Data Collection and Preprocessing
Now that you're familiar with the problem we aim to solve, let's get our hands a little dirty with data! Data collection and preprocessing are essential steps that lay the foundation for any machine learning project. If you think of machine learning as cooking, then data is your key ingredient. The better the quality, the tastier the result!
Data Collection
In a real-world scenario, data collection would involve gathering data from various sources like databases, logs, or external APIs. For our capstone project, we've provided a dataset named product_interactions.csv. This file contains interactions of users with different products, as we discussed in the Problem Statement section.
You can read this dataset into a DataFrame using the following code snippet:
import pandas as pd
# Read the CSV file into a DataFrame
df = pd.read_csv('product_interactions.csv')
# Show the first few rows of the DataFrame
df.head()
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