StatLab: Data Science Portfolio Dashboard 📊

📖 Overview

StatLab is an interactive web application designed to demonstrate core Data Science and Statistical Analysis competencies.

Unlike static notebooks, this project features a dynamic synthetic data generator that creates realistic employee datasets on the fly using statistical probability distributions. It enables users to perform real-time hypothesis testing, regression analysis, and receive automated insights via Google Gemini AI.

🔬 Statistical Methods Implemented

This project showcases the practical application of the following statistical concepts, essential for any Data Scientist role:

1. Data Generation (Probability Distributions)

• Normal Distribution (Gaussian): Implemented the Box-Muller Transform to generate realistic distributions for Age and Performance Scores.
• Skewed Distributions: Modeled Years of Experience to reflect real-world seniority hierarchies.
• Probabilistic Modeling: Used conditional probability logic to model Churn based on Salary, Overwork, and Performance factors.

2. Inferential Statistics (Hypothesis Testing)

• Independent Samples T-Test: Compares means between two independent groups (e.g., Engineering vs. Sales salaries).
• Significance Testing: Calculates T-Statistic and P-Value to accept or reject null hypotheses.

3. Regression Analysis

• Simple Linear Regression: Implements the Least Squares method from scratch (y = mx + c) to model relationships.
• Correlation: Calculates Pearson Correlation Coefficient (r) and Coefficient of Determination (R²) to measure relationship strength.

4. Descriptive Statistics

• Central Tendency: Mean, Median.
• Dispersion: Standard Deviation, Variance, Range.
• Skewness: Measures the asymmetry of the probability distribution.

✨ Key Features

• Synthetic Dataset Generator: Instantly generate N=200+ records of employee data with realistic noise and correlations.
• Interactive Dashboard:
  • Overview: Key KPIs (Churn Rate, Avg Salary) and raw data view.
  • Hypothesis Lab: Interactive T-Test tool with visualization.
  • Regression Lab: Scatter plots with dynamic trend lines and fit metrics.
• AI Analyst Integration:
  • Uses Google Gemini API to generate plain-English "Data Scientist" interpretations of statistical results.
• CSV Export: Download the generated dataset for further analysis in Python/R.

🛠️ Tech Stack

• Frontend: React 18, TypeScript
• Styling: Tailwind CSS
• Visualization: Recharts
• AI/LLM: Google GenAI SDK
• Icons: Lucide React

🚀 Getting Started

To run this project locally:

1. Clone the repository

   git clone https://github.com/your-username/statlab-data-science-portfolio.git
   cd statlab-data-science-portfolio

2. Install Dependencies

   npm install

3. Set up Environment

   • Create a .env file in the root directory.
   • Add your Gemini API Key (Required for AI features):

     VITE_API_KEY=your_google_gemini_api_key

   Note: If you don't have a key, the statistical features will still work, but AI insights will be disabled.

4. Run the Development Server

   npm run dev

📂 Project Structure

/src
  ├── services/
  │   ├── statsService.ts   # Core statistical algorithms (T-Test, Regression, StdDev)
  │   ├── dataService.ts    # Synthetic data generation logic
  │   └── geminiService.ts  # AI integration
  ├── components/           # Reusable UI components
  ├── types.ts              # TypeScript definitions
  └── App.tsx               # Main application logic

📝 License

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

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Created to demonstrate full-stack data science capabilities.