Surprise_Housing_Price_Prediction_Model
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. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below.
Business Goal
We have to make an model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for management to understand the pricing dynamics of a new market.
Table of Contents
General Info
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. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below. We have to make an model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for management to understand the pricing dynamics of a new market.
Conclusions
A linear regression model that does not use regularization can learn the training data too well, resulting in overfitting. Ridge and Lasso regressions are regularization techniques that can help to prevent overfitting by adding a penalty to the model's complexity. In high-dimensional data, Lasso regression is often preferred over ridge regression because it can automatically select features, which makes the model more interpretable.
Analysis
Univariate Analysis Bivariate conclusion
Technologies Used
Python 3.X numpy - 1.22.4 pandas - 1.3.5 seaborn - 0.11.2 matplotlib - 3.5.3 scikit-learn 1.2.2 Windows 10
Contact
Created by [@iamnarendrasingh] - feel free to contact me!