HousePrices-ML

Prediction of housing prices using Machine Learning

In this project we explore a housing pricing dataset using Python libraries such as pandas, numpy, matplotlib, seaborn, and dython. After that, we will prepare the dataset for model training, after that we will consider four different ML models using sklearn and xgboost libraries, and then assess the performance of each model using cross validation. Ultimately, we will choose the best ML model, select the best parameters for this model using GridSearchCV, and apply the model on the test dataset to predict the housing prices in Boston.

This notebook is also available on Kaggle.