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+# Austin Bikeshare data analysis
+
+As a part of Stream Processing and Stream Analytics project.
+
+### Project contributers
+- [Mr Philosopher](https://github.com/7ze)
+- [Usama Tahir](https://github.com/Usama00004)
+
+> **Project Disclaimer**
+>Please note that this project is currently under active development. The
+>information provided in this readme and associated documentation is subject to
+>change without prior notice. While we strive to provide accurate and
+>up-to-date details, certain aspects of the project may be modified, added, or
+>removed as development progresses.
+
+### Analysis Summary
+---
+
+This project aims to conduct comprehensive data analysis on Austin Bikeshare
+trips using real-time streaming data. Through advanced data transformations and
+insights generation, this analysis seeks to uncover valuable patterns and
+trends that can inform decision-making and operational improvements within the
+bikeshare system.
+
+#### Analysis Ideas
+
+1. *Subscriber Type Analysis:*
+   - Explore the distribution of subscriber types (e.g., Pay-as-you-ride,
+     Local365).
+   - Identify trends in usage patterns based on different subscriber
+     categories.
+   
+2. *Popular Stations and Routes:*
+   - Determine the most frequently used start and end stations.
+   - Uncover common routes taken by bikeshare users for optimization.
+
+3. *Duration Analysis:*
+   - Analyze trip durations to understand average length and identify
+     exceptional cases.
+   - Explore correlations between trip duration and other factors.
+
+4. *Bike Usage Analysis:*
+   - Investigate bike usage patterns to identify popular bikes and maintenance
+     trends.
+   - Examine the relationship between bike type and usage frequency.
+
+5. *Time-based Patterns:*
+   - Study usage patterns based on time of day, weekdays vs. weekends, and
+     months.
+   - Identify peak usage hours and seasonal trends.
+
+6. *Subscriber Behavior:*
+   - Analyze how subscriber types influence trip behaviors such as trip
+     duration and station choices.
+   - Determine if different subscriber types have distinct usage patterns.
+
+7. *Station Popularity Over Time:*
+   - Track popularity changes of specific stations over time.
+   - Investigate factors influencing shifts in station popularity.
+
+8. *Subscriber Retention Analysis:*
+   - Explore whether different subscriber types exhibit varying retention
+     rates.
+   - Understand which types of users tend to continue using the service over
+     time.
+
+9. *Bike Availability:*
+   - Investigate bike availability at stations and its impact on user behavior.
+   - Analyze how bike availability affects station popularity.
+
+10. *Subscriber Demographics (If Available):*
+    - Correlate subscriber types with demographic information if available.
+    - Understand if certain types of users are more likely to use the service.
+
+11. *Ride Patterns by Day of the Week:*
+    - Observe how usage patterns change across different days of the week.
+    - Determine if certain days have higher usage due to specific events or
+      reasons.
+
+#### Final Dataset Schema idea
+
+```plaintext 
+Table: analysis
+
+Fields:
+- trip_id: STRING (Primary Key)
+- subscriber_type: STRING
+- bike_id: STRING
+- bike_type: STRING
+- start_time: TIMESTAMP
+- start_station_id: INTEGER
+- start_station_name: STRING
+- end_station_id: INTEGER
+- end_station_name: STRING
+- duration_minutes: INTEGER
+- day_of_week: INTEGER
+- hour_of_day: INTEGER
+- month: INTEGER
+- route: STRING
+- is_weekend: BOOLEAN
+- is_peak_hour: BOOLEAN
+- subscriber_demographics: STRING
+- subscriber_retention: BOOLEAN
+- bike_availability: INTEGER
+- station_popularity: INTEGER
+- ...
+
+Indexes:
+- trip_id (Primary Key) ```