https://github.com/kelvintechnical/6-python-machine-learning-projects

https://github.com/kelvintechnical/6-python-machine-learning-projects

Last synced: 27 days ago
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• Host: GitHub
• URL: https://github.com/kelvintechnical/6-python-machine-learning-projects
• Owner: kelvintechnical
• Created: 2024-11-13T01:41:22.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-04T20:18:19.000Z (about 1 month ago)
• Last Synced: 2024-12-04T21:22:44.537Z (about 1 month ago)
• Size: 40 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Python Machine Learning Projects

### Description

**Python Machine Learning Projects** is a curated collection of machine learning projects, progressing from beginner to advanced. Each project covers key concepts in machine learning, including data preprocessing, classification, regression, natural language processing, and computer vision. This repository is designed to provide hands-on experience and serves as an evolving portfolio of machine learning applications. 

### Prerequisites

To run these projects, make sure you have Python and essential machine learning libraries installed on your system.

- **Install Libraries**: Use the following command to install required libraries:

  ```bash

  pip install scikit-learn tensorflow pandas numpy matplotlib

## Follow the output and instructions in the script to explore the code and results.

## Project List

The following projects are organized from beginner to advanced. As each project is completed, this list will be updated with links to the code and detailed explanations.

### [Data Preprocessing with Scikit-Learn](https://github.com/kelvintechnical/Data-Preprocessing-with-Scikit-Learn)

**What I Learned:** I learned how to clean and prepare data for machine learning algorithms, handle missing values, encode categorical variables, and scale data to improve model performance.

### [Linear Regression Model](https://github.com/kelvintechnical/Linear-Regression-Model/blob/main/README.md)

**What I Learned:** I learned how to build and evaluate a simple linear regression model for predicting continuous values, understanding cost functions, and optimization techniques like gradient descent.

### [Logistic Regression for Binary Classification](https://github.com/kelvintechnical/Logistic-Regression-for-Binary-Classification-)

**What I Learned:** I learned how to apply logistic regression for binary classification tasks, including understanding sigmoid functions, decision boundaries, and model evaluation metrics like accuracy and confusion matrices.

### [K-Nearest Neighbors (KNN) Classifier](https://github.com/kelvintechnical/K-Nearest-Neighbors-Classifier)

**What I Learned:** I learned how to implement K-Nearest Neighbors (KNN) for classification tasks, understanding the importance of distance metrics and how to choose an optimal K value for improved performance.

### [Naive Bayes Text Classifier](https://github.com/kelvintechnical/K-Nearest-Neighbors-KNN-Classifier/blob/main/README.md)

**What I Learned:** I learned how to use the Naive Bayes algorithm for text classification, understanding concepts like conditional probability and the assumption of feature independence.

### [Decision Tree Classifier](https://github.com/kelvintechnical/Decision-Tree-Classifier/blob/main/README.md)

**What I Learned:** I learned how to implement a decision tree for classification tasks, including splitting criteria, tree pruning, and handling overfitting to improve the model's accuracy and robustness.