House Price Prediction

Chandramouli Yalamanchili
Updated - 06/05/2021 [Created - 03/28/2021]
View Project Code on GitHub

Introduction

The goal of this project is to build the linear regression model to house price prediction.
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https://www.kaggle.com/kamelyounes/house-prices-prediction/?select=train.csv
I have pulled the home price data from Kaggle link provided above and used it for the project.
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Download the dataset from the kaggle link provided above and place it in the path Data/train.csv
Install all of the Python packages needed.
Execute the jupyter notebook in the Jupyter server of choice.
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Used Python and Jupyter notebook for this project.
Perform data analysis and summary functions to understand the distribution of the data as well as to identify the any missing values present in the data.
Performed few data engineering steps to prepare the data for the modeling: - Handled the missing values, by replacing missing value to mean for numeric fields and to a constant of “Missing” for non-numeric fields. - One Hot encoding to make all non-numeric fields numeric as well. - Applied scaling to normalize the feature values.
Created 3 types of regression models - linear regression, decision tree regression, and xgboost regression models.
Compared all three trained models and it seems like xgboost has exhibited better performance with this data and for the amount of testing done.
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Based on the regression scores, it seems like xgboost has performed better than the remaining two regression models.
- Linear Regression - 0.74
- Decision tree grid search - 0.74
- XGBoost - 0.88
Also, the score on training set and testing set are very close indicating that there is no overfitting of the training data in the model.