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)

This project focuses on analyzing real estate data and building predictive insights on house prices. The dataset contains features such as house size, number of bedrooms, and other key attributes.

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.

House sale prediction on regression analysis

This repo includes the deployment code for house price prediction model

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.

A custom Gradient Boosting Machine built entirely from scratch using Python and NumPy to minimize Squared Error loss. Features manual weak learner integration and hyperparameter tuning to predict housing prices without relying on XGBoost or LightGBM.

This contains property listings from various cities across Bangladesh, specifically including Dhaka, Chattogram, Cumilla, Narayanganj City, and Gazipur, with prices listed in Bangladeshi Taka (৳).