House-Price-Prediction

Overview

This project aims to predict house prices using machine learning techniques. The goal is to develop a model that can accurately estimate the price of a house based on various features such as crime rate, number of bedrooms, population, and other relevant factors. The project utilizes a dataset containing historical housing data, and the machine learning model is trained on this data to make predictions for new, unseen instances.

Dataset

The dataset used for this project is sourced from house.csv. It includes various features such as:

1. CRIM      per capita crime rate by town 
2. ZN        proportion of residential land zoned for lots over 
             25,000 sq. Ft.
3. INDUS     proportion of non-retail business acres per town
4. CHAS      Charles River dummy variable (= 1 if tract bounds 
             river; 0 otherwise)
5. NOX       nitric oxides concentration (parts per 10 million)
6. RM        average number of rooms per dwelling
7. AGE       proportion of owner-occupied units built prior to 1940
8. DIS       weighted distances to five Boston employment centres
9. RAD       index of accessibility to radial highways
10. TAX      full-value property-tax rate per $10,000
11. PTRATIO  pupil-teacher ratio by town
12. B        1000(Bk - 0.63) ^2 where Bk is the proportion of blacks 
             by town
13. LSTAT    % lower status of the population.
14. MEDV     Median value of owner-occupied homes in $1000's

Project Structure

The project is organized into the following directories:

• data: Contains the dataset used for training and testing the machine learning model.

• src: Python scripts containing modular code for data preprocessing, feature engineering, and model training. This promotes code reusability and maintainability.

• models: Saved machine learning models in Joblib. These models can be loaded and used for making predictions on new data.

Dependencies

• Python 3.x
• Libraries: NumPy, Pandas, Scikit-Learn, Matplotlib, Seaborn, etc. (provide a comprehensive list in a requirements.txt file)

Usage

Clone the repository:

git clone https://github.com/shreya1m/House-Price-Prediction.git

Navigate to the project directory:

cd House-Price-Prediction

Install dependencies:

• pip install -r requirements.txt
• Execute python script.

Future Enhancements

• Fine-tuning hyperparameters to improve model performance.
• Exploring additional features for better prediction accuracy.
• Deploying the model as a web application or API for real-time predictions.

Feel free to contribute, open issues, or provide feedback to make this project more robust and effective in predicting house prices.