Predict House Prices in depth EDA

In this project House Prices - Advanced Regression Techniques Kaggle's competition dataset is used.

While doing this we'll go through:

• Removing outliers using IQR and z-score methods
• Visualizing categorical and continuous variables
• How to process string dtype columns for building machine learning model
• Dealing with missing values

Table of contents

• Getting started
• Findings
• Machine learning model
• License

Getting started

The notebook is available on Kaggle to work in the same environment where this notebook was created i.e. use the same version packages used, etc...

Findings

To know about findings in the EDA stage go to the notebook

Machine learning model

Learning curve

RMS and R2 scores

Visualizing our predictions against actual values

License

APACHE LICENSE, VERSION 2.0