regressor-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b4cd2024",
   "metadata": {},
   "source": [
    "---\n",
    "# YAML data for Mercury App\n",
    "title: Prediction using XGBoost\n",
    "description: Stock Market Prediction using XGBoost method\n",
    "show-code: True\n",
    "author: Anurag M Bagde\n",
    "show-prompt: True\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66a62ddf",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:38.840881Z",
     "iopub.status.busy": "2021-12-28T05:11:38.840229Z",
     "iopub.status.idle": "2021-12-28T05:11:40.075429Z",
     "shell.execute_reply": "2021-12-28T05:11:40.075939Z",
     "shell.execute_reply.started": "2021-12-28T05:10:26.506068Z"
    },
    "papermill": {
     "duration": 1.258832,
     "end_time": "2021-12-28T05:11:40.076260",
     "exception": false,
     "start_time": "2021-12-28T05:11:38.817428",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Done by Anurag M Bagde (2019506120) \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from xgboost import XGBRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7ffa0fd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.106890Z",
     "iopub.status.busy": "2021-12-28T05:11:40.106298Z",
     "iopub.status.idle": "2021-12-28T05:11:40.169340Z",
     "shell.execute_reply": "2021-12-28T05:11:40.169848Z",
     "shell.execute_reply.started": "2021-12-28T05:10:27.711163Z"
    },
    "papermill": {
     "duration": 0.078401,
     "end_time": "2021-12-28T05:11:40.170030",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.091629",
     "status": "completed"
    },
    "tags": []
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ts_polarity</th>\n",
       "      <th>twitter_volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-26</th>\n",
       "      <td>51.47</td>\n",
       "      <td>51.80</td>\n",
       "      <td>51.26</td>\n",
       "      <td>51.62</td>\n",
       "      <td>51.12</td>\n",
       "      <td>104174400</td>\n",
       "      <td>0.072340</td>\n",
       "      <td>888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-27</th>\n",
       "      <td>51.97</td>\n",
       "      <td>52.14</td>\n",
       "      <td>50.88</td>\n",
       "      <td>51.04</td>\n",
       "      <td>50.54</td>\n",
       "      <td>103493200</td>\n",
       "      <td>0.117541</td>\n",
       "      <td>962.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-28</th>\n",
       "      <td>51.03</td>\n",
       "      <td>51.43</td>\n",
       "      <td>50.83</td>\n",
       "      <td>51.38</td>\n",
       "      <td>50.88</td>\n",
       "      <td>63755200</td>\n",
       "      <td>0.061477</td>\n",
       "      <td>895.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-29</th>\n",
       "      <td>52.13</td>\n",
       "      <td>52.33</td>\n",
       "      <td>51.67</td>\n",
       "      <td>52.25</td>\n",
       "      <td>51.74</td>\n",
       "      <td>83962000</td>\n",
       "      <td>0.056460</td>\n",
       "      <td>1083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30</th>\n",
       "      <td>52.54</td>\n",
       "      <td>52.61</td>\n",
       "      <td>51.80</td>\n",
       "      <td>52.19</td>\n",
       "      <td>51.67</td>\n",
       "      <td>84573600</td>\n",
       "      <td>0.106096</td>\n",
       "      <td>1005.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Adj Close     Volume  ts_polarity  \\\n",
       "Date                                                                        \n",
       "2019-08-26  51.47  51.80  51.26  51.62      51.12  104174400     0.072340   \n",
       "2019-08-27  51.97  52.14  50.88  51.04      50.54  103493200     0.117541   \n",
       "2019-08-28  51.03  51.43  50.83  51.38      50.88   63755200     0.061477   \n",
       "2019-08-29  52.13  52.33  51.67  52.25      51.74   83962000     0.056460   \n",
       "2019-08-30  52.54  52.61  51.80  52.19      51.67   84573600     0.106096   \n",
       "\n",
       "            twitter_volume  \n",
       "Date                        \n",
       "2019-08-26           888.0  \n",
       "2019-08-27           962.0  \n",
       "2019-08-28           895.0  \n",
       "2019-08-29          1083.0  \n",
       "2019-08-30          1005.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read APPL.csv contains open, high, low, close, Adj close, Volume of Apple stock with twitter polarity scores and twitter volume\n",
    "df = pd.read_csv('../input/tweets/AAPL.csv', index_col=\"Date\", infer_datetime_format=True, parse_dates=True)\n",
    "\n",
    "# Drop null values\n",
    "df.dropna(inplace=True)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc7a21b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.196973Z",
     "iopub.status.busy": "2021-12-28T05:11:40.196053Z",
     "iopub.status.idle": "2021-12-28T05:11:40.208128Z",
     "shell.execute_reply": "2021-12-28T05:11:40.208796Z",
     "shell.execute_reply.started": "2021-12-28T05:10:27.770801Z"
    },
    "papermill": {
     "duration": 0.027209,
     "end_time": "2021-12-28T05:11:40.209112",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.181903",
     "status": "completed"
    },
    "tags": []
   },
   "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>Adj Close</th>\n",
       "      <th>ts_polarity</th>\n",
       "      <th>twitter_volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>24.44</td>\n",
       "      <td>0.070389</td>\n",
       "      <td>1133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>23.83</td>\n",
       "      <td>0.133635</td>\n",
       "      <td>1430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>23.36</td>\n",
       "      <td>0.072042</td>\n",
       "      <td>1949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>22.38</td>\n",
       "      <td>0.074369</td>\n",
       "      <td>2289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>22.50</td>\n",
       "      <td>0.051595</td>\n",
       "      <td>2235.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close  ts_polarity  twitter_volume\n",
       "Date                                              \n",
       "2016-01-04      24.44     0.070389          1133.0\n",
       "2016-01-05      23.83     0.133635          1430.0\n",
       "2016-01-06      23.36     0.072042          1949.0\n",
       "2016-01-07      22.38     0.074369          2289.0\n",
       "2016-01-08      22.50     0.051595          2235.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe with Adj close, ts_polarity, twitter_volume of APPL\n",
    "df = df[[\"Adj Close\", \"ts_polarity\", \"twitter_volume\"]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b89af7a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.238296Z",
     "iopub.status.busy": "2021-12-28T05:11:40.237255Z",
     "iopub.status.idle": "2021-12-28T05:11:40.262143Z",
     "shell.execute_reply": "2021-12-28T05:11:40.262669Z",
     "shell.execute_reply.started": "2021-12-28T05:10:27.787124Z"
    },
    "papermill": {
     "duration": 0.040605,
     "end_time": "2021-12-28T05:11:40.262855",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.222250",
     "status": "completed"
    },
    "tags": []
   },
   "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>Adj Close</th>\n",
       "      <th>ts_polarity</th>\n",
       "      <th>twitter_volume</th>\n",
       "      <th>Pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>23.83</td>\n",
       "      <td>0.133635</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>-0.024959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>23.36</td>\n",
       "      <td>0.072042</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>-0.019723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>22.38</td>\n",
       "      <td>0.074369</td>\n",
       "      <td>2289.0</td>\n",
       "      <td>-0.041952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>22.50</td>\n",
       "      <td>0.051595</td>\n",
       "      <td>2235.0</td>\n",
       "      <td>0.005362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>22.86</td>\n",
       "      <td>0.019443</td>\n",
       "      <td>1222.0</td>\n",
       "      <td>0.016000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close  ts_polarity  twitter_volume  Pct_change\n",
       "Date                                                          \n",
       "2016-01-05      23.83     0.133635          1430.0   -0.024959\n",
       "2016-01-06      23.36     0.072042          1949.0   -0.019723\n",
       "2016-01-07      22.38     0.074369          2289.0   -0.041952\n",
       "2016-01-08      22.50     0.051595          2235.0    0.005362\n",
       "2016-01-11      22.86     0.019443          1222.0    0.016000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pct change based on Adj close value\n",
    "df[\"Pct_change\"] = df[\"Adj Close\"].pct_change()\n",
    "\n",
    "# Drop null values\n",
    "df.dropna(inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e520e8a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.291000Z",
     "iopub.status.busy": "2021-12-28T05:11:40.290060Z",
     "iopub.status.idle": "2021-12-28T05:11:40.297102Z",
     "shell.execute_reply": "2021-12-28T05:11:40.297565Z",
     "shell.execute_reply.started": "2021-12-28T05:10:27.820129Z"
    },
    "papermill": {
     "duration": 0.022592,
     "end_time": "2021-12-28T05:11:40.297752",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.275160",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# This function \"window_data\" accepts the column number for the features (X) and the target (y)\n",
    "# It chunks the data up with a rolling window of Xt-n to predict Xt\n",
    "# It returns a numpy array of X any y\n",
    "def window_data(df, window, feature_col_number1, feature_col_number2, feature_col_number3, target_col_number):\n",
    "    # Create empty lists \"X_close\", \"X_polarity\", \"X_volume\" and y\n",
    "    X_close = []\n",
    "    X_polarity = []\n",
    "    X_volume = []\n",
    "    y = []\n",
    "    for i in range(len(df) - window):\n",
    "        \n",
    "        # Get close, ts_polarity, tw_vol, and target in the loop\n",
    "        close = df.iloc[i:(i + window), feature_col_number1]\n",
    "        ts_polarity = df.iloc[i:(i + window), feature_col_number2]\n",
    "        tw_vol = df.iloc[i:(i + window), feature_col_number3]\n",
    "        target = df.iloc[(i + window), target_col_number]\n",
    "        \n",
    "        # Append values in the lists\n",
    "        X_close.append(close)\n",
    "        X_polarity.append(ts_polarity)\n",
    "        X_volume.append(tw_vol)\n",
    "        y.append(target)\n",
    "        \n",
    "    return np.hstack((X_close,X_polarity,X_volume)), np.array(y).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fcec13d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.333748Z",
     "iopub.status.busy": "2021-12-28T05:11:40.332676Z",
     "iopub.status.idle": "2021-12-28T05:11:40.789733Z",
     "shell.execute_reply": "2021-12-28T05:11:40.789161Z",
     "shell.execute_reply.started": "2021-12-28T05:10:27.830344Z"
    },
    "papermill": {
     "duration": 0.480035,
     "end_time": "2021-12-28T05:11:40.789930",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.309895",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Predict Closing Prices using a 3 day window of previous closing prices\n",
    "window_size = 3\n",
    "\n",
    "# Column index 0 is the `Adj Close` column\n",
    "# Column index 1 is the `ts_polarity` column\n",
    "# Column index 2 is the `twitter_volume` column\n",
    "feature_col_number1 = 0\n",
    "feature_col_number2 = 1\n",
    "feature_col_number3 = 2\n",
    "target_col_number = 0\n",
    "X, y = window_data(df, window_size, feature_col_number1, feature_col_number2, feature_col_number3, target_col_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4711a25a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.817658Z",
     "iopub.status.busy": "2021-12-28T05:11:40.817051Z",
     "iopub.status.idle": "2021-12-28T05:11:40.822027Z",
     "shell.execute_reply": "2021-12-28T05:11:40.821489Z",
     "shell.execute_reply.started": "2021-12-28T05:10:28.360993Z"
    },
    "papermill": {
     "duration": 0.020001,
     "end_time": "2021-12-28T05:11:40.822166",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.802165",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Use 70% of the data for training and 30% for testing\n",
    "X_split = int(0.7 * len(X))\n",
    "y_split = int(0.7 * len(y))\n",
    "\n",
    "# Set X_train, X_test, y_train, t_test\n",
    "X_train = X[: X_split]\n",
    "X_test = X[X_split:]\n",
    "y_train = y[: y_split]\n",
    "y_test = y[y_split:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf1a755",
   "metadata": {
    "papermill": {
     "duration": 0.011905,
     "end_time": "2021-12-28T05:11:40.846526",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.834621",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Scaling Data with MinMax scaler!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4bad3135",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.875507Z",
     "iopub.status.busy": "2021-12-28T05:11:40.874381Z",
     "iopub.status.idle": "2021-12-28T05:11:40.878496Z",
     "shell.execute_reply": "2021-12-28T05:11:40.878998Z",
     "shell.execute_reply.started": "2021-12-28T05:10:28.368408Z"
    },
    "papermill": {
     "duration": 0.019865,
     "end_time": "2021-12-28T05:11:40.879237",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.859372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db0ba2e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.908198Z",
     "iopub.status.busy": "2021-12-28T05:11:40.907244Z",
     "iopub.status.idle": "2021-12-28T05:11:40.914241Z",
     "shell.execute_reply": "2021-12-28T05:11:40.914718Z",
     "shell.execute_reply.started": "2021-12-28T05:10:28.382495Z"
    },
    "papermill": {
     "duration": 0.022629,
     "end_time": "2021-12-28T05:11:40.914900",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.892271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Use the MinMaxScaler to scale data between 0 and 1.\n",
    "x_train_scaler = MinMaxScaler()\n",
    "x_test_scaler = MinMaxScaler()\n",
    "y_train_scaler = MinMaxScaler()\n",
    "y_test_scaler = MinMaxScaler()\n",
    "\n",
    "# Fit the scaler for the Training Data\n",
    "x_train_scaler.fit(X_train)\n",
    "y_train_scaler.fit(y_train)\n",
    "\n",
    "# Scale the training data\n",
    "X_train = x_train_scaler.transform(X_train)\n",
    "y_train = y_train_scaler.transform(y_train)\n",
    "\n",
    "# Fit the scaler for the Testing Data\n",
    "x_test_scaler.fit(X_test)\n",
    "y_test_scaler.fit(y_test)\n",
    "\n",
    "# Scale the y_test data\n",
    "X_test = x_test_scaler.transform(X_test)\n",
    "y_test = y_test_scaler.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1d5f91a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.946560Z",
     "iopub.status.busy": "2021-12-28T05:11:40.945756Z",
     "iopub.status.idle": "2021-12-28T05:11:40.948154Z",
     "shell.execute_reply": "2021-12-28T05:11:40.948703Z",
     "shell.execute_reply.started": "2021-12-28T05:10:28.397072Z"
    },
    "papermill": {
     "duration": 0.020865,
     "end_time": "2021-12-28T05:11:40.948885",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.928020",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create the XG Boost regressor instance\n",
    "model = XGBRegressor(objective='reg:squarederror', n_estimators=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "307ce0b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:40.977942Z",
     "iopub.status.busy": "2021-12-28T05:11:40.976291Z",
     "iopub.status.idle": "2021-12-28T05:11:45.634398Z",
     "shell.execute_reply": "2021-12-28T05:11:45.633800Z",
     "shell.execute_reply.started": "2021-12-28T05:10:28.407176Z"
    },
    "papermill": {
     "duration": 4.673278,
     "end_time": "2021-12-28T05:11:45.634535",
     "exception": false,
     "start_time": "2021-12-28T05:11:40.961257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "             gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.300000012,\n",
       "             max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=1000, n_jobs=4,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "model.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3becbb90",
   "metadata": {
    "papermill": {
     "duration": 0.012284,
     "end_time": "2021-12-28T05:11:45.660485",
     "exception": false,
     "start_time": "2021-12-28T05:11:45.648201",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Model performance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "855aa7a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:45.691397Z",
     "iopub.status.busy": "2021-12-28T05:11:45.690780Z",
     "iopub.status.idle": "2021-12-28T05:11:45.700668Z",
     "shell.execute_reply": "2021-12-28T05:11:45.701164Z",
     "shell.execute_reply.started": "2021-12-28T05:10:31.941148Z"
    },
    "papermill": {
     "duration": 0.02833,
     "end_time": "2021-12-28T05:11:45.701366",
     "exception": false,
     "start_time": "2021-12-28T05:11:45.673036",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Make some predictions\n",
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d6554f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:45.733740Z",
     "iopub.status.busy": "2021-12-28T05:11:45.732543Z",
     "iopub.status.idle": "2021-12-28T05:11:45.736750Z",
     "shell.execute_reply": "2021-12-28T05:11:45.737238Z",
     "shell.execute_reply.started": "2021-12-28T05:10:31.952943Z"
    },
    "papermill": {
     "duration": 0.023302,
     "end_time": "2021-12-28T05:11:45.737406",
     "exception": false,
     "start_time": "2021-12-28T05:11:45.714104",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error: 0.04766297161947317\n",
      "R-squared : 0.9591504900093728\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, predicted)))\n",
    "print('R-squared :', metrics.r2_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b13ae0a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:45.768956Z",
     "iopub.status.busy": "2021-12-28T05:11:45.766406Z",
     "iopub.status.idle": "2021-12-28T05:11:45.771679Z",
     "shell.execute_reply": "2021-12-28T05:11:45.771068Z",
     "shell.execute_reply.started": "2021-12-28T05:10:31.963144Z"
    },
    "papermill": {
     "duration": 0.021267,
     "end_time": "2021-12-28T05:11:45.771835",
     "exception": false,
     "start_time": "2021-12-28T05:11:45.750568",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Recover the original prices instead of the scaled version\n",
    "predicted_prices = y_test_scaler.inverse_transform(predicted.reshape(-1, 1))\n",
    "real_prices = y_test_scaler.inverse_transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0c8682d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:45.811650Z",
     "iopub.status.busy": "2021-12-28T05:11:45.810743Z",
     "iopub.status.idle": "2021-12-28T05:11:45.815145Z",
     "shell.execute_reply": "2021-12-28T05:11:45.814415Z",
     "shell.execute_reply.started": "2021-12-28T05:10:31.973203Z"
    },
    "papermill": {
     "duration": 0.029029,
     "end_time": "2021-12-28T05:11:45.815304",
     "exception": false,
     "start_time": "2021-12-28T05:11:45.786275",
     "status": "completed"
    },
    "tags": []
   },
   "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>Real</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-27</th>\n",
       "      <td>46.39</td>\n",
       "      <td>47.278915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-30</th>\n",
       "      <td>46.13</td>\n",
       "      <td>45.850224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>46.22</td>\n",
       "      <td>46.335148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>48.95</td>\n",
       "      <td>45.902985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-02</th>\n",
       "      <td>50.38</td>\n",
       "      <td>48.946964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Real  Predicted\n",
       "Date                        \n",
       "2018-07-27  46.39  47.278915\n",
       "2018-07-30  46.13  45.850224\n",
       "2018-07-31  46.22  46.335148\n",
       "2018-08-01  48.95  45.902985\n",
       "2018-08-02  50.38  48.946964"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame of Real and Predicted values\n",
    "stocks = pd.DataFrame({\n",
    "    \"Real\": real_prices.ravel(),\n",
    "    \"Predicted\": predicted_prices.ravel()\n",
    "}, index = df.index[-len(real_prices): ]) \n",
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32548076",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-28T05:11:45.850743Z",
     "iopub.status.busy": "2021-12-28T05:11:45.850070Z",
     "iopub.status.idle": "2021-12-28T05:11:46.198262Z",
     "shell.execute_reply": "2021-12-28T05:11:46.197765Z",
     "shell.execute_reply.started": "2021-12-28T05:10:31.990609Z"
    },
    "papermill": {
     "duration": 0.369462,
     "end_time": "2021-12-28T05:11:46.198424",
     "exception": false,
     "start_time": "2021-12-28T05:11:45.828962",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the real vs predicted values as a line chart\n",
    "#stocks.hvplot(title = \"Real vs Predicted values of APPL\")\n",
    "plt.plot(real_prices, label = 'Real')\n",
    "plt.plot(predicted_prices, label = 'Predicted values of APPL')\n",
    "\n",
    "#plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Apple Stock Price')\n",
    "plt.title('Stock Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Apple Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 19.135045,
   "end_time": "2021-12-28T05:11:46.923516",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-12-28T05:11:27.788471",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}