Machine Learning Classification Models

In this project I am going to demonstrat and analyze the data of bank customers and identify the customers who will leave their credit card services and reason for same and any majors to prevent this using the available data.

As I am mostly focusing on coding and the data science technique to understand data and get most of it using Classification models as per my knowledge and understanding in this notebook. I have skipped the theory part for the Classification and it's functioning which you will get plenty of theoretical material on internet. Following are the few articles which will help you to get basic understanding and theoretical concept about Logistic Regression and other classification models models

Reference materials

• 4 Types of Classification Tasks in Machine Learning - MachineLearningMastery.com
• A Complete guide to Understand Classification in Machine Learning (analyticsvidhya.com)
• Classification In Machine Learning | by Amit Upadhyay | Analytics Vidhya | Medium
• Classification In Machine Learning | Classification Algorithms | Edureka

Simple and short best video

• [StatQuest: Logistic Regression - YouTube] (https://www.youtube.com/watch?v=yIYKR4sgzI8)

Project Discription:

The bank Customers’ leaving credit cards services would lead bank to loss, so the bank wants to analyze the data of customers and identify the customers who will leave their credit card services and reason for same and any majors to prevent this using the avaible data . Customers’ leaving credit cards services would lead bank to loss, so the bank wants to analyze the data of customers and identify the customers who will leave their credit card services and reason for same – so that bank could improve upon those areas

Problem Statment :

As data scientist ,my job is to come up with a model that will help the bank improve its services so that customers do not renounce their credit cards and provide the bank key insights from avaible data to improve services.

Data Dictionary:

• CLIENTNUM: Client number. Unique identifier for the customer holding the account
• Attrition_Flag: Internal event (customer activity) variable - if the account is closed then "Attrited Customer" else "Existing Customer"
• Customer_Age: Age in Years
• Gender: Gender of the account holder
• Dependent_count: Number of dependents
• Education_Level: Educational Qualification of the account holder - Graduate, High School, Unknown, Uneducated, - College(refers to a college student), Post-- - Graduate, Doctorate.
• Marital_Status: Marital Status of the account holder
• Income_Category: Annual Income Category of the account holder
• Card_Category: Type of Card
• Months_on_book: Period of relationship with the bank
• Total_Relationship_Count: Total no. of products held by the customer
• Months_Inactive_12_mon: No. of months inactive in the last 12 months
• Contacts_Count_12_mon: No. of Contacts between the customer and bank in the last 12 months
• Credit_Limit: Credit Limit on the Credit Card
• Total_Revolving_Bal: The balance that carries over from one month to the next is the revolving balance
• Avg_Open_To_Buy: Open to Buy refers to the amount left on the credit card to use (Average of last 12 months)
• Total_Trans_Amt: Total Transaction Amount (Last 12 months)
• Total_Trans_Ct: Total Transaction Count (Last 12 months)
• Total_Ct_Chng_Q4_Q1: Ratio of the total transaction count in 4th quarter and the total transaction count in 1st quarter
• Total_Amt_Chng_Q4_Q1: Ratio of the total transaction amount in 4th quarter and the total transaction amount in 1st quarter
• Avg_Utilization_Ratio: Represents how much of the available credit the customer spent

What Steps we are performing to achive above objective:

1. Importing base packages.

2. Data cleaning and summarization.

3. Missing value treatment.

4. Feature engineering and text Columns formatting.

5. Testing multiple classification models and choosing best one.

6. Hyper tuning for the model.

7. Predicting test scores with different matrices.

8. Printing the important features for the prediction as per best model.

9. Conclusion.