House-sale-price-prediction-using-python
In this project I pretended to be a Data Analyst working at a Real Estate Investment Trust. The Trust wanted to start investing in Residential real estate. I was tasked with determining the market price of a house given a set of features. I analyzed and predicted housing prices using attributes such as square footage, number of bedrooms, number of floors, and so on. I used the dataset of house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.
The dataset used to complete this task was obtained from: here
I used python codes to do data cleaning, data analysis, creating models for price prediction, evaluating and refining models.
Major skillsets covered include: Statistical Analysis of data using correlation, R-Squared, linear and polynomial regression Graphical representation of data using boxplot, and seaborn's regplot Model refinement using ridge regression object Polynomial transformation of training and test data
