🚀 ML Project Templates

A collection of init notebooks for quickly starting any Machine Learning project. These templates include structured steps and essential boilerplate code so you don’t have to rewrite everything from scratch.

---

📂 Repository Structure

ml-project-templates/
│
├── init_classification.ipynb # Template for classification problems
├── init_regression.ipynb # Template for regression problems
├── init_clustering.ipynb # Template for clustering (unsupervised learning)
└── README.md

---

📘 Templates Overview

1. Classification

• Data loading & exploration
• Train/test split
• Baseline models (Logistic Regression, RandomForest, XGBoost)
• Evaluation metrics: Accuracy, Precision, Recall, F1, ROC-AUC
• Confusion Matrix & ROC curve visualization

2. Regression

• Data loading & exploration
• Train/test split
• Baseline models (Linear Regression, RandomForest, XGBoost)
• Evaluation metrics: RMSE, MAE, R²
• Residual plots

3. Clustering

• Data loading & exploration
• Preprocessing (scaling, optional PCA)
• Algorithms: KMeans, Agglomerative, DBSCAN
• Evaluation metrics: Silhouette Score, Davies-Bouldin Index
• 2D visualization with PCA

---

🚀 Usage

1. Clone the repo:

   git clone https://github.com/ysfa7md/ml-project-templates.git
   cd ml-project-templates

2. Open any notebook in Jupyter / Colab / VSCode.

3. Replace data.csv with your dataset.

4. Run step by step.