https://github.com/dbshan25/roda

Retail Order Data Analysis
https://github.com/dbshan25/roda

kaggle python3 sqlworkbench streamlit

Last synced: 4 months ago
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Retail Order Data Analysis

• Host: GitHub
• URL: https://github.com/dbshan25/roda
• Owner: DbShan25
• Created: 2025-02-17T13:16:37.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-03-04T08:25:54.000Z (10 months ago)
• Last Synced: 2025-06-16T19:53:36.745Z (6 months ago)
• Topics: kaggle, python3, sqlworkbench, streamlit
• Language: Python
• Homepage:
• Size: 9.77 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# RODA

Retail Order Data Analysis

Using Kaggle Api, Python, SQL, Streamlit to analyze and optimize sales performance by identifying key trends, top-performing products, and growth opportunities using a dataset of sales transactions.

Data set is downloaded from: !kaggle datasets download ankitbansal06/retail-orders -f orders.csv

Extracted zip file using python code in Jupyter Notebook.

Used Pandas DataFrame to do the below.

  -Read the extracted file

  -Replace the missing values

  -Standardize the column names for clarity and SQL compatibility

  -Calculating new fields discount , sale price and profit

  -Converting datetime field from text

  -Splitted into 2 different DataFrames, One with Order details and other with location details.

  -Imported the oridinal dataframe and 2 splitted data frame to SQL database creating new Database and required tables.

  -Created a foreign key link to the splitted dataframes.

Queried Key Highlights, Requested Insights and Additional Insights using SQL queries.

Displayed the data in StreamLit UI with Data Table/Chart for the SQL queries.