Housing Price Prediction Project

This project aims to predict housing prices using a machine learning model based on the Boston Housing Dataset. The process includes data preprocessing, model training, evaluation, and visualization of results. The primary objective is to develop an accurate and interpretable model.

Project Structure

Predict-Housing-Price/
├── Dataset/
│   └── Boston_Housing_Dataset.csv
├── src/
│   ├── data_preprocessing.py
│   ├── train.py
│   ├── model_evaluation.py
│   └── visualization.py
├── Report/
│   └── Housing_Price_Prediction_Report.pdf
├── main.py
├── Requirements.txt
└── README.md

Folders and Files

• Dataset/: Contains the dataset used for training the model.

  • housing.csv: The Boston Housing Dataset.

• src/: Contains the source code for various stages of the project.

  • data_preprocessing.py: Script for data cleaning, handling missing values, removing outliers, feature engineering, and scaling.
  • train.py: Script for training the machine learning model.
  • model_evaluation.py: Script for evaluating the trained model.
  • visualization.py: Script for visualizing results such as feature importance, actual vs predicted prices, and residuals.

• Report/: Contains the detailed project report.

  • Housing_Price_Prediction_Report.pdf: The final report documenting the entire project, including methodology, results, and analysis.

• main.py: The main script to run the entire pipeline from data preprocessing to model training and evaluation.

• Requirements.txt: List of Python dependencies required to run the project.

Getting Started

Prerequisites

Ensure you have Python 3.6 or higher installed on your system. Install the required dependencies using:

pip install -r Requirements.txt

Running the Project

1. Data Preprocessing: Clean and preprocess the data.

   python src/data_preprocessing.py

2. Model Training: Train the machine learning model.

   python src/train.py

3. Model Evaluation: Evaluate the trained model.

   python src/model_evaluation.py

4. Visualization: Generate visualizations for analysis.

   python src/visualization.py

Alternatively, you can run the entire pipeline using the main.py script:

python main.py

Project Report

The detailed project report is available in the Report/ folder. It includes comprehensive information about the data preprocessing steps, model training process, evaluation metrics, results, and analysis.

Contributing

If you wish to contribute to this project, please fork the repository and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

References

• Dataset source: Boston Housing Dataset
• plotly : Link
• Random Forest Regressor : Link