Data Science Challenge: Analyzing Customer Purchase Patterns

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:

Code:

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:

1. Total Sales: Calculate the total sales revenue for each customer.
   
   
2. Purchase Frequency: Determine how often each customer makes a purchase.
   
   
3. Most Popular Products: Identify the top 3 most purchased products.
   
   
4. Customer 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!

 

What have you written so far?
You should not expect members of this forum to code your homework assignments for you. Do the coding yourself, that way you learn how to do it.