Applied Machine Learning (predicting Students Exam Scores) Lab

Applied Machine Learning – Lab Sheet3- M3
(Predicting Student Exam Scores)
Module 3: Linear Regression with Gradient Descent, Linear
Regression with Least squares, Polynomial regression using
Python.


Task 1: Implementing Linear Regression with Gradient
Descent
Step 1: Preprocessing the Data
import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler, OneHotEncoder

from sklearn.compose import ColumnTransformer

from sklearn.pipeline import Pipeline

from sklearn.impute import SimpleImputer

import matplotlib.pyplot as plt



# Load the dataset

url = 'https://drive.google.com/uc?id=1TwOizNpaHfITQK_kWbFVBTlHAw0e1bxW'

data = pd.read_csv(url)



# Display the first few rows of the dataset

print(data.head())

# Display dataset information

print(data.info())

, # Check for missing values

print(data.isnull().sum())



# Separate features and target

X = data.drop('Exam_Scores', axis=1)

y = data['Exam_Scores']



# Identify categorical and numeric columns

categorical_features = ['Parental_Education', 'Ethnicity']

numeric_features = ['Hours_Studied', 'Previous_Exams']



# Preprocessing pipeline

numeric_transformer = Pipeline(steps=[

('imputer', SimpleImputer(strategy='mean')),

('scaler', StandardScaler())])



categorical_transformer = Pipeline(steps=[

('imputer', SimpleImputer(strategy='most_frequent')),