Facial-Expression-Image-Classification

Problem Statement.

• The objective of this project was to develop a machine learning model capable of classifying facial expressions in images into one of seven emotion categories: anger, disgust, fear, happiness, sadness, surprise, and neutral.

Approach

The approach involved the following steps:

1. Imported necessary libraries for data processing and model building.
2. Data preparation, including loading the dataset and preprocessing.
3. Feature extraction to convert the image into numerical features.
4. Model Trained using a classification algorithm.
5. Evaluated model's performance using appropriate metrics.

Results:

Achieved a accuracy of 97.67% on the test dataset, indicating the model's ability to accurately classify images.

Technologies Used:

Python, pandas, scikit-learn, Jupyter Notebook.

Skills Demonstrated:

Data preprocessing, feature extraction, classification modeling, model evaluation.