Working for a new-age insurance company and employ mutiple outreach plans to sell term insurance to the customers. Telephonic marketing campaigns still remain one of the most effective way to reach out to people however they incur a lot of cost. Hence, it is important to identify the customers that are most likely to convert beforehand so that they can be specifically targeted via call. We are given the historical marketing data of the insurance company and are required to build a ML model that will predict if a client will subscribe to the insurance.
--> CLeaning the Data ( Identifying and Removing Outliers, Dropping Duplicates, Checking Null Values) --> Exploratory Data Analysis ( Plots for Features and Target & Feature Vs Target) --> Encoding ( Encoding Categorical Data ) --> Imputation ( Imputing missing values using KNNImputer) --> Splitting ( Splitting the data into train test and test set) --> Scaling ( Scaling the data using StandardScaler) --> Balancing the Data (using SMOTE Techniques to balance the data) --> Model Building ( ML algorithms like Logistic Regression, Random Forest, XGBoost, CatBoost, LightGBM, KNN, SVM are used)