Churn prediction case study
- Converted ipynb files to html for ease of evaluation
- Report is present as pdf in the current directory
- Report highlights all the steps taken to solve this case study
Structure of repository:
./
├── README.md
├── Report.pdf
├── html
│ ├── 000_Undestanding_Problem.html
│ ├── 001_EDA_part1.html
...
├── notebooks
│ ├── 000_Undestanding_Problem.ipynb
│ ├── 001_EDA_part1.ipynb
│ ├── 002_EDA_part2.ipynb
│ ├── 003_EDA_part3.ipynb
│ ├── 004_feature_engineering.ipynb
│ ├── 005_feature_selection_classification.ipynb
│ ├── 006_feature_selection_regression.ipynb
│ ├── 007_modelling_classification_RandomForest.ipynb
│ ├── 008_modelling_classification_XgBoost.ipynb
│ ├── 009_modelling_regression_RandomForest.ipynb
│ ├── 010_modelling_regression_XgBoost.ipynb
│ └── Report.ipynb
└── problem-stmt