ML_PROJECT

Campaign for selling personal loans

Bank is has a growing customer base. The bank wants to increase borrowers (asset customers) base to bring in more loan business and earn more through the interest on loans. So , bank wants to convert the liability based customers to personal loan customers. (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success.

The department wants to build a model that will help them identify the potential customers who have higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign.

Dataset Description

The file given below contains data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign

Objective

The classification goal is to predict the likelihood of a liability customer buying personal loans.

Libraries Used:

 Pandas  Numpy  matplotlib  seaborn  sklearn

Models used

 Logistic regression  KNN  Decisio Tree  Bayes Naive  Random Forest

Learning Outcomes:

 Exploratory Data Analysis  Preparing the data to train a model  Training and making predictions using a classification model  Model evaluation  Lable Coding  Comparision between different classification algorithmComparision between different algorithms

Output

decision tree is the most fit model as it got the highest accuracy.