🐍 Sales Data Analysis — Python & Google Colab

A complete end-to-end sales data analysis project using Python — covering data cleaning, exploratory data analysis (EDA), and multi-dimensional visualizations across sales reps, regions, product categories, customer types, and payment methods.

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📌 Project Overview

This project analyzes a sales dataset using Python in Google Colab, following a structured workflow from raw data ingestion to actionable business insights — producing 6 charts covering every key business dimension.

Detail	Info
Tool	Python (Google Colab)
Libraries	Pandas · Matplotlib · Seaborn
Dataset	Sales.csv — multi-rep, multi-region sales data
Dimensions	Month · Region · Sales Rep · Product · Customer Type · Payment Method

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📊 Analysis & Visualizations

1. Total Sales Amount Over Months

Monthly sales trend showing growth from ~$250K in January to a peak of ~$450K in Oct–Nov, then a slight dip in December. Clear seasonal upward trend through Q3–Q4.

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2. Total Sales by Region

Regional distribution is nearly equal across all 4 regions — North leads at 27.3%, followed by East (25.1%), West (24.6%), and South (23.0%). No dominant region — balanced market penetration.

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3. Total Sales by Sales Representative

David is the top-performing sales rep with ~$1.1M+ in total sales. Bob follows closely. Charlie is the lowest performer. All reps fall in the $850K–$1.1M range — a fairly competitive team.

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4. Total Quantity Sold per Sales Rep

David also leads in quantity sold (~6,000 units), followed by Alice and Bob (~5,000). Charlie has the lowest quantity at ~4,200 units — consistent with his lower revenue performance.

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5. Total Quantity Sold by Product Category

Clothing leads product categories with ~7,000 units, followed closely by Furniture (~6,700). Food has the lowest quantity at ~5,500 units. All categories show strong demand.

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6. Total Sales by Customer Type

Nearly perfect split between Returning (50.1%) and New customers (49.9%) — indicating strong customer retention AND healthy new customer acquisition simultaneously.

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7. Total Sales by Payment Method

Credit Card leads at $1.76M, followed by Bank Transfer ($1.72M), with Cash slightly lower (~$1.54M). Digital payment methods dominate.

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💡 Key Insights

• 📈 Sales grow steadily from Jan to Nov — strong Q3/Q4 performance with a Dec dip
• 🏆 David is the top rep in both revenue (~$1.1M) and quantity (~6,000 units)
• 🌍 All 4 regions are nearly equal — no single dominant market
• 👕 Clothing is the best-selling category by volume at ~7,000 units
• 🔄 50/50 split between new and returning customers — excellent retention rate
• 💳 Credit Card is the preferred payment method, but all 3 methods are well-distributed

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🛠️ Tools & Libraries

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Library	Usage
Pandas	Data loading, cleaning, grouping, aggregation
Matplotlib	Line chart, Donut charts, layout control
Seaborn	Bar charts with viridis/magma color palettes

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📁 Files in This Repo

File	Description
Sales_Data.ipynb	Full Jupyter Notebook with all code and outputs
Sales.csv	Raw sales dataset
1.png – 6.png	Output chart screenshots

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🚀 How to Run

1. Open Google Colab
2. Upload Sales_Data.ipynb and Sales.csv
3. Run all cells (Runtime → Run all)
4. All 7 charts will render inline

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👤 Author

Belal Farrag — Data Analyst