• Overview
• Dataset
• Installation
• Usage
• Model
• Results
• Contributing
• Acknowledgements

This project aims to predict house prices in Boston using various machine learning algorithms. The dataset used is the famous Boston Housing Dataset, which includes features like crime rate, number of rooms, and distance to employment centers.

The dataset contains 506 samples with 13 features each. The target variable is the median value of owner-occupied homes in $1000's. The features are:

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 centers
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 Black residents by town
13. LSTAT: % lower status of the population

1. Clone the repository:

   https://github.com/1073rajan/bostonhousepricing.git

2. Create and activate a virtual environment:

   conda create -p venv python==3.7 -y

3. Install the required packages:

   pip install -r requirements.txt

1. Exploratory Data Analysis (EDA):

   • Navigate to the notebooks directory and open eda.ipynb to explore the dataset and visualize the features.

2. Model Training:

   • Run the train_model.py script to train the models:

   python train_model.py

3. Prediction:

   • Use the predict.py script to make predictions on new data:

   python predict.py --input data/new_data.csv --output data/predictions.csv

Various regression models are used in this project:

• Linear Regression
• Ridge Regression
• Lasso Regression

Each model is evaluated based on metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score.

The performance of the models can be found in the results directory. The model_comparison.csv file contains the evaluation metrics for each model.

Contributions are welcome! Please create a pull request with a detailed description of the changes.

• This project uses the [Boston Housing Dataset](Sk-Learn Dataset) from the SK-learn Machine Learning Repository.
• Special thanks to the scikit-learn team for providing such an excellent library.

1. Githubs Account
2. Vs Code IDE
3. HerokuAccount
4. GitCLI

https://bostonhouseprice.pythonanywhere.com/