A complete end to end case study for Loan Decision dataset in Python.
The data set comprises of parameters such as Gender, Applicant Income, Coapplicant Income, Credit History etc. The goal was to conduct an end to end analysis and to form a Machine learning model to predict Loan Decision status for various applicants.
The data was thoroughly explored and imputed where needed. Unnecessary features were dropped. Finally complete tuning and model creation was performed for the following model types:
- Naive Bayes
- Logistic Regression
- Gradient Boosting Classifier
- Random Forest Classifier
- Support Vector Machine (SVC)
Along with complete classification performance report and visualisations for which parameters were relatively more important for the same. COMPLETE CASE STUDY IS AVAILABLE AS A PYTHON NOTEBOOK (task.ipynb)