model_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:19.737290500Z",
     "start_time": "2024-04-29T00:05:13.564026200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\nikola\\anaconda3\\lib\\site-packages (2.1.4)\n",
      "Requirement already satisfied: numpy<2,>=1.23.2 in c:\\users\\nikola\\anaconda3\\lib\\site-packages (from pandas) (1.26.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\nikola\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\nikola\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\nikola\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\nikola\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:20.463222600Z",
     "start_time": "2024-04-29T00:05:19.736291400Z"
    }
   },
   "id": "6dbc9bf56430bf9a"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "import pandas as pd"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:20.936293Z",
     "start_time": "2024-04-29T00:05:20.466812200Z"
    }
   },
   "id": "63a75bbd4a1ecd9e"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# data from 21.04.2020 - 21.04.2024\n",
    "data = pd.read_excel(\"KursnaLista.xlsx\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:21.433192400Z",
     "start_time": "2024-04-29T00:05:20.939294Z"
    }
   },
   "id": "f758e0f53a1f5592"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.Rate.plot()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:21.656110900Z",
     "start_time": "2024-04-29T00:05:21.438199100Z"
    }
   },
   "id": "dc193d302bee07a6"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# time series plot\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(data.index, data['Rate'], color='blue')\n",
    "plt.title('Course Rate Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Rate')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:21.827514800Z",
     "start_time": "2024-04-29T00:05:21.656110900Z"
    }
   },
   "id": "1d4bfc9ec6625e94"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x500 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.hist(data['Rate'], bins=20, color='green', alpha=0.7)\n",
    "plt.title('Distribution of Course Rates')\n",
    "plt.xlabel('Rate')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:22.008799200Z",
     "start_time": "2024-04-29T00:05:21.830163200Z"
    }
   },
   "id": "a294247bbcece261"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#box plot\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.boxplot(data['Rate'], vert=False)\n",
    "plt.title('Box Plot of Course Rates')\n",
    "plt.xlabel('Rate')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:22.148461400Z",
     "start_time": "2024-04-29T00:05:22.011126600Z"
    }
   },
   "id": "4c10305fe7c75683"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot \n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.scatter(data.index, data['Rate'], color='red', label='Course Rate')\n",
    "plt.plot(data.index, data['Rate'].rolling(window=30).mean(), color='blue', label='30-Day Moving Average')\n",
    "plt.title('Course Rate Over Time with Trend Line')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Rate')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:22.371056900Z",
     "start_time": "2024-04-29T00:05:22.135477200Z"
    }
   },
   "id": "4cede3fb32cf15bf"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "past = 7\n",
    "future = 7"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:22.373912900Z",
     "start_time": "2024-04-29T00:05:22.367253500Z"
    }
   },
   "id": "cad3628bbed9b03b"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "money = data.Rate"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:22.379978400Z",
     "start_time": "2024-04-29T00:05:22.373386200Z"
    }
   },
   "id": "e139af4abae4cd9e"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "0       56.6633\n1       56.9300\n2       56.7728\n3       57.2739\n4       57.1250\n         ...   \n1457    57.8078\n1458    57.8041\n1459    57.5850\n1460    57.7255\n1461    57.7255\nName: Rate, Length: 1462, dtype: float64"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "money"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:22.395012400Z",
     "start_time": "2024-04-29T00:05:22.377981500Z"
    }
   },
   "id": "9a3126aaf9493a84"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "93    53.2864\n94    53.3279\n95    53.1487\n96    53.1487\n97    53.1487\n98    52.4617\n99    52.6543\nName: Rate, dtype: float64"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking random days \n",
    "# taking 7 days from the past and 7 days from the future \n",
    "money[100-past:100]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:22.397480400Z",
     "start_time": "2024-04-29T00:05:22.387976300Z"
    }
   },
   "id": "8889eddefca29907"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "100    52.6183\n101    52.5386\n102    52.0723\n103    52.0723\n104    52.0723\n105    52.0723\n106    52.4394\nName: Rate, dtype: float64"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7 future days\n",
    "money[100:100+future]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:22.526671800Z",
     "start_time": "2024-04-29T00:05:22.397480400Z"
    }
   },
   "id": "69ccce02a1384aa9"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "1462"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data set to train model\n",
    "# first day cannot be taken as there will not be data for past 7 days \n",
    "# model will be trained on past 7 days and future 7 days in time series \n",
    "\n",
    "# calculate length of data \n",
    "len(money)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T00:05:22.529747700Z",
     "start_time": "2024-04-29T00:05:22.405172600Z"
    }
   },
   "id": "20ec4da2698ff280"
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is 1462 values, and we will look from 7 till 1455 value\n"
     ]
    }
   ],
   "source": [
    "# taking data set\n",
    "start = past\n",
    "end = len(money) - future\n",
    "print(f\"There is {len(money)} values, and we will look from {past} till {end} value\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T08:57:13.980839800Z",
     "start_time": "2024-04-29T08:57:13.933025700Z"
    }
   },
   "id": "b2ff90d8708848c8"
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "outputs": [],
   "source": [
    "table = []\n",
    "\n",
    "# take past and future for each of those days \n",
    "for i in range(start,end) :\n",
    "    s = money[(i-past):(i+future)]\n",
    "    table.append(list(s))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T08:57:18.210356500Z",
     "start_time": "2024-04-29T08:57:18.174558800Z"
    }
   },
   "id": "309dbe44ead6d29b"
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "outputs": [],
   "source": [
    "past_columns = []\n",
    "for i in range(past):\n",
    "    past_columns.append(f\"past_{i}\")\n",
    "\n",
    "\n",
    "future_columns = []\n",
    "for i in range(future):\n",
    "    future_columns.append(f\"future_{i}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T08:57:23.107965200Z",
     "start_time": "2024-04-29T08:57:23.094991100Z"
    }
   },
   "id": "6773754d9ef642c8"
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "outputs": [],
   "source": [
    "data_frame = pd.DataFrame(data = table, columns = (past_columns + future_columns)) "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T08:57:24.966130200Z",
     "start_time": "2024-04-29T08:57:24.956572600Z"
    }
   },
   "id": "a2821d26e54017d4"
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [
    {
     "data": {
      "text/plain": "       past_0   past_1   past_2   past_3   past_4   past_5   past_6  future_0  \\\n0     56.6633  56.9300  56.7728  57.2739  57.1250  57.1250  57.1250   56.8525   \n1     56.9300  56.7728  57.2739  57.1250  57.1250  57.1250  56.8525   56.7206   \n2     56.7728  57.2739  57.1250  57.1250  57.1250  56.8525  56.7206   56.9036   \n3     57.2739  57.1250  57.1250  57.1250  56.8525  56.7206  56.9036   56.7258   \n4     57.1250  57.1250  57.1250  56.8525  56.7206  56.9036  56.7258   56.7258   \n...       ...      ...      ...      ...      ...      ...      ...       ...   \n1443  57.2100  57.0296  56.6643  56.7245  56.7245  56.7245  56.8188   56.5888   \n1444  57.0296  56.6643  56.7245  56.7245  56.7245  56.8188  56.5888   56.5888   \n1445  56.6643  56.7245  56.7245  56.7245  56.8188  56.5888  56.5888   57.3166   \n1446  56.7245  56.7245  56.7245  56.8188  56.5888  56.5888  57.3166   57.7295   \n1447  56.7245  56.7245  56.8188  56.5888  56.5888  57.3166  57.7295   57.7295   \n\n      future_1  future_2  future_3  future_4  future_5  future_6  \n0      56.7206   56.9036   56.7258   56.7258   56.7258   56.7258  \n1      56.9036   56.7258   56.7258   56.7258   56.7258   56.3829  \n2      56.7258   56.7258   56.7258   56.7258   56.3829   56.8872  \n3      56.7258   56.7258   56.7258   56.3829   56.8872   57.0734  \n4      56.7258   56.7258   56.3829   56.8872   57.0734   57.1872  \n...        ...       ...       ...       ...       ...       ...  \n1443   56.5888   57.3166   57.7295   57.7295   57.7295   57.7073  \n1444   57.3166   57.7295   57.7295   57.7295   57.7073   57.8078  \n1445   57.7295   57.7295   57.7295   57.7073   57.8078   57.8041  \n1446   57.7295   57.7295   57.7073   57.8078   57.8041   57.5850  \n1447   57.7295   57.7073   57.8078   57.8041   57.5850   57.7255  \n\n[1448 rows x 14 columns]",
      "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>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>56.6633</td>\n      <td>56.9300</td>\n      <td>56.7728</td>\n      <td>57.2739</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>56.8525</td>\n      <td>56.7206</td>\n      <td>56.9036</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>56.9300</td>\n      <td>56.7728</td>\n      <td>57.2739</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>56.8525</td>\n      <td>56.7206</td>\n      <td>56.9036</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.3829</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>56.7728</td>\n      <td>57.2739</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>56.8525</td>\n      <td>56.7206</td>\n      <td>56.9036</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.3829</td>\n      <td>56.8872</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>57.2739</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>56.8525</td>\n      <td>56.7206</td>\n      <td>56.9036</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.3829</td>\n      <td>56.8872</td>\n      <td>57.0734</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>56.8525</td>\n      <td>56.7206</td>\n      <td>56.9036</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.3829</td>\n      <td>56.8872</td>\n      <td>57.0734</td>\n      <td>57.1872</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1443</th>\n      <td>57.2100</td>\n      <td>57.0296</td>\n      <td>56.6643</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n    </tr>\n    <tr>\n      <th>1444</th>\n      <td>57.0296</td>\n      <td>56.6643</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n      <td>57.8078</td>\n    </tr>\n    <tr>\n      <th>1445</th>\n      <td>56.6643</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n      <td>57.8078</td>\n      <td>57.8041</td>\n    </tr>\n    <tr>\n      <th>1446</th>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n      <td>57.8078</td>\n      <td>57.8041</td>\n      <td>57.5850</td>\n    </tr>\n    <tr>\n      <th>1447</th>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n      <td>57.8078</td>\n      <td>57.8041</td>\n      <td>57.5850</td>\n      <td>57.7255</td>\n    </tr>\n  </tbody>\n</table>\n<p>1448 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T08:57:28.699800Z",
     "start_time": "2024-04-29T08:57:28.663809100Z"
    }
   },
   "id": "a0124188b21ab5ba"
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "outputs": [
    {
     "data": {
      "text/plain": "       past_0   past_1   past_2   past_3   past_4   past_5   past_6\n0     56.6633  56.9300  56.7728  57.2739  57.1250  57.1250  57.1250\n1     56.9300  56.7728  57.2739  57.1250  57.1250  57.1250  56.8525\n2     56.7728  57.2739  57.1250  57.1250  57.1250  56.8525  56.7206\n3     57.2739  57.1250  57.1250  57.1250  56.8525  56.7206  56.9036\n4     57.1250  57.1250  57.1250  56.8525  56.7206  56.9036  56.7258\n...       ...      ...      ...      ...      ...      ...      ...\n1443  57.2100  57.0296  56.6643  56.7245  56.7245  56.7245  56.8188\n1444  57.0296  56.6643  56.7245  56.7245  56.7245  56.8188  56.5888\n1445  56.6643  56.7245  56.7245  56.7245  56.8188  56.5888  56.5888\n1446  56.7245  56.7245  56.7245  56.8188  56.5888  56.5888  57.3166\n1447  56.7245  56.7245  56.8188  56.5888  56.5888  57.3166  57.7295\n\n[1448 rows x 7 columns]",
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>56.6633</td>\n      <td>56.9300</td>\n      <td>56.7728</td>\n      <td>57.2739</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>56.9300</td>\n      <td>56.7728</td>\n      <td>57.2739</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>56.8525</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>56.7728</td>\n      <td>57.2739</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>56.8525</td>\n      <td>56.7206</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>57.2739</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>56.8525</td>\n      <td>56.7206</td>\n      <td>56.9036</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>57.1250</td>\n      <td>56.8525</td>\n      <td>56.7206</td>\n      <td>56.9036</td>\n      <td>56.7258</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1443</th>\n      <td>57.2100</td>\n      <td>57.0296</td>\n      <td>56.6643</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n    </tr>\n    <tr>\n      <th>1444</th>\n      <td>57.0296</td>\n      <td>56.6643</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n    </tr>\n    <tr>\n      <th>1445</th>\n      <td>56.6643</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n    </tr>\n    <tr>\n      <th>1446</th>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n    </tr>\n    <tr>\n      <th>1447</th>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n      <td>57.7295</td>\n    </tr>\n  </tbody>\n</table>\n<p>1448 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting input values on which prediction should be made\n",
    "X = data_frame[past_columns] \n",
    "X"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T08:57:56.000162500Z",
     "start_time": "2024-04-29T08:57:55.954557100Z"
    }
   },
   "id": "81549179a267fe25"
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "      future_0  future_1  future_2  future_3  future_4  future_5  future_6\n0      56.8525   56.7206   56.9036   56.7258   56.7258   56.7258   56.7258\n1      56.7206   56.9036   56.7258   56.7258   56.7258   56.7258   56.3829\n2      56.9036   56.7258   56.7258   56.7258   56.7258   56.3829   56.8872\n3      56.7258   56.7258   56.7258   56.7258   56.3829   56.8872   57.0734\n4      56.7258   56.7258   56.7258   56.3829   56.8872   57.0734   57.1872\n...        ...       ...       ...       ...       ...       ...       ...\n1443   56.5888   56.5888   57.3166   57.7295   57.7295   57.7295   57.7073\n1444   56.5888   57.3166   57.7295   57.7295   57.7295   57.7073   57.8078\n1445   57.3166   57.7295   57.7295   57.7295   57.7073   57.8078   57.8041\n1446   57.7295   57.7295   57.7295   57.7073   57.8078   57.8041   57.5850\n1447   57.7295   57.7295   57.7073   57.8078   57.8041   57.5850   57.7255\n\n[1448 rows x 7 columns]",
      "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>0</th>\n      <td>56.8525</td>\n      <td>56.7206</td>\n      <td>56.9036</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>56.7206</td>\n      <td>56.9036</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.3829</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>56.9036</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.3829</td>\n      <td>56.8872</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.3829</td>\n      <td>56.8872</td>\n      <td>57.0734</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.7258</td>\n      <td>56.3829</td>\n      <td>56.8872</td>\n      <td>57.0734</td>\n      <td>57.1872</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1443</th>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n    </tr>\n    <tr>\n      <th>1444</th>\n      <td>56.5888</td>\n      <td>57.3166</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n      <td>57.8078</td>\n    </tr>\n    <tr>\n      <th>1445</th>\n      <td>57.3166</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n      <td>57.8078</td>\n      <td>57.8041</td>\n    </tr>\n    <tr>\n      <th>1446</th>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n      <td>57.8078</td>\n      <td>57.8041</td>\n      <td>57.5850</td>\n    </tr>\n    <tr>\n      <th>1447</th>\n      <td>57.7295</td>\n      <td>57.7295</td>\n      <td>57.7073</td>\n      <td>57.8078</td>\n      <td>57.8041</td>\n      <td>57.5850</td>\n      <td>57.7255</td>\n    </tr>\n  </tbody>\n</table>\n<p>1448 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target data \n",
    "Y = data_frame[future_columns] \n",
    "Y"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T08:57:58.671269400Z",
     "start_time": "2024-04-29T08:57:58.642799800Z"
    }
   },
   "id": "fc69ef8598ecd386"
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "outputs": [],
   "source": [
    "test_size = int(0.4 * len(data_frame))\n",
    "\n",
    "# Training data (first 60% of records)\n",
    "X = data_frame[past_columns][:int(0.6 * len(data_frame))]\n",
    "Y = data_frame[future_columns][:int(0.6 * len(data_frame))]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:05:08.574244800Z",
     "start_time": "2024-04-29T09:05:08.558220700Z"
    }
   },
   "id": "bd99a0f6ca40a593"
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "outputs": [],
   "source": [
    "# hidden data\n",
    "\n",
    "X_test = data_frame[past_columns][-test_size:]\n",
    "Y_test = data_frame[future_columns][-test_size:]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:05:09.298517500Z",
     "start_time": "2024-04-29T09:05:09.288815900Z"
    }
   },
   "id": "19e070a65b6ca435"
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in c:\\users\\nikola\\anaconda3\\lib\\site-packages (1.2.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\nikola\\anaconda3\\lib\\site-packages (from scikit-learn) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\nikola\\anaconda3\\lib\\site-packages (from scikit-learn) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\nikola\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\nikola\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scikit-learn"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:05:16.623303600Z",
     "start_time": "2024-04-29T09:05:10.171301400Z"
    }
   },
   "id": "73db0a9cbf797a61"
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP MAE = 0.23739490278333517\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "mlp = MLPRegressor(random_state=500, max_iter=20000, hidden_layer_sizes=(256, 128, 64, 32), activation='relu')\n",
    "mlp.fit(X, Y)\n",
    "\n",
    "mlp_prediction = mlp.predict(X_test)[0]\n",
    "\n",
    "plt.plot(mlp_prediction, label=\"MLP Prediction\")\n",
    "plt.plot(Y_test.iloc[0], label=\"Real data\")\n",
    "plt.legend()\n",
    "\n",
    "mae = mean_absolute_error(Y_test.iloc[0], mlp_prediction)\n",
    "print(f\"MLP MAE = {mae}\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:11:21.985578800Z",
     "start_time": "2024-04-29T09:11:21.026400500Z"
    }
   },
   "id": "fbf1b952da329482"
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [],
   "source": [
    "import pickle"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:11:33.877280Z",
     "start_time": "2024-04-29T09:11:33.865886300Z"
    }
   },
   "id": "f6586c4eb5af6468"
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [],
   "source": [
    "pickle.dump(mlp, open(\"mlp_model\",\"wb\"))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:11:34.568235Z",
     "start_time": "2024-04-29T09:11:34.562292600Z"
    }
   },
   "id": "92b17e314afe8e8a"
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [],
   "source": [
    "# Using Grid Search CV\n",
    "\n",
    "#Reason: Model should give the minimal absolute error"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:11:35.140061400Z",
     "start_time": "2024-04-29T09:11:35.132254900Z"
    }
   },
   "id": "6b7b549f1789defd"
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:11:35.836842600Z",
     "start_time": "2024-04-29T09:11:35.823286100Z"
    }
   },
   "id": "e9559d0b559e351f"
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "outputs": [],
   "source": [
    "from numpy import linspace"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:11:36.305593900Z",
     "start_time": "2024-04-29T09:11:36.291288Z"
    }
   },
   "id": "8278f03c4173625b"
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.001  , 0.00325, 0.0055 , 0.00775, 0.01   ])"
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linspace(0.001, 0.01, 5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:11:36.739351700Z",
     "start_time": "2024-04-29T09:11:36.701310Z"
    }
   },
   "id": "c4ed322bdc2ef39"
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "outputs": [],
   "source": [
    "model = MLPRegressor(random_state=500)\n",
    "param_grid = {\n",
    "    \"max_iter\": [5000, 10000, 20000],\n",
    "    \"learning_rate_init\": linspace(0.001, 0.01, 5),\n",
    "    \"activation\": [\"logistic\", \"relu\"]\n",
    "}\n",
    "\n",
    "GS = GridSearchCV (model, param_grid, scoring=\"neg_mean_absolute_error\", cv=3)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:12:46.733487200Z",
     "start_time": "2024-04-29T09:12:46.677842400Z"
    }
   },
   "id": "f0527322833e5d50"
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "outputs": [
    {
     "data": {
      "text/plain": "GridSearchCV(cv=3, estimator=MLPRegressor(random_state=500),\n             param_grid={'activation': ['logistic', 'relu'],\n                         'learning_rate_init': array([0.001  , 0.00325, 0.0055 , 0.00775, 0.01   ]),\n                         'max_iter': [5000, 10000, 20000]},\n             scoring='neg_mean_absolute_error')",
      "text/html": "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=MLPRegressor(random_state=500),\n             param_grid={&#x27;activation&#x27;: [&#x27;logistic&#x27;, &#x27;relu&#x27;],\n                         &#x27;learning_rate_init&#x27;: array([0.001  , 0.00325, 0.0055 , 0.00775, 0.01   ]),\n                         &#x27;max_iter&#x27;: [5000, 10000, 20000]},\n             scoring=&#x27;neg_mean_absolute_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=MLPRegressor(random_state=500),\n             param_grid={&#x27;activation&#x27;: [&#x27;logistic&#x27;, &#x27;relu&#x27;],\n                         &#x27;learning_rate_init&#x27;: array([0.001  , 0.00325, 0.0055 , 0.00775, 0.01   ]),\n                         &#x27;max_iter&#x27;: [5000, 10000, 20000]},\n             scoring=&#x27;neg_mean_absolute_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: MLPRegressor</label><div class=\"sk-toggleable__content\"><pre>MLPRegressor(random_state=500)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPRegressor</label><div class=\"sk-toggleable__content\"><pre>MLPRegressor(random_state=500)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.fit(X,Y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:13:15.164983800Z",
     "start_time": "2024-04-29T09:12:48.461663200Z"
    }
   },
   "id": "262ec237e023c482"
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "outputs": [
    {
     "data": {
      "text/plain": "{'activation': 'relu',\n 'learning_rate_init': 0.0032500000000000003,\n 'max_iter': 5000}"
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:13:18.050443700Z",
     "start_time": "2024-04-29T09:13:18.007080100Z"
    }
   },
   "id": "17d8cb1874a1745b"
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "outputs": [],
   "source": [
    "best_model = GS.best_estimator_"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:13:18.812964100Z",
     "start_time": "2024-04-29T09:13:18.796724200Z"
    }
   },
   "id": "3695fc6877c51802"
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "outputs": [
    {
     "data": {
      "text/plain": "       past_0   past_1   past_2   past_3   past_4   past_5   past_6\n869   61.9402  62.2104  62.2104  61.1951  61.1951  61.1951  60.5564\n870   62.2104  62.2104  61.1951  61.1951  61.1951  60.5564  60.4373\n871   62.2104  61.1951  61.1951  61.1951  60.5564  60.4373  61.5566\n872   61.1951  61.1951  61.1951  60.5564  60.4373  61.5566  61.5442\n873   61.1951  61.1951  60.5564  60.4373  61.5566  61.5442  61.7792\n...       ...      ...      ...      ...      ...      ...      ...\n1443  57.2100  57.0296  56.6643  56.7245  56.7245  56.7245  56.8188\n1444  57.0296  56.6643  56.7245  56.7245  56.7245  56.8188  56.5888\n1445  56.6643  56.7245  56.7245  56.7245  56.8188  56.5888  56.5888\n1446  56.7245  56.7245  56.7245  56.8188  56.5888  56.5888  57.3166\n1447  56.7245  56.7245  56.8188  56.5888  56.5888  57.3166  57.7295\n\n[579 rows x 7 columns]",
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>869</th>\n      <td>61.9402</td>\n      <td>62.2104</td>\n      <td>62.2104</td>\n      <td>61.1951</td>\n      <td>61.1951</td>\n      <td>61.1951</td>\n      <td>60.5564</td>\n    </tr>\n    <tr>\n      <th>870</th>\n      <td>62.2104</td>\n      <td>62.2104</td>\n      <td>61.1951</td>\n      <td>61.1951</td>\n      <td>61.1951</td>\n      <td>60.5564</td>\n      <td>60.4373</td>\n    </tr>\n    <tr>\n      <th>871</th>\n      <td>62.2104</td>\n      <td>61.1951</td>\n      <td>61.1951</td>\n      <td>61.1951</td>\n      <td>60.5564</td>\n      <td>60.4373</td>\n      <td>61.5566</td>\n    </tr>\n    <tr>\n      <th>872</th>\n      <td>61.1951</td>\n      <td>61.1951</td>\n      <td>61.1951</td>\n      <td>60.5564</td>\n      <td>60.4373</td>\n      <td>61.5566</td>\n      <td>61.5442</td>\n    </tr>\n    <tr>\n      <th>873</th>\n      <td>61.1951</td>\n      <td>61.1951</td>\n      <td>60.5564</td>\n      <td>60.4373</td>\n      <td>61.5566</td>\n      <td>61.5442</td>\n      <td>61.7792</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1443</th>\n      <td>57.2100</td>\n      <td>57.0296</td>\n      <td>56.6643</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n    </tr>\n    <tr>\n      <th>1444</th>\n      <td>57.0296</td>\n      <td>56.6643</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n    </tr>\n    <tr>\n      <th>1445</th>\n      <td>56.6643</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n    </tr>\n    <tr>\n      <th>1446</th>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n    </tr>\n    <tr>\n      <th>1447</th>\n      <td>56.7245</td>\n      <td>56.7245</td>\n      <td>56.8188</td>\n      <td>56.5888</td>\n      <td>56.5888</td>\n      <td>57.3166</td>\n      <td>57.7295</td>\n    </tr>\n  </tbody>\n</table>\n<p>579 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:13:19.784291300Z",
     "start_time": "2024-04-29T09:13:19.739176900Z"
    }
   },
   "id": "78ccbf812948d8e4"
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP MAE = 0.3848698487692315\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlp = best_model\n",
    "mlp.fit(X, Y)\n",
    "mlp_prediction = mlp.predict(X_test)[0]\n",
    "\n",
    "plt.plot(mlp_prediction, label=\"MLP Prediction\")\n",
    "plt.plot(Y_test.iloc[0], label=\"Real data\")\n",
    "plt.legend()\n",
    "\n",
    "mae = mean_absolute_error(Y_test.iloc[0], mlp_prediction)\n",
    "print(f\"MLP MAE = {mae}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:13:21.022474400Z",
     "start_time": "2024-04-29T09:13:20.753964300Z"
    }
   },
   "id": "bf50457b8d9e00ba"
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------+------------------+\n",
      "| Future   |   Real Data |   Predicted Data |\n",
      "+==========+=============+==================+\n",
      "| future_0 |     60.4373 |          61.6177 |\n",
      "+----------+-------------+------------------+\n",
      "| future_1 |     61.5566 |          61.6081 |\n",
      "+----------+-------------+------------------+\n",
      "| future_2 |     61.5442 |          61.6851 |\n",
      "+----------+-------------+------------------+\n",
      "| future_3 |     61.7792 |          61.1284 |\n",
      "+----------+-------------+------------------+\n",
      "| future_4 |     61.7792 |          61.3225 |\n",
      "+----------+-------------+------------------+\n",
      "| future_5 |     61.7792 |          61.5989 |\n",
      "+----------+-------------+------------------+\n",
      "| future_6 |     61.5566 |          61.59   |\n",
      "+----------+-------------+------------------+\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "import numpy as np\n",
    "\n",
    "Y_test_values = Y_test.iloc[0].values\n",
    "\n",
    "Y_test_values_array = np.array(Y_test_values)\n",
    "\n",
    "comparison_table_data = [[\"future_\" + str(i), Y_test_values_array[i], mlp_prediction[i]] for i in range(len(Y_test_values_array))]\n",
    "\n",
    "print(tabulate(comparison_table_data, headers=[\"Future\", \"Real Data\", \"Predicted Data\"], tablefmt='grid'))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:13:36.062465900Z",
     "start_time": "2024-04-29T09:13:36.013369600Z"
    }
   },
   "id": "b1f06e57989aa34e"
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+-------------+------------------+\n",
      "| Future    |   Real Data |   Predicted Data |\n",
      "+===========+=============+==================+\n",
      "| Monday    |     60.4373 |          61.6177 |\n",
      "+-----------+-------------+------------------+\n",
      "| Tuesday   |     61.5566 |          61.6081 |\n",
      "+-----------+-------------+------------------+\n",
      "| Wednesday |     61.5442 |          61.6851 |\n",
      "+-----------+-------------+------------------+\n",
      "| Thursday  |     61.7792 |          61.1284 |\n",
      "+-----------+-------------+------------------+\n",
      "| Friday    |     61.7792 |          61.3225 |\n",
      "+-----------+-------------+------------------+\n",
      "| Saturday  |     61.7792 |          61.5989 |\n",
      "+-----------+-------------+------------------+\n",
      "| Sunday    |     61.5566 |          61.59   |\n",
      "+-----------+-------------+------------------+\n"
     ]
    }
   ],
   "source": [
    "import calendar\n",
    "Y_test_values = Y_test.iloc[0].values\n",
    "Y_test_values_array = np.array(Y_test_values)\n",
    "days_of_week = [calendar.day_name[i] for i in range(7)]\n",
    "\n",
    "comparison_table_data = [[days_of_week[i], Y_test_values_array[i], mlp_prediction[i]] for i in range(len(Y_test_values_array))]\n",
    "\n",
    "print(tabulate(comparison_table_data, headers=[\"Future\", \"Real Data\", \"Predicted Data\"], tablefmt='grid'))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:13:41.834474100Z",
     "start_time": "2024-04-29T09:13:41.826237800Z"
    }
   },
   "id": "81f035d67a819501"
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "real_data = [row[1] for row in comparison_table_data]\n",
    "predicted_data = [row[2] for row in comparison_table_data]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(days_of_week, real_data, marker='o', label='Real Data')\n",
    "plt.plot(days_of_week, predicted_data, marker='x', label='Predicted Data')\n",
    "plt.title('Comparison of Real and Predicted Data')\n",
    "plt.xlabel('Day of the Week')\n",
    "plt.ylabel('Value')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:13:42.991559100Z",
     "start_time": "2024-04-29T09:13:42.822326100Z"
    }
   },
   "id": "e4822991d7c942f8"
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "outputs": [
    {
     "data": {
      "text/plain": "['model.pkl']"
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(mlp, 'model.pkl')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-29T09:13:55.652669300Z",
     "start_time": "2024-04-29T09:13:55.613360900Z"
    }
   },
   "id": "33da98a3c97bd21d"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "dd84f5fd983e968f"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "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": 5
}