Case Study: Cult.Fit 

Objective: Analyze user engagement, workout preferences, and retention using data analysis tools in Python.

Scenario:

Cult.Fit collects data from its users about their workouts, app usage, locations, and fitness goals. Your goal is to perform EDA to uncover insights like:

• Which workout formats are most popular?
• How often do users attend sessions?
• Is there a pattern of drop-offs?

Data Columns Example:

• user_id: Unique identifier
• session_date: Date of attendance
• workout_type: Yoga, HIIT, Dance, etc.
• location: Center name
• duration_minutes: Time spent in session
• active_days: Number of days user worked out this month

Key Python Steps:

Insight Use:

• Recommend popular workouts to new users
• Predict churn using low activity data
• Optimize scheduling based on high-demand slots

Case Study: Zomato

Objective: Analyze user reviews, cuisine popularity, and restaurant ratings using Pandas & EDA.

Scenario:

Zomato has user-generated data such as reviews, ratings, cuisines, and locations. Your job is to find:

• Top cuisines per city
• Correlation between price and rating
• Patterns in delivery vs dine-in ratings

Data Columns Example:

• restaurant_name
• location
• cuisine
• average_cost_for_two
• user_rating
• delivery_available (Yes/No)

Key Python Steps:

Insight Use:

• Recommend pricing ranges for new restaurant partners
• Help users discover top-rated cuisine in their area
• Identify user sentiment differences between delivery and dine-in