Chennai House Price Prediction

Description

Chennai House Price Prediction is a machine learning project aimed at predicting house prices in the Chennai region. This project leverages a combination of data preprocessing, exploratory data analysis, feature engineering, and various regression models to achieve accurate predictions.

Frontend

• React: The frontend of the application is built using React for a dynamic and interactive user interface.
• Tailwind CSS: Tailwind CSS is used for styling to create a modern and responsive design.

Backend

• Python & Flask: The backend is built using Python and Flask, and it is deployed on Render.
• Dataset: Data is loaded from Kaggle for analysis and model training.

Steps Performed

Data Preprocessing

• Data Loading: Imported the dataset from Kaggle.
• Data Cleaning: Handled missing values and outliers.
• Feature Encoding: Converted categorical variables into numerical values.
• Feature Selection: Selected relevant features for the model.
• Data Scaling: Normalized the data to ensure consistent scaling.

Exploratory Data Analysis (EDA)

• Univariate Analysis: Analyzed individual features.
• Bivariate Analysis: Examined relationships between pairs of features.
• Multivariate Analysis: Studied interactions among multiple features.

Model Building

• Regression Models: Tested various regression models including Linear Regression, Decision Trees, and Random Forests.
• XGBoost Regressor: Achieved the highest accuracy of 97%.
• Cross-Validation: Performed to assess model performance.
• Hyperparameter Tuning: Optimized model parameters for better results.

Tools and Libraries

• scikit-learn: Used for machine learning tasks and model evaluation.
• Plotly: Utilized for creating interactive visualizations.
• Pickle: Employed for model serialization and saving.

Installation

Prerequisites

• Python 3.x
• Node.js and npm
• Flask
• Dependencies: scikit-learn, xgboost, plotly, pickle, etc.

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/chennai-house-price-prediction.git
   cd chennai-house-price-prediction

2. Install backend dependencies:

   pip install -r requirements.txt

3. Setup environment variables: Create a .env file for backend configuration.

4. Run the backend server:

   python app.py

5. Install frontend dependencies:

   cd frontend
   npm install

6. Run the frontend development server:

   npm start

7. Open your browser and navigate to http://localhost:3000.

Screenshots

Frontend

Landing Page

Prediction Page

Prediction Result

Contributing

Contributions are welcome! Please read our Contributing Guidelines before submitting a pull request.

License

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

Contact

For any inquiries or support, please contact:

• Email: [email protected]
• LinkedIn: Lingesh Patturaj

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Thank you for exploring Chennai House Price Prediction. Feel free to contribute or reach out if you have any questions!

![House Prediction](https://media.giphy.com/media/3