Face Detection And Emotion Recognition

Detecting The face in a image and Recognising their emotions.

In this project, I have trained a CNN classifier using "FER-2013 dataset" with Keras and tensorflow backened. The classifier sucessfully predicted the various types of emotions of human. And the highest accuracy obtained with the model was 60.1%.
Then used Open-CV to detect the face in an image and then pass the face to the classifer to predict the emotion of a person.

Open Cv CNN Image Classification

AI FOR SOCIAL GOOD

Classification of Messages of social media platform to identity if a person is affectd by any metal illness and thus helping in sucide prevention.

NLP LSTM TFIDF XGBOOST Classification

Spam Email Classifier

Detection of Spam Email.

In this project. I have used various method of text cleaning methods and then using bag of words and TfIdf to vectorize the text and using various types of Machine Learning Alogrithms like "Naive bayes" , "Random Forest" and "Xgboost" to detect if a mail was a spam mail or not.

NLP Ml Alogorithms Bag Of Words

Plant AI

Detection of diseased leaves of plant and Predicting the disease.

In this project, I have used the "plant disease dataset" from kaggle and trained a image classifer model using PyTorch framework using CNN and Transfer Learning with 38 classes of various plant leaves.The model was successfully able to detect diseased and healthy leaves of 14 unique plants. I was able to achive an accuracy of 98% by using Resnet34 pretrained model.

Image Classification CNN Transfer Learning