Data Science Challenge: Analyzing Customer Purchase Patterns

[Sachin Bhatt]

I'm working on a data science project where I have a dataset of customer transactions and I need to analyze customer purchase patterns using Python. The dataset includes the following columns: customer_id, transaction_date, product_id, quantity, and price.

Here's a simplified version of the data:

import pandas as pd

data = {
    'customer_id': [101, 102, 101, 103, 102, 104],
    'transaction_date': ['2023-01-15', '2023-02-10', '2023-02-25', '2023-03-05', '2023-03-12', '2023-03-20'],
    'product_id': [1, 2, 1, 3, 2, 1],
    'quantity': [2, 1, 3, 2, 1, 4],
    'price': [20.0, 30.0, 25.0, 40.0, 30.0, 15.0]
}

df = pd.DataFrame(data)

I want to perform the following analyses using Python:

• list item Total Sales: Calculate the total sales revenue for each customer.

• list itemPurchase Frequency: Determine how often each customer makes a purchase.

• list itemMost Popular Products: Identify the top 3 most purchased products.

• list itemCustomer Retention: Analyze customer retention by calculating the percentage of customers who make repeat purchases within 30 days.

Could you provide Python code examples and explanations for each of these analyses using the provided dataset? Thank you for your assistance in analyzing these customer purchase patterns!

[friedemannkleint]

That sounds like a question for ChatGPT ...

[JonB]

@Sachin-Bhatt said in Data Science Challenge: Analyzing Customer Purchase Patterns:

Could you provide Python code examples and explanations for each of these analyses using the provided dataset?

Since this is a Python question, nothing to do with Qt, I suggest you ask in a Python forum.

Also, I hate to ask, but is the point of this "challenge" that you figure the stuff for yourself rather than asking others to give the answer?