8th sem Final year Project of VTU

This project is about predicting house price of Boston city using supervised machine learning algorithms. In this we used three models Multiple Linear Regression, Decision Tree and Random Forest and finally choose the best one. Furthermore, we briefly introduced Regression, the data set, analyzed and visualized the dataset.

Predicting House Prices in Ames,Iowa using advanced regression techniques (penalized regressions)

Participated in the Kaggle "Houses Price Competition" and successfully solved the challenge. Leveraged various machine learning techniques to predict house prices accurately. My solution encompasses data preprocessing, feature engineering, and ensemble models to achieve competitive results.

This is a house price prediction system that helps users predict house prices based on locality, number of of bedrooms, bathrooms and total sqft area.

This repo includes the deployment code for house price prediction model