Library Management Data Analysis

Project Overview

This project analyzes library transaction data using Python, Pandas, NumPy, and Matplotlib.

The objective is to identify borrowing patterns, popular books, active students, genre preferences, and library usage trends through data analysis and visualization.

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Objectives

• Analyze library borrowing records.
• Identify most borrowed books.
• Identify most active students.
• Analyze genre popularity.
• Study borrowing duration trends.
• Generate meaningful insights through visualizations.

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Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib

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Dataset Features

• Transaction ID
• Student ID
• Student Name
• Book ID
• Book Title
• Genre
• Issue Date
• Return Date
• Status

Total Records: 150

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Project Workflow

Data Loading

• Imported dataset using Pandas.

Data Cleaning

• Checked missing values.
• Checked duplicate records.

Feature Engineering

• Borrow Duration
• Issue Month

Exploratory Data Analysis

• Most Borrowed Books
• Most Active Students
• Popular Genres
• Borrowing Trends

Data Visualization

• Bar Charts
• Pie Charts
• Histograms

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Key Insights

• Most popular book categories identified.
• Most active library users identified.
• Monthly borrowing trends analyzed.
• Average borrowing duration calculated.

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Skills Demonstrated

• Data Cleaning
• Data Analysis
• Exploratory Data Analysis
• Data Visualization
• Feature Engineering
• Business Insight Generation

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Author

Gaurav Rawat

BCA Student | Aspiring Data Analyst

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Project Status

Completed as part of the #100DaysOfDataAnalytics Challenge.