FedEx Logistics Performance Analysis in Python | End-to-End Data Analysis Project
In this video, I have built a complete end-to-end data analysis project in Python on FedEx Logistics Performance using a real-world logistics dataset. This tutorial is designed for beginners and aspiring data analysts who want to understand how real business data is cleaned, analyzed, and visualized using Python.
The project uses Python libraries such as pandas, numpy, matplotlib, and seaborn to perform data cleaning, feature engineering, KPI calculation, and data visualization. Special attention is given to solving real-life data issues such as object data types in numeric columns, missing values, and inconsistent entries in columns like Weight (Kilograms) and Freight Cost (USD).
📂 Resources:
Dataset & Project's GitHub Link : https://github.com/rishav7575/FedEx-L...
📌 What you will learn in this video:
✔ How to load and explore a real logistics dataset in Python
✔ How to clean messy data and convert object columns to numeric values
✔ How to calculate key logistics KPIs like Lead Time, On-Time Delivery Rate, and Freight Cost per Kg
✔ How to perform Exploratory Data Analysis (EDA) using pandas and numpy
✔ How to create meaningful visualizations using matplotlib and seaborn
✔ How to analyze shipment performance by country, vendor, and shipment mode
✔ How to draw business insights from logistics data
This project is ideal for Python beginners, data analyst aspirants, students, and professionals building a portfolio. It demonstrates how Python can be used for real-world supply chain and logistics analytics, making it suitable for interview preparation and practical learning.
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