The California House Price Prediction project leverages machine learning to forecast house prices based on various factors such as location, median income, and average rooms per household. By building a comprehensive data pipeline, we aim to predict house prices with high accuracy and provide actionable insights.

• 🔍 Data Preprocessing & Cleaning: Handle missing values, detect outliers, and normalize data for optimal model performance.
• 📊 Exploratory Data Analysis (EDA): Visualize correlations and trends to extract meaningful insights.
• 🛠 Machine Learning Pipeline: Automated feature engineering, model selection, and hyperparameter tuning.
• 📈 Support for Multiple Regression Models: Compare different models to achieve the best prediction accuracy.

california-house-price-prediction/
│
├── data/                                 # Dataset files (raw and processed)
├── Notebooks/                            # Jupyter notebooks for analysis and experiments
│   ├──California_house_model (1).ipynb   # Jupyter Notebook Code      
├── src/                                  # Source code for data processing and model training
│   ├──california_house_model.py          # Python Code
├── requirements.txt                      # Python dependencies
├── .gitignore                            # Ignored files and directories
└── README.md                             # Project documentation

1. Clone the Repository:

   git clone https://github.com/Fujelhrx/California-House-Model.git
   cd california-house-price-prediction

2. Install Dependencies:

   pip install -r requirements.txt

3. Run the Jupyter Notebook:

   jupyter notebook

• Open California_house_model.ipynb and run all cells to preprocess the data, train the model, and evaluate predictions.
• For command-line usage, execute the training script:

  python src/california_house_model.py

The script demonstrates how to build custom transformers using BaseEstimator and TransformerMixin, such as:

• StandardScalerClone: A custom standard scaler.
• ClusterSimilarity: Computes RBF kernel similarity to cluster centers.

• Add machine learning model training and evaluation.
• Optimize the feature engineering process.
• Implement hyperparameter tuning for better prediction accuracy.

We welcome contributions from the community! Here’s how you can help:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Submit a pull request with a detailed explanation.

This project is licensed under the MIT License. See the LICENSE file for details.

• scikit-learn: For providing the essential machine learning tools.
• California Housing Dataset: The backbone of our data.
• Open-source Contributors: For their continuous support and contributions.

We’d love to hear from you! If you have any questions, feedback or suggestions, feel free to reach out:

• 📧 Email: machhaliyafujel@gmail.com
• 💼 LinkedIn: Fujel Machhaliya
• 🖥️ GitHub: Fujelhrx

Q: What dataset is used?
A: The dataset is a publicly available California housing dataset that includes features such as median income, location, and the number of rooms.

Q: Can I adapt this project for another region?
A: Absolutely! Modify the preprocessing and data handling steps to fit your custom dataset.