🏠 Pakistani Housing Price Prediction

This project focuses on predicting real estate housing prices in Pakistan using machine learning models. The goal is to analyze key property features such as location, city, area size, number of bedrooms, and bathrooms, and then build predictive models to estimate property prices with high accuracy.

📊 Dataset: The dataset includes real estate listings with the following important columns: City, Location, Province Area Size, Area Type, Area Category Bedrooms, Baths Purpose (Sale / Rent) Price (Target variable)

⚙️ Approach: Data Preprocessing Handling missing values Encoding categorical features (Label Encoding) Log-transform applied on skewed features (e.g., Price)

Modeling: Random Forest Regressor (improved performance) Cross-validation for model robustness Evaluation Metrics R² Score (Coefficient of Determination) MAE (Mean Absolute Error) MSE (Mean Squared Error)

📈 Results: Random Forest Regressor R² Score: 0.99 MAE: 4,205 MSE: 5.02e+10 ✅ Random Forest performed significantly better and provided highly accurate predictions.

🔍 Visualization: The graph below shows Actual vs Predicted Prices. The red dashed line represents the perfect prediction line. Blue points are the predicted values. The closer they are to the red line, the better the model performance.

🚀 Future Work: Hyperparameter tuning with GridSearchCV Try Gradient Boosting and XGBoost models Deploy the model with Streamlit or Flask for real-world usage

👨‍💻 Author: Developed by Zain Data Scientist | Machine Learning Enthusiast