Customer Sales Analysis

Project Overview

This project analyzes over a decade of customer sales transaction data to uncover sales trends, customer purchasing behavior, and product performance.

The dataset initially contained inconsistencies, missing values, and data quality issues which were cleaned and transformed before analysis.

The goal of this project is to demonstrate practical data cleaning, transformation, and exploratory analysis skills using Python.

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Dataset Description

The dataset contains sales transaction records including:

• Transaction dates
• Customer information
• Product purchased
• Sales locations
• Quantity and price
• Revenue generated
• Product return information

Both dirty and cleaned versions of the dataset are included for demonstration purposes.

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Libraries & Tools Used

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook
• Seaborn

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Project Workflow

The project followed these steps:

1. Data loading and inspection
2. Handling missing and inconsistent values
3. Outlier detection and correction
4. Feature engineering
5. Exploratory data analysis
6. Visualization of key trends

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Key Analysis Areas

• Sales performance trends
• Product performance comparison
• Customer purchasing patterns
• Relationship between returns and ratings
• Revenue distribution insights

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Project Structure

customer-sales-analysis/
│
├── data/
│   ├── cleaned_customer_sales_data.csv
│   └── customer_sales_data_dirty.csv
│
├── notebooks/
│   ├── sales_analysis.ipynb
│   └── requirements.txt
│
├── scripts/
├── visuals/
└── README.md

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How to Run the Project

1. Clone the repository
2. Install dependencies
3. Open the notebook in Jupyter Notebook
4. Run all cells to reproduce the analysis

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Author

Briggs Jobi Data Analyst