Image_Identification_using_ML

Image Identification ML model involving data preparation, network design, training with backpropagation, and performance evaluation. The project focuses on selecting suitable datasets and tuning neural network parameters. Additionally, implemented data augmentation techniques to further improve the model robustness and generalization capabilities.

This repository contains a Jupyter Notebook for training and evaluating a machine learning model. The notebook walks through the steps of data preprocessing, model training, evaluation, and visualization.

File Structure

• Model.ipynb — Main Jupyter Notebook containing the code and analysis.

Features

• Data loading and preprocessing
• Model training and evaluation
• Visualization of results
• Built using popular Python libraries

Getting Started

1. Clone the repository

git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name

###2. Set up the environment Create a virtual environment (optional but recommended), then install the required libraries.

python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate
pip install -r requirements.txt

If requirements.txt is not available, manually install:

pip install numpy pandas matplotlib seaborn scikit-learn

3. Run the notebook

jupyter notebook Model.ipynb

Libraries Used

numpy pandas matplotlib seaborn scikit-learn

Output

The notebook includes training results, evaluation metrics, and visualizations to understand model performance.