From 584542178cbb6b55a2fef8c1b827e3225cf103a1 Mon Sep 17 00:00:00 2001 From: Orrm23 Date: Thu, 20 Feb 2025 01:26:13 -0800 Subject: [PATCH] exam marks prediction --- ...k_prediction_using_Linear_Regression.ipynb | 1174 +++++++++++++++++ 1 file changed, 1174 insertions(+) create mode 100644 12___Exam_mark_prediction_using_Linear_Regression.ipynb diff --git a/12___Exam_mark_prediction_using_Linear_Regression.ipynb b/12___Exam_mark_prediction_using_Linear_Regression.ipynb new file mode 100644 index 0000000..df8a32d --- /dev/null +++ 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": [ + "\"Open" + ] + }, + { + "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": [ + "array([[ 6.83 , 15. , 1. ],\n", + " [ 6.56 , 16. , 0. ],\n", + " [ 6.98142857, 17. , 1. ],\n", + " [ 5.67 , 18. , 0. ],\n", + " [ 8.67 , 19. , 1. ],\n", + " [ 7.55 , 20. , 0. ],\n", + " [ 6.67 , 15. , 0. ],\n", + " [ 8.99 , 16. , 0. ],\n", + " [ 5.19 , 17. , 1. ],\n", + " [ 6.75 , 18. , 0. ],\n", + " [ 6.59 , 19. , 0. ],\n", + " [ 8.56 , 20. , 1. ],\n", + " [ 7.75 , 15. , 0. ],\n", + " [ 7.9 , 16. 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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