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.