House Selling Price

Here buisness requrement to get model which explain which variable is important for calulating selling price of any property so that bussines person can make some conclusions

Table of Contents

• General Info
• Technologies Used
• Conclusions

General Information

• Project have dataset consist of numeical and categorical data, where we need to build a model which is less complex and also not overfitted on data
• Outcome of this project is to comeup with a model which have best fitting over on training and testing data. Model is able to predict price of house based on diffent params like area, type, etc...
• Build two model Ridge and Lasso with Grid CV Seraching for getting optimal hyperpamamters

Conclusions

• For ridge model found 80 as optimal params which provide solution with 89% R2 score on train and 87% on test data.
• For lasso model found 0.0001 as optimal params which provide solution with 89% R2 score on train data and 88% on test data.

Technologies Used

• matplotlib - 3.3.1
• seaborn - 0.11.0
• sklearn - 0.23.1
• numpy - 1.22.2
• pandas - 1.1.4

Contact

Created by @shubham-sri - feel free to contact me!

License

This project is open source and available under the ... License.