Prediction of housing prices using regression model
Use case- This model is used to predict/determine the price of housing using housing determinant factors(variables).
Analysis- The dataset was then analyzed by checking different relationships between the independents variables and between the independent and dependent variables.
Methods used in the analysis are - Using Sea-born(sns), Bar-plot and Line-plot were plotted to show these relationships.
Building the Model: Regression Models were used - RidgeRegression and LinearRegression were used for the analysis
The Mean Absolute Error, the Mean Squared Error and the R2 Score was checked using Sklearn.metrics
The Linear Regression had a higher R2 (Performance) score making it the most suitable model to train our dataset with.
YellowBricks was used to visualize the Train and the Test of the Regression Model.