{"id":25560063,"url":"https://github.com/shellynagar27/good-cabs-data-analysis-project","last_synced_at":"2026-01-25T20:49:24.321Z","repository":{"id":268435746,"uuid":"904347255","full_name":"shellynagar27/Good-Cabs-Data-Analysis-Project","owner":"shellynagar27","description":"This project is part of CodeBasics Challenge #13, where the goal was to provide actionable insights to the Chief of Operations at Goodcabs, a cab service provider in tier-2 cities of India. 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The goal is to understand key business metrics, identify growth opportunities, and optimize services based on data analysis.\n\n---\n## 😵 Business Problem\nGoodCabs is facing challenges in key areas like **revenue growth**, **repeat passenger rate**, and **operational efficiency**. The business requires actionable insights to improve performance, enhance customer experience, and adapt to emerging trends.\n\n---\n## 🔑 Key Objectives\n- Identify patterns in revenue, trips, passengers, and ratings.\n- Analyze city-specific performance to find growth opportunities.\n- Establish correlations between customer behavior, trip distance, and fare.\n- Enhance the decision-making process with interactive dashboards.\n\n---\n## 🪜 Data Analysis Methodology\n\n1. **Business Problem Understanding**:  \n   - Gathered insights from stakeholders to understand key objectives and requirements.\n\n2. **Exploratory Data Analysis (EDA)**:  \n   - Conducted a thorough EDA using **Power Query** and **SQL** to explore the data, identify patterns, and summarize key findings.\n\n3. **Ad-Hoc Analysis**:  \n   - Performed ad-hoc data analysis using **SQL** queries to answer specific business questions.\n   - Visualized results using **Excel** for easy interpretation.\n\n4. **Data Cleaning \u0026 Transformation**:  \n   - Cleaned and transformed raw data to ensure **data integrity** and **consistency**.\n   - Applied **Star** and **Snowflake schemas** to establish relationships between different datasets.\n\n5. **Dashboard Creation**:  \n   - Created an interactive **Power BI** dashboard to visualize key performance indicators (KPIs) such as revenue, trip types, passenger ratings, etc.\n\n6. **Results Presentation**:  \n   - Compiled key findings and insights into a [**PowerPoint presentation**](https://github.com/shellynagar27/Good-Cabs-Data-Analysis-Project/blob/main/Final%20PPT.pdf) for stakeholders.\n---\n## 📃 Ad-Hoc Requests\n  - [Results](https://github.com/shellynagar27/Good-Cabs-Data-Analysis-Project/tree/main/SQL%20Solution)\n---\n## 🔍 Data Set Overview\n\nThis project utilizes data from two primary databases: `trips_db` and `targets_db`. These databases contain detailed and aggregated data on Goodcabs' operations, including trip information, passenger behavior, city-specific performance, and monthly targets for growth. \n\n### trips_db\nThis database stores detailed and aggregated data on trips, passenger types, and repeat trip behavior across Goodcabs’ operations in tier-2 cities. The data is organized by city, month, and day type (weekday or weekend) for comprehensive analysis of travel patterns, demographics, and repeat usage trends.\n\n1. **dim_city**\n   - Purpose: Provides city-specific details for location-based analysis of trips and passenger behavior.\n   - Key Columns: \n     - `city_id`: Unique city identifier (e.g., RJ01 for Jaipur).\n     - `city_name`: City name (e.g., Jaipur, Lucknow).\n\n2. **dim_date**\n   - Purpose: Provides date-specific details for time-based grouping and analysis of trip patterns across days, months, and weekends vs weekdays.\n   - Key Columns: \n     - `date`: The specific date of the entry (YYYY-MM-DD).\n     - `start_of_month`: The first day of the respective month.\n     - `month_name`: Name of the month.\n     - `day_type`: Weekday or weekend indicator.\n\n3. **fact_passenger_summary (Aggregated Data)**\n   - Purpose: Provides aggregated passenger counts (new and repeat) for each city by month.\n   - Key Columns:\n     - `month`: Start date of the month.\n     - `city_id`: City identifier.\n     - `total_passengers`: Total count of all passengers.\n     - `new_passengers`: Count of new passengers.\n     - `repeat_passengers`: Count of repeat passengers.\n\n4. **dim_repeat_trip_distribution (Aggregated Data)**\n   - Purpose: Provides a breakdown of repeat trip behavior, categorized by trip frequency (up to 10 trips per month) for each city.\n   - Key Columns:\n     - `month`: Start date of the month.\n     - `city_id`: City identifier.\n     - `trip_count`: Number of trips taken by repeat passengers.\n     - `repeat_passenger_count`: Count of repeat passengers for each trip frequency.\n\n5. **fact_trips**\n   - Purpose: Provides detailed trip-level data, including distance, fare, and ratings for each trip.\n   - Key Columns:\n     - `trip_id`: Unique trip identifier.\n     - `date`: Date of the trip.\n     - `city_id`: City identifier.\n     - `passenger_type`: New or repeat passenger.\n     - `distance_travelled (km)`: Distance of the trip in kilometers.\n     - `fare_amount`: Fare amount paid.\n     - `passenger_rating`: Passenger rating (1-10).\n     - `driver_rating`: Driver rating (1-10).\n\n### targets_db\nThis database contains monthly targets for each city, including trip counts, new passenger acquisition, and average passenger ratings. It helps in evaluating Goodcabs' performance against the company's established goals.\n\n1. **city_target_passenger_rating**\n   - Purpose: Stores target average passenger ratings for each city.\n   - Key Columns:\n     - `city_id`: Unique city identifier.\n     - `target_avg_passenger_rating`: Target average passenger rating.\n\n2. **monthly_target_new_passengers**\n   - Purpose: Stores the target number of new passengers to acquire for each city in a given month.\n   - Key Columns:\n     - `month`: Start date of the target month (YYYY-MM-DD).\n     - `city_id`: City identifier.\n     - `target_new_passengers`: Target new passenger count.\n\n3. **monthly_target_trips**\n   - Purpose: Stores the target number of total trips to achieve for each city and month.\n   - Key Columns:\n     - `month`: Start date of the target month (YYYY-MM-DD).\n     - `city_id`: City identifier.\n     - `total_target_trips`: Target total trips count.\n---\n## 🗄️Data Model\n![Screenshot 2024-12-15 154118](https://github.com/user-attachments/assets/0a3599a8-2bdf-4fa7-a422-33a3c3f634d7)\n\n---\n\n## 🚀 **Recommendations**  \n1. **Optimize Fare Strategy:**  \n   - Introduce dynamic pricing models to attract passengers during off-peak hours.  \n   - Maintain competitive pricing to retain repeat customers.  \n\n2. **Boost Passenger Satisfaction:**  \n   - Address key pain points: **driver behavior**, and **repeat passenger rate**.  \n   - Introduce driver training programs and incentivize quality service.  \n\n3. **Targeted Marketing:**  \n   - Promote services during **tourism seasons** and **local events** to boost demand.  \n   - Run campaigns to attract **new passengers** while offering loyalty programs to retain repeat users.  \n\n4. **Partnership Opportunities:**  \n   - Collaborate with local businesses (hotels, malls, events) to drive demand in high-footfall areas.  \n\n5. **Data Collection \u0026 Analysis:**  \n   - Implement feedback mechanisms to gather more insights on passenger preferences.  \n   - Analyze socio-economic patterns to tailor operations city-wise.  \n\n---\n\n## 🎯 **Outcome**  \nThe project provided actionable insights to optimize Goodcabs' operations, improve customer satisfaction, and increase repeat passenger rates. The recommendations aim to drive **sustainable growth** and enhance **operational efficiency** across cities through data-driven decisions.\n\n---\n## 🛠️ Tools \u0026 Technologies\n- **SQL**: For data extraction and ad-hoc analysis.\n- **Power Query**: For data cleaning and transformation.\n- **Excel**: For visualizing analysis results.\n- **Power BI**: For interactive dashboard creation.\n- **Canva**: For presenting insights to stakeholders.\n- [**Flaticon**](https://www.flaticon.com/): For icons\n- [**Adobe Color**](https://color.adobe.com/create/color-wheel) \u0026 [**Coolors**](https://coolors.co/): For generating color palette\n- [**Adobe Stock**](https://stock.adobe.com/in/) \u0026 [**shutterctock**](https://www.shutterstock.com/): For images\n---\n\n### **👨‍💻 Skills Demonstrated:**  \n- Data Analysis  \n- Visualization  \n- Critical Thinking  \n- Business Strategy  \n- Communication  \n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshellynagar27%2Fgood-cabs-data-analysis-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshellynagar27%2Fgood-cabs-data-analysis-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshellynagar27%2Fgood-cabs-data-analysis-project/lists"}