Part 1: Simple Linear Regression

The Objective of this experiment is to implement Simple Linear Regression on Salary Dataset to regress Salaries with corresponding Years of Experience.

Step 1: Load 'Salary_dataset.csv'

Step 2: Define Years of Experience as X and Salary as Y.

Step 3: Split the dataset into training and testing sets.

Step 4: Train the Linear Regression model.

Step 5: Evaluate performance using MAE, MSE, RMSE, and R².

Step 6: Plot the:

• Scatter plot of actual data
• Regression line
• Residual distribution

Part 2: Multiple Linear Regression

The objective of this experiment is to implement Multiple Linear Regression on Car Dataset using categorical features – Fuel types (Petrol, Diesel or CNG), Seller types (Dealer or Individual) and Transmission (Manual or Automatic) – to regress on Selling Price of cars.

Step 1: Import required libraries: pandas, numpy, matplotlib, seaborn, and sklearn.

Step 2: Load the dataset 'car data.csv'

Step 3: Perform exploratory data analysis using:

• head(), tail(), info(), describe()
• Check missing values using isnull().sum()

Step 4: Encode categorical variables:

• Fuel_Type: Petrol = 0, Diesel = 1, CNG = 2
• Seller_Type: Dealer = 0, Individual = 1
• Transmission: Manual = 0, Automatic = 1

Step 5: Define features X by removing Car_Name and Selling_Price.

Step 6: Define target variable Y as Selling_Price.

Step 7: Split the dataset into training and testing sets.

Step 8: Train the Linear Regression model.

Step 9: Evaluate the model using MAE, MSE, RMSE, and R².

Step 10: Plot residuals vs predicted values.