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

   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:

   pip install -r requirements.txt

3. Run Jupyter Notebook

   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