PRODIGY_ML_04

ML Internship at Prodigy InfoTech

Task - 04

Hand-Gesture recognition model

This repository contains code on Developing a hand gesture recognition model that can accurately identify and classify different hand gestures from image or video data, enabling intuitive human-computer interaction and gesture-based control systems.

Algorithm:

1. Data Preprocessing:

   • Split the dataset into training and validation sets.

2. Model Definition:

   • Create a Convolutional Neural Network (CNN) model using TensorFlow and Keras.

3. Data Augmentation:

   • Apply data augmentation techniques using the ImageDataGenerator to create variations in the training set.

4. Model Compilation:

   • Compile the model with an appropriate optimizer (e.g., Adam), categorical crossentropy loss, and accuracy as the metric.

5. Model Training:

   • Train the model using the augmented training set.
   • Specify the number of epochs and monitor the validation accuracy.

6. Model Evaluation:

   • Evaluate the model on the validation set to assess its performance.
   • Display the accuracy achieved on the validation set.

7. Confusion Matrix:

   • Use scikit-learn's confusion_matrix to compute the confusion matrix based on the model's predictions on the validation set.

Usage:

1. Dataset from Kaggle Hand Gesture.
2. Used Jupiter Notebook for Python Coding.

Techniques:

The following techniques are implemented in this project:

• CNN model