STOCK_PRICE_PREDICTION_TECHNOCOLABS (SVM_linear_model).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NjRhJ-573r8g"
   },
   "outputs": [],
   "source": [
    "##### File Created by RAHUL CHAWLA #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd    #importing necessary libraries\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "A-dRovU43r8m",
    "outputId": "cf881267-b2dd-4bfd-c6c1-7e76952a3449"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22805, 8)\n"
     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:16</td>\n",
       "      <td>13997.90</td>\n",
       "      <td>14020.85</td>\n",
       "      <td>13991.35</td>\n",
       "      <td>14013.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:17</td>\n",
       "      <td>14014.85</td>\n",
       "      <td>14018.55</td>\n",
       "      <td>14008.15</td>\n",
       "      <td>14009.05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:18</td>\n",
       "      <td>14008.05</td>\n",
       "      <td>14013.10</td>\n",
       "      <td>14005.05</td>\n",
       "      <td>14012.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:19</td>\n",
       "      <td>14013.65</td>\n",
       "      <td>14019.10</td>\n",
       "      <td>14013.65</td>\n",
       "      <td>14016.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:20</td>\n",
       "      <td>14015.45</td>\n",
       "      <td>14017.80</td>\n",
       "      <td>14011.95</td>\n",
       "      <td>14015.45</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Type      Date   Time      open      high       low     close  Unnamed: 7\n",
       "0  NIFTY  20210101  09:16  13997.90  14020.85  13991.35  14013.15         NaN\n",
       "1  NIFTY  20210101  09:17  14014.85  14018.55  14008.15  14009.05         NaN\n",
       "2  NIFTY  20210101  09:18  14008.05  14013.10  14005.05  14012.70         NaN\n",
       "3  NIFTY  20210101  09:19  14013.65  14019.10  14013.65  14016.20         NaN\n",
       "4  NIFTY  20210101  09:20  14015.45  14017.80  14011.95  14015.45         NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('DataFrame.csv')      #Reading csv file and creating DataFrame\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "FLzCWg_g3r8q"
   },
   "outputs": [],
   "source": [
    "data.drop('Unnamed: 7' , axis = 1 , inplace = True)   #Dropping unnecessary Column:-> Unnamed: 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "uCSRAwj_3r8r"
   },
   "outputs": [],
   "source": [
    "data.rename(columns = str.upper , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "WKlfXbvG3r8s",
    "outputId": "8ee015ea-a500-430b-9bc9-a13035c3d064"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TYPE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>HIGH</th>\n",
       "      <th>LOW</th>\n",
       "      <th>CLOSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:16</td>\n",
       "      <td>13997.90</td>\n",
       "      <td>14020.85</td>\n",
       "      <td>13991.35</td>\n",
       "      <td>14013.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:17</td>\n",
       "      <td>14014.85</td>\n",
       "      <td>14018.55</td>\n",
       "      <td>14008.15</td>\n",
       "      <td>14009.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:18</td>\n",
       "      <td>14008.05</td>\n",
       "      <td>14013.10</td>\n",
       "      <td>14005.05</td>\n",
       "      <td>14012.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:19</td>\n",
       "      <td>14013.65</td>\n",
       "      <td>14019.10</td>\n",
       "      <td>14013.65</td>\n",
       "      <td>14016.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210101</td>\n",
       "      <td>09:20</td>\n",
       "      <td>14015.45</td>\n",
       "      <td>14017.80</td>\n",
       "      <td>14011.95</td>\n",
       "      <td>14015.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>22800</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210331</td>\n",
       "      <td>15:27</td>\n",
       "      <td>14687.35</td>\n",
       "      <td>14694.35</td>\n",
       "      <td>14686.30</td>\n",
       "      <td>14690.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22801</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210331</td>\n",
       "      <td>15:28</td>\n",
       "      <td>14690.90</td>\n",
       "      <td>14690.90</td>\n",
       "      <td>14686.05</td>\n",
       "      <td>14689.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22802</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210331</td>\n",
       "      <td>15:29</td>\n",
       "      <td>14690.30</td>\n",
       "      <td>14694.10</td>\n",
       "      <td>14688.55</td>\n",
       "      <td>14691.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22803</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210331</td>\n",
       "      <td>15:30</td>\n",
       "      <td>14689.85</td>\n",
       "      <td>14693.55</td>\n",
       "      <td>14689.45</td>\n",
       "      <td>14693.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22804</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>20210331</td>\n",
       "      <td>15:31</td>\n",
       "      <td>14690.70</td>\n",
       "      <td>14690.70</td>\n",
       "      <td>14690.70</td>\n",
       "      <td>14690.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22805 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TYPE      DATE   TIME      OPEN      HIGH       LOW     CLOSE\n",
       "0      NIFTY  20210101  09:16  13997.90  14020.85  13991.35  14013.15\n",
       "1      NIFTY  20210101  09:17  14014.85  14018.55  14008.15  14009.05\n",
       "2      NIFTY  20210101  09:18  14008.05  14013.10  14005.05  14012.70\n",
       "3      NIFTY  20210101  09:19  14013.65  14019.10  14013.65  14016.20\n",
       "4      NIFTY  20210101  09:20  14015.45  14017.80  14011.95  14015.45\n",
       "...      ...       ...    ...       ...       ...       ...       ...\n",
       "22800  NIFTY  20210331  15:27  14687.35  14694.35  14686.30  14690.60\n",
       "22801  NIFTY  20210331  15:28  14690.90  14690.90  14686.05  14689.30\n",
       "22802  NIFTY  20210331  15:29  14690.30  14694.10  14688.55  14691.30\n",
       "22803  NIFTY  20210331  15:30  14689.85  14693.55  14689.45  14693.05\n",
       "22804  NIFTY  20210331  15:31  14690.70  14690.70  14690.70  14690.70\n",
       "\n",
       "[22805 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VD3JAUN23r8t"
   },
   "source": [
    "# CONVERTING STRING INTO DATE TIME SERIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "O7UfjCPS3r8u"
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "data['DATE'] = pd.to_datetime(data['DATE'] , format = \"%Y%m%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "FuvyQBwV3r8v",
    "outputId": "0613a3cc-ac98-4751-8e3f-b49eefd12d36"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "        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>TYPE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>HIGH</th>\n",
       "      <th>LOW</th>\n",
       "      <th>CLOSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>09:16</td>\n",
       "      <td>13997.90</td>\n",
       "      <td>14020.85</td>\n",
       "      <td>13991.35</td>\n",
       "      <td>14013.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>09:17</td>\n",
       "      <td>14014.85</td>\n",
       "      <td>14018.55</td>\n",
       "      <td>14008.15</td>\n",
       "      <td>14009.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>09:18</td>\n",
       "      <td>14008.05</td>\n",
       "      <td>14013.10</td>\n",
       "      <td>14005.05</td>\n",
       "      <td>14012.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>09:19</td>\n",
       "      <td>14013.65</td>\n",
       "      <td>14019.10</td>\n",
       "      <td>14013.65</td>\n",
       "      <td>14016.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NIFTY</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>09:20</td>\n",
       "      <td>14015.45</td>\n",
       "      <td>14017.80</td>\n",
       "      <td>14011.95</td>\n",
       "      <td>14015.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TYPE       DATE   TIME      OPEN      HIGH       LOW     CLOSE\n",
       "0  NIFTY 2021-01-01  09:16  13997.90  14020.85  13991.35  14013.15\n",
       "1  NIFTY 2021-01-01  09:17  14014.85  14018.55  14008.15  14009.05\n",
       "2  NIFTY 2021-01-01  09:18  14008.05  14013.10  14005.05  14012.70\n",
       "3  NIFTY 2021-01-01  09:19  14013.65  14019.10  14013.65  14016.20\n",
       "4  NIFTY 2021-01-01  09:20  14015.45  14017.80  14011.95  14015.45"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "f3xURQ1p3r8w",
    "outputId": "dab3f08e-8619-43a6-a0fa-9d46466a6fc3"
   },
   "outputs": [],
   "source": [
    "import missingno as msno       #Checking for the missing entries\n",
    "msno.bar(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "Q0BEaHXq3r8y",
    "outputId": "c33fd056-fd19-40d4-9f03-4588670f9c04"
   },
   "outputs": [],
   "source": [
    "data.describe()   #Statistics of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uxRDT7K83r8z",
    "outputId": "0251229b-7eeb-4fe4-8c2a-0027545dda54"
   },
   "outputs": [],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "id": "HHqLZ_m43r81",
    "outputId": "fe75e837-86c4-40df-ebdc-2703365a7d5d"
   },
   "outputs": [],
   "source": [
    "data[data.duplicated()]       #Checking for the duplicated entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cGuTPbTe3r83"
   },
   "source": [
    "# CONSIDERING ANOTHER DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "p9eZLJhV3r84",
    "outputId": "d611c0aa-ce8e-4138-f9e4-05cf442186e3"
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('MSFT.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "v-s6ikLC3r85"
   },
   "outputs": [],
   "source": [
    "df2.rename(columns = str.upper , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DWBbzXhK3r85"
   },
   "outputs": [],
   "source": [
    "df2['DATE'] = pd.to_datetime(df2['DATE'] , format = \"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "5KYU3hn93r86",
    "outputId": "4ad127d0-9399-4f5b-cfe5-b77623fbc7ef"
   },
   "outputs": [],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "id": "Cvr7LE7F3r86",
    "outputId": "e8e28935-cbab-4757-ea8a-ae894dd8456e"
   },
   "outputs": [],
   "source": [
    "df2[df2.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "alpxXG9N3r87",
    "outputId": "a72065c2-134c-4bc1-80ea-6398ed6e26b6"
   },
   "outputs": [],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n3Epkzcx3r88",
    "outputId": "89d61f81-0e1a-4b9b-95ee-4745b5545172"
   },
   "outputs": [],
   "source": [
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f8yBb5cI3r88",
    "outputId": "559b375e-1007-48c0-a3df-def5d3bafac4"
   },
   "outputs": [],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pTvCEwya3r8-"
   },
   "source": [
    "# EDA ANALYSIS (DataFrame.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lxRBbFCK3r8-"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "1a5p5a3S3r8_",
    "outputId": "c96c2235-6ea2-4339-eda2-8847f15986cc"
   },
   "outputs": [],
   "source": [
    "d1 = data.copy()\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lTOXpQG0gnKM"
   },
   "outputs": [],
   "source": [
    "d1.columns\n",
    "d1 = d1.set_index(['TYPE','DATE','TIME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EPEEmJ43gsA5"
   },
   "outputs": [],
   "source": [
    "feature = ['OPEN', 'HIGH', 'LOW', 'CLOSE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dGLAlmT7hEMD",
    "outputId": "f0f8362d-cf8a-44ef-f78d-9457f4762252"
   },
   "outputs": [],
   "source": [
    "list(enumerate(feature))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 750
    },
    "id": "-ML4KQrChMwC",
    "outputId": "722a8aeb-cd0a-43df-d85e-4cd7a47dcf2e"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,12))\n",
    "for i in enumerate(feature):\n",
    "    plt.subplot(2,2,i[0]+1)\n",
    "    base_color = sns.color_palette()[i[0]]\n",
    "    sns.distplot(d1[i[1]] , kde = False , color = base_color)\n",
    "    plt.xlabel(i[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 742
    },
    "id": "8bLKIkpiO2ms",
    "outputId": "377627ab-13c9-42ce-b058-5fb5b499bd16"
   },
   "outputs": [],
   "source": [
    "color = sns.PairGrid(d1)\n",
    "color.map_upper(sns.scatterplot,color = 'green')\n",
    "color.map_lower(sns.scatterplot,color = 'orange')\n",
    "color.map_diag(plt.hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 606
    },
    "id": "5FlVb2YnRkrJ",
    "outputId": "42b7781c-22ef-4ed7-9049-2e704168d6fd"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,10))         #creating Box plots of various attributes\n",
    "for i in enumerate(feature):\n",
    "    plt.subplot(2,2,i[0]+1)\n",
    "    base_color = sns.color_palette()[i[0]]\n",
    "    sns.boxplot(x = d1[i[1]] , color = base_color)\n",
    "    plt.xlabel(i[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NfBIycHg3r9D",
    "outputId": "94680bf3-c093-4905-f476-40b093570bc1"
   },
   "outputs": [],
   "source": [
    "sns.heatmap(d1.corr() , vmin = -1 , vmax = 1 , annot = True , cmap = 'Blues')     #heat map to determine correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_A_96PAs3r9D"
   },
   "source": [
    "# EDA ANALYSIS (MSFT.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "w3aVDnWq3r9E",
    "outputId": "67be431e-0b8f-4d22-b82c-260382391a5c"
   },
   "outputs": [],
   "source": [
    "d2 = df2.copy()\n",
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dTaQ9g0RSW_P"
   },
   "outputs": [],
   "source": [
    "d2 = d2.set_index('DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "n3qoL67LShlF"
   },
   "outputs": [],
   "source": [
    "feat = d2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Pfm4hI7St4J",
    "outputId": "c0300137-5859-4783-aa2d-5fbfd6782bda"
   },
   "outputs": [],
   "source": [
    "list(enumerate(feat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "_rvS3m4ySLW3",
    "outputId": "a5c107c9-4b43-4df0-aec2-e0e78f91bde7"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,15))         #creating distribution plots of various attributes\n",
    "for i in enumerate(feat):\n",
    "    plt.subplot(3,2,i[0]+1)\n",
    "    base_color = sns.color_palette()[i[0]]\n",
    "    sns.distplot(d2[i[1]] , color = base_color , hist = False)\n",
    "    plt.xlabel(i[1]);\n",
    "    plt.title(f'Distribution plot of {i[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "RwxQhpaaTygA",
    "outputId": "93e0e9e5-4c38-4b3b-e783-bf6f682a605f"
   },
   "outputs": [],
   "source": [
    "sns.pairplot(data = d2)      #Plots b/w various attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 606
    },
    "id": "GOnGmbGRUls9",
    "outputId": "34b7c8fe-f6ac-4f6b-b1b8-f0829a912d0c"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,10))         #creating Box plots of various attributes\n",
    "for i in enumerate(feat):\n",
    "    plt.subplot(3,2,i[0]+1)\n",
    "    base_color = sns.color_palette()[i[0]]\n",
    "    sns.boxplot(x = d2[i[1]] , color = base_color)\n",
    "    plt.xlabel(i[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 606
    },
    "id": "wZTUBeCyU-Kv",
    "outputId": "f4d3d23b-b683-4579-a478-54f1a3302ae3"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,10))         #creating Violin plots of various attributes\n",
    "for i in enumerate(feat):\n",
    "    plt.subplot(3,2,i[0]+1)\n",
    "    base_color = sns.color_palette()[i[0]]\n",
    "    sns.violinplot(x = d2[i[1]] , color = base_color)\n",
    "    plt.xlabel(i[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329
    },
    "id": "R9Eo9fU83r9I",
    "outputId": "a28a7d16-e7fd-47ac-acfc-725fecfd910b"
   },
   "outputs": [],
   "source": [
    "sns.heatmap(d2.corr() , vmin = -1 , vmax = 1 , annot = True , cmap = 'Paired_r')     #heat map to determine correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CwfuYYiv3r9J"
   },
   "source": [
    "# AS SEEN THE PRESENCE OF OUTLIERS IN MSFT, SO SUPRESSING THEIR EFFECT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr1 = d2['OPEN'].quantile(0.75) - d2['OPEN'].quantile(0.25) \n",
    "iqr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_whisker = d2['OPEN'].quantile(0.75)+(iqr1*1.5)\n",
    "lower_whisker = d2['OPEN'].quantile(0.25)-(iqr1*1.5)\n",
    "print(upper_whisker)\n",
    "print(lower_whisker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr2 = d2['CLOSE'].quantile(0.75) - d2['CLOSE'].quantile(0.25) \n",
    "iqr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_whisker = d2['CLOSE'].quantile(0.75)+(iqr2*1.5)\n",
    "lower_whisker = d2['CLOSE'].quantile(0.25)-(iqr2*1.5)\n",
    "print(upper_whisker)\n",
    "print(lower_whisker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr3 = d2['HIGH'].quantile(0.75) - d2['HIGH'].quantile(0.25) \n",
    "iqr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_whisker = d2['HIGH'].quantile(0.75)+(iqr3*1.5)\n",
    "lower_whisker = d2['HIGH'].quantile(0.25)-(iqr3*1.5)\n",
    "print(upper_whisker)\n",
    "print(lower_whisker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr4 = d2['LOW'].quantile(0.75) - d2['LOW'].quantile(0.25) \n",
    "iqr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_whisker = d2['LOW'].quantile(0.75)+(iqr4*1.5)\n",
    "lower_whisker = d2['LOW'].quantile(0.25)-(iqr4*1.5)\n",
    "print(upper_whisker)\n",
    "print(lower_whisker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr5 = d2['ADJ CLOSE'].quantile(0.75) - d2['ADJ CLOSE'].quantile(0.25) \n",
    "iqr5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_whisker = d2['ADJ CLOSE'].quantile(0.75)+(iqr5*1.5)\n",
    "lower_whisker = d2['ADJ CLOSE'].quantile(0.25)-(iqr5*1.5)\n",
    "print(upper_whisker)\n",
    "print(lower_whisker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr6 = d2['VOLUME'].quantile(0.75) - d2['VOLUME'].quantile(0.25) \n",
    "iqr6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_whisker = d2['VOLUME'].quantile(0.75)+(iqr6*1.5)\n",
    "lower_whisker = d2['VOLUME'].quantile(0.25)-(iqr6*1.5)\n",
    "print(upper_whisker)\n",
    "print(lower_whisker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2.loc[d2['OPEN'] >= 86.05937385559082 , 'OPEN'] = 86.05937385559082\n",
    "d2.loc[d2['CLOSE'] >= 85.87265515327454 , 'CLOSE'] = 85.87265515327454\n",
    "d2.loc[d2['HIGH'] >= 86.6875 , 'HIGH'] = 86.6875\n",
    "d2.loc[d2['LOW'] >= 84.5562515258789 , 'LOW'] = 84.5562515258789\n",
    "d2.loc[d2['ADJ CLOSE'] >= 64.37626564502716 , 'ADJ CLOSE'] = 64.37626564502716\n",
    "d2.loc[d2['VOLUME'] >= 130092200.0 , 'VOLUME'] = 130092200.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_remo_outlier = d2\n",
    "d_remo_outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = ['OPEN' , 'HIGH' , 'LOW' , 'CLOSE' , 'ADJ CLOSE' , 'VOLUME']\n",
    "list(enumerate(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,12))#creating Box plots of various columns after removing outliers\n",
    "for i in enumerate(l):\n",
    "    plt.subplot(3,2,i[0]+1)\n",
    "    base_color = sns.color_palette()[i[0]]\n",
    "    sns.boxplot(x = d2[i[1]] , color = base_color)\n",
    "    plt.xlabel(i[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODELLING PART FOR DATAFRAME.CSV DATASET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SEPERATING THE DATE COLUMN AND TIME COLUMN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1 = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1.drop(['TYPE'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1['DAY']=pd.to_datetime(d_1['DATE'], format=\"%Y/%m/%d\").dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1['month']=pd.to_datetime(d_1['DATE'], format='%Y/%m/%d').dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1['YEAR']=pd.to_datetime(d_1['DATE'], format='%Y/%m/%d').dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1.drop(['DATE'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1['Time_hour']=pd.to_datetime(d_1['TIME']).dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1['time_minute']=pd.to_datetime(d_1['TIME']).dt.minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1.drop(['TIME'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>OPEN</th>\n",
       "      <th>HIGH</th>\n",
       "      <th>LOW</th>\n",
       "      <th>CLOSE</th>\n",
       "      <th>DAY</th>\n",
       "      <th>month</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>Time_hour</th>\n",
       "      <th>time_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13997.90</td>\n",
       "      <td>14020.85</td>\n",
       "      <td>13991.35</td>\n",
       "      <td>14013.15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14014.85</td>\n",
       "      <td>14018.55</td>\n",
       "      <td>14008.15</td>\n",
       "      <td>14009.05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14008.05</td>\n",
       "      <td>14013.10</td>\n",
       "      <td>14005.05</td>\n",
       "      <td>14012.70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14013.65</td>\n",
       "      <td>14019.10</td>\n",
       "      <td>14013.65</td>\n",
       "      <td>14016.20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14015.45</td>\n",
       "      <td>14017.80</td>\n",
       "      <td>14011.95</td>\n",
       "      <td>14015.45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       OPEN      HIGH       LOW     CLOSE  DAY  month  YEAR  Time_hour  \\\n",
       "0  13997.90  14020.85  13991.35  14013.15    1      1  2021          9   \n",
       "1  14014.85  14018.55  14008.15  14009.05    1      1  2021          9   \n",
       "2  14008.05  14013.10  14005.05  14012.70    1      1  2021          9   \n",
       "3  14013.65  14019.10  14013.65  14016.20    1      1  2021          9   \n",
       "4  14015.45  14017.80  14011.95  14015.45    1      1  2021          9   \n",
       "\n",
       "   time_minute  \n",
       "0           16  \n",
       "1           17  \n",
       "2           18  \n",
       "3           19  \n",
       "4           20  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>OPEN</th>\n",
       "      <th>HIGH</th>\n",
       "      <th>LOW</th>\n",
       "      <th>DAY</th>\n",
       "      <th>month</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>Time_hour</th>\n",
       "      <th>time_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13997.90</td>\n",
       "      <td>14020.85</td>\n",
       "      <td>13991.35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14014.85</td>\n",
       "      <td>14018.55</td>\n",
       "      <td>14008.15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14008.05</td>\n",
       "      <td>14013.10</td>\n",
       "      <td>14005.05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14013.65</td>\n",
       "      <td>14019.10</td>\n",
       "      <td>14013.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14015.45</td>\n",
       "      <td>14017.80</td>\n",
       "      <td>14011.95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>20</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22800</th>\n",
       "      <td>14687.35</td>\n",
       "      <td>14694.35</td>\n",
       "      <td>14686.30</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22801</th>\n",
       "      <td>14690.90</td>\n",
       "      <td>14690.90</td>\n",
       "      <td>14686.05</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22802</th>\n",
       "      <td>14690.30</td>\n",
       "      <td>14694.10</td>\n",
       "      <td>14688.55</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22803</th>\n",
       "      <td>14689.85</td>\n",
       "      <td>14693.55</td>\n",
       "      <td>14689.45</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22804</th>\n",
       "      <td>14690.70</td>\n",
       "      <td>14690.70</td>\n",
       "      <td>14690.70</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22805 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           OPEN      HIGH       LOW  DAY  month  YEAR  Time_hour  time_minute\n",
       "0      13997.90  14020.85  13991.35    1      1  2021          9           16\n",
       "1      14014.85  14018.55  14008.15    1      1  2021          9           17\n",
       "2      14008.05  14013.10  14005.05    1      1  2021          9           18\n",
       "3      14013.65  14019.10  14013.65    1      1  2021          9           19\n",
       "4      14015.45  14017.80  14011.95    1      1  2021          9           20\n",
       "...         ...       ...       ...  ...    ...   ...        ...          ...\n",
       "22800  14687.35  14694.35  14686.30   31      3  2021         15           27\n",
       "22801  14690.90  14690.90  14686.05   31      3  2021         15           28\n",
       "22802  14690.30  14694.10  14688.55   31      3  2021         15           29\n",
       "22803  14689.85  14693.55  14689.45   31      3  2021         15           30\n",
       "22804  14690.70  14690.70  14690.70   31      3  2021         15           31\n",
       "\n",
       "[22805 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = d_1.loc[:,d_1.columns!='CLOSE']\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        14013.15\n",
       "1        14009.05\n",
       "2        14012.70\n",
       "3        14016.20\n",
       "4        14015.45\n",
       "           ...   \n",
       "22800    14690.60\n",
       "22801    14689.30\n",
       "22802    14691.30\n",
       "22803    14693.05\n",
       "22804    14690.70\n",
       "Name: CLOSE, Length: 22805, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=d_1['CLOSE']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18244, 8) (4561, 8) (18244,) (4561,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.05314965,  1.04448231,  1.04056985, ...,  0.        ,\n",
       "         0.57862783,  1.57763269],\n",
       "       [ 1.11093268,  1.12650169,  1.11958297, ...,  0.        ,\n",
       "        -1.03034734, -1.47114428],\n",
       "       [-2.22510865, -2.15991211, -2.28835392, ...,  0.        ,\n",
       "        -1.5666724 , -0.76758037],\n",
       "       ...,\n",
       "       [ 1.20151495,  1.22261814,  1.21178628, ...,  0.        ,\n",
       "        -1.5666724 ,  0.52228681],\n",
       "       [ 1.07569912,  1.06575609,  1.06528546, ...,  0.        ,\n",
       "         0.04230277,  1.57763269],\n",
       "       [-0.57515319, -0.57860421, -0.56504839, ...,  0.        ,\n",
       "        -1.03034734,  0.87406877]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = StandardScaler()\n",
    "sc.fit_transform(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file = open('dataframe_linear_model_pickle_sc.pkl' , 'wb')\n",
    "pickle.dump(sc , file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc1 = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc1.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file1 = open('dataframe_linear_model_pickle_sc1.pkl' , 'wb')\n",
    "pickle.dump(sc1 , file1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM BASED APPROACH (LINEAR MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr = SVR(kernel='linear', C = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = svr.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = svr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.DataFrame({'Actual' : y_test , 'Predicted' : y_test_pred}).reset_index()[['Actual' , 'Predicted']]\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.plot(result['Actual'],label = 'Actual' , color = 'black')\n",
    "plt.plot(result['Predicted'],label = 'Predicted' , color = 'pink')\n",
    "plt.title('Plot of Actual v/s Predicted results')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = mean_squared_error(y_test,y_test_pred)\n",
    "print(\"Mean_squared_error is: \", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2score = r2_score(y_test , y_test_pred)\n",
    "print(\"R2 score is: \" , r2score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PICKLE FILE FOR DATAFRAME DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file = open('dataframe_linear_model_pickle.pkl' , 'wb')\n",
    "pickle.dump(svr , file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = open('dataframe_linear_model_pickle.pkl' , 'rb')\n",
    "loaded_model = pickle.load(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODELLING PART FOR MSFT.CSV DATASET WITH REMOVED OUTLIERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_3 = d_remo_outlier.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_3.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_3['YEAR'] = pd.to_datetime(d_3['DATE'] , format = '%Y-%m-%d').dt.year\n",
    "d_3['MONTH'] = pd.to_datetime(d_3['DATE'] , format = '%Y-%m-%d').dt.month\n",
    "d_3['DAY'] = pd.to_datetime(d_3['DATE'] , format = '%Y-%m-%d').dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_3.drop('DATE' , axis =1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_3.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = d_3.loc[: , d_3.columns!= 'CLOSE']\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = d_3['CLOSE']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc2 = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = sc2.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file1 = open('MSFT_linear_model_pickle_sc2.pkl' , 'wb')\n",
    "pickle.dump(sc2 , file1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc3 = StandardScaler()\n",
    "x_test = sc3.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file1 = open('MSFT_linear_model_pickle_sc3.pkl' , 'wb')\n",
    "pickle.dump(sc3, file1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM BASED APPROACH ON MSFT DATASET ON REMOVAL OF OUTLIERS (LINEAR MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "model = SVR(kernel = 'linear' , C = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(x_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_2 = pd.DataFrame({'Actual' : y_test , 'Predicted' : y_test_pred}).reset_index()[['Actual' , 'Predicted']]\n",
    "result_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.plot(result_2['Actual'],label = 'Actual' , color = 'black')\n",
    "plt.plot(result_2['Predicted'],label = 'Predicted' , color = 'gold')\n",
    "plt.title('Plot of Actual v/s Predicted results')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = mean_squared_error(y_test,y_test_pred)\n",
    "print(\"Mean_squared_error is: \", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2score = r2_score(y_test , y_test_pred)\n",
    "print(\"R2 score is: \" , r2score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PICKLE FILE FOR MSFT(REMOVED_OUTLIERS) DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file1 = open('MSFT_linear_model_pickle.pkl' , 'wb')\n",
    "pickle.dump(model , file1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = open('MSFT_linear_model_pickle.pkl' , 'rb')\n",
    "loaded_model = pickle.load(model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "STOCK PRICE PREDICTION TECHNOCOLABS.ipynb",
   "provenance": []
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}