🚲 Bike-Sharing Data Dashboard

Features:

Upload any CSV file (e.g., from the UCI Bike Sharing Dataset).
Dropdown lets you choose Temperature, Humidity, or Windspeed.
Interactive scatter plot shows relationship with rentals.
Second plot shows Predicted vs Actual rentals (currently a dummy prediction, but you can replace with model output).

📊 Explanation of Result

1.Bike Rentals vs Temperature (Scatter Plot). Each blue dot represents the number of rentals at a given normalized temperature value. The trend shows a positive correlation: as temperature increases, bike rentals generally rise. Rentals peak around mid-range temperatures (0.6–0.7 normalized), then taper off slightly at very high temperatures.This suggests that moderate weather encourages more bike usage.

2.Predicted vs Actual Rentals (Regression Plot). Each red dot compares the model’s prediction (based on temperature) against the actual rental count.The diagonal trend indicates the model captures the general relationship, but variance shows it’s not perfect.The spread of points around the diagonal line highlights prediction errors — meaning temperature alone explains part of the demand, but other factors (humidity, windspeed, seasonality) also matter.