Data Analysis Dashboard

This project is a Data Analysis Dashboard built using Flask, Bootstrap, Seaborn, and Matplotlib. It allows users to upload datasets, analyze them interactively, and visualize various features using machine learning models and exploratory data analysis tools.

---

Features

1. Upload Dataset: Users can upload CSV files for analysis.
2. Target Variable Analysis:
   • Choose a target variable for analysis.
   • Generate correlation heatmaps, pairplots, and distribution plots.
3. Data Exploration:
   • View summary statistics for numeric and non-numeric columns.
   • Explore feature correlations.
   • Detect and handle missing values.
   • Detect and handle outliers.
4. Machine Learning:
   • Predict outcomes based on a simple regression or classification model.
   • Display model scores for the target variable.
5. Dark/Light Theme Toggle: Switch between themes for better usability.

---

Installation

Prerequisites

• Python 3.7 or above
• pip
• Virtual environment (recommended)

Steps

1. Clone the repository:

   git clone https://github.com/your_username/data-analysis-dashboard.git
   cd data-analysis-dashboard

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   python app.py

5. Open your browser and navigate to:

   http://127.0.0.1:5000/
   

---

Usage

1. Upload a CSV file using the Upload Dataset section.
2. Select a target variable for analysis in the Choose Target Variable section.
3. Use the Explore Your Data section to:
   • View summary statistics.
   • Check correlations.
   • Explore distributions.
   • Handle missing values and outliers.
4. Visualize data using heatmaps, pairplots, and distribution plots in the Data Visualizations section.
5. Use the Prediction Section to make predictions based on the dataset and target variable.

---

File Structure

project-folder/
├── app.py                  # Main Flask app
├── static/
│   ├── styles.css          # Custom CSS
│   ├── script.js           # JavaScript logic
├── templates/
│   ├── index.html          # Main HTML template
├── uploads/                # Uploaded datasets
├── requirements.txt        # Python dependencies
├── README.md               # Project documentation

---

Technologies Used

• Backend: Flask
• Frontend: HTML, CSS (Bootstrap), JavaScript
• Visualization: Matplotlib, Seaborn
• Machine Learning: Scikit-learn

---

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or bug fix:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Description of your changes"

4. Push to your branch:

   git push origin feature-name

5. Open a pull request.

---

Contact

For any questions or feedback, feel free to reach out:

• GitHub: sneMoDi

---

Happy Analyzing! 🎉