⚠️ I got lazy to create a jupyter notebook, so enjoy the way this is

⚠️ Most of these contents are not included yet, but eventually i will in there future. I have to first focus on mi national exam

Machine Learning Algorithms in Python

Welcome to the Machine Learning Algorithms in Python repository! This repository contains a collection of Python implementations for various machine learning algorithms. Each project demonstrates the application of fundamental and advanced algorithms in machine learning, from basic regression techniques to complex neural networks.

Table of Contents

• Introduction
• Projects
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • K-Means Clustering
  • Principal Component Analysis (PCA)
  • Neural Networks
• Getting Started
• Contributing
• License
• Contact

Introduction

This repository is designed for educational purposes and to help developers and data scientists understand the implementation of various machine learning algorithms. Each project includes clear explanations, example code, and usage instructions.

Projects

Linear Regression

• Description: Implements the simple linear regression algorithm to predict continuous values.
• Files: linear_regression.py, data.csv
• Usage:

  from linear_regression import LinearRegression
  model = LinearRegression()
  model.fit(X_train, y_train)
  predictions = model.predict(X_test)

Logistic Regression

• Description: Implements logistic regression for binary classification tasks.
• Files: logistic_regression.py, data.csv
• Usage:

  from logistic_regression import LogisticRegression
  model = LogisticRegression()
  model.fit(X_train, y_train)
  predictions = model.predict(X_test)

Decision Trees

• Description: Implements decision tree classifier and regressor.
• Files: decision_trees.py, data.csv
• Usage:

  from decision_trees import DecisionTreeClassifier
  model = DecisionTreeClassifier()
  model.fit(X_train, y_train)
  predictions = model.predict(X_test)

Random Forest

• Description: Implements the Random Forest algorithm for classification and regression.
• Files: random_forest.py, data.csv
• Usage:

  from random_forest import RandomForestClassifier
  model = RandomForestClassifier()
  model.fit(X_train, y_train)
  predictions = model.predict(X_test)

Support Vector Machines (SVM)

• Description: Implements SVM for classification and regression tasks.
• Files: svm.py, data.csv
• Usage:

  from svm import SVC
  model = SVC()
  model.fit(X_train, y_train)
  predictions = model.predict(X_test)

K-Nearest Neighbors (KNN)

• Description: Implements the K-Nearest Neighbors algorithm for classification and regression.
• Files: knn.py, data.csv
• Usage:

  from knn import KNeighborsClassifier
  model = KNeighborsClassifier()
  model.fit(X_train, y_train)
  predictions = model.predict(X_test)

K-Means Clustering

• Description: Implements the K-Means clustering algorithm for unsupervised learning.
• Files: kmeans.py, data.csv
• Usage:

  from kmeans import KMeans
  model = KMeans(n_clusters=3)
  model.fit(X)
  clusters = model.predict(X)

Principal Component Analysis (PCA)

• Description: Implements PCA for dimensionality reduction.
• Files: pca.py, data.csv
• Usage:

  from pca import PCA
  model = PCA(n_components=2)
  reduced_data = model.fit_transform(X)

Neural Networks

• Description: Implements a basic neural network for classification and regression.
• Files: neural_network.py, data.csv
• Usage:

  from neural_network import NeuralNetwork
  model = NeuralNetwork(hidden_layers=[64, 32])
  model.fit(X_train, y_train)
  predictions = model.predict(X_test)

Getting Started

To get started with any of these algorithms, follow these steps:

1. Clone the repository:

   git clone https://github.com/ndizeyedavid/machine-learning-cornflakes.git
   cd machine-learning-cornflakes

2. Install dependencies: Enter into each folder, then perform that command

   pip install -r requirements.txt

3. Run the example: Navigate to the desired algorithm directory and follow the usage instructions provided above.

Contributing

I welcome contributions! If you'd like to add a new algorithm, improve existing implementations, or fix bugs, please follow these steps: Please ensure that your code adheres to our coding standards and includes appropriate tests and documentation.

License

No license, do what you want😊

Contact

For questions or feedback, please contact:

• Author: David Ndizeye
• Email: davidndizeye101@gmail.com
• GitHub: ndizeyedavid