🚀 Machine Learning Projects

This repository contains multiple end-to-end Machine Learning projects built for learning, experimentation, and real-world problem solving.

The goal of this repository is to practice:

• Data Cleaning & Preprocessing
• Exploratory Data Analysis (EDA)
• Model Building
• Model Evaluation
• Deployment (where applicable)

---

📂 Repository Structure

ML-Projects/
│
├── Project-1/
│   ├── data/
│   ├── notebooks/
│   ├── src/
│   ├── models/
│   └── README.md
│
├── Project-2/
│   ├── data/
│   ├── notebooks/
│   ├── src/
│   ├── models/
│   └── README.md
│
└── requirements.txt

Each project folder contains:

• Dataset (if permitted)

• Jupyter notebooks

• Python scripts

• Individual project README

---

🛠 Tech Stack

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-Learn
• Flask (for deployment projects)

---

🔥 Machine Learning Concepts Covered

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines
• K-Nearest Neighbors
• Feature Engineering
• Hyperparameter Tuning
• Model Evaluation Metrics
• ANN

---

⚙️ How to Run a Project

1. Clone the repository:

git clone https://github.com/Captain-23/ML-Projects.git

2. Navigate into a project folder:

cd Project-Name

3. Create a virtual environment:

python -m venv venv

4. Activate the environment:

Mac/Linux:

source venv/bin/activate

Windows:

venv\Scripts\activate

5. Install dependencies:

pip install -r requirements.txt

6. Run the notebook or Python file.

---

📊 Purpose of This Repository

This repoository contains several machine learning projects that I have made when I was learning machine learning.

---

👨‍💻 Author

Captain

• Student at bennett University