Machine Learning Projects

What this Repository is About

This Repository contains all of the Machine Learning as well as Deep Learning Projects, that i have made for any Competition or have done only for Practice. In this repo. all the projects will have at least one Complete Notebook, and the number of inComplete notebooks, i don’t know. In some of the Projects the Dataset is not uploaded, along with the notebook, it is because the Dataset is very huge in size, but I have mentioned the Source or link of the dataset, if by mistake i have not mentioned it in any of the projects, let me know!. Hope you get benefitted with this repo of mine.

List of Projects Till Date

1. HackerEarth: Emotion Detection - Tom and Jerry Cartoon

Detects emotion of Tom and Jerry characters in the video, using CNN with 25,696,261 parameters. Dataset used is from HackerEarth Competition with name “Detect emotions of your favorite toons”.
Training Accuracy: 0.9904
Validation Accuracy: 0.6556
HackerEarth Score: 26.98841 ( without Image Augmentation )

2. HackerEarth: Mother’s Day with Machine Learning

Extracts sentiment hidden in the tweets posted during Mother’s Day, using NLTK, tf-idf, SGD Classifier. Dataset used is from HackerEarth Competition with name “Machine Learning with Mother’s Day”.
Training F1 Score: 0.7489
HackerEarth Score: 0.3680

3. HackerEarth: Identify the Dance Form

Detects the Dance form in which the person in the image is posing, using VGG-16 pre-trained model, by transfer learning with 21,139,528 parameters. Dataset used is from HackerEarth Competition with name “Identify the Dance Form”.
Training Accuracy: 0.9841
Validation Accuracy: 0.9075
HackerRarth Score: 57.51433 ( with Image Augmentation )
Training Accuracy: 0.9940
Validation Accuracy: 0.9144
HackerRarth Score: 62.61918 ( with Image Augmentation and K-Fold Validation )

4. Image Classification on CIFAR10

Detects objects in the frame, from one of the ten classes included in the training dataset i.e. [Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck] , using CNN with 1,250,858 parameters. Dataset used is CIFAR-10.
Training Accuracy: 0.7256
Validation Accuracy: 0.7332 ( without Image Augmentation )

5. Kaggle: Handwritten Digit Recogniser MNIST

Detects the Handwritten digits in the given image, using CNN with 600,810 parameters. Dataset used for training is MNIST.
Validation Accuracy: 0.98904
Kaggle Score: 0.98878

Machine_Learning_Projects