Assignment: Analyzing Sales Performance Using Pandas

[Pankaj-Str]

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

1. Upload your completed Jupyter Notebook (.ipynb).
2. Include the dataset (dataset.csv) if required.
3. If applicable, include your summary report (report.pdf).
4. Submit via [Specify Submission Method].

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[ritvik-patil]

https://github.com/ritvik-patil/Sales-Data-Analysis-with-Pandas