Update README.md

README.md

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-# Hyperparameter-Tuning
-Attrition Models (Hyperparameter Tuning) for Credit Card Attrition
+# Hyperparameter-Tuning (World Bank Project)
 
-
-Problem Statement
-Business Context
-
-The Thera bank recently saw a steep decline in the number of users of their credit card, credit cards are a good source of income for banks because of different kinds of fees charged by the banks like annual fees, balance transfer fees, and cash advance fees, late payment fees, foreign transaction fees, and others. Some fees are charged to every user irrespective of usage, while others are charged under specified circumstances.
+World Bank recently saw a steep decline in the number of users of our Visa-sponsored credit card, credit cards are a good source of income for our credit union because of different kinds of fees charged by us like annual fees, balance transfer fees, and cash advance fees, late payment fees, foreign transaction fees, and others. Some fees are charged to every user irrespective of usage, while others are charged under specified circumstances.
 
 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
 
-You as a Data scientist at Thera bank need to come up with a classification model that will help the bank improve its services so that customers do not renounce their credit cards
-
-Data Description
-
-CLIENTNUM: Client number. Unique identifier for the customer holding the account
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-Attrition_Flag: Internal event (customer activity) variable - if the account is closed then "Attrited Customer" else "Existing Customer"
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-Customer_Age: Age in Years
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-Gender: Gender of the account holder
-Dependent_count: Number of dependents
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-Education_Level: Educational Qualification of the account holder - Graduate, High School, Unknown, Uneducated, College(refers to college student), Post-Graduate, Doctorate
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-Marital_Status: Marital Status of the account holder
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-Income_Category: Annual Income Category of the account holder
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-Card_Category: Type of Card
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-Months_on_book: Period of relationship with the bank (in months)
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-Total_Relationship_Count: Total no. of products held by the customer
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-Months_Inactive_12_mon: No. of months inactive in the last 12 months
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-Contacts_Count_12_mon: No. of Contacts in the last 12 months
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-Credit_Limit: Credit Limit on the Credit Card
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-Total_Revolving_Bal: Total Revolving Balance on the Credit Card
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-Avg_Open_To_Buy: Open to Buy Credit Line (Average of last 12 months)
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-Total_Amt_Chng_Q4_Q1: Change in Transaction Amount (Q4 over Q1)
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-Total_Trans_Amt: Total Transaction Amount (Last 12 months)
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-Total_Trans_Ct: Total Transaction Count (Last 12 months)
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-Total_Ct_Chng_Q4_Q1: Change in Transaction Count (Q4 over Q1)
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-Avg_Utilization_Ratio: Average Card Utilization Ratio
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-What Is a Revolving Balance?
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-If we don't pay the balance of the revolving credit account in full every month, the unpaid portion carries over to the next month. That's called a revolving balance
-What is the Average Open to buy?
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-'Open to Buy' means the amount left on your credit card to use. Now, this column represents the average of this value for the last 12 months.
-What is the Average utilization Ratio?
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-The Avg_Utilization_Ratio represents how much of the available credit the customer spent. This is useful for calculating credit scores.
-Relation b/w Avg_Open_To_Buy, Credit_Limit and Avg_Utilization_Ratio:
-
-( Avg_Open_To_Buy / Credit_Limit ) + Avg_Utilization_Ratio = 1
+I came up with classification models that will help the bank improve its services so that customers do not renounce their credit cards