This project explores the relationship between weather conditions and bike-sharing trends in urban areas. Utilizing data from the Capital Bikeshare system in Washington D.C. for 2011-2012, our analysis integrates various statistical techniques to uncover patterns in bike-sharing rental trends and their correlation with different weather conditions.

The dataset includes hourly and daily aggregates of bike-sharing counts along with weather and seasonal information for 2011 and 2012. Key variables include temperature, humidity, wind speed, and weather situation.

• Data Preprocessing: Data normalization reversal, handling of missing values, and data type conversion.
• Exploratory Data Analysis (EDA): Statistical summaries and graphical illustrations to identify trends.
• Hypothesis Testing: ANOVA and T-tests to examine the impact of weather on bike-sharing trends.
• Regression Analysis: To quantify the relationship between weather conditions and bike rentals.

• Temperature, humidity, and wind speed significantly affect bike rental patterns.
• Clear and lightly cloudy days see higher user activity compared to rainy or snowy days.
• Seasonal variations (spring and summer) attract more users due to more favorable weather conditions.