---

Information Integration Project (IIA)

This repository contains the code and resources for the Information Integration Project (IIA), a project aimed at integrating and processing information from multiple sources to provide meaningful insights. The project is designed to handle data extraction, transformation, and loading (ETL) processes, as well as data analysis and visualization.

---

Table of Contents

1. Project Overview
2. Repository Structure
3. Detailed File Analysis
4. Setup and Installation
5. Usage
6. Contributing
7. License

---

Project Overview

The Information Integration Project (IIA) is a data integration and analysis tool that processes data from various sources, performs transformations, and generates insights. The project is built using Python and leverages libraries such as Pandas, NumPy, and Matplotlib for data processing and visualization. It also includes scripts for automating data extraction and loading processes.

Key features of the project:

• Data Extraction: Fetch data from multiple sources (e.g., CSV files, APIs).
• Data Transformation: Clean, normalize, and transform raw data into a usable format.
• Data Loading: Store processed data in a structured format (e.g., databases, CSV files).
• Data Analysis: Perform statistical analysis and generate insights.
• Visualization: Create visual representations of the data using charts and graphs.

---

Detailed File Analysis

1. data/ Directory

• raw_data/: Contains raw data files fetched from external sources. These files are in formats such as CSV or JSON and serve as the input for the data processing pipeline.
• processed_data/: Stores cleaned and transformed data files. These files are generated after running the data cleaning and transformation scripts.

2. scripts/ Directory

• data_extraction.py: This script is responsible for fetching data from external sources (e.g., APIs, databases) and saving it in the raw_data/ directory.
• data_cleaning.py: This script cleans and transforms the raw data. It handles tasks such as removing duplicates, handling missing values, and normalizing data formats.
• data_analysis.py: This script performs statistical analysis on the processed data. It calculates metrics such as mean, median, and standard deviation, and generates summary reports.
• visualization.py: This script creates visualizations (e.g., bar charts, line graphs) using libraries like Matplotlib and Seaborn. The visualizations are saved as image files or displayed in the console.

3. docs/ Directory

• project_report.pdf: A detailed report explaining the project's objectives, methodology, and results. It includes insights derived from the data analysis and visualizations.

4. requirements.txt

• This file lists all the Python libraries required to run the project. It includes dependencies such as Pandas, NumPy, Matplotlib, and Requests.

---

Setup and Installation

To set up the project locally, follow these steps:

1. Clone the Repository: bash git clone https://github.com/aditya22041/InformationIntegrationProject-IIA-.git cd InformationIntegrationProject-IIA-

2. Install Dependencies: bash pip install -r requirements.txt

3. Run the Scripts:

   • Extract data: bash python scripts/data_extraction.py

   • Clean and transform data: bash python scripts/data_cleaning.py

   • Analyze data: bash python scripts/data_analysis.py

   • Generate visualizations: bash python scripts/visualization.py

---

Usage

1. Data Extraction:

   • Modify the data_extraction.py script to specify the data sources (e.g., API endpoints, file paths).
   • Run the script to fetch and save raw data.

2. Data Cleaning:

   • Use the data_cleaning.py script to clean and transform the raw data. Customize the cleaning logic as needed.

3. Data Analysis:

   • Run the data_analysis.py script to perform statistical analysis on the processed data.

4. Visualization:

   • Use the visualization.py script to generate charts and graphs. Modify the script to customize the visualizations.

---

Contributors

1. Wasif Ali A-WASIF
2. Aditya Yadav @aditya22041
3. Aastha Singh @aastha1708

---

License

This project is licensed under the MIT License. See the LICENSE file for details.

---

For any questions or issues, please contact the repository owner: WASIF.

---