Analyzing data from bike sharing services,identifying usage patterns depending on time features and weather conditions. Furthermore, applying concepts such as visual analysis, hypothesis testing, and time series analysis to the available data. Finally,working with time series data and apply some of the main data analysis techniques to business scenarios.
A) Understanding the Data
B) Data Preprocessing
- Preprocessing Temporal and Weather Features
- Registered versus Casual Use Analysis
- Analyzing Seasonal Impact on Rides
C) Hypothesis Tests
- Estimating Average Registered Rides
- Hypothesis Testing on Registered Rides
D) Analysis of Weather-Related Features
- Evaluating the Difference between the Pearson and Spearman Correlations.
- Correlation Matrix Plot
E) Time Series Analysis
- Time Series Decomposition in Trend, Seasonality, and Residual Components.
F) ARIMA Models
- ACF and PACF Plots for Registered Rides
- Investigating the Impact of Weather Conditions on Rides
G) Summary :
we studied a business problem related to bike sharing services. We started by presenting some of the main visual techniques
in data analysis,such as bar plots, scatter plots, and time series visualizations. We also analyzed customer behaviors based
on different time frames and weatherconditions. We got introduced to hypothesis testing and some of its main applications.
Finally, we presented the basics of time series analysis,and how to identify the best time series models when dealing with
nonstationary time series.
- Bike Sharing Analysis
Done✅ - Absenteeism at Work
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