Machine learning demonstration of the Gradient Boosting algorithm and it's effectiveness on a regression dataset of house prices.

Data Analysis Project using Python(Numpy, Pandas, Seaborn, matplotlib)

This project is based on House Prices dataset which consists of 2073 rows and 81 columns. Objective is to predict the price of the houses by a Regression Technique called Linear Regression after performing Principal Component Analysis.

Investigate the response variable (dependent variable) life expectancy in the year 2016 and use other indicators (predictor variables) of the dataset to develop a linear model which explains the life expectancies 2016.