The project uses the following steps:
- 𝐃𝐚𝐭𝐚 𝐩𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: The data is cleaned and preprocessed to remove any errors or inconsistencies.
- 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: New features are created from the existing features to improve the accuracy of the model.
- 𝗠𝗼𝗱𝗲𝗹 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴: The data is trained on a variety of machine learning models, including KNN, Decision tree, Linear Regression, gradient boosting model and finally using ML Ensemble.
- 𝗠𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: The models are evaluated on a holdout dataset to determine their accuracy.
Use the package manager #pip to install all these module.
pip install pandas
pip install matplotlib
pip install seaborn
pip install sklearn
- Read data and data preprocessing using pandas.
- Visualize insights using viz libraries.
- For improve accuracy of the model create some new feature from the existing feature using LabelEncoder.
- Using Linear Regression, KNN, Decision Tree, Gradient Boosting model.
The best model achieved an accuracy of 99%