Sales Data Analysis Using Python

Project Overview

This project analyzes sales and product data using Python and the Pandas library to demonstrate practical data handling and exploratory analysis skills.
The analysis explores datasets containing sales transactions and product information, highlighting techniques such as data loading, inspection, cleaning, and summarization.

The goal is to extract insights from raw data and showcase essential Python data analysis capabilities.

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Table of Contents

1. Project Overview
2. Files in Repository
3. Journey of the Data
   • Datasets
   • Data Cleaning and Exploration
4. Script Functionality
5. Key Learnings
6. Recommendations
7. Tools and Technologies
8. Contact
9. Acknowledgement

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Files in Repository

File Name	Description
script.ipynb	Jupyter Notebook containing the analysis code
sales.xlsx	Dataset containing sales records
products.xlsx	Dataset containing product information
Project_Documentation - Sales Data Analysis.docx	Full project documentation

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Journey of the Data

Datasets

• sales.xlsx – Contains individual sales transactions, including product IDs, quantities, and totals.
• products.xlsx – Contains product information such as product ID, name, and category.

Data Cleaning and Exploration

• Verified data consistency across both datasets.
• Checked for null values, duplicates, and data type mismatches.
• Conducted exploratory analysis to understand data structure and relationships.

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Script Functionality

Import Libraries

• pandas – For data analysis and manipulation.
• os – For managing file paths and directories.

Load Datasets

import pandas as pd
import os

sales_data = pd.read_excel('sales.xlsx')
products_data = pd.read_excel('products.xlsx')