https://github.com/vipulbunny/ml-learning_projects
A collection of machine learning projects implemented in Python, showcasing core concepts like regression, classification, clustering, and model evaluation techniques. Ideal for learners and data science enthusiasts.
https://github.com/vipulbunny/ml-learning_projects
classification clustering data-analysis data-science data-visualization decision-trees jupyter-notebook machine-learning model-evaluation random-forest regression supervised-learning unsupervised-learning
Last synced: 11 months ago
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A collection of machine learning projects implemented in Python, showcasing core concepts like regression, classification, clustering, and model evaluation techniques. Ideal for learners and data science enthusiasts.
- Host: GitHub
- URL: https://github.com/vipulbunny/ml-learning_projects
- Owner: VIPULbunny
- Created: 2025-03-24T10:47:09.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-21T04:17:41.000Z (11 months ago)
- Last Synced: 2025-07-21T06:19:10.553Z (11 months ago)
- Topics: classification, clustering, data-analysis, data-science, data-visualization, decision-trees, jupyter-notebook, machine-learning, model-evaluation, random-forest, regression, supervised-learning, unsupervised-learning
- Language: Jupyter Notebook
- Homepage: https://github.com/VIPULbunny/ML-Learning_Projects
- Size: 30.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Support: Support Vector Regression/Diabetes_Dataset.csv
Awesome Lists containing this project
README
ML-Learning_Projects
Hands-on Machine Learning Projects for Practice & Portfolio
---
## 🔍 Overview
This public repository contains curated machine learning mini-projects aimed at learning core ML concepts using Python.
Each project is implemented using real-world datasets and popular ML libraries. Ideal for:
- Students & Freshers
- Data Science Enthusiasts
- Interview Preparation
- Portfolio Building
---
## 🧠 Included Projects
### 🔢 Supervised Learning
| Project | Techniques Used |
|----------------------------|-------------------------------------|
| 🔹 Linear Regression | Regression, Evaluation Metrics |
| 🔹 Logistic Regression | Binary Classification |
| 🔹 Decision Tree | Tree-based Classification |
| 🔹 Random Forest | Ensemble Methods, Feature Importance |
| 🔹 Support Vector Regression| SVM with kernels |
| 🔹 K-Nearest Neighbors | Lazy Learning, Distance Metrics |
### 🔍 Unsupervised Learning
| Project | Techniques Used |
|----------------|------------------------|
| 🔹 Clustering | K-Means, Elbow Method |
### 📐 Model Validation
| Project | Techniques Used |
|--------------------------|-----------------------------------------|
| 🔹 Cross Validation | K-Fold, Stratified K-Fold, ShuffleSplit |
### ❤️ Applied Projects
| Project | Domain |
|------------------------|-------------------|
| 🔹 Heart Disease Prediction | Healthcare AI |
---
## 🛠️ Technologies Used
- Python 3.x
- Jupyter Notebook
- `scikit-learn`, `pandas`, `numpy`, `matplotlib`, `seaborn`
> Tip: These notebooks are best viewed via [Jupyter Notebook Viewer](https://nbviewer.org/) or directly on GitHub.
---
## 🚀 Getting Started
## 🚀 Getting Started
```bash
# Clone the repository
git clone https://github.com/VIPULbunny/ML-Learning_Projects.git
cd ML-Learning_Projects
# Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
---
## 📁 Project Structure
```
ML-Learning_Projects
├── Clustering
│ ├── Association
│ │ └── Apriori
│ │ └──Eclat
│ └── KMeans
├── Cross Validation Techniques
│ └── Cross_Validation.ipynb
├── Decision Tree
│ └── Car_Price_prediction.ipynb
├── Heart disease Prediction
│ └── Heart_Disease_Prediction.ipynb
├── KNN
│ ├── MOVIES_project
│ └── KNN_Iphone_purchesed.ipynb
├── Linear Regression
│ ├── HousePrice.ipynb
│ └── Linear_Regression_Model.ipynb
├── Logistic Regression
│ ├── Titanic_suvival_project.ipynb
│ └── Logistic_Regression_Model.ipynb
├── Random Forest
│ ├── Credit Card Fraud Detection
│ └── Random_F_Regressor.ipynb
├── Support Vector Regression
│ ├── SVM(SVR)
│ ├── SVM.ipynb
│ ├──SVM_with_STD.ipynb
│ └── SVM_without_STD.ipynb
└── README.md
```
---
## 🔭 Live Preview Options
- 📘 [View on NBViewer](https://nbviewer.org/github/VIPULbunny/ML-Learning_Projects/)
- ⚙️ [Run on Google Colab](https://colab.research.google.com/github/VIPULbunny/ML-Learning_Projects/)
---
## 👨💻 Author
**Vipul Solanki**
📍 Computer Engineering Student – RGIT, Mumbai
💼 Data Science & AI Enthusiast
📫 Email: [vipulsolanki339@gmail.com](mailto:vipulsolanki339@gmail.com)
🔗 [LinkedIn](https://www.linkedin.com/in/vipulsolanki777/)
💻 [GitHub](https://github.com/VIPULbunny)
---
## ⭐ Support
If you find this repository helpful:
- ⭐ Star the repo
- 🍴 Fork it
- 🧠 Share it
- 💬 Connect with me
**Contributions are welcome!**
Feel free to open an issue or submit a pull request.