Data Analytics Project (python)

Dataset: rideshare_trips_2021_2023.csv
Solution File: solution.ipynb
Tool Used: Jupyter Notebook (Python & Pandas)

---

Question / Task Description

You're given a CSV file containing ride-sharing trip data from 2021 to 2023.
Each row contains the following columns:

• Driver ID
• Trip Date
• Trip Fare
• City
• Vehicle Type

Objectives

1. For each row, determine how many trips that driver had already completed before that trip.
2. Based on that, assign a Driver Tier:
   • New Driver: First trip
   • Regular Driver: 1–49 previous trips
   • Veteran Driver: 50+ previous trips
3. Calculate the total earnings contributed by each Driver Tier (rounded to 2 decimal places).
4. Create two visualizations:
   • A multi-line chart showing monthly total fares by City (Jan 2021 – Dec 2023).
   • A bar chart comparing total fares by Vehicle Type, using 2023 data only.

---

Repository Contents

File	Description
rideshare_trips_2021_2023.csv	Raw dataset containing trip records
solution.ipynb	Jupyter notebook containing full solution and analysis
line-chart.png	Monthly total fares by City (Line Chart)
bar-chart.png	2023 total fares by Vehicle Type (Bar Chart)
README.md	Project documentation

---

Total Earnings by Driver Tier

Driver Tier	Definition	Total Earnings ($)
New Driver	First trip	[2282890.27]
Regular Driver	1–49 previous trips	[35798376.70]

---

Visualizations

1 ️Monthly Total Fares by City (2021–2023)

This line chart shows how total fares evolved across cities over time.

2 Total Fares by Vehicle Type (2023)

This bar chart compares fare totals for each vehicle type in 2023.

---

Solution Overview (Python Steps)

1. Load Dataset

   import pandas as pd
   df = pd.read_csv('rideshare_trips_2021_2023.csv')

2. Sort and Group

   df = df.sort_values(['Driver ID', 'Trip Date']) df['Prev Trips'] = df.groupby('Driver ID').cumcount()

3. Assign Driver Tier

   def tier(x): if x == 0: return 'New Driver' elif x <= 49: # or x < 50: return 'Regular Driver' else: return 'Veteran Driver' df['Driver Tier'] = df['Prev Trips']

4. Calculate Total Earnings per Tier

   earnings = df.groupby('Driver Tier')['Trip Fare'].sum().round(2) print(earnings)

5. Create Charts

   Line chart: Monthly revenue trend by City

   Bar chart: Total fare by Vehicle Type (2023 only)

Insights

Veteran Drivers contributed the highest earnings overall.

Regular Drivers showed consistent engagement with stable earnings.

New Drivers made fewer trips but represent growth potential.

Some cities experienced seasonal revenue changes between 2022 and 2023.

Author

Project by [Abdulkarim Abdulrazak]