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:
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:
Total Sales: Calculate the total sales revenue for each customer.
Purchase Frequency: Determine how often each customer makes a purchase.
Most Popular Products: Iden
