House Prices Advanced Regression Techniques Walkthrough

This project has been done for a Kaggle competition.

Kaggle is a platform for predictive modelling and analytics competitions in which statisticians and data miners compete to produce the best models for predicting and describing the datasets uploaded by companies and users.

Selected project to this assignment

• House Prices; Advanced Regression Techniques
• Kaggle link: https://www.kaggle.com/c/house-prices-advanced-regression-techniques

Competition Description

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges us to predict the final price of each home.

Methodology

• Selected Algorithm: Linear Regression
• Used Technologies:
  • Python 3
  • PyCharm

Link to my submission1.csv file: https://drive.google.com/file/d/1vavOuZf1bp4FfD9AJ-uKhnN34nuTrT1J/view?usp=sharing

YouTube link to walkthrough: https://youtu.be/Qq2X-K2Ku3s

Kaggle Updates: https://www.kaggle.com/nimeshikaranasinghe/competitions

Kaggle Kernel Link: https://www.kaggle.com/nimeshikaranasinghe/kernel55d4b2c230