(Group 3) SAL_BW_Project_3 - Data Preparation, SQL Insights, and (EDA) Data Visualization 📊

🎥 Project Demo

👉 Watch Video on Google Drive

Project Overview 🔍

This project focuses on cleaning and merging multiple retail CSV files, loading them into SQL, extracting business insights, and performing sales trend analysis using Pandas, Seaborn, and Matplotlib.

Project Workflow 🛠️

1. Data Preparation: Collected multiple retail sales data files from Kaggle. Cleaned, merged, and converted the data into 'cleaned_build_week_data.csv'.

2. SQL Insights: Loaded the dataset into MySQL Workbench and generated insights using SQL queries.

3. Exploratory Data Analysis (EDA) & Visualization: Analyzed data trends and visualized them using Python libraries.

Dataset 📁:

• 'cleaned_build_week_data.csv': Contains retail sales data with the following columns:
  • Order_Date
  • Customer_ID
  • Country
  • UnitPrice
  • Quantity
  • TotalPrice
  • Order_ID

Files in this Repository 📂:

Process	Filename	Description
1. Data Collection & Preparation	BW_Data_Preparation.ipynb	Data collection, cleaning, and preparation.
2. Exploratory Data Analysis (EDA)	BW_EDA_Visualization.ipynb	Visualizations and exploratory data analysis (EDA).
3. SQL Queries	BW_SQL_Queries.sql	SQL queries in MySQL Workbench.
4. Cleaned Data	cleaned_build_week_data.csv	Cleaned data in CSV format.
5. Insights & Presentation	BW_Insights	Presentation images summarizing insights.

How to Run the Project 🚀

1. Data Preparation

Run BW_Data_Preparation.ipynb to clean the data and save it as cleaned_build_week_data.csv.

2. Load Data into SQL

Create a database and table using BW_SQL_Queries.sql. Import cleaned_build_week_data.csv into the database.

3. SQL Analysis Execute queries in BW_SQL_Queries.sql to generate insights:

1. Top 10 customers by total spending.
2. Total sales by country.
3. Find the month with the highest sales.
4. Average order value per customer.

4. Exploratory Data Analysis (EDA)

Run BW_EDA_Visualization.ipynb to:

1. Use Pandas for group-by aggregations and filtering.
2. Visualizations:
   • Time series line plot: Monthly sales trend.
   • Bar chart: Top countries by revenue.
   • Pie chart: Contribution of top 5 customers.
   • Heatmap: Sales by month and country.

Insights from Analysis 📊

1. Sales Trends 📈:

We observed a significant increase in sales starting from December 2009, peaking at approximately 70,000. However, the growth was followed by a gradual decline in the subsequent months and years. To boost future sales, we suggest:

• Price reductions,
• Offering more discounts, or
• Promoting high-rated products.

2. Top Contributing Countries 🌍:

• United States was the top contributor to sales every month.
• United Kingdom saw a spike in December, emerging as the highest contributor for that month.
• France showed the lowest sales revenue.

3. Top Customers 🏆:

• Customer ID 16393 contributed 27% of the total orders.
• Customer ID 15542 had the smallest contribution at 15%. This insight could help target top customers with personalized offers or loyalty programs.

4. Monthly and Country-wise Sales Distribution 🌐:

• United States led in sales every month, while United Kingdom dominated in December.
• Identifying seasonal trends and focusing marketing efforts on high-performing countries during peak months could maximize revenue.

Project Submission 📥:

• GitHub Repository: SAL_BW_Project_3

Contributors 👥:

• Meena
• SahilKadolkar