Classification-models

Supervised Machine Learning techniques to test results of classification on bank data. Techniques involved are Logistic Regression, Decision Trees and Random Forest. The model uses german_credit.csv file for training and Test_50.csv for testing. For feature extraction, PCA has been used to determine how many features should be used for actual classification process and the parameters were identified using chi-squared test. Libraries used are numpy, pandas and sklearn.