Loan_Status_Classification-with-python

Dataset: Loan_train.csv and Loan_test.csv files are directly downloaded from the links mentioned in the cells of notebook.

Workflow:

1. Data Cleaning.
2. Feature Extraction and Feature Selection.
3. Converting Categorical to Numerical Values.
4. One Hot Encoding.
5. Normalization.
6. Train-Test Split.
7. Model Prediction(Loan Case is paid off or not).
8. Results(Evaluation Metrics).

Classifier Models Built are:

1. k-Nearest Neighbour.
2. Decision Tree.
3. Support Vector Machine.
4. Logistic Regression.

Evaluation Metrics used are:

1. Jaccard Index.
2. F1-score.
3. Logloss.