Ridge-and-Lasso-Regression

Project Overview

This project demonstrates the application of Ridge and Lasso regression techniques on a housing dataset. The goal is to build predictive models that estimate housing prices based on various features.

Dataset

The dataset used for this project is housingdata.csv. This dataset includes multiple features relevant to housing prices, such as the number of rooms, age of the property, distance to employment centers, and more. The dataset was taken from kaggle.

Features

• CRIM: Per capita crime rate by town.
• ZN: Proportion of residential land zoned for lots over 25,000 sq. ft.
• INDUS: Proportion of non-retail business acres per town.
• CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise).
• NOX: Nitric oxides concentration (parts per 10 million).
• RM: Average number of rooms per dwelling.
• AGE: Proportion of owner-occupied units built prior to 1940.
• DIS: Weighted distances to five Boston employment centers.
• RAD: Index of accessibility to radial highways.
• TAX: Full-value property tax rate per $10,000.
• PTRATIO: Pupil-teacher ratio by town.
• LSTAT: Percentage of lower status of the population.
• MEDV: Median value of owner-occupied homes in $1000s.

Requirements

• Python 3.x
• Pandas
• NumPy
• Scikit-learn
• Matplotlib (for plotting, if necessary)
• Jupyter Notebook (optional, for interactive exploration)