Machine_Learning/Predicting Exam Score Using linear Regression/Model/Applying Linear Regression Model to Predict Exam Score.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":6200064,"sourceType":"datasetVersion","datasetId":2353567}],"dockerImageVersionId":30761,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **Applying Linear Regression Model For Predicting Exam Score**","metadata":{}},{"cell_type":"code","source":"#Importing Packages and libraries\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#Reading CSV File\ndf = pd.read_csv(\"/kaggle/input/student-study-hours/score_updated.csv\")\n\n#Checkng Colummn Names and Format\ndf.head()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-09-22T12:11:30.936077Z","iopub.execute_input":"2024-09-22T12:11:30.936547Z","iopub.status.idle":"2024-09-22T12:11:30.955194Z","shell.execute_reply.started":"2024-09-22T12:11:30.936501Z","shell.execute_reply":"2024-09-22T12:11:30.954005Z"},"trusted":true},"execution_count":62,"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"   Hours  Scores\n0    2.5      21\n1    5.1      47\n2    3.2      27\n3    8.5      75\n4    3.5      30","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>Hours</th>\n      <th>Scores</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.5</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.1</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.2</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.5</td>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.5</td>\n      <td>30</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#Plotting Datapoints in Dataset\nplt.figure(figsize=(10, 6))\nplt.scatter(df['Hours'], df['Scores'], c = 'black' , marker = 'o')\n\n#Adding Title and Labels\nplt.title('Hours vs Score')\nplt.xlabel('Score')\nplt.ylabel('Hours')\n\n#Display \nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-22T12:11:30.963185Z","iopub.execute_input":"2024-09-22T12:11:30.964225Z","iopub.status.idle":"2024-09-22T12:11:31.267575Z","shell.execute_reply.started":"2024-09-22T12:11:30.964174Z","shell.execute_reply":"2024-09-22T12:11:31.266280Z"},"trusted":true},"execution_count":63,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"#Creating Array for X\nx = np.array(df['Hours'])\n\n#Creating Array for Y\ny = np.array(df['Scores'])","metadata":{"execution":{"iopub.status.busy":"2024-09-22T12:11:31.269608Z","iopub.execute_input":"2024-09-22T12:11:31.270001Z","iopub.status.idle":"2024-09-22T12:11:31.276460Z","shell.execute_reply.started":"2024-09-22T12:11:31.269960Z","shell.execute_reply":"2024-09-22T12:11:31.274789Z"},"trusted":true},"execution_count":64,"outputs":[]},{"cell_type":"code","source":"#Creating Gradient Dscent Function\ndef Gradient_Descent(w, b, x, y, L):\n    n = len(x)\n    for i in range(n):\n                   linear = w * x[i] + b\n                   mean_error = (linear - y[i]) \n                   w -= L * (mean_error * x[i])\n                   b -= L * mean_error\n    return w, b","metadata":{"execution":{"iopub.status.busy":"2024-09-22T12:11:31.277945Z","iopub.execute_input":"2024-09-22T12:11:31.278343Z","iopub.status.idle":"2024-09-22T12:11:31.296045Z","shell.execute_reply.started":"2024-09-22T12:11:31.278305Z","shell.execute_reply":"2024-09-22T12:11:31.294466Z"},"trusted":true},"execution_count":65,"outputs":[]},{"cell_type":"markdown","source":"   **Linear Regression Implementaion**","metadata":{}},{"cell_type":"code","source":"#Initializing Values\nw = 0\nb = 0\nL = 0.01\niterations = 1000\n\nfor i in range(iterations):\n    w, b = Gradient_Descent(w, b, x, y, L)\n    \n#Creatimg Plot\nplt.figure(figsize=(10, 6))\nplt.scatter(x, y, c='black', marker = 'o')\nplt.plot(df['Hours'], w * df['Hours'] + b, c='blue')\n\n#Displaying Graph\nplt.show()\n\n","metadata":{"execution":{"iopub.status.busy":"2024-09-22T12:11:31.298972Z","iopub.execute_input":"2024-09-22T12:11:31.299577Z","iopub.status.idle":"2024-09-22T12:11:31.639759Z","shell.execute_reply.started":"2024-09-22T12:11:31.299501Z","shell.execute_reply":"2024-09-22T12:11:31.638551Z"},"trusted":true},"execution_count":66,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}