Predicting Boston Housing Prices using Regression Models

#100daysofdatascience #machinelearning #scikitlearn #regression

I recently worked on a project where I used various regression models to predict the prices of houses in the Boston Housing dataset. The goal was to identify which model would perform the best in terms of accuracy.

The dataset consists of 506 samples and 14 attributes, including the median value of owner-occupied homes in $1000's. I performed data preprocessing and feature selection to prepare the data for model training.

I trained and tested six different regression models: Linear Regression, Ridge Regression, Lasso Regression, ElasticNet Regression, Decision Tree Regression, and Random Forest Regression. I used scikit-learn library and visualized the results using Matplotlib.

The best-performing model was Random Forest Regression, with an accuracy of 89%.

You can find the details of the project and the code in the notebook link attached below:

Notebook Link

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