Bank Dataset:- Problem Statement:- Find the best stratigies to improve for the next marketing campaign.how can the financial institution have a greater effectiveness for future marketing campaigns? in order to answer this ,we have to analyze the last marketing campaign the bank performed and identify the patterns that will help us find conclusions in order to develop future strategies.
Context Age - Age of the customer - Integer value job - Job of the customer - Categorical feature marital - Marital status of the customer- Categorical feature education - eduction status - categorical feature default - whether the custome is defaulter or not - categorical feature balance - yearly account balance of the customer - continueous feature housing - housing status of the customer - categorical feature loan - whether the customer availed any loans - categorical feature contact - how many times the customer has been contacted - categorical feature day - day from last contact - discrete feature month - month from last contacted date - categorical feature. duration - duration of last contact in hours - contineous feature campaign - contact with how many campaign - categorical feature
Description Done EDA (Exploratory Data Analysis) on bank data Cleaning of raw Data Done Visualization to know important features and their correlation Train model with multiple algorithms After Training XG boost algo performs well with accuracy of 88 Random forest algorithm gives accuracy of 88