Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/djdhairya/india_estate_price-map


https://github.com/djdhairya/india_estate_price-map

data-science data-visualization eda hyperparameter-tuning keras lightgbm matplotlib metrics model numpy pandas scikit-learn seaborn tree xgboost

Last synced: about 2 months ago
JSON representation

Awesome Lists containing this project

README 

        

# Indian House Price Prediction  



## Project Overview  

This project focuses on predicting house prices in major Indian metropolitan cities: **Delhi, Mumbai, Kolkata, Bangalore, Chennai, and Hyderabad**. The prediction is based on various features, including property details, amenities, and facilities provided. Using machine learning techniques and visualization tools, we aim to create an accurate and user-friendly house price prediction model.



---



## Features Considered for Prediction  

The model predicts house prices based on the following segments:  



1. **Property Details**:  

   - Area  

   - Location  

   - Number of Bedrooms  

   - Resale Value  



2. **Amenities**:  

   - Maintenance Staff  

   - Gymnasium  

   - Swimming Pool  

   - Landscaped Gardens  

   - Jogging Track  

   - Rain Water Harvesting  

   - Indoor Games  

   - Sports Facility  



3. **Facilities**:  

   - Shopping Mall  

   - Intercom  

   - ATM  

   - Clubhouse  

   - School Nearby  

   - 24x7 Security  

   - Power Backup  

   - Car Parking  

   - Staff Quarter  

   - Hospital Nearby  



4. **Appliances and Additional Features**:  

   - Washing Machine  

   - Gas Connection  

   - Air Conditioner (AC)  

   - Wifi  

   - Children’s Play Area  

   - Lift Available  

   - Bed  

   - Vaastu Compliance  

   - Microwave  

   - Golf Course Access  

   - Television (TV)  

   - Dining Table  

   - Sofa  

   - Wardrobe  

   - Refrigerator  



---



## Libraries Used  

This project makes use of the following Python libraries:  



- **Data Processing and Analysis**:  

  - Pandas  

  - NumPy  



- **Data Visualization**:  

  - Matplotlib  

  - Seaborn  



- **Machine Learning**:  

  - Scikit-learn  

  - XGBoost  

  - LightGBM  



- **Model Evaluation**:  

  - Scikit-learn metrics  

  - Hyperparameter Tuning with GridSearchCV  



---



## Steps Involved  



### 1. Data Collection  

- Datasets sourced from reliable real estate platforms like  Kaggle, or government portals.  

- Data for Delhi, Mumbai, Kolkata, Bangalore, Chennai, and Hyderabad.  



### 2. Data Preprocessing  

- Handling missing values.  

- Encoding categorical variables.  

- Normalizing numerical features.  



### 3. Exploratory Data Analysis (EDA)  

- Visualizing relationships between price and various features using graphs like:  

  - Heatmaps for correlations.  

  - Boxplots for outliers.  

  - Pair plots for feature relationships.  



### 4. Model Building  

- Implemented machine learning models like:  

  - Linear Regression  

  - Decision Trees  

  - Random Forests  

  - Gradient Boosting (XGBoost/LightGBM)  



### 5. Model Evaluation  

- Metrics used for performance evaluation:  

  - Mean Squared Error (MSE)  

  - R-squared (R²) Score  



### 6. Prediction  

- Predicting house prices for given input features using the trained model.  



---



## Results  

- **Visualization**: Graphs and heatmaps to highlight feature importance and price trends in different cities.  

- **Accuracy**: Achieved high prediction accuracy using advanced models like XGBoost and Random Forest.  



---



## Usage Instructions  



1. **Clone the Repository**  

   ```bash  

   git clone https://github.com/djdhairya/India_Estate_Price-Map.git  

     

   ```  



2. **Install Dependencies**  

   ```bash  

   pip install -r requirements.txt  

   ```  



3. **Run the Application**  

   ```bash  

   python ipynb file  

   ```  



4. **Input Features**  

   Enter details like Area, Location, and other amenities to predict the house price.  



---



## Contributions  

Feel free to contribute to the project by:  

- Improving the model accuracy.  

- Adding more cities or features.  

- Enhancing visualization and UI.  



---



## Acknowledgments  

- Datasets sourced from  Kaggle, and public repositories.  

- Special thanks to open-source contributors for developing the libraries used in this project.  



--- 



## License  

This project is licensed under the MIT License.  



---