CodeAlpha Data Analytics Internship

This repository contains the projects I completed during my Data Analytics Internship at CodeAlpha.

The internship focuses on applying Python and data analytics concepts to different real-world style projects.

Internship Tasks

Task 1 — Web Scraping

Project: E-Commerce Book Data Web Scraping using Python

For this task, I scraped book information from Books to Scrape using Python.

The dataset contains 1,000 books and includes information such as:

• Book title
• Product URL
• Price
• Rating
• Availability
• Category

Tools used:

• Python
• Requests
• BeautifulSoup
• Pandas

Files:

• task1_web_scraping/data/books_dataset.csv
• task1_web_scraping/notebook/web_scraping.ipynb
• task1_web_scraping/src/scraper.py

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Task 2 — Exploratory Data Analysis

Project: E-Commerce Book Dataset EDA

For this task, I explored the book dataset collected during Task 1.

The analysis included:

• Dataset overview
• Data types
• Missing value checking
• Duplicate checking
• Price analysis
• Rating analysis
• Category analysis
• Statistical summaries
• Basic visualizations

Tools used:

• Python
• Pandas
• Matplotlib

File:

• task2_eda/eda_analysis.ipynb

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Task 3 — Data Visualization

Project: E-Commerce Book Dataset Visualization

For this task, I created different visualizations to understand patterns in book prices, ratings, and categories.

The visualizations included:

• Rating distribution
• Book price distribution
• Top book categories
• Average price by rating
• Average price by category
• Book price vs rating
• Price distribution by rating

Tools used:

• Python
• Pandas
• Matplotlib

File:

• task3_data_visualization/data_visualization.ipynb

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Task 4 — Sentiment Analysis

Project: Sentiment Analysis of Book Reviews

For this task, I analyzed a dataset containing 1,209 book reviews and identified three sentiment categories:

• Positive
• Negative
• Neutral

The analysis included:

• Checking the dataset for missing values and duplicates
• Cleaning review text
• Converting sentiment labels into readable names
• Analyzing common words
• Comparing words across different sentiments
• Analyzing review length
• Visualizing sentiment distribution
• Calculating sentiment percentages

Tools used:

• Python
• Pandas
• Matplotlib
• Regular Expressions

File:

• task4_sentiment_analysis/sentiment_analysis.ipynb

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Repository Structure

CodeAlpha_WebScraping/
│
├── task1_web_scraping/
│   ├── data/
│   │   └── books_dataset.csv
│   ├── notebook/
│   │   └── web_scraping.ipynb
│   └── src/
│       └── scraper.py
│
├── task2_eda/
│   └── eda_analysis.ipynb
│
├── task3_data_visualization/
│   └── data_visualization.ipynb
│
├── task4_sentiment_analysis/
│   ├── g_reviews.csv
│   └── sentiment_analysis.ipynb
│
├── requirements.txt
└── README.md