StatsPrograms

A comprehensive collection of Python programs demonstrating statistical analysis, data manipulation, and machine learning techniques. This repository contains 12 programs covering various statistical concepts and implementations.

📋 Programs Overview

01.py - Data Manipulation with Pandas

• DataFrame merging and joining operations
• Pivot tables and data reshaping
• Data frame comparison techniques

02.py - String Manipulation and Regular Expressions

• String methods (upper, title, strip, etc.)
• String splitting and joining
• Regular expression pattern matching
• Email and phone number extraction and substitution

03.py - Time Series Analysis

• Time series data generation
• Daily mean calculations
• ARIMA model implementation
• Forecasting and visualization

04.py - Descriptive Statistics

• Mean, median, and mode calculations
• Frequency distribution handling
• Statistical analysis with NumPy and SciPy

05.py - Cross-Validation Techniques

• K-Fold cross-validation
• Leave-One-Out cross-validation
• Model evaluation with R-squared metrics
• Regression model validation

06.py - Probability Distributions

• Normal distribution
• Binomial distribution
• Poisson distribution
• Bernoulli distribution
• Distribution visualization with Matplotlib

07.py - Hypothesis Testing

• One-sample t-test
• Two-sample t-test
• Statistical significance testing
• Sample mean and standard deviation calculations

08.py - Analysis of Variance (ANOVA)

• One-way ANOVA
• Two-way ANOVA
• Sum of squares calculations
• Between-group and within-group variance analysis

09.py - Correlation and Regression

• Pearson correlation coefficient
• Linear regression implementation
• Scatter plots and regression line visualization
• Correlation analysis

10.py - Principal Component Analysis (PCA)

• PCA implementation on Wisconsin Breast Cancer dataset
• Dimensionality reduction
• Variance analysis
• PCA visualization

11.py - Linear Discriminant Analysis (LDA)

• LDA implementation on Iris dataset
• Classification boundary visualization
• Feature transformation
• Discriminant analysis

12.py - Linear Regression with Sklearn

• Model training and testing
• Prediction accuracy evaluation
• Mean Squared Error (MSE) calculation
• R-squared score analysis
• Actual vs. Predicted visualization

📁 Project Structure

StatsPrograms/
│
├── .github/
│   └── workflows/          # GitHub Actions workflows
│
├── outputs/                # Generated visualizations and results
│
├── 01.py                   # Data Manipulation with Pandas
├── 02.py                   # String Manipulation and Regular Expressions
├── 03.py                   # Time Series Analysis
├── 04.py                   # Descriptive Statistics
├── 05.py                   # Cross-Validation Techniques
├── 06.py                   # Probability Distributions
├── 07.py                   # Hypothesis Testing
├── 08.py                   # Analysis of Variance (ANOVA)
├── 09.py                   # Correlation and Regression
├── 10.py                   # Principal Component Analysis (PCA)
├── 11.py                   # Linear Discriminant Analysis (LDA)
├── 12.py                   # Linear Regression with Sklearn
│
├── requirements.txt        # Python dependencies
└── README.md              # Project documentation

🛠️ Technologies Used

• Python 3.x
• NumPy - Numerical computing
• Pandas - Data manipulation and analysis
• Matplotlib - Data visualization
• Seaborn - Statistical data visualization
• SciPy - Scientific computing
• Scikit-learn - Machine learning algorithms
• Statsmodels - Statistical modeling

📦 Installation

1. Clone the repository:

git clone https://github.com/toxicbishop/StatsPrograms.git
cd StatsPrograms

1. Install required dependencies:

pip install -r requirements.txt

🚀 Usage

Run any program individually:

python 01.py
python 02.py
# ... and so on

📊 Outputs

Program outputs (including visualizations) are saved in the outputs/ directory.

📝 License

This project is free to use without requiring a specific license.

👤 Author

ToxicBishop

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This repository serves as a learning resource for statistical analysis and data science with Python.