House-Price-Prediction-ML

🏠 House-Price-Prediction-ML

A Machine Learning regression project for predicting housing prices using data preprocessing, feature engineering, and performance evaluation metrics.

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📌 Overview

This project implements a supervised Machine Learning model to predict house prices based on various input features such as area, number of rooms, and other housing attributes.

The goal is to build a complete ML workflow including:

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Feature engineering
• Model training using regression algorithms
• Model evaluation using performance metrics

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📊 Dataset

The dataset contains housing-related features and a target variable representing house prices.

Files included:

• data.csv – Housing dataset
• housing.names – Dataset attribute description
• ML House Prediction.ipynb – Jupyter Notebook implementation

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⚙️ Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib / Seaborn
• Scikit-learn
• Jupyter Notebook

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🧠 Machine Learning Workflow

1️⃣ Data Preprocessing

• Handling missing values
• Feature scaling (if applied)
• Encoding categorical variables (if any)

2️⃣ Model Training

Regression algorithms such as:

• Linear Regression
• (Other models if implemented)

3️⃣ Model Evaluation

Performance metrics used:

• MAE (Mean Absolute Error)
• MSE (Mean Squared Error)
• RMSE (Root Mean Squared Error)
• R² Score

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📈 Results

The model was evaluated on the test dataset using standard regression metrics.

Metric	Value
MAE	2.0590
MSE	8.6957
RMSE	2.9488
R²	0.8771

Since MEDV represents house prices in $1000s, the RMSE value indicates that the model’s predictions are on average off by approximately $2,949.
The R² score of 0.8771 shows that the model explains about 87.71% of the variance in housing prices.

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🚀 How to Run

1. Clone the repository:

git clone https://github.com/Abhrankan-Chakrabarti/House-Price-Prediction-ML.git

1. Navigate to the project folder:

cd House-Price-Prediction-ML

1. Install dependencies:

pip install -r requirements.txt

1. Open the notebook:

jupyter notebook

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📂 Project Structure

House-Price-Prediction-ML/
│
├── ML House Prediction.ipynb
├── data.csv
├── housing.names
├── README.md
└── LICENSE

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🎯 Future Improvements

• Add multiple regression models (Random Forest, XGBoost, etc.)
• Hyperparameter tuning
• Model comparison visualization
• Deployment using Flask / Streamlit

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👨‍💻 Author

Abhrankan Chakrabarti Machine Learning Enthusiast | Programmer | Student

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📜 License

This project is licensed under the MIT License.

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