Butelka770

🚀 Analytics_Portfolio_Dual_Projects - Your Guide to Data Insights and Analysis

📝 Description

Welcome to the Analytics Portfolio showcasing two comprehensive data science projects. These projects include Employee Attrition Analysis and Customer Sentiment Analysis. You will find detailed exploration of data, natural language processing, machine learning, and visualizations made with Tableau. This portfolio aims to help you grasp complex data insights easily.

📦 Key Features

• Employee Attrition Analysis: Understand what drives employees to leave their jobs.
• Customer Sentiment Analysis: Analyze customer opinions and feedback on various products.
• Data Visualizations: Clear and engaging Tableau dashboards for sharing insights.
• End-to-End Projects: Each project covers data exploration, preprocessing, modeling, and evaluation.

🖥️ System Requirements

To run this application smoothly, please ensure you have the following installed on your machine:

• Windows 10 or later, macOS, or a modern Linux distribution.
• Python 3.6 or newer.
• Jupyter Notebook.
• An internet connection for downloading necessary libraries.

🚀 Getting Started

To get started with the Analytics Portfolio, follow these simple steps:

1. Visit the Releases page to download the files: Download Link.

2. Locate the most recent version under “Latest Release.”

3. Download the zip file that contains all project files.

4. Unzip the downloaded file on your computer.

5. Open Jupyter Notebook to access the project files.

💾 Download & Install

You can easily get our software by visiting the following link. This page contains all the files you need to run the projects.

Visit the Releases Page to Download

Once you’ve downloaded and unzipped the files, locate the “README.md” file inside the project folder for further instructions.

🔍 Projects Overview

Each project has been designed to provide a thorough understanding of common data science tasks. Below is a brief overview of each project.

📈 Employee Attrition Analysis

• Objective: Identify factors influencing employee turnover.
• Data Used: Employee survey data focusing on satisfaction and performance metrics.
• Methods: Linear regression, classification, and data visualization techniques.

🌟 Customer Sentiment Analysis

• Objective: Understand how customers feel about products based on reviews.
• Data Used: Text data from customer reviews scraped from online platforms.
• Methods: Natural language processing, sentiment analysis, and visualization tools.

🔧 Technical Details

This portfolio uses common Python libraries such as:

• Pandas: For data manipulation and analysis.
• Scikit-learn: For machine learning algorithms.
• Natural Language Toolkit (NLTK): For natural language processing tasks.

📊 Data Visualization

Visual representation of data helps in better understanding. Each project includes Tableau dashboards that represent key insights in an easily digestible format. You can explore these dashboards once you run the projects.

⚙️ Running the Projects

To run the projects after setting up, follow these steps:

1. Open Jupyter Notebook.
2. Navigate to the project folder you unzipped.
3. Open the relevant notebook file (e.g., Employee_Attrition_Analysis.ipynb).
4. Run each cell sequentially to execute the code and view results.

📚 Helpful Resources

• Python Installation: Download Python
• Jupyter Notebook Installation: Installing Jupyter
• Tableau Tutorials: Tableau Learning

📫 Support

For any questions or issues, please open an issue on the GitHub repository. We will do our best to assist you promptly.

🚀 Join the Community

Feel free to connect with other users and share your experiences. Discussion and collaboration can help everyone improve their skills in data science.

Visit the Releases page again to get started on your analytics journey: Download Link.