Machine Learning Classification Techniques

This repository explores fundamental classification algorithms in machine learning and provides practical examples of their implementation. Classification is the task of assigning data points to pre-defined categories or classes, making it essential for many applications.

Why Classification Matters

• Email Spam Detection: Identify whether an email is spam or legitimate.
• Medical Diagnosis Predict the presence or absence of a disease based on patient symptoms
• Image Classification: Recognize objects in images (e.g., cat vs. dog classification)
• Customer Churn Prediction: Determine the likelihood of a customer leaving a service

Algorithms This repository covers the following widely-used classification algorithms:

1. Decision Tree: Builds a tree-like structure of rules to make predictions.

Check the Decision Tree notebook

2. K-Nearest Neighbors (KNN): Classifies a new data point based on the majority vote of its 'k' nearest neighbors.

Check the KNN notebook

3. Kernel SVM: A powerful extension of Support Vector Machines, using kernels to handle non-linearly separable data.

Check the KNN notebook

4. Logistic Regression: Models the probability of a data point belonging to a class using a logistic function. This is a way to explain what binary classification is by using linear and logistic regression.
   

This is another way of showing how logistic regression classifies two classes.

Check the Logistic Regression Classifier notebook

5. Naive Bayes: Applies Bayes' theorem for classification, based on the assumption of independence between features. Check on this image how Naives Bayes classifies different datapoints.

Check the Naive Bayes Notebook

6. Random Forest: Combines multiple decision trees to improve predictions and reduce overfitting. Random forest is a variation of decision tree, where lot of trees are serving and composing the forest.

Check Random Forest Classifier Notebook

7. Support Vector Machine (SVM): Finds the best-fitting hyperplane to separate data points belonging to different classes.

Check the SVM Notebook

Jupyter Notebooks: Step-by-Step Learning

Each algorithm has a dedicated Jupyter Notebook, including:

• Theoretical Explanations: Understand the intuition behind each algorithm.
• Code Implementations: Learn how to implement models in Python.
• Examples on Datasets: Apply the algorithms to real-world classification problems.

Designed For:

• Beginners: New to machine learning seeking to understand classification.
• Students: Looking to reinforce concepts with practical examples.
• Practitioners: Needing a refresher or exploring different classification techniques.

Let's Start Classifying!

1. Clone this repository.
2. Install the required libraries (details within notebooks)
3. Explore the notebooks and experiment with the code.
4. Compare with each model, which is the one with more accuracy when predicting the target variable.

Contribute and Collaborate

Found a bug? Want to improve the examples? Feel free to open an issue or submit a pull request. Let's build a fantastic learning resource together!