Data Science Challenge: Analyzing Customer Purchase Patterns

[soni21]

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:

[ol ]
[li]Total Sales: Calculate the total sales revenue for each customer.[/li]
[li][/li]
[li]Purchase Frequency: Determine how often each customer makes a purchase.[/li]
[li][/li]
[li]Most Popular Products: Identify the top 3 most purchased products.[/li]
[li][/li]
[li]Customer Retention: Analyze customer retention by calculating the percentage of customers who make repeat purchases within 30 days.[/li]
[/ol]

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!