    "value = money['curs']\n",
    "len(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = past\n",
    "end = len(value)  - future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df = [] \n",
    "for i in range(start,end):\n",
    "    past_and_future_value = value[(i-past):(i+future)]   # для каждого элемента i (каждого дня) вводим переменную \n",
    "    # (временной диапазон за 28 дней до и 7 дней после для каждого дня\n",
    "    raw_df.append(list(past_and_future_value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "past_columns = [f\"past_{i}\" for i in range(past)]\n",
    "future_columns = [f\"future_{i}\" for i in range(future)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>past_0</th>\n",
       "      <th>past_1</th>\n",
       "      <th>past_2</th>\n",
       "      <th>past_3</th>\n",
       "      <th>past_4</th>\n",
       "      <th>past_5</th>\n",
       "      <th>past_6</th>\n",
       "      <th>past_7</th>\n",
       "      <th>past_8</th>\n",
       "      <th>past_9</th>\n",
       "      <th>...</th>\n",
       "      <th>past_25</th>\n",
       "      <th>past_26</th>\n",
       "      <th>past_27</th>\n",
       "      <th>future_0</th>\n",
       "      <th>future_1</th>\n",
       "      <th>future_2</th>\n",
       "      <th>future_3</th>\n",
       "      <th>future_4</th>\n",
       "      <th>future_5</th>\n",
       "      <th>future_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.0463</td>\n",
       "      <td>56.8734</td>\n",
       "      <td>56.9957</td>\n",
       "      <td>56.6019</td>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>...</td>\n",
       "      <td>57.7701</td>\n",
       "      <td>57.5899</td>\n",
       "      <td>56.5918</td>\n",
       "      <td>56.3554</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.8734</td>\n",
       "      <td>56.9957</td>\n",
       "      <td>56.6019</td>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>56.4115</td>\n",
       "      <td>...</td>\n",
       "      <td>57.5899</td>\n",
       "      <td>56.5918</td>\n",
       "      <td>56.3554</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "      <td>56.3742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56.9957</td>\n",
       "      <td>56.6019</td>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>56.4115</td>\n",
       "      <td>56.3888</td>\n",
       "      <td>...</td>\n",
       "      <td>56.5918</td>\n",
       "      <td>56.3554</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "      <td>56.3742</td>\n",
       "      <td>56.4334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.6019</td>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>56.4115</td>\n",
       "      <td>56.3888</td>\n",
       "      <td>55.9288</td>\n",
       "      <td>...</td>\n",
       "      <td>56.3554</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "      <td>56.3742</td>\n",
       "      <td>56.4334</td>\n",
       "      <td>56.6616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>56.4115</td>\n",
       "      <td>56.3888</td>\n",
       "      <td>55.9288</td>\n",
       "      <td>55.8288</td>\n",
       "      <td>...</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "      <td>56.3742</td>\n",
       "      <td>56.4334</td>\n",
       "      <td>56.6616</td>\n",
       "      <td>57.1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<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>past_0</th>\n",
       "      <th>past_1</th>\n",
       "      <th>past_2</th>\n",
       "      <th>past_3</th>\n",
       "      <th>past_4</th>\n",
       "      <th>past_5</th>\n",
       "      <th>past_6</th>\n",
       "      <th>past_7</th>\n",
       "      <th>past_8</th>\n",
       "      <th>past_9</th>\n",
       "      <th>...</th>\n",
       "      <th>past_25</th>\n",
       "      <th>past_26</th>\n",
       "      <th>past_27</th>\n",
       "      <th>future_0</th>\n",
       "      <th>future_1</th>\n",
       "      <th>future_2</th>\n",
       "      <th>future_3</th>\n",
       "      <th>future_4</th>\n",
       "      <th>future_5</th>\n",
       "      <th>future_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.0463</td>\n",
       "      <td>56.8734</td>\n",
       "      <td>56.9957</td>\n",
       "      <td>56.6019</td>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>...</td>\n",
       "      <td>57.7701</td>\n",
       "      <td>57.5899</td>\n",
       "      <td>56.5918</td>\n",
       "      <td>56.3554</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.8734</td>\n",
       "      <td>56.9957</td>\n",
       "      <td>56.6019</td>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>56.4115</td>\n",
       "      <td>...</td>\n",
       "      <td>57.5899</td>\n",
       "      <td>56.5918</td>\n",
       "      <td>56.3554</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "      <td>56.3742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56.9957</td>\n",
       "      <td>56.6019</td>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>56.4115</td>\n",
       "      <td>56.3888</td>\n",
       "      <td>...</td>\n",
       "      <td>56.5918</td>\n",
       "      <td>56.3554</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "      <td>56.3742</td>\n",
       "      <td>56.4334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.6019</td>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>56.4115</td>\n",
       "      <td>56.3888</td>\n",
       "      <td>55.9288</td>\n",
       "      <td>...</td>\n",
       "      <td>56.3554</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "      <td>56.3742</td>\n",
       "      <td>56.4334</td>\n",
       "      <td>56.6616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56.3569</td>\n",
       "      <td>56.3878</td>\n",
       "      <td>56.5925</td>\n",
       "      <td>56.7597</td>\n",
       "      <td>56.5892</td>\n",
       "      <td>56.6261</td>\n",
       "      <td>56.4115</td>\n",
       "      <td>56.3888</td>\n",
       "      <td>55.9288</td>\n",
       "      <td>55.8288</td>\n",
       "      <td>...</td>\n",
       "      <td>56.3438</td>\n",
       "      <td>56.5201</td>\n",
       "      <td>56.6537</td>\n",
       "      <td>56.7608</td>\n",
       "      <td>55.9208</td>\n",
       "      <td>55.6717</td>\n",
       "      <td>56.3742</td>\n",
       "      <td>56.4334</td>\n",
       "      <td>56.6616</td>\n",
       "      <td>57.1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(raw_df, columns=past_columns+future_columns)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>future_0</th>\n",
       "      <th>future_1</th>\n",
       "      <th>future_2</th>\n",
       "      <th>future_3</th>\n",
       "      <th>future_4</th>\n",
       "      <th>future_5</th>\n",
       "      <th>future_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>65.6196</td>\n",
       "      <td>65.6046</td>\n",
       "      <td>65.9735</td>\n",
       "      <td>66.2608</td>\n",
       "      <td>66.4127</td>\n",
       "      <td>66.7471</td>\n",
       "      <td>66.4897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<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>future_0</th>\n",
       "      <th>future_1</th>\n",
       "      <th>future_2</th>\n",
       "      <th>future_3</th>\n",
       "      <th>future_4</th>\n",
       "      <th>future_5</th>\n",
       "      <th>future_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>65.6196</td>\n",
       "      <td>65.6046</td>\n",
       "      <td>65.9735</td>\n",
       "      <td>66.2608</td>\n",
       "      <td>66.4127</td>\n",
       "      <td>66.7471</td>\n",
       "      <td>66.4897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[past_columns][:-1]   # на данные, на основе которых мы будем делать предсказание, не вклюбчая последний ряд (строчка)\n",
    "y = df[future_columns][:-1] # на данные, которые мы будем предсказывать\n",
    "X_test = df[past_columns][-1:]   # тестовые данные для проверки модели\n",
    "y_test = df[future_columns][-1:] \n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "future_0    65.6196\nfuture_1    65.6046\nfuture_2    65.9735\nfuture_3    66.2608\nfuture_4    66.4127\nfuture_5    66.7471\nfuture_6    66.4897\nName: 373, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[65.6196, 65.6046, 65.9735, 66.2608, 66.4127, 66.7471, 66.4897]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(y_test.iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------обучение модели--------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[66.255999794934752,\n 66.302815103539672,\n 66.326570906206825,\n 66.31823111344363,\n 66.274208066962586,\n 66.206925922119552,\n 66.178591856498826]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "LinReg  = LinearRegression()\n",
    "LinReg.fit(X,y)# метод fit для обучения модели\n",
    "LinReg.predict(X_test) # предсказанные результаты - массив массивов\n",
    "prediction = LinReg.predict(X_test)[0] # массив, если взять только первый элемент из LinReg.predict(X_test)\n",
    "list(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 66.25599979,  66.3028151 ,  66.32657091,  66.31823111,\n         66.27420807,  66.20692592,  66.17859186]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array([prediction])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1950827395156198"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.norm(y_test - prediction) # отклонение данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39069872464912897"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sklearn.metrics.mean_absolute_error(np.array([list(y_test.iloc[0])]), np.array([prediction]))\n",
    "#sklearn.metrics.mean_absolute_error(np.array([y_test.iloc[0]]), np.array([prediction]))\n",
    "sklearn.metrics.mean_absolute_error(y_test.iloc[0], prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x139fdd78da0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYizxG2NME/P/nnFY+5fNDr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x139fbcfb3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(prediction, label=\"prediction\")\n",
    "plt.plot(list(y_test.iloc[0]), label=\"real\")\n",
    "#plt.plot(y_test, label=\"real\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n          metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n          weights='uniform')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "KNN = KNeighborsRegressor(n_neighbors=1)\n",
    "KNN.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x139fee4af60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x139fddbc048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction = KNN.predict(X_test)[0]\n",
    "plt.plot(prediction, label=\"prediction\")\n",
    "plt.plot(list(y_test.iloc[0]), label=\"real\")\n",
    "#plt.plot(y_test, label=\"real\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29404285714285755"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.mean_absolute_error(y_test.iloc[0], prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n       hidden_layer_sizes=(100,), learning_rate='constant',\n       learning_rate_init=0.001, max_iter=2000, momentum=0.9,\n       nesterovs_momentum=True, power_t=0.5, random_state=None,\n       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "MLP = MLPRegressor(max_iter = 2000)\n",
    "MLP\n",
    "MLP.fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x139fef1dcc0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6EII4SMkoQshhI+QhC6EED7i/wMUM69D2Ba0DQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x139fede94e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction = MLP.predict(X_test)[0]\n",
    "plt.plot(prediction, label=\"prediction\")\n",
    "plt.plot(list(y_test.iloc[0]), label=\"real\")\n",
    "#plt.plot(y_test, label=\"real\")\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.7260838173866251"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.mean_absolute_error(y_test.iloc[0], prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}