An Analytics Vidhya Hackathon

This is a Machine Learning Classification task as a part of the Loan Prediction Hackathon organized by Analytics Vidhya. The problem statements involves identifying which customers' loan requests would be rejected or approved.

Metric to increase was ACCURACY

Click here to view loan prediction competition - https://datahack.analyticsvidhya.com/contest/all/ (THE COMPETITION MIGHT GET REMOVED AFTER DURATION FOR COMPETITION EXPIRES)

• Datatype of certain columns were wrongly identified. Corrected them.
• Categories names in certain feature not making sense. Categories names made clear.

• Skewness and outliers detected in numerical features.
• Missing values detected in categorical and numerical features.
• No imbalance reported.
• Numerical features not useful in classifying records.
• Few categorical features showing patterns in differentiating approved and rejected loan requests.
• No multicollinearity.

• Data split into train and test to avoid data leakage.
• To impute numerical features, used the simple imputer.
• To impute categorical features, used the weight of evidence method. Also encoding performed by woe.

• Outliers couldnt be dropped due to high proportion and couldnt be capped due to large outliers.
• Numerical features were transformed.

• Logistic regression, KNN, SVC, Decision tree, Random Forest Model, Adaboost, Gradient Boosting and XGB Classifiers were used.
• Decision tree and ensemble techniques gave accuracy greater than 70%. Used for further improvement

• Best parameters were found for each model using Grid Search.
• Accuracy scores touching 78% to 81%.
• Highest accuracy calculated for tuned decision tree on test i.e 81%.

• Models were clubbed together to create super models using voting and stacking classifier.
• No improvement in performance.
• Best overall score on test - 80%

• Best features found for stacked classifier using Forward Feature Elimination
• No improvement in accuracy.

• Stacked classifier with Tuned Decision Tree, Random Forest, Gradient, XGB and Adaboost classifier.