Description Data Exploration
Load the dataset and display the first five rows.
Check for missing values and handle them appropriately.
Get summary statistics of numerical columns.
Find the number of unique stores and unique products in the dataset.
Sales Analysis
Calculate the total number of transactions recorded in the dataset.
Identify the store location with the highest total sales revenue.
List the top 5 cities in terms of total sales.
Determine the most sold product category.
Find the store with the highest average transaction value.
Calculate the total revenue generated by each store.
Identify stores with the lowest total revenue.
Advanced Insights
Compute the revenue generated per employee for each store.
Find the city with the highest number of transactions.
Analyze how revenue trends over time (e.g., monthly or yearly).
Identify the best-selling product based on quantity sold.
Determine which store has the highest number of transactions.
Compute the average revenue per transaction across all stores.
Identify the month with the highest sales.
Find out which day of the week has the highest sales.
Compare sales between weekdays and weekends.
Calculate total revenue based on different payment methods.
Analyze the correlation between discounts and revenue.
📊 Visualization (Optional)
Use bar charts, line graphs, and heatmaps to present key insights.
📌 Submission Guidelines
Upload sales_analysis.ipynb (Jupyter Notebook) with well-commented code.
Include visualizations (if any) in the images/ folder.
Write a short report (200–300 words) summarizing key findings.
🚀 Tools & Libraries
Python 3.x Pandas Matplotlib & Seaborn Jupyter Notebook
📩 How to Submit
Upload your completed Jupyter Notebook (.ipynb).
Include the dataset (dataset.csv) if required.
If applicable, include your summary report (report.pdf).
Submit via [Specify Submission Method] .
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