🏙 NYC Real Estate Sales Prediction

This project uses machine learning models to predict real estate sales prices in New York City from 2016-2017. The dataset includes property details like borough, neighborhood, building class, and sales prices.

Models used

• Linear Regression
• Random Forest Regressor
• Gradient Boosting Regressor
• XGBoost Regressor

📊 Features

• Data Preprocessing: Handled missing values, removed unnecessary columns, and transformed skewed data using log transformation.
• Modeling: Developed models using Linear Regression, Random Forest, Gradient Boosting, XGBoost, and others.
• Best Model: Random Forest with RMSE of 0.45 and R² of 0.76 was chosen for its strong performance and feature importance insights.

🛠️ Technologies Used

• Python: Core language.
• Pandas, NumPy: Data manipulation.
• Seaborn, Matplotlib: Visualization.
• Scikit-learn, XGBoost: Machine learning algorithms.

🚀 How to Run

1. Clone this repository:

   git clone <your-repo-url>

2. Install dependencies:

   pip install -r requirements.txt

3. Run the model:

   python real_estate_sales_prediction.py
   

📈 Results

• Random Forest achieved the best results with an R² of 0.76.
• Featured models were evaluated based on RMSE and R².

🧪 Tests

• Train-Test Split: Split data into training (80%) and testing (20%) sets.
• Cross-validation: Performed 5-fold cross-validation to evaluate model performance.
• Hyperparameter Tuning: Used GridSearchCV to tune the hyperparameters for Random Forest and XGBoost models.
• Model Evaluation: Assessed models based on RMSE, MAE, and R².