This project showcases the analysis and visualization of sales data from a company. It demonstrates data cleaning using SQL and the creation of an interactive dashboard using PostgreSQL and visualization tools.

The goal of this project is to transform raw sales data into actionable insights. It starts with importing the data into a database using a SQL script, followed by cleaning and managing it in PostgreSQL, and ends with building a dynamic dashboard.

1. Data Import:

   • Imported raw sales data into the SQL database using a structured .sql file.

2. Data Cleaning:

   • Removed unnecessary strings, duplicates, null values, and unwanted records (e.g., entries with negative salaries) using SQL queries.

3. Database Management:

   • The cleaned data was stored in PostgreSQL for structured management.

4. Interactive Dashboard:

   • Connected the PostgreSQL database to a visualization tool for building an interactive dashboard.

PostgreSQL: For data import, cleaning, and transformations.

Dashboard Tool: Power BI for creating visualizations.

• db_dump.sql : SQL file for importing raw data into the database and cleaning it. (Note:It's syntax is according to MySQL Environment,For other environment either convert it or do table creation manually.
• salesinsight.pbix : Power BI dashboard file

The project involves five tables that represent different facets of the company’s sales data:

1. Customers Table

   • customer_code,customer_name and customer_type

2. date

   • date,cy_date,year,month_name and date_yy_mmm
   • Removed unwanted strings from columns like '/r' from date_yy_mmm
   • SQL Query :update date set date_yy_mmm = replace(date_yy_mmm,'\r','') where date_yy_mmm like '%\r%';

3. markets

   • markets_code,markets_name and zone
   • Removed null values
   • SQL Query : Delete from markets m where m.zone='';

4. products

   • product_code and product_type
   • Removed unwanted strings from columns like '/r' from product_type
   • SQL Syntax :update products set product_type = Replace(product_type,'\r','') where product_type like '%\r%';

5. transactions

   • product_code,customer_code,market_code,order_date,sales_qty,sales_amount and currency
   • Removed records with negative sales_amount
   • SQL Syntax : Delete from transactions where sales_amount <=0;
   • Removed duplicates as here currency Contains INR/USD and INR/r or USD/r
   • SQL Syntax : Delete from transactions where currency NOT like '%\r%';

The interactive dashboard includes:

1. Top Customers: Highlight best customers (in terms of revenue and quantity ordered) through bar chart.
2. Sales Trends: Time-series visualization to track sales performance.
3. Regional Analysis: Showing revenue generation across different regions.

Dashboard Images:

• Name : Ankit Rai
• LinkedIn: Ankit Rai