├── Data
└── Data_preprocessing.ipynb


/Data:
--------------------------------------------------------------------------------
1 | 
2 | 


--------------------------------------------------------------------------------
/Data_preprocessing.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "cell_type": "markdown",
  5 |       "metadata": {
  6 |         "id": "view-in-github",
  7 |         "colab_type": "text"
  8 |       },
  9 |       "source": [
 10 |         "<a href=\"https://colab.research.google.com/github/NandhanaRameshkumar/Data_Preprocessing/blob/main/Data_preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 11 |       ]
 12 |     },
 13 |     {
 14 |       "cell_type": "code",
 15 |       "execution_count": null,
 16 |       "metadata": {
 17 |         "id": "vgSP2IAvTSio"
 18 |       },
 19 |       "outputs": [],
 20 |       "source": [
 21 |         "import pandas as pd\n",
 22 |         "from sklearn.model_selection import train_test_split\n",
 23 |         "from sklearn.preprocessing import StandardScaler  # For feature scaling\n",
 24 |         "from sklearn.linear_model import LinearRegression  # Replace with your chosen model"
 25 |       ]
 26 |     },
 27 |     {
 28 |       "cell_type": "code",
 29 |       "execution_count": null,
 30 |       "metadata": {
 31 |         "id": "xNbXMWtSXLkL"
 32 |       },
 33 |       "outputs": [],
 34 |       "source": [
 35 |         "# Access a specific column (e.g., Close price)\n",
 36 |         "close_prices = data['Close']\n",
 37 |         "\n",
 38 |         "# Calculate statistics on a column (e.g., average Close price)\n",
 39 |         "average_close = close_prices.mean()\n",
 40 |         "\n",
 41 |         "# Filter data based on a condition (e.g., Close price higher than 100)\n",
 42 |         "high_close_days = data[data['Close'] > 100]"
 43 |       ]
 44 |     },
 45 |     {
 46 |       "cell_type": "code",
 47 |       "execution_count": null,
 48 |       "metadata": {
 49 |         "id": "FJXkayR_XT_f"
 50 |       },
 51 |       "outputs": [],
 52 |       "source": [
 53 |         "# Add a new column (e.g., Daily Change)\n",
 54 |         "data['Daily Change'] = data['Close'] - data['Open']\n",
 55 |         "\n",
 56 |         "# Drop a column (e.g., Adj Close)\n",
 57 |         "data.drop('Adj Close', axis=1, inplace=True)  # Modifies original data\n",
 58 |         "\n",
 59 |         "# Rename a column (e.g., Low to Min Price)\n",
 60 |         "data.rename(columns={'Low': 'Min Price'}, inplace=True)  # Modifies original data"
 61 |       ]
 62 |     },
 63 |     {
 64 |       "cell_type": "code",
 65 |       "execution_count": null,
 66 |       "metadata": {
 67 |         "id": "8sPWK1hwZULW"
 68 |       },
 69 |       "outputs": [],
 70 |       "source": [
 71 |         "data = pd.read_csv('/content/MarketData2.csv')"
 72 |       ]
 73 |     },
 74 |     {
 75 |       "cell_type": "code",
 76 |       "execution_count": null,
 77 |       "metadata": {
 78 |         "id": "EcVLTIYYZbmI"
 79 |       },
 80 |       "outputs": [],
 81 |       "source": [
 82 |         "# Define the target variable (e.g., predict future closing price)\n",
 83 |         "target_variable = 'Close'"
 84 |       ]
 85 |     },
 86 |     {
 87 |       "cell_type": "code",
 88 |       "execution_count": null,
 89 |       "metadata": {
 90 |         "id": "I74ACYImZgZb"
 91 |       },
 92 |       "outputs": [],
 93 |       "source": [
 94 |         "features = ['Open', 'High', 'Low', 'Volume']"
 95 |       ]
 96 |     },
 97 |     {
 98 |       "cell_type": "code",
 99 |       "execution_count": null,
100 |       "metadata": {
101 |         "colab": {
102 |           "background_save": true
103 |         },
104 |         "id": "Su_Cw0k2ZkiP"
105 |       },
106 |       "outputs": [],
107 |       "source": [
108 |         "X = data[features]\n",
109 |         "y = data[target_variable]"
110 |       ]
111 |     },
112 |     {
113 |       "cell_type": "code",
114 |       "execution_count": null,
115 |       "metadata": {
116 |         "id": "FXwhV8HfZp_u"
117 |       },
118 |       "outputs": [],
119 |       "source": [
120 |         "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
121 |       ]
122 |     }
123 |   ],
124 |   "metadata": {
125 |     "colab": {
126 |       "provenance": [],
127 |       "authorship_tag": "ABX9TyNt1GTt78FG3n2oICFBtDyO",
128 |       "include_colab_link": true
129 |     },
130 |     "kernelspec": {
131 |       "display_name": "Python 3",
132 |       "name": "python3"
133 |     },
134 |     "language_info": {
135 |       "name": "python"
136 |     }
137 |   },
138 |   "nbformat": 4,
139 |   "nbformat_minor": 0
140 | }


--------------------------------------------------------------------------------