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Prediction Models/Facial_expression_prediction/Readme.md

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+# Facial Expression Detection Model using CNN
+
+This project implements a Convolutional Neural Network (CNN) to classify facial expressions from images. The model is trained to detect various emotions such as "happy," "sad," "angry," "surprise," and "neutral." It uses the FER2013 dataset for training and evaluation, with TensorFlow and Keras as the main frameworks.
+
+## Table of Contents
+
+- [Project Description](#project-description)
+- [Problem Statement](#problem-statement)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Model Architecture](#model-architecture)
+
+
+## Project Description
+
+This project is designed to classify facial expressions based on images of faces. Using a deep CNN model, it processes grayscale images and identifies the emotions displayed. The model’s potential applications include enhancing user experience in human-computer interaction systems and improving real-time emotion analysis in sectors like healthcare, security, and customer service.
+
+## Problem Statement
+
+Human facial expressions are essential for understanding emotions in a non-verbal context. However, accurately classifying these expressions from images is challenging due to the variety in facial structures, lighting, and subtle differences in expressions. This project seeks to provide a robust model capable of detecting facial expressions in real-time, assisting applications in fields like healthcare, security, and AI-driven customer interactions.
+