20newsGroup datasets that consists of 20K post of 20 different class.Built a text classifier using Naive Bayes algorithm to classify text into 20 different categories. Achieved accuracy of about 79%.In Text classification assigning text according to categories. Text classification is the process of assigning tags or categories to text according to its content. It’s one of the fundamental tasks in Natural Language Processing (NLP) with broad applications such as sentiment analysis, topic labeling, spam detection, and intent detection.Unstructured data in the form of text is everywhere: emails, chats, web pages, social media, support tickets, survey responses, and more

Cifar-10 is a standard computer vision dataset used for image recognition. It is a subset of the 80 million tiny images dataset and consists of 60,000 32×32 color images containing one of 10 object classes, with 6000 images per class. There are 50000 training images and 10000 test images. Trained a CNN model to classify images into 10 different classes. Training was done with 50,000 images, performed testing over 10,000 images. Accuracy achieved > 85%.used CNN for the classification tasks, the original row vector is not appropriate. In order to feed an image data into a CNN model.

The game's objective is to slide numbered tiles on a grid to combine them to create a tile with the number 2048. Here is a Python version that uses TKinter!

Huffman Code

Insatgram web Scrapping using selenium and beautifulsoup.

Fetched zomato data using zomato API.