Supply Chain Data Analysis & Exploratory Data Analysis (EDA)

A comprehensive exploratory data analysis of supply chain data, focusing on data cleaning, visualization, and deriving actionable business insights to optimize supply chain operations.

📊 Project Overview

This project demonstrates end-to-end data analysis skills by examining supply chain performance metrics, identifying key trends, and providing data-driven recommendations for operational improvements.

🚀 Features

• Data Cleaning & Preprocessing: Handled missing values, data type conversions, and feature engineering
• Statistical Analysis: Descriptive statistics, correlation analysis, and trend identification
• Interactive Visualizations: Created using Plotly, Matplotlib, and Seaborn
• Business Insights: Delivery performance analysis, cost optimization opportunities, efficiency metrics

📁 Project Structure

supply-chain-eda/ │ ├── supply-chain-analysis.ipynb # Main Jupyter notebook with EDA ├── supply-chain-presentation.pptx # Project presentation ├── README.md # Project documentation └── data/ # Dataset directory

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🔗 Quick Links

• 📓 Kaggle Notebook - View the complete analysis
• 📊 Presentation Slides - Project overview and insights
• 👤 Portfolio - More of my projects

🛠️ Technical Stack

• Programming: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Plotly
• Tools: Jupyter Notebook, Canva for presentations
• Skills: Data Cleaning, EDA, Statistical Analysis, Data Visualization

📈 Key Insights

• Identified critical factors affecting supply chain efficiency
• Analyzed delivery performance across different regions
• Discovered cost optimization opportunities
• Provided actionable recommendations for process improvements

👩‍💻 Author

Riddhima Singhal

• GitHub: @riddhima-7321
• LinkedIn: Riddhima Singhal
• Kaggle: riddhimasinghal7321