Sales Data Analysis using Pandas 🔹 Objective
The goal of this project is to analyze sales data from a CSV file using Python and Pandas. The analysis helps us extract meaningful business insights such as total revenue, profit trends, and product-wise performance.
🔹 Tools & Technologies
Python 🐍
Pandas (Data analysis)
Matplotlib / Seaborn (Data visualization)
Jupyter Notebook / Google Colab (for interactive execution)
🔹 Steps Performed
Load the dataset
Used pandas.read_csv() to import the sales CSV file.
Explore the dataset
Checked the first few rows using .head().
Verified column names and data types.
Data Cleaning (if needed)
Handled missing values (like blank Sales entries).
Corrected any typos (e.g., “Mopbile” → “Mobile”).
Analysis Performed
✅ Total Revenue → df["Sales"].sum()
✅ Total Profit → df["Profit"].sum()
✅ Group by Month → Find monthly sales trends
✅ Group by Product → Find best-selling products
✅ Group by Category → Understand performance across categories
Visualization
Bar charts for Product-wise sales
Line chart for Month-wise sales
Pie chart for Product contribution
🔹 Outcomes / Insights
Found total sales revenue and profit.
Identified top-performing products (e.g., Laptop, Mobile, Tablet).
Observed sales seasonality across months.
Discovered how discounts impact overall sales.
🔹 Deliverables
Python Script (data_analysis.py)
Jupyter Notebook (data_analysis.ipynb) with code + charts
Charts (saved as PNGs or displayed in notebook)