User-Defined-Machine-Learning-Algorithms

This Repository contains most of the Machine Learning Algorithms, Step By Step through User defined functions and also some statistical concepts . Additionally, some datasets are included for experimentation.

📂 Notebooks

Classification Algorithms

• Bayes_Classifier.ipynb - Implements a Bayesian classifier for probabilistic classification.
• Euclidean_Distance_Classifier.ipynb - A classifier based on Euclidean distance.
• KNN.ipynb - Implements the k-Nearest Neighbors algorithm.
• Logistic_Regression.ipynb - Logistic Regression for binary classification.
• Perceptron_Direct.ipynb - Direct solution for the Perceptron algorithm.
• Perceptron_Iterative.ipynb - Iterative implementation of the Perceptron algorithm.
• Mahalanobis_Classifier.ipynb - Classification using Mahalanobis distance.

Regression Algorithms

• Closedform_LinearReg.ipynb - Closed-form solution for Linear Regression.
• Closedform_RidgeReg.ipynb - Ridge Regression using a closed-form solution.
• GradientDesc_LinearReg.ipynb - Linear Regression using gradient descent.
• GradientDesc_RidgeReg.ipynb - Ridge Regression with gradient descent.
• MultipleDegree_PolynomialReg.ipynb - Polynomial Regression with multiple degrees.
• Polynomial_Regression.ipynb - Basic implementation of Polynomial Regression.

Statistical Concepts

• Covariance_Matrix.ipynb - Computing and analyzing the covariance matrix.
• Multiclass_Confusion_Matrix.ipynb - Generating a confusion matrix for multiclass classification.
• Binary_Confusion_Matrix.ipynb - Creating a confusion matrix for binary classification.
• Multivariate_Normal_Dist.ipynb - Working with the multivariate normal distribution.
• Univariate_Normal_Dist.ipynb - Understanding and visualizing the univariate normal distribution.

Data Splitting

• TestTrainsplit.ipynb - Train-test split implementation for datasets.

📂 Datasets

• heart.csv - A dataset related to heart disease classification.
• Housing.csv - Housing price dataset for regression tasks.
• Iris.csv - Classic Iris dataset for classification.

Feel free to explore and modify the notebooks to gain a deeper understanding of machine learning algorithms!

(This repository will be constantly updated to include more and more algorithms along with more efficient codes for existing algos)