🚲 Bike Share Dataset (bike_share_year_0.csv)

• Total Rows: 17,290

• Total Columns: 15

• Key Columns:

  • dteday: Date of the ride.
  • season: Season (1: Winter, 2: Spring, 3: Summer, 4: Fall).
  • yr: Year indicator (0 = First year in dataset).
  • mnth: Month of the ride.
  • hr: Hour of the ride.
  • holiday: Indicates if the day is a holiday.
  • weekday: Day of the week.
  • workingday: Whether it is a working day.
  • weathersit: Weather condition (e.g., Clear, Mist, Rain).
  • temp: Normalized temperature.
  • atemp: Normalized “feels-like” temperature.
  • hum: Normalized humidity.
  • windspeed: Normalized wind speed.
  • rider_type: Type of rider (casual or registered).
  • riders: Count of riders.

• Data Period: Covers the first year of recorded bike usage.

🚲 Bike Share Dataset (bike_share_year_1.csv)

• Total Rows: 17,468
• Total Columns: 15 (Same structure as bike_share_year_0.csv).
• Differences from bike_share_year_0.csv:
  
  • Covers the second year of recorded bike usage (yr = 1).
  • Slightly higher number of rows.
• Data Period: Covers the second year of bike usage data.

💵 Cost Dataset (cost.csv)

• Total Rows: 2

• Total Columns: 3

• Columns:

  • yr: Year indicator (0 for the first year, 1 for the second year).
  • price: The price per bike rental.
  • COGS: Cost of goods sold (COGS) per rental.

• Purpose:

  • Provides revenue-related information to calculate profit margins and cost trends over the two years.

🔎 Key Insights:

✅Bike Usage Trends:

• The bike0 and bike1 datasets allow for a year-over-year comparison of rental trends based on time of day, weather, season, and user type.

✅Revenue & Cost Analysis:

• The cost.csv file provides rental pricing and cost structure for profitability analysis.

✅Seasonal & Weather Impact:

• By merging bike0 and bike1, we can analyze seasonal fluctuations in bike usage and how weather conditions affect ridership.

✅User Behavior Analysis:

• The rider_type column allows us to distinguish between casual and registered riders, helping to identify customer preferences.