Vendor Performance Analysis Project

Project Description

This project analyzes vendor sales and performance data to help solve business problems using a complete data analytics workflow. The project uses Python for data ingestion, cleaning, exploratory data analysis, and performance analysis; MySQL for storing structured datasets; and Tableau for building an interactive business dashboard.

The main goal of this project is to understand vendor performance, identify sales patterns, compare business metrics, and generate insights that can support better decision-making. By combining Python, MySQL, and Tableau, the project turns raw data into meaningful summaries, reports, and visual dashboards.

Tools and Technologies Used

• Python: Used for data cleaning, data processing, exploratory data analysis, and performance analysis.
• MySQL: Used to store and manage datasets in a structured database.
• Pandas / Jupyter Notebook: Used for analysis, transformation, and summary generation.
• Tableau: Used to create a dashboard for visualizing vendor performance and business insights.
• CSV Dataset: Used as the processed summary dataset for dashboarding and reporting.

Project Files

• ingestion_db.ipynb: Loads and stores data into the database.
• cleaning.ipynb: Cleans and prepares the data for analysis.
• EDA.ipynb: Performs exploratory data analysis to understand trends and patterns.
• get_vendor_summary.ipynb: Creates vendor-level summary data.
• performance_analysis.ipynb: Analyzes vendor performance and business metrics.
• vendor_sales_summary.csv: Final summary dataset used for reporting and dashboard creation.
• Vendor Performance Dashboard.twb: Tableau dashboard file.
• Vendor Performance Report.pdf: Final report containing key findings and analysis.

Business Problem

Businesses need to understand which vendors are performing well, which vendors need improvement, and how sales trends affect overall business performance. Without proper analysis, it is difficult to make informed decisions about vendor relationships, inventory, sales strategy, and revenue growth.

This project helps answer questions such as:

• Which vendors generate the highest sales?
• Which vendors have lower performance and may need attention?
• What trends can be seen in vendor sales data?
• How can business teams use data to improve decision-making?
• Which performance indicators are most important for vendor evaluation?

Project Workflow

1. Data Collection and Storage

   • Raw datasets are collected and stored using MySQL.
   • Python is used to connect with the database and manage the data.

2. Data Cleaning

   • Missing values, duplicate records, and inconsistent data are handled.
   • The dataset is prepared for accurate analysis.

3. Exploratory Data Analysis

   • Python is used to explore sales patterns, vendor trends, and key metrics.
   • The analysis helps identify important relationships in the data.

4. Vendor Performance Analysis

   • Vendor-level summaries are created.
   • Business metrics are calculated to compare vendor performance.

5. Dashboard Creation

   • Tableau is used to create a visual dashboard.
   • The dashboard helps business users understand insights quickly and interactively.

6. Reporting

   • Key findings are documented in a PDF report.
   • The report explains the analysis and business insights clearly.

Key Insights

This project focuses on generating insights related to:

• Vendor sales performance
• Sales trends and patterns
• Vendor comparison
• Business decision-making
• Performance improvement opportunities

Outcome

The final outcome of this project is a complete analytics solution that transforms raw vendor data into useful business insights. The Tableau dashboard and report make it easier to understand vendor performance and support decisions that can improve business operations.

Conclusion

This project demonstrates how Python, MySQL, and Tableau can be used together to analyze business data and solve real-world business problems. It shows the full process of storing data, cleaning it, analyzing it, and presenting insights through dashboards and reports.