https://github.com/tolumie/ai-machine-learning-projects

A collection of hands-on Artificial Intelligence (AI) and Machine Learning (ML) projects covering supervised and unsupervised learning, ensemble methods, deep learning, and model evaluation. This repository provides practical implementations using real-world datasets, making it ideal for both beginners and experienced data scientists.
https://github.com/tolumie/ai-machine-learning-projects

artificial-intelligence classification clustering-methods data-analysis data-science decision-trees deep-learning ensemble-learning feature-engineering gradient-boosting-classifier hyperparameter-tuning keras-tensorflow machine-learning neural-networks pca python random-forest regression supervised-learning unsupervised-learning

Last synced: about 2 months ago
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A collection of hands-on Artificial Intelligence (AI) and Machine Learning (ML) projects covering supervised and unsupervised learning, ensemble methods, deep learning, and model evaluation. This repository provides practical implementations using real-world datasets, making it ideal for both beginners and experienced data scientists.

• Host: GitHub
• URL: https://github.com/tolumie/ai-machine-learning-projects
• Owner: Tolumie
• Created: 2025-03-08T07:49:01.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-03-09T13:54:03.000Z (7 months ago)
• Last Synced: 2025-06-03T01:43:23.696Z (4 months ago)
• Topics: artificial-intelligence, classification, clustering-methods, data-analysis, data-science, decision-trees, deep-learning, ensemble-learning, feature-engineering, gradient-boosting-classifier, hyperparameter-tuning, keras-tensorflow, machine-learning, neural-networks, pca, python, random-forest, regression, supervised-learning, unsupervised-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 77.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# **AI & Machine Learning Projects**  

This repository contains a collection of **AI and Machine Learning projects**, including classification, regression, clustering, boosting, and deep learning models. It serves as a comprehensive resource for hands-on machine learning concepts, data preprocessing, model evaluation, and hyperparameter tuning.  

## **📂 Repository Structure**  

### **1️⃣ Supervised Learning**  

- **Regression Models**  

  - `linear regression.ipynb` – Implementation of linear regression.  

  - `Polynomial Regression.ipynb` – Polynomial regression modeling.  

  - `Regression-Keras.ipynb` – Neural network regression using Keras.  

  - `Regression_Train_Test_Split.ipynb` – Train-test split strategies for regression.  

  - `Regularization.ipynb` – Ridge and Lasso regression techniques.  

- **Classification Models**  

  - `Logistic Regression _Error_Metrics.ipynb` – Logistic regression and evaluation metrics.  

  - `Multi-class_Classification.ipynb` – Handling multi-class classification problems.  

  - `Support Vector Machines(SVM).ipynb` – SVM for classification tasks.  

  - `Decision Tree.ipynb` – Decision tree modeling for classification.  

  - `Ramdom_forest.ipynb` – Random forest classifier implementation.  

### **2️⃣ Ensemble Learning**  

- `Ada_Boost.ipynb` – Adaptive boosting algorithm.  

- `Bootstrap Aggregating (Bagging).ipynb` – Bagging technique for model stability.  

- `Gradient_Boosting.ipynb` – Gradient boosting implementation.  

- `Stacking__For_Classification_with_Python.ipynb` – Stacking classifiers for improved accuracy.  

### **3️⃣ Clustering & Unsupervised Learning**  

- `KMeans Clustering.ipynb` – K-Means clustering for pattern discovery.  

- `Mean Shift Clustering.ipynb` – Mean shift clustering for density-based segmentation.  

- `DBSCAN.ipynb` – Density-based clustering with DBSCAN.  

- `Gaussian Mixture Models (GMM).ipynb` – GMM for probabilistic clustering.  

- `PCA.ipynb` – Principal Component Analysis for dimensionality reduction.  

### **4️⃣ Deep Learning & Neural Networks**  

- `Intro_Neural Network.ipynb` – Basics of artificial neural networks.  

- `Keras_Intro.ipynb` – Using Keras for building and training neural networks.  

- `Forward_Propagation.ipynb` – Understanding forward propagation in neural networks.  

- `Gradient_Descent_DEMO.ipynb` – Demonstration of gradient descent optimization.  

### **5️⃣ Feature Engineering & Data Preprocessing**  

- `LAB_Transforming_Target.ipynb` – Transforming target variables for better predictions.  

- `Imbalanced_Data.ipynb` – Techniques for handling imbalanced datasets (SMOTE, undersampling, etc.).  

- `Matrix_Review.ipynb` – Basics of matrix operations in ML.  

### **6️⃣ Cross-Validation & Model Selection**  

- `Cross_Validation LAB.ipynb` – Hands-on implementation of cross-validation techniques.  

- `LAB_Regularization.ipynb` – Regularization methods for improving model performance.  

## **📊 Datasets Included**  

- `Ames_Housing_Sales.csv` – Housing price dataset.  

- `boston_house_prices.csv` – Boston housing dataset.  

- `tumor.csv` – Medical dataset for tumor classification.  

- `Wine_Quality_Data.csv` – Wine quality dataset for regression/classification.  

- `churndata_processed.csv` – Customer churn dataset.  

- `Wholesale_Customers_Data.csv` – Customer segmentation dataset.  

## **🚀 How to Use the Repository**  

1. **Clone the Repository**  

   ```bash

   git clone https://github.com/Tolumie/Ai-Machine-Learning-Projects.git

   cd Ai-Machine-Learning-Projects

   ```  

2. **Install Dependencies**  

   Ensure you have Python and Jupyter Notebook installed. You may install required libraries using:  

   ```bash

   pip install -r requirements.txt

   ```  

3. **Run Jupyter Notebook**  

   ```bash

   jupyter notebook

   ```  

4. **Open any `.ipynb` file** and explore the projects.  

## **🛠 Requirements**  

- Python 3.x  

- Jupyter Notebook  

- Pandas, NumPy, Scikit-learn, TensorFlow/Keras, Matplotlib, Seaborn  

## **📌 Future Updates**  

✅ Deep learning projects with TensorFlow/Keras  

✅ Time series forecasting models  

✅ Reinforcement learning experiments  

## **👨‍💻 Author**  

**Tolulope Israel Ogunbodede** – [GitHub Profile](https://github.com/Tolumie)