Machine Learning Algorithms 🤖📊

A comprehensive repository featuring essential Machine Learning algorithms! This repository covers a mix of Supervised Learning, Unsupervised Learning, Neural Networks, Bayesian Learning, and Optimization Techniques to aid your understanding and exploration of ML concepts.

---

📝 Features

1.📊 Dealing with Data

• Objective: Work with libraries like NumPy, Pandas, and Statistics.
• Task: Analyze and manipulate datasets effectively.

---

2.📈 Data Analysis & Visualization

• Objective: Perform exploratory data analysis.
• Task: Visualize insights using the Diwali Sales dataset.

---

3.🔢 Linear and Logistic Regression

• Objective: Implement basic regression techniques.
• Task: Train and test linear and logistic regression models.

---

4.🧮 Naïve Bayesian Classifier

• Objective: Classify data using the naïve Bayesian approach.
• Task: Compute and evaluate accuracy on sample .CSV datasets.

---

5.📚 Text Classification with Naïve Bayesian Classifier

• Objective: Classify text documents into categories.
• Task: Use a naïve Bayesian model to demonstrate document classification.

---

6.🌳 Decision Tree with ID3 Algorithm

• Objective: Build and understand decision trees.
• Task: Implement the ID3 algorithm to classify data.

---

7.🌸 K-Nearest Neighbor (KNN)

• Objective: Learn instance-based learning.
• Task: Classify the Iris dataset using the KNN algorithm.

---

8.🧩 Clustering Algorithms

• Objective: Explore unsupervised learning.
• Task: Apply:
  • Expectation-Maximization (EM) for clustering.
  • K-Means for comparison using the same .CSV dataset.

---

9.🩺 Bayesian Network for Medical Diagnosis

• Objective: Model probabilistic relationships for diagnostics.
• Task: Construct a Bayesian network and diagnose heart disease using the Heart Disease dataset.

---

10.🧪 Comparison of Supervised Learning Algorithms

• Objective: Evaluate the performance of different supervised learning methods.
• Task: Compare algorithms like Linear Regression, SVM, and Decision Trees on suitable datasets.

---

11.🔍 Comparison of Unsupervised Learning Algorithms

• Objective: Understand clustering techniques.
• Task: Compare methods like K-Means and K-Mode.

---

12.🤖 Artificial Neural Network with Backpropagation

• Objective: Understand the workings of neural networks.
• Task: Build and train an ANN using the backpropagation algorithm on appropriate datasets.

---

📂 Folder Structure

📁 Machine-Learning-Algorithms
├── ID3_DecisionTree/
├── NeuralNetwork_Backpropagation/
├── NaiveBayes_Classifier/
├── NaiveBayes_TextClassification/
├── BayesianNetwork_HeartDisease/
├── Clustering/
├── KNN_Iris/
├── Regression/
├── Supervised_Algorithms_Comparison/
├── Unsupervised_Algorithms_Comparison/

🚀 Getting Started

1. Clone the repository:

git clone https://github.com/your-username/Machine-Learning-Algorithms.git
cd Machine-Learning-Algorithms

2. Install required dependencies:

pip install -r requirements.txt

3. Run individual scripts in their respective folders to explore each algorithm.

---

🛠 Tools & Libraries

• Python 🐍
• NumPy 📐
• Pandas 🗂
• Scikit-learn 🤖

---

🤝 Contributions

Contributions are welcome! Feel free to fork this repository and submit pull requests with new implementations or improvements.

📜 License

This project is licensed under the MIT License.

---

Happy Learning! 😊