problem_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "monetary-casino",
   "metadata": {},
   "source": [
    "# Predicting Stock Prices using Machine Learning 📈\n",
    "The purpose of this notebook is to create a Regression Model which could predict the Stock price for the given dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "muslim-substitute",
   "metadata": {},
   "source": [
    "Since we already have a dataset, we'll approach the problem with the following machine learning modelling framework.\n",
    "More specifically, we'll look at the following topics.\n",
    "\n",
    "* **Exploratory data analysis (EDA)** - the process of going through a dataset and finding out more about it.\n",
    "* **Model training** - create model(s) to learn to predict a target variable based on other variables.\n",
    "* **Model evaluation** - evaluating a models predictions using problem-specific evaluation metrics. \n",
    "* **Model comparison** - comparing several different models to find the best one.\n",
    "* **Cross-validation** - if we do build a good model, can we be sure it will work on unseen data?\n",
    "* **Predicting the Test Data** - We now finally, predict the target using out trained model and the test data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "renewable-application",
   "metadata": {},
   "source": [
    "## 1. Importing required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "blessed-excess",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regular EDA and plotting libraries\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from yellowbrick.regressor import PredictionError\n",
    "\n",
    "#To get rid of warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# We want our plots to appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "## Models\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "## Model evaluators\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proud-label",
   "metadata": {},
   "source": [
    "## 2. Data Exploration\n",
    "\n",
    "Data can be found in `Data/` Folder. \n",
    "The ones we are going to use:\n",
    "* `Train_dataset_ - Train_Aug10.csv`\n",
    "\n",
    "\n",
    "Once we have imported our data, we are going to explore and get to know about the different Attributes and their patterns with the data.\n",
    "\n",
    "Data Exploration is one of most important tasks in Machine Learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "endless-diploma",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock Index</th>\n",
       "      <th>Index</th>\n",
       "      <th>Industry</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>General Index</th>\n",
       "      <th>NAV</th>\n",
       "      <th>P/E Ratio</th>\n",
       "      <th>Volumes Traded</th>\n",
       "      <th>Inventory Turnover</th>\n",
       "      <th>Covid Impact (Beta)</th>\n",
       "      <th>Tracking Error</th>\n",
       "      <th>Dollar Exchange Rate</th>\n",
       "      <th>Put-Call Ratio</th>\n",
       "      <th>P/B Ratio</th>\n",
       "      <th>Stock Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA1</td>\n",
       "      <td>NYSE</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>700.29</td>\n",
       "      <td>12765.84</td>\n",
       "      <td>32.38</td>\n",
       "      <td>45.35</td>\n",
       "      <td>1208599.0</td>\n",
       "      <td>5.05</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>0.0520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>9.20</td>\n",
       "      <td>156.987852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA2</td>\n",
       "      <td>NYSE</td>\n",
       "      <td>Information Tech</td>\n",
       "      <td>214.52</td>\n",
       "      <td>12765.84</td>\n",
       "      <td>33.25</td>\n",
       "      <td>170.56</td>\n",
       "      <td>13753356.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.91</td>\n",
       "      <td>560.859622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA3</td>\n",
       "      <td>NYSE</td>\n",
       "      <td>Information Tech</td>\n",
       "      <td>943.42</td>\n",
       "      <td>12765.84</td>\n",
       "      <td>88.27</td>\n",
       "      <td>143.58</td>\n",
       "      <td>6892003.0</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0396</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.09</td>\n",
       "      <td>1279.732874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA4</td>\n",
       "      <td>BSE</td>\n",
       "      <td>Materials</td>\n",
       "      <td>828.13</td>\n",
       "      <td>38182.08</td>\n",
       "      <td>73.66</td>\n",
       "      <td>151.76</td>\n",
       "      <td>8316793.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0431</td>\n",
       "      <td>74.9</td>\n",
       "      <td>1.11</td>\n",
       "      <td>5.60</td>\n",
       "      <td>1121.787632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AA5</td>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3351.28</td>\n",
       "      <td>23.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3943312.0</td>\n",
       "      <td>8.06</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.07</td>\n",
       "      <td>9.46</td>\n",
       "      <td>382.962534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Stock Index    Index          Industry    VWAP  General Index    NAV  \\\n",
       "0         AA1     NYSE       Real Estate  700.29       12765.84  32.38   \n",
       "1         AA2     NYSE  Information Tech  214.52       12765.84  33.25   \n",
       "2         AA3     NYSE  Information Tech  943.42       12765.84  88.27   \n",
       "3         AA4      BSE         Materials  828.13       38182.08  73.66   \n",
       "4         AA5  S&P 500       Real Estate     NaN        3351.28  23.79   \n",
       "\n",
       "   P/E Ratio  Volumes Traded  Inventory Turnover  Covid Impact (Beta)  \\\n",
       "0      45.35       1208599.0                5.05                -0.43   \n",
       "1     170.56      13753356.0                3.10                 0.23   \n",
       "2     143.58       6892003.0                7.70                 0.23   \n",
       "3     151.76       8316793.0                4.80                 0.03   \n",
       "4        NaN       3943312.0                8.06                -0.43   \n",
       "\n",
       "   Tracking Error  Dollar Exchange Rate  Put-Call Ratio  P/B Ratio  \\\n",
       "0          0.0520                   1.0            0.61       9.20   \n",
       "1          0.0164                   1.0             NaN       6.91   \n",
       "2          0.0396                   1.0             NaN       6.09   \n",
       "3          0.0431                  74.9            1.11       5.60   \n",
       "4          0.0244                   1.0            1.07       9.46   \n",
       "\n",
       "   Stock Price  \n",
       "0   156.987852  \n",
       "1   560.859622  \n",
       "2  1279.732874  \n",
       "3  1121.787632  \n",
       "4   382.962534  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"data/Train_dataset_ - Train_Aug10.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "optical-abortion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7383 entries, 0 to 7382\n",
      "Data columns (total 15 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Stock Index           7383 non-null   object \n",
      " 1   Index                 7383 non-null   object \n",
      " 2   Industry              7383 non-null   object \n",
      " 3   VWAP                  7345 non-null   float64\n",
      " 4   General Index         7321 non-null   float64\n",
      " 5   NAV                   7322 non-null   float64\n",
      " 6   P/E Ratio             7149 non-null   float64\n",
      " 7   Volumes Traded        7115 non-null   float64\n",
      " 8   Inventory Turnover    6984 non-null   float64\n",
      " 9   Covid Impact (Beta)   7007 non-null   float64\n",
      " 10  Tracking Error        7312 non-null   float64\n",
      " 11  Dollar Exchange Rate  7306 non-null   float64\n",
      " 12  Put-Call Ratio        7298 non-null   float64\n",
      " 13  P/B Ratio             7358 non-null   float64\n",
      " 14  Stock Price           7383 non-null   float64\n",
      "dtypes: float64(12), object(3)\n",
      "memory usage: 865.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info() #Gives Insight about different Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disabled-powell",
   "metadata": {},
   "source": [
    "### 2.A Filling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "danish-costa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stock Index               0\n",
       "Index                     0\n",
       "Industry                  0\n",
       "VWAP                     38\n",
       "General Index            62\n",
       "NAV                      61\n",
       "P/E Ratio               234\n",
       "Volumes Traded          268\n",
       "Inventory Turnover      399\n",
       "Covid Impact (Beta)     376\n",
       "Tracking Error           71\n",
       "Dollar Exchange Rate     77\n",
       "Put-Call Ratio           85\n",
       "P/B Ratio                25\n",
       "Stock Price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum() #Gives the number of missing values in each column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sexual-impact",
   "metadata": {},
   "source": [
    "By exploring the data we can observe that there are patterns between a few columns. These patterns could be used to our advantage to fill the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "silver-durham",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Dollar Exchange Rate</th>\n",
       "      <th>1.0</th>\n",
       "      <th>17.7</th>\n",
       "      <th>74.9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BSE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JSE</th>\n",
       "      <td>0</td>\n",
       "      <td>1463</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NYSE</th>\n",
       "      <td>1454</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S&amp;P 500</th>\n",
       "      <td>1458</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Dollar Exchange Rate  1.0   17.7  74.9\n",
       "Index                                 \n",
       "BSE                      0     0  1447\n",
       "JSE                      0  1463     0\n",
       "NSE                      0     0  1484\n",
       "NYSE                  1454     0     0\n",
       "S&P 500               1458     0     0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_data['Index'],train_data['Dollar Exchange Rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expensive-richmond",
   "metadata": {},
   "source": [
    "**Pattern 1 : Index vs. Dollar Exchange Rate**\n",
    "The Dollar Exchange Rate values are depended on the Index Column.\n",
    "The Dollar Exchange Rates for the following Indexes are :\n",
    "\n",
    "*  **NSE**  : 74.9\n",
    "*  **NYSE** : 1.0\n",
    "*  **BSE**  : 74.9\n",
    "*  **JSE**  : 17.7\n",
    "*  **S&P 500**  : 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "failing-sitting",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.loc[train_data[\"Index\"]==\"NSE\", [\"Dollar Exchange Rate\"]] = 74.9\n",
    "train_data.loc[train_data[\"Index\"]==\"NYSE\", [\"Dollar Exchange Rate\"]] = 1.0\n",
    "train_data.loc[train_data[\"Index\"]==\"BSE\", [\"Dollar Exchange Rate\"]] = 74.9\n",
    "train_data.loc[train_data[\"Index\"]==\"JSE\", [\"Dollar Exchange Rate\"]] = 17.7\n",
    "train_data.loc[train_data[\"Index\"]==\"S&P 500\", [\"Dollar Exchange Rate\"]] = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ready-extension",
   "metadata": {},
   "source": [
    "**Pattern 2 : Industry vs. Covid Impact (Beta)**\n",
    "The Covid Impact (Beta) values are depended on the Industru Column.\n",
    "The Covid Impact (Beta) for the following Indexes are :\n",
    "\n",
    "*  **Real Estate**  : -0.43\n",
    "*  **Energy** : 0.11\n",
    "*  **Healthcare**  : 0.78\n",
    "*  **Materials**  : 0.03\n",
    "*  **Information Tech**  : 0.23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sunset-honey",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Covid Impact (Beta)</th>\n",
       "      <th>-0.43</th>\n",
       "      <th>0.03</th>\n",
       "      <th>0.11</th>\n",
       "      <th>0.23</th>\n",
       "      <th>0.78</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industry</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>Energy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information Tech</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1389</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Materials</th>\n",
       "      <td>0</td>\n",
       "      <td>1445</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Estate</th>\n",
       "      <td>1383</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Covid Impact (Beta)  -0.43   0.03   0.11   0.23   0.78\n",
       "Industry                                              \n",
       "Energy                   0      0   1431      0      0\n",
       "Healthcare               0      0      0      0   1359\n",
       "Information Tech         0      0      0   1389      0\n",
       "Materials                0   1445      0      0      0\n",
       "Real Estate           1383      0      0      0      0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_data['Industry'],train_data['Covid Impact (Beta)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "congressional-matrix",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.loc[train_data[\"Industry\"]==\"Real Estate\", [\"Covid Impact (Beta)\"]] = -0.43\n",
    "train_data.loc[train_data[\"Industry\"]==\"Energy\", [\"Covid Impact (Beta)\"]] = 0.11\n",
    "train_data.loc[train_data[\"Industry\"]==\"Healthcare\", [\"Covid Impact (Beta)\"]] = 0.78\n",
    "train_data.loc[train_data[\"Industry\"]==\"Materials\", [\"Covid Impact (Beta)\"]] = 0.03\n",
    "train_data.loc[train_data[\"Industry\"]==\"Information Tech\", [\"Covid Impact (Beta)\"]] = 0.23"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacterial-personality",
   "metadata": {},
   "source": [
    "**Pattern 3 : Index vs. General Index**\n",
    "The General Index values are depended on the Index Column.\n",
    "The General Indexs for the following Indexes are :\n",
    "\n",
    "*  **NSE**      : 11270.15\n",
    "*  **NYSE**     : 12765.84\n",
    "*  **BSE**      : 38182.02\n",
    "*  **JSE**      : 55722.00\n",
    "*  **S&P 500**  : 3351.28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "respiratory-canberra",
   "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>General Index</th>\n",
       "      <th>3351.28</th>\n",
       "      <th>11270.15</th>\n",
       "      <th>12765.84</th>\n",
       "      <th>38182.08</th>\n",
       "      <th>55722.00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</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>BSE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JSE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSE</th>\n",
       "      <td>0</td>\n",
       "      <td>1485</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NYSE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1458</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S&amp;P 500</th>\n",
       "      <td>1461</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "General Index  3351.28   11270.15  12765.84  38182.08  55722.00\n",
       "Index                                                          \n",
       "BSE                   0         0         0      1453         0\n",
       "JSE                   0         0         0         0      1464\n",
       "NSE                   0      1485         0         0         0\n",
       "NYSE                  0         0      1458         0         0\n",
       "S&P 500            1461         0         0         0         0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_data['Index'],train_data['General Index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "operational-missouri",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.loc[train_data[\"Index\"]==\"NSE\", [\"General Index\"]] = 11270.15\n",
    "train_data.loc[train_data[\"Index\"]==\"NYSE\", [\"General Index\"]] = 12765.84\n",
    "train_data.loc[train_data[\"Index\"]==\"BSE\", [\"General Index\"]] = 38182.08\n",
    "train_data.loc[train_data[\"Index\"]==\"JSE\", [\"General Index\"]] = 55722.00\n",
    "train_data.loc[train_data[\"Index\"]==\"S&P 500\", [\"General Index\"]] = 3351.28"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "guided-sussex",
   "metadata": {},
   "source": [
    "Few colums like `Volumes Traded`, `P/E Ratio` and etc have high missing data. So below code will fill those missing values with the mean of the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "blessed-destiny",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Volumes Traded\"].fillna(train_data[\"Volumes Traded\"].mean(),inplace = True)\n",
    "train_data[\"P/E Ratio\"].fillna(train_data[\"P/E Ratio\"].mean(),inplace = True)\n",
    "train_data[\"Inventory Turnover\"].fillna(train_data[\"Inventory Turnover\"].mean(),inplace = True)\n",
    "train_data[\"Tracking Error\"].fillna(train_data[\"Tracking Error\"].mean(),inplace = True)\n",
    "train_data[\"Put-Call Ratio\"].fillna(train_data[\"Put-Call Ratio\"].mean(),inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disabled-router",
   "metadata": {},
   "source": [
    "Dropping the reamaing missing data as its not a big number "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dutch-cameroon",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "incorporated-bread",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stock Index             0\n",
       "Index                   0\n",
       "Industry                0\n",
       "VWAP                    0\n",
       "General Index           0\n",
       "NAV                     0\n",
       "P/E Ratio               0\n",
       "Volumes Traded          0\n",
       "Inventory Turnover      0\n",
       "Covid Impact (Beta)     0\n",
       "Tracking Error          0\n",
       "Dollar Exchange Rate    0\n",
       "Put-Call Ratio          0\n",
       "P/B Ratio               0\n",
       "Stock Price             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum() #Finally all the Missing Data is handled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "opposed-surname",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7285"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data) #Length of the Data is 7285"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amateur-jason",
   "metadata": {},
   "source": [
    "### 2.B Spliting the Data\n",
    "The Data should be splitted into **train** and **test** .\n",
    "\n",
    "* **Train** Data is used to train the model.\n",
    "\n",
    "* **Test** Data is used to evaluate the model.\n",
    "\n",
    "Note : This Test data is only used to check the validity of the model. There is another Test data which will be finally used to predict the Stock Prices\n",
    "\n",
    "### Label Encoding the Data\n",
    "Before splitting the Data there is a important task to be done : **Label Encoding**\n",
    "\n",
    "**Label Encoding** refers to converting the labels into numeric form so as to convert it into the machine-readable form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "resident-framing",
   "metadata": {},
   "outputs": [],
   "source": [
    "le =LabelEncoder()\n",
    "\n",
    "label = le.fit_transform(train_data[\"Industry\"])  #Label encoded the Industry column\n",
    "label1 = le.fit_transform(train_data[\"Index\"])    #Label encoded the Index column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "typical-priest",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(\"Industry\",axis=1,inplace=True)\n",
    "train_data[\"Industry\"] = label\n",
    "#Dropped the Previous column and added new encoded column for Industry.\n",
    "\n",
    "train_data.drop(\"Index\",axis=1,inplace=True)\n",
    "train_data[\"Index\"] = label1\n",
    "#Dropped the Previous column and added new encoded column for Index.\n",
    "\n",
    "train_data.drop(\"Stock Index\", axis=1, inplace=True)\n",
    "#The Stock Index is dropped because its just index and not used to predict anything."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sticky-brown",
   "metadata": {},
   "source": [
    "#### Splitting the Data into X & Y\n",
    "\n",
    "Y : The Column which need to be Predicted : `Stock Price`\n",
    "\n",
    "X : Remaing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "juvenile-disability",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_data.drop(\"Stock Price\", axis =1)\n",
    "Y = train_data[\"Stock Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "legendary-teddy",
   "metadata": {
    "scrolled": false
   },
   "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>VWAP</th>\n",
       "      <th>General Index</th>\n",
       "      <th>NAV</th>\n",
       "      <th>P/E Ratio</th>\n",
       "      <th>Volumes Traded</th>\n",
       "      <th>Inventory Turnover</th>\n",
       "      <th>Covid Impact (Beta)</th>\n",
       "      <th>Tracking Error</th>\n",
       "      <th>Dollar Exchange Rate</th>\n",
       "      <th>Put-Call Ratio</th>\n",
       "      <th>P/B Ratio</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>700.29</td>\n",
       "      <td>12765.84</td>\n",
       "      <td>32.38</td>\n",
       "      <td>45.35</td>\n",
       "      <td>1208599.0</td>\n",
       "      <td>5.05</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>0.0520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>9.20</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>214.52</td>\n",
       "      <td>12765.84</td>\n",
       "      <td>33.25</td>\n",
       "      <td>170.56</td>\n",
       "      <td>13753356.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.944722</td>\n",
       "      <td>6.91</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>943.42</td>\n",
       "      <td>12765.84</td>\n",
       "      <td>88.27</td>\n",
       "      <td>143.58</td>\n",
       "      <td>6892003.0</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0396</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.944722</td>\n",
       "      <td>6.09</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>828.13</td>\n",
       "      <td>38182.08</td>\n",
       "      <td>73.66</td>\n",
       "      <td>151.76</td>\n",
       "      <td>8316793.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0431</td>\n",
       "      <td>74.9</td>\n",
       "      <td>1.110000</td>\n",
       "      <td>5.60</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>259.61</td>\n",
       "      <td>38182.08</td>\n",
       "      <td>10.63</td>\n",
       "      <td>153.05</td>\n",
       "      <td>19263869.0</td>\n",
       "      <td>8.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>74.9</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>5.51</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     VWAP  General Index    NAV  P/E Ratio  Volumes Traded  \\\n",
       "0  700.29       12765.84  32.38      45.35       1208599.0   \n",
       "1  214.52       12765.84  33.25     170.56      13753356.0   \n",
       "2  943.42       12765.84  88.27     143.58       6892003.0   \n",
       "3  828.13       38182.08  73.66     151.76       8316793.0   \n",
       "5  259.61       38182.08  10.63     153.05      19263869.0   \n",
       "\n",
       "   Inventory Turnover  Covid Impact (Beta)  Tracking Error  \\\n",
       "0                5.05                -0.43          0.0520   \n",
       "1                3.10                 0.23          0.0164   \n",
       "2                7.70                 0.23          0.0396   \n",
       "3                4.80                 0.03          0.0431   \n",
       "5                8.04                 0.03          0.0344   \n",
       "\n",
       "   Dollar Exchange Rate  Put-Call Ratio  P/B Ratio  Industry  Index  \n",
       "0                   1.0        0.610000       9.20         4      3  \n",
       "1                   1.0        0.944722       6.91         2      3  \n",
       "2                   1.0        0.944722       6.09         2      3  \n",
       "3                  74.9        1.110000       5.60         3      0  \n",
       "5                  74.9        1.150000       5.51         3      0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "pediatric-harris",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting the Data into train and test(Validation) Data \n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "registered-integer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5828, 13), (1457, 13))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "apart-colon",
   "metadata": {},
   "source": [
    "## Training Our Model\n",
    "We've explored the data, now we'll try to use machine learning to predict our target variable : **Stock Price**\n",
    "Few best Algorithms for this problem are :\n",
    "* LinearRegressor\n",
    "* RidgeRegressor\n",
    "* RandomForestRegressor\n",
    "\n",
    "All of the algorithms in the Scikit-Learn library use the same functions, for training a model, `model.fit(X_train, y_train)` and for scoring a `model model.score(X_test, y_test). score()` returns the ratio of correct predictions (1.0 = 100% correct)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "obvious-liberty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8705496484626631"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(36) #Seed to make our results re-producable\n",
    "\n",
    "model1 = LinearRegression()\n",
    "model1.fit(x_train,y_train)\n",
    "model1.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "optical-mozambique",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8705840197836443"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(36) #Seed to make our results re-producable\n",
    "\n",
    "model2 = linear_model.Ridge(alpha=20)\n",
    "model2.fit(x_train,y_train)\n",
    "model2.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "demanding-tactics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.979810913140061"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "model3 = RandomForestRegressor()\n",
    "model3.fit(x_train,y_train)\n",
    "model3.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developed-theorem",
   "metadata": {},
   "source": [
    "#### Choosing the Right Estimator (Model)\n",
    "Amoung the three above models **RanbomForestRegressor** gives us the best resuts : 96.6% Accuracy\n",
    "\n",
    "But Accuracy alone cannot determine our decision. There are other parameters that should be checked before determining the best model.\n",
    "\n",
    "## Evaluating & Comparing Models\n",
    "Currently our models are :\n",
    "\n",
    "**model1** : `LinearRegression()`\n",
    "\n",
    "**model2** : `linear_model.Ridge(alpha=20)`\n",
    "\n",
    "**model3** : `RandomForestRegressor()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "planned-daughter",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting values with our different models\n",
    "y_pred1 = model1.predict(x_test) #Predicted Values by Model1\n",
    "y_pred2 = model2.predict(x_test) #Predicted Values by Model2\n",
    "y_pred3 = model3.predict(x_test) #Predicted Values by Model3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "swiss-credit",
   "metadata": {},
   "source": [
    "### 1. R2 Score \n",
    "**RandomForest** (model3) performed best among other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "removed-brother",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r2_scores = pd.DataFrame(data={\"LinearRegressor\":r2_score(y_test, y_pred1),\n",
    "             \"RidgeRegressor\":r2_score(y_test, y_pred2),\n",
    "             \"RandomForest\":r2_score(y_test, y_pred3)}, index = [\"R2_score\"])\n",
    "r2_scores.T.plot.bar(color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collect-diameter",
   "metadata": {},
   "source": [
    "### 2. Mean Absolute Error\n",
    "**RandomForest** (model3) performed best among other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "acoustic-characteristic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mae_scores = pd.DataFrame(data={\"LinearRegressor\":mean_absolute_error(y_test, y_pred1),\n",
    "             \"RidgeRegressor\":mean_absolute_error(y_test, y_pred2),\n",
    "             \"RandomForest\":mean_absolute_error(y_test, y_pred3)}, index = [\"Mean Absolute Error\"])\n",
    "mae_scores.T.plot.bar(color='blue');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "otherwise-husband",
   "metadata": {},
   "source": [
    "### 3. Mean Squared Error\n",
    "**RandomForest** (model3) performed best among other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "novel-booking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mse_scores = pd.DataFrame(data={\"LinearRegressor\":mean_squared_error(y_test, y_pred1),\n",
    "             \"RidgeRegressor\":mean_squared_error(y_test, y_pred2),\n",
    "             \"RandomForest\":mean_squared_error(y_test, y_pred3)}, index = [\"Mean Squared Error\"])\n",
    "mse_scores.T.plot.bar(color='pink');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "living-level",
   "metadata": {},
   "source": [
    "Among the three models the `RandomForestRegressor()` clearly dominates so we will be evluating on it further on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cordless-welsh",
   "metadata": {},
   "source": [
    "## Evaluating Our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fitted-carbon",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model3 #RandomForest\n",
    "y_pred = y_pred3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compound-friend",
   "metadata": {},
   "source": [
    "### 1. Evaluating Values Manually\n",
    "The below is a DataFrame containing the predicted and true value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "accompanied-thesaurus",
   "metadata": {
    "scrolled": false
   },
   "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>Predicted</th>\n",
       "      <th>Original</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2477</th>\n",
       "      <td>941.104537</td>\n",
       "      <td>939.101287</td>\n",
       "      <td>2.003250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2504</th>\n",
       "      <td>578.699996</td>\n",
       "      <td>579.125737</td>\n",
       "      <td>-0.425741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2679</th>\n",
       "      <td>714.546256</td>\n",
       "      <td>717.000042</td>\n",
       "      <td>-2.453786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2522</th>\n",
       "      <td>735.843351</td>\n",
       "      <td>735.844817</td>\n",
       "      <td>-0.001466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2384</th>\n",
       "      <td>1297.791755</td>\n",
       "      <td>1319.956860</td>\n",
       "      <td>-22.165105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Predicted     Original  Difference\n",
       "2477   941.104537   939.101287    2.003250\n",
       "2504   578.699996   579.125737   -0.425741\n",
       "2679   714.546256   717.000042   -2.453786\n",
       "2522   735.843351   735.844817   -0.001466\n",
       "2384  1297.791755  1319.956860  -22.165105"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = pd.DataFrame(data={\"Predicted\":y_pred,\"Original\":y_test,\"Difference\":y_pred-y_test})\n",
    "values.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conventional-atlanta",
   "metadata": {},
   "source": [
    "### 2. Evaluating Values by Visualizer\n",
    "The below Graph plots PredictionError."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "level-harris",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualizer = PredictionError(model,size=(500,500))\n",
    "\n",
    "visualizer.fit(x_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(x_test, y_test)  # Evaluate the model on the validation data\n",
    "visualizer.poof();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "announced-demonstration",
   "metadata": {},
   "source": [
    "### 3. Evaluating using `cross_val_score`\n",
    "`cross_val_score` takes different pairs of Test and Train data. And gives scores for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "married-liabilities",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99152613, 0.94662341, 0.99557742])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "cross_val_score(model, X, Y, cv=3, scoring=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wired-driver",
   "metadata": {},
   "source": [
    "## Predicting Values using Test Data\n",
    "\n",
    "**Note :** The test data imported below is cleaned and filled in another jupyter notebook.\n",
    "\n",
    "It has the same Data Cleaning Steps as we used in this notebook. You refer to it as `test_dataprep.ipynb`.\n",
    "From that Notebook Cleaned data is exported and imported here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "alien-adaptation",
   "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>Stock Index</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>General Index</th>\n",
       "      <th>NAV</th>\n",
       "      <th>P/E Ratio</th>\n",
       "      <th>Volumes Traded</th>\n",
       "      <th>Inventory Turnover</th>\n",
       "      <th>Covid Impact (Beta)</th>\n",
       "      <th>Tracking Error</th>\n",
       "      <th>Dollar Exchange Rate</th>\n",
       "      <th>Put-Call Ratio</th>\n",
       "      <th>P/B Ratio</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AC3235</td>\n",
       "      <td>53.30</td>\n",
       "      <td>11270.15</td>\n",
       "      <td>44.59</td>\n",
       "      <td>185.09</td>\n",
       "      <td>12067855.0</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0330</td>\n",
       "      <td>74.9</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>4.66</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AC3236</td>\n",
       "      <td>749.34</td>\n",
       "      <td>55722.00</td>\n",
       "      <td>74.20</td>\n",
       "      <td>34.01</td>\n",
       "      <td>6172474.0</td>\n",
       "      <td>6.78</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0464</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>6.11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AC3237</td>\n",
       "      <td>567.75</td>\n",
       "      <td>1461.00</td>\n",
       "      <td>88.41</td>\n",
       "      <td>177.40</td>\n",
       "      <td>17472488.0</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0401</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>4.99</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AC3238</td>\n",
       "      <td>646.78</td>\n",
       "      <td>11270.15</td>\n",
       "      <td>79.36</td>\n",
       "      <td>105.44</td>\n",
       "      <td>15553159.0</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>74.9</td>\n",
       "      <td>1.090000</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AC3239</td>\n",
       "      <td>380.33</td>\n",
       "      <td>12765.84</td>\n",
       "      <td>19.90</td>\n",
       "      <td>139.40</td>\n",
       "      <td>12525784.0</td>\n",
       "      <td>5.93</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.938828</td>\n",
       "      <td>6.18</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Stock Index    VWAP  General Index    NAV  P/E Ratio  Volumes Traded  \\\n",
       "0      AC3235   53.30       11270.15  44.59     185.09      12067855.0   \n",
       "1      AC3236  749.34       55722.00  74.20      34.01       6172474.0   \n",
       "2      AC3237  567.75        1461.00  88.41     177.40      17472488.0   \n",
       "3      AC3238  646.78       11270.15  79.36     105.44      15553159.0   \n",
       "4      AC3239  380.33       12765.84  19.90     139.40      12525784.0   \n",
       "\n",
       "   Inventory Turnover  Covid Impact (Beta)  Tracking Error  \\\n",
       "0                2.78                 0.03          0.0330   \n",
       "1                6.78                 0.11          0.0464   \n",
       "2                4.36                 0.23          0.0401   \n",
       "3                2.77                 0.78          0.0187   \n",
       "4                5.93                 0.03          0.0261   \n",
       "\n",
       "   Dollar Exchange Rate  Put-Call Ratio  P/B Ratio  Industry  Index  \n",
       "0                  74.9        0.800000       4.66         3      2  \n",
       "1                  17.7        0.860000       6.11         0      1  \n",
       "2                   1.0        0.930000       4.99         2      4  \n",
       "3                  74.9        1.090000       1.26         1      2  \n",
       "4                   1.0        0.938828       6.18         3      3  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_test = pd.read_csv(\"data/final_test.csv\")\n",
    "cleaned_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "personal-switzerland",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "predicted = model.predict(cleaned_test.drop(\"Stock Index\",axis=1))\n",
    "# Stock Index is not included while predicting because it isnt used for training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "understood-steps",
   "metadata": {
    "scrolled": false
   },
   "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>Predicted Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock Index</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AC3235</th>\n",
       "      <td>809.645666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC3236</th>\n",
       "      <td>253.193978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC3237</th>\n",
       "      <td>1553.837529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC3238</th>\n",
       "      <td>729.469454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC3239</th>\n",
       "      <td>266.254825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD2734</th>\n",
       "      <td>136.907358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD2735</th>\n",
       "      <td>592.683479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD2736</th>\n",
       "      <td>497.838118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD2737</th>\n",
       "      <td>348.017712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD2738</th>\n",
       "      <td>1303.893809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3331 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Predicted Values\n",
       "Stock Index                  \n",
       "AC3235             809.645666\n",
       "AC3236             253.193978\n",
       "AC3237            1553.837529\n",
       "AC3238             729.469454\n",
       "AC3239             266.254825\n",
       "...                       ...\n",
       "AD2734             136.907358\n",
       "AD2735             592.683479\n",
       "AD2736             497.838118\n",
       "AD2737             348.017712\n",
       "AD2738            1303.893809\n",
       "\n",
       "[3331 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans = pd.DataFrame(predicted,index=cleaned_test['Stock Index'],columns=[\"Predicted Values\"])\n",
    "ans.to_csv(\"Answer_files/file_01.csv\")\n",
    "ans\n",
    "# Imported the final Answer as csv file \"file_01.csv\" as asked in the Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "alive-buddy",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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