Interactive statistics and machine learning playground built with Streamlit for exploring probability, distributions, hypothesis testing, confidence intervals, and ML models.
🌐 Live App | Tech Stack: Python, Streamlit, NumPy, pandas, SciPy, scikit-learn, Matplotlib, Plotly
Statistical vocabulary, chart principles, probability rules, Central Limit Theorem, Law of Large Numbers, and Monty Hall simulation.
Discrete: Binomial, Poisson, custom probability distributions
Continuous: Normal (with inverse), Triangular, Uniform, Exponential
Calculate margin of error and inverse calculations for sample size determination.
Z-tests, t-tests, proportion tests, Chi-Square tests with visualizations and automatic p-value calculations.
Supervised: Linear regression, gradient descent
Unsupervised: K-means clustering
Exploratory data analysis tools for uploaded datasets.
Prerequisites: Python 3.9+, uv
# Clone repository
git clone https://github.com/<your-username>/Stats-Calculator.git
cd Stats-Calculator
# Create virtual environment
uv venv .venv
# Activate (Windows)
.venv\Scripts\activate
# Activate (macOS/Linux)
source .venv/bin/activate
# Install dependencies
uv sync
# Run app
uv run streamlit run Introduction.pyStats-Calculator/
├── Introduction.py # Home page
├── pages/ # Streamlit pages
│ ├── 1_Theory.py
│ ├── 2_Distribution.py
│ ├── 3_Confidence_Interval.py
│ ├── 4_Hypothesis_Test.py
│ ├── 5_Machine_Learning.py
│ └── 6_Data_Exploration.py
├── distributions/ # Distribution calculators
├── hypothesis_test/ # Hypothesis testing tools
├── moe/ # Margin of error calculators
├── ml/ # Machine learning models
├── probability/ # Probability concepts
├── theorem/ # Statistical theorems
├── theory/ # Chart gallery & vocabulary
├── utils.py # Shared UI components
└── images/ # Screenshots and assets
See LICENSE file for details.





