Project 1: House Price Prediction

Predict house prices using features like size, location, number of rooms, etc.

Difficulty

Beginner

Time Estimate

2-3 days

Skills You'll Practice

• Regression
• Feature Engineering
• Exploratory Data Analysis (EDA)
• Data Cleaning

Learning Objectives

By completing this project, you will learn to:

• Clean and preprocess real-world data
• Perform exploratory data analysis
• Engineer features from raw data
• Apply linear regression and regularization
• Evaluate regression models using multiple metrics
• Handle missing values and outliers

Dataset

Option 1: California Housing Dataset (Built-in)

from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()

Option 2: Kaggle House Prices Competition

• House Prices: Advanced Regression Techniques
• More features and real-world complexity

Project Steps

Step 1: Load and Explore Data

• Load the dataset
• Check data shape and basic statistics
• Identify missing values
• Explore feature distributions
• Check for outliers

Step 2: Data Preprocessing

• Handle missing values
• Remove or transform outliers
• Encode categorical variables (if using Kaggle dataset)
• Feature scaling/normalization

Step 3: Feature Engineering

• Create new features (e.g., total rooms per household, bedrooms per room)
• Handle skewed distributions (log transformation)
• Feature selection

Step 4: Model Training

• Split data into train/test sets
• Train Linear Regression model
• Try Ridge and Lasso regression
• Compare model performance

Step 5: Model Evaluation

• Calculate RMSE, MAE, R²
• Visualize predictions vs actual values
• Analyze residuals
• Identify areas for improvement

Step 6: Model Improvement

• Tune hyperparameters
• Try polynomial features
• Feature selection
• Final model selection

Expected Deliverables

1. Jupyter Notebook with complete analysis:

   • EDA with visualizations
   • Data preprocessing steps
   • Model training and evaluation
   • Results and conclusions

2. Python Script (optional):

   • Clean, well-commented code
   • Can be run independently

3. Results Summary:

   • Best model performance metrics
   • Key insights from EDA
   • Feature importance

Evaluation Metrics

• RMSE (Root Mean Squared Error): Primary metric
• MAE (Mean Absolute Error): Average prediction error
• R² Score: Proportion of variance explained

Tips

• Start with the California Housing dataset (simpler)
• Visualize everything: distributions, correlations, residuals
• Try different feature combinations
• Don't forget to scale features for regularization
• Compare multiple models before choosing the best one

Resources

• Scikit-learn Linear Regression
• California Housing Dataset Documentation
• Kaggle House Prices Competition

Next Steps

After completing this project:

• Try the Kaggle competition for more challenge
• Experiment with ensemble methods
• Move to Project 2: Iris Classification for classification practice