Steps to Perform

Task 1: Feature engineering (45 mins)

1. Analyze the provided dataset and select relevant features
2. Create new features such as:
   • Interaction features
3. Encode categorical variables and handle missing values
4. Scale the numerical features using StandardScaler
5. Save the processed dataset as "FloridaBikeRentals.csv"https://drive.google.com/file/d/1Q2dmG1iBmu-lfZ9pNVo28Vc5EfyLw2Yj/view?usp=drive_link Task 2: Model building (75 mins)
   1. Implement various regression models including:
      • Linear Regression
      • Ridge Regression (L2 Regularization)
      • Lasso Regression (L1 Regularization)
      • Elastic Net Regression
   2. Perform hyperparameter tuning using GridSearchCV
   3. Evaluate model performance using:
      • Mean Absolute Error (MAE)
      • Mean Squared Error (MSE)
      • R-squared (R)

Task 3: Model building with polynomial features (45 mins)

1. 1. Create polynomial features for selected numerical columns
   2. Train models with polynomial features to capture non-linear relationships
   3. Compare results with linear models to assess improvements
   4. Save the best-performing model

Task 4: Model evaluation and validation (45 mins)

1. 1. Perform cross-validation techniques to validate model performance (on both models- With Polynomial Features and without Polynomial Features)
   2. Assess models using test data
   3. Compare results across different regression models

Task 5: Reporting and insights (30 mins)

1. Summarize findings and key takeaways from the analysis 2. Discuss feature importance and business implications 3. Provide recommendations for further improvements

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