🌟 Machine Learning Projects

Welcome to my collection of machine learning projects! This repository contains various projects showcasing different techniques and applications in the field of machine learning.

📂 Project Structure

Here is the directory structure for the machine learning projects:

machine-learning-projects/
├── Project1/
│   ├── data/
│   │   ├── raw/
│   │   ├── processed/
│   │   └── README.md
│   ├── notebooks/
│   │   ├── 01_eda.ipynb
│   │   ├── 02_preprocessing.ipynb
│   │   ├── 03_model_training.ipynb
│   │   ├── 04_evaluation.ipynb
│   │   └── README.md
│   ├── scripts/
│   │   ├── data_preprocessing.py
│   │   ├── train_model.py
│   │   ├── evaluate_model.py
│   │   └── README.md
│   ├── models/
│   │   ├── model.h5
│   │   ├── model.pkl
│   │   └── README.md
│   ├── results/
│   │   ├── figures/
│   │   ├── metrics/
│   │   └── README.md
│   ├── requirements.txt
│   └── README.md
├── Project2/
│   ├── notebooks/
│   │   ├── HDC.ipynb              # The single Jupyter notebook containing all steps
│   │   └── README.md              # Overview of the notebook
│   ├── results/
│   │   ├── confusion_matrix.png   # Confusion matrix visualization
│   │   ├── sample_predictions.png # Predictions on sample data
│   │   ├── training_history.png   # Training history graph
├── README.md                  # Project overview

├── Project3/
│   ├── ...
├── .gitignore
├── LICENSE
└── README.md

📝 Projects Overview

Project 1: Titanic Survival Prediction 🚢

Predicting the survival of passengers on the Titanic using various machine learning algorithms. 
This project involves data cleaning, feature engineering, and model training.

Project 2: Handwritten Digit Classification With OpenCV✍️

Using a neural network to classify handwritten digits from the MNIST dataset. 

### 1. Notebook Details
The notebook `HDC.ipynb` contains:
- Exploratory Data Analysis: Visualizing and understanding the dataset.
- Data Preprocessing: Preparing the dataset for training, including normalization and splitting.
- Model Training: Training a Convolutional Neural Network (CNN) on the MNIST dataset.
- Evaluation: Testing the trained model and visualizing results.

### 2. Key Results
- Confusion Matrix: 

- Sample Predictions:

- Training History:

🚀 Getting Started

To get started with any of the projects, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/machine-learning-projects.git
   cd machine-learning-projects

2. Navigate to the project directory:

   cd Project1

3. Install the required dependencies:

   pip install -r requirements.txt

📄 License

This repository is licensed under the MIT License. See the LICENSE file for more information.

📫 Contact

If you have any questions or feedback, feel free to reach out to me at [email protected]