Sales Performance Analysis Project

Overview

This project analyzes a sales dataset containing 9,800 records of orders, spanning customer details, product categories, shipping information, and sales figures. The goal is to uncover trends, identify key drivers of revenue, and provide actionable insights for a hypothetical e-commerce or retail business. Built as a portfolio piece, it showcases data cleaning, exploratory data analysis (EDA), visualization, and advanced analytics skills.

• Dataset: 18 columns including Order Date, Sales, Category, Region, etc.
• Tools: Python (Pandas, Matplotlib, Seaborn), Jupyter Notebook
• Skills Demonstrated: Data wrangling, visualization, statistical analysis, storytelling

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Project Structure

1. Data Cleaning

   • Handled 11 missing Postal Code values
   • Converted Order Date and Ship Date to datetime
   • Engineered Shipping Time feature

2. Exploratory Data Analysis (EDA)

   • Visualized sales trends, category performance, regional distribution, and more
   • Key visuals:
     • Line chart: Monthly sales trends
     • Bar chart: Sales by category
     • Heatmap: Correlations (e.g., Sales vs. Shipping Time)
     • Bar chart: Sales by region
     • Box plot: Sales distribution by sub-category

3. Insights

   • Identified seasonal sales peaks (e.g., Q4 spikes)
   • Highlighted top-performing categories and regions
   • Analyzed shipping efficiency

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Key Visualizations

Below are sample outputs from the analysis (replace with your actual images):

• Monthly Sales Trend
  

• Sales by Category
  

• Correlation Heatmap
  

• Average Shipping Distribution
  

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Setup and Usage

Prerequisites

• Python 3.x

• Libraries: pandas, matplotlib, seaborn

  Install dependencies

  pip install -r requirements.txt

  Dataset The dataset is not included due to size/privacy. Replace cleaned_data.csv in the notebook with your own sales data in a similar format (see structure in notebook).

About This Project This project was developed to enhance my data analysis skills and serve as a portfolio piece. It demonstrates my ability to:

• Clean and preprocess messy data
• Visualize insights effectively
• Derive actionable business recommendations
• Feel free to explore the notebook, adapt the code, or suggest improvements!

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Notes for Customization

1. Images: After running your notebook, save the plots (e.g., plt.savefig('images/monthly_sales_trend.png')) and upload them to an images/ folder in your GitHub repo. Update the paths in the README.
2. Username: Replace yourusername with your GitHub handle and update links.
3. Findings: Adjust the "Key Findings" section based on your actual results (e.g., specific months, categories, or regions).
4. Requirements File: Create a requirements.txt with:

• pandas

• matplotlib

• seaborn

• jupyter

  Run pip freeze > requirements.txt if you use additional libraries.

5. Repo Name: Name your GitHub repository something catchy like sales-analysis-project or retail-insights.

How to Use

• Copy this Markdown into a file named README.md in your project directory.
• Push it to GitHub with your notebook and any images.
• Test the rendering by viewing it on GitHub—it should look polished and professional!

Let me know if you’d like to refine any section further or add more details!