Data Science and Statistics Experiments

A comprehensive collection of Jupyter notebooks covering fundamental data science concepts, statistical analysis, and machine learning algorithms. This repository serves as a learning resource for data acquisition, data preprocessing, visualization, and various machine learning classification and regression techniques.

📋 Table of Contents

• Overview
• Features
• Experiments
• Prerequisites
• Technologies Used
• Repository Structure
• Getting Started
• Usage

🎯 Overview

This repository contains hands-on experiments that demonstrate essential data science workflows from data acquisition to advanced machine learning model implementation. Each notebook focuses on a specific topic with practical examples and implementations.

✨ Features

• Data Acquisition & Preprocessing: Learn to load and clean datasets
• Statistical Analysis: Explore central tendency measures and data exploration
• Data Visualization: Create various chart types using matplotlib
• Machine Learning: Implement supervised learning algorithms
• Model Evaluation: Understand accuracy metrics and performance evaluation

📚 Experiments

1. Data Acquisition using Pandas

• Loading CSV files
• Basic data exploration
• Understanding data structure

2. Central Tendency Measures using NumPy

• Mean, median, and mode calculations
• Statistical measures with NumPy

3. Basics of DataFrame

• DataFrame operations
• Data manipulation techniques

4. Missing Value Treatment

• Identifying missing values
• Handling null values in datasets

5. Creation of Arrays using NumPy

• Array creation and manipulation
• NumPy array operations

6. Data Visualization

• Line charts
• Bar charts
• Scatter plots
• Pie charts
• Histograms

7. Simple Linear Regression

• Implementation of linear regression
• Model training and prediction

8. Logistic Regression

• Binary classification
• Model evaluation metrics
• Train-test split methodology

9. K-Nearest Neighbors (KNN)

• KNN classifier implementation
• Classification problems

10. Support Vector Machine (SVM)

• SVM classifier
• Classification with support vectors

11. Decision Tree

• Decision tree classifier
• Tree-based classification

12. Random Forest Classifier

• Ensemble learning
• Hyperparameter tuning with GridSearchCV
• Model evaluation and metrics

🔧 Prerequisites

Before running the notebooks, ensure you have the following installed:

• Python 3.7 or higher
• Jupyter Notebook or JupyterLab
• Required Python packages (install using pip install -r requirements.txt)

🛠️ Technologies Used

• Python - Programming language
• Pandas - Data manipulation and analysis
• NumPy - Numerical computing
• Matplotlib - Data visualization
• Scikit-learn - Machine learning algorithms
• Jupyter Notebook - Interactive development environment

📁 Repository Structure

Data-Science-and-Statistics-main/
│
├── Experiment 1 -_ Data acquisition using pandas.ipynb
├── Experiment 2-_  Central tendency of measures Mean, median mode using numpy.ipynb
├── Experiment 3 -_ Basic of Data Frame.ipynb
├── Experiment 4-_ Missing value treatment.ipynb
├── Experiment 5-_ Creation of arrays using numpy.ipynb
├── Experiment 6 -_ Data Visualisation.ipynb
├── Experiment 7 -_Simple linear Regression (1).ipynb
├── Experiment 7 -_Simple linear Regression.ipynb
├── Experiment 8 -_ Logistic Regression.ipynb
├── Experiment 9 -_ KNN .ipynb
├── Experiment 10 -_ Suport Vector Machine.ipynb
├── Experiment 11 -_ Decision Tree.ipynb
└── Experiment 12 -_ # Random Forest Classifier.ipynb

🚀 Getting Started

1. Clone the repository

   git clone https://github.com/yourusername/Data-Science-and-Statistics-main.git
   cd Data-Science-and-Statistics-main
   

2. Create a virtual environment (recommended)

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

3. Install required packages

   pip install jupyter pandas numpy matplotlib scikit-learn
   

4. Launch Jupyter Notebook

   jupyter notebook
   

💻 Usage

1. Open the desired experiment notebook in Jupyter
2. Ensure you have the required datasets (CSV files) in the appropriate directory
3. Update file paths in the notebooks to match your system paths
4. Run cells sequentially to execute the experiments
5. Modify parameters and explore different configurations

Note on Data Files

Some notebooks require external datasets (e.g., diabetes.csv, heart.csv). Make sure to:

• Download the required datasets
• Update the os.chdir() path in the notebooks to point to your dataset location
• Alternatively, place datasets in the same directory as the notebooks

📊 Learning Outcomes

After completing these experiments, you will have hands-on experience with:

• Data loading and preprocessing techniques
• Statistical analysis and measures
• Creating effective data visualizations
• Implementing various machine learning algorithms
• Evaluating model performance
• Understanding the complete data science workflow

📝 Notes

• Each notebook is self-contained and can be run independently
• Experiment with different parameters to see how they affect results
• Modify datasets or use your own data to practice

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

📄 License

This project is open source and available for educational purposes.

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Happy Learning! 🎓