The Analytics_Portfolio_Dual_Projects showcases two comprehensive data science projects: Employee Attrition Analysis and Customer Sentiment Analysis. This portfolio includes in-depth exploratory data analysis (EDA), natural language processing (NLP), machine learning models, and visually engaging Tableau dashboards.
This guide walks you through downloading and running the projects. No programming knowledge is necessary!
- Operating System: Windows 10/11, macOS, or Linux
- Storage Space: At least 500 MB free
- Software: The application requires Python 3.x and Jupyter Notebook to run the notebooks with ease.
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Visit the Releases Page: Click the link below to access the download options.
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Download the Suitable File:
- Look for the most recent release version. Click on the package that matches your operating system.
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Extract the Files:
- If your download is a ZIP file, right-click on it and select "Extract All" to access the contents.
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Open Jupyter Notebook:
- Launch Jupyter Notebook from your applications or command line. Navigate to the folder where you extracted the files.
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Run the Notebooks:
- Click on the notebook files (ending with .ipynb) to start exploring the data science projects.
This project focuses on understanding the reasons why employees leave companies. It uses EDA to visualize data patterns and machine learning to predict attrition. You'll find graphs, charts, and models that illustrate key findings.
Explore how customers feel about products through this NLP-focused project. It takes unstructured text data from reviews and analyzes sentiment. You'll see clear visualizations that summarize customer feedback and mood.
- Exploratory Data Analysis (EDA): Gain insights through visual tools.
- Machine Learning Models: Learn predictions based on data patterns.
- Natural Language Processing: Analyze text to understand sentiment.
- Tableau Dashboards: Engage with dynamic visual reports.
This repository covers a range of essential data science topics, including:
- Customer Sentiment
- Data Science
- Data Visualization
- Human Resource Analytics
- Jupyter Notebook Usage
- Machine Learning Techniques
- Natural Language Processing
- Pandas for Data Handling
- Python Programming
- Scikit-Learn for Machine Learning
- Text Analysis
A: No, you do not need programming skills. Follow the instructions to use Jupyter Notebook.
A: Yes, the application is compatible with Windows, macOS, and Linux.
A: Ensure you have a stable internet connection and try accessing the releases page again.
If you face difficulties:
- Check your system requirements.
- Ensure Jupyter Notebook is correctly installed.
- Refer to online resources for help with Python or Jupyter-related issues.
If you want to contribute, please clone the repo and send a pull request. Your input helps enhance the projects!
This project is licensed under the MIT License. Feel free to use and modify the projects as needed.
Explore the datasets and dive into the world of data science with confidence!