Analysis of Value of Bank Customers

I had two datasets:
First dataset:

• Date
• Account ID Number
• Opening Date of Account
• Account Status: Active or Close
• Account Type
• Account Balance at the end of the day

Second dataset:

• Account ID Number
• Transaction Amount
• Transaction Category
• Transaction Time

Data Primary Analysis

I found one null value, I dropped it since it had no information.

Feature Introduction: I introduced 6 features based on my knowledge of bank transactions.

• Feature 1: The total number of transactions for each account in these three months.
• Feature 2: The amount average of each transaction.
• Feature 3: The variance of amount of the transaction for each customer.
• Feature 4: The average of amount of account balance at end of each day.
• Feature 5: Account balance variance
• Feature 6: The duration of the account activity, which includes two parts: from the day of account opening to the beginning of the dataset, plus the active dates of accounts in the dataset. I should convert system date from Gregorian to Jalali to make dataset dates and opening dates of the same type.

Scaling

I scaled financial features using log function and min-max scaling. (I test the model without the scaling I realized that scaling ends in a better clustering) I scaled active time duration using min-max scaling.

Concatenating All Features

I made a dataframe from all features.
Then, I used scatter_matrix to visualize the features.

Drop Outliers

I used box plot to show the existence of outliers:

I used a standard score. Then I dropped those data that correspond to standard scores greater than 3. Then, the boxplot become:

Kmean

I used the elbow curve of inertia, to specify the number of clusters:

For clustering kmeans with n=6, we clustered the data. I used the scatter matrix to visualize it:

clusters correspond the label=5,6 reperesnt cusstomers, with :

• longer active time
• bigger account balance
• bigger amount of transaction
• greater number of transaction.

Second Kmeans

I did a second kmeans on clusters with label=5,6. Using Elbow curve, I consider n=3.

Then, as we see in this clustering, the cluster with label=2 corresponds, to customers with:

• bigger account balance
• bigger amount of transaction

DBSCAN

As a second method, I used DBSCAN. I used KNN to specify the value of epsilon:

DBSCAN on datas resulted in following clustering:

Clustering wasn't successful in this method.