SabinAdkh/Financila-Inclusion-in-Africa-using-Machine-Learning

## This is a Classification Task where the we have to preidct the likelihood of a person having a bank ## Dataset description ### The dataset is collected from the online paltform called Zindi. ``` Financial Inclusion in Afri… ``` ### The dataset contains 13 features including target variable * More description of the variables can be found in VariableDefinition.csv file ### Target Varialbe is a categorical variable with object dtype. * Bank_account: "Yes", "No" ## Data Imbalance * Handled data imbalance issue by using oversampling technique called SMOTE(Synthetic Minority Oversampling Technique) * ## Machine Learning Algorithms Used * LogisticRegression * SupportVectorMachine * KNN * DecisionTree * RandomForest * GradientBoosting * XGBoost * HistGradientBoosting * AdaBoost ### Model Evaluation Metrics Used * Precision * Recall * Accuracy Score * F1-score