CaliPrice-Estimator 🚀

A machine learning project predicting housing prices in California based on real-world datasets.

🗂️ Project Overview

• Analyze California housing data for trends and insights.
• Train regression models (Random Forest, XGBoost, LightGBM).
• Evaluate model performance with visualizations.

Dataset

• Source: California Housing Prices on Kaggle

🛠️ Tools Used

• Languages: Python, Jupyter Notebook
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, XGBoost
• Visualization: Matplotlib, Plotly
• Deployment: Streamlit (optional)

🚀 Features

1. Data cleaning and preprocessing.
2. Feature engineering (crime rate, proximity to schools).
3. Model training and evaluation.
4. Interactive visualizations.

Visualizations

• Heatmaps
• Correlation analysis
• Scatter plots
• Feature importance