nitink08 / housing-price-prediction

Housing Price Prediction

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price.

The company is looking at prospective properties to buy to enter the market.

The company wants to know:

• Which variables are significant in predicting the price of a house, and
• How well those variables describe the price of a house.

Conclusions

The Optimal Lambda values in case of Ridge and Lasso are:

• Ridge - 5
• Lasso - 0.0004

The Mean Squared error in case of Ridge and Lasso are:

• Ridge - 0.01371078357735112
• Lasso - 0.013535752335222997

The Mean Squared Error of Lasso is slightly lower than that of Ridge.

As we know Lasso helps in feature reduction (as the coefficient value of one of the feature became 0), Lasso has a better edge over Ridge.

So based on Lasso, the factors that generally affect the price are the

• Zoning classification
• Living area square feet
• Overall quality and condition of the house
• Foundation type of the house
• Number of cars that can be accomodated in the garage
• Total basement area in square feet and the Basement finished square feet.

Therefore, the variables predicted by Lasso in the above bar chart as significant variables for predicting the price of a house.

Technologies Used

• numpy - version 1.21.5

• pandas - version 1.4.4

• matplotlib.pyplot - version 3.5.2

• seaborn - version 0.11.2

• sklearn.preprocessing -> scale - version 1.0.2

• sklearn.model_selection -> train_test_split - version 1.0.2

• sklearn.linear_model -> Ridge, Lasso - version 1.0.2

• sklearn.metrics -> mean_squared_error - version 1.0.2

• sklearn.model_selection -> GridSearchCV - version 1.0.2

• sklearn.feature_selection -> RFE - version 1.0.2

• sklearn.linear_model -> LinearRegression - version 1.0.2

• statsmodels.api - version 0.13.2

• sklearn -> metrics - version 1.0.2

Acknowledgements

• This project was inspired by US-based housing company named Surprise Housing.

Contact

Created by [@nitink08] - feel free to contact me!