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+++ b/12___Exam_mark_prediction_using_Linear_Regression.ipynb
@@ -0,0 +1,1174 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "I1VRs4tZkbvW"
+ },
+ "source": [
+ "# **Day-12 | Exam mark prediction using Linear Regression-multipleVariable**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SAFLqwkKk8rK"
+ },
+ "source": [
+ "### *Import Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EgF2lvr_jzVL"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.linear_model import LinearRegression"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XWe_7j6UjxRj"
+ },
+ "source": [
+ "### *Load Dataset from Local Directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vKrHCJk_jwfJ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "110e0b18-ab68-4ac6-9e32-733af1698dcc"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " Upload widget is only available when the cell has been executed in the\n",
+ " current browser session. Please rerun this cell to enable.\n",
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Saving data.csv to data.csv\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6gXowmSom462"
+ },
+ "source": [
+ "### *Load Dataset*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6JLDHSdym6wP"
+ },
+ "source": [
+ "dataset = pd.read_csv('data.csv')"
+ ],
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-DdkIy1ZnDfA"
+ },
+ "source": [
+ "### *Load Summarize*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "OlElQViRnGFp",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "cfb430ee-c9d1-4ae7-b909-7eaf0fb9f3c5"
+ },
+ "source": [
+ "print(dataset.shape)\n",
+ "print(dataset.head(5))"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "(201, 4)\n",
+ " hours age internet marks\n",
+ "0 6.83 15 1 78.50\n",
+ "1 6.56 16 0 76.74\n",
+ "2 NaN 17 1 78.68\n",
+ "3 5.67 18 0 71.82\n",
+ "4 8.67 19 1 84.19\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-Zb9xIa-kOx5"
+ },
+ "source": [
+ "### *Finding & Removing NA values from our Features X*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "UwyBQ5nZkTpf",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "fbdf10d5-f2b7-4942-a800-d47f2c78eb8a"
+ },
+ "source": [
+ "dataset.columns[dataset.isna().any()]"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Index(['hours'], dtype='object')"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8LxSw9aHlJTC"
+ },
+ "source": [
+ "dataset.hours = dataset.hours.fillna(dataset.hours.mean())"
+ ],
+ "execution_count": 13,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JRyfB6prpJDP"
+ },
+ "source": [
+ "### *Segregate Dataset into Input X & Output Y*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "x9dQcTohpK1X",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "64f7c047-e8c2-4395-d502-e16b8526f1da"
+ },
+ "source": [
+ "X = dataset.iloc[:, :-1].values\n",
+ "print(X.shape)\n",
+ "X"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "(201, 3)\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
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+ " [ 6.35 , 17. , 1. ],\n",
+ " [ 7.53 , 18. , 0. ],\n",
+ " [ 8.56 , 19. , 1. ],\n",
+ " [ 8.94 , 20. , 1. ],\n",
+ " [ 6.6 , 15. , 1. ],\n",
+ " [ 8.35 , 16. , 1. ],\n",
+ " [ 4.15 , 15. , 0. ]])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SqxVaBO0pf1W",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5793f7be-3c73-48d1-ac38-11179a8161fe"
+ },
+ "source": [
+ "Y = dataset.iloc[:, -1].values\n",
+ "Y"
+ ],
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([78.5 , 76.74, 78.68, 71.82, 84.19, 81.18, 76.99, 85.46, 70.66,\n",
+ " 77.82, 75.37, 83.88, 79.5 , 80.76, 83.08, 76.03, 76.04, 85.11,\n",
+ " 82.5 , 80.58, 82.18, 83.36, 70.67, 75.02, 70.96, 83.33, 74.75,\n",
+ " 75.65, 74.15, 80.17, 82.27, 76.14, 71.1 , 84.35, 83.08, 76.76,\n",
+ " 81.24, 78.21, 73.08, 83.23, 70.27, 86.41, 71.1 , 82.84, 82.38,\n",
+ " 72.96, 77.46, 70.11, 72.38, 71.41, 72.22, 77.77, 84.44, 71.45,\n",
+ " 82.21, 85.48, 75.03, 86.65, 70.9 , 71.7 , 73.61, 79.41, 76.19,\n",
+ " 80.43, 85.78, 70.06, 81.25, 81.7 , 69.27, 82.79, 71.8 , 71.79,\n",
+ " 74.97, 78.61, 77.59, 72.33, 72.08, 77.33, 70.05, 73.34, 84. ,\n",
+ " 82.93, 76.63, 75.36, 77.29, 72.87, 73.4 , 81.74, 71.85, 84.6 ,\n",
+ " 79.56, 82.1 , 72.08, 79.1 , 81.01, 76.48, 75.39, 68.57, 83.64,\n",
+ " 82.3 , 75.18, 82.03, 82.99, 79.26, 77.55, 77.07, 72.1 , 73.25,\n",
+ " 74.25, 70.58, 81.08, 75.04, 76.38, 80.86, 78.42, 74.44, 70.34,\n",
+ " 85.04, 73.61, 75.55, 76.2 , 82.69, 76.83, 79.53, 83.57, 85.95,\n",
+ " 76.02, 77.65, 77.01, 74.49, 73.19, 71.86, 75.8 , 72.46, 78.39,\n",
+ " 83.48, 83.15, 71.22, 85.98, 83.91, 84.58, 80.31, 82.55, 75.52,\n",
+ " 83.82, 85.15, 82.75, 74.34, 82.02, 86.12, 71.87, 76.7 , 81.7 ,\n",
+ " 70.78, 78.45, 70.2 , 83.37, 75.52, 81.57, 80.72, 80.81, 79.49,\n",
+ " 79.17, 77.07, 82.04, 71.94, 81.6 , 70.79, 82.68, 83.08, 71.18,\n",
+ " 77.63, 77.78, 70.4 , 73.02, 71.11, 85.96, 73.64, 84.24, 78.17,\n",
+ " 77.19, 71.83, 86.99, 83.87, 71.5 , 79.63, 85.1 , 72.01, 77.27,\n",
+ " 79.87, 73.14, 70.51, 84.03, 79.64, 74.24, 81.67, 84.68, 86.75,\n",
+ " 78.05, 83.5 , 81.45])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KsDoGjjbpmjk"
+ },
+ "source": [
+ "### *Training Dataset using Linear Regression*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nKmEySI1poV_",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 80
+ },
+ "outputId": "875ea4c7-b893-4617-bce9-011f7fe33c6d"
+ },
+ "source": [
+ "model = LinearRegression()\n",
+ "model.fit(X,Y)"
+ ],
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "LinearRegression()"
+ ],
+ "text/html": [
+ "LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "n-UeFqpGpw9p"
+ },
+ "source": [
+ "### *Predicted Price for Land sq.Feet of custom values*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ollo3wTcpyKQ",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c88cfbbe-6fa4-4be4-e1bc-747df623e40a"
+ },
+ "source": [
+ "a=[[9.2,20,0]]\n",
+ "PredictedmodelResult = model.predict(a)\n",
+ "print(PredictedmodelResult)"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[86.26599847]\n"
+ ]
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file