Aim of the Project : To build an End-to-End Web App using Data Science & Machine Learning for an International E-commerce Company to predict whether their products will reach on committed Delivery Time or not. Life Cycle of the Project : Collected the dataset from Kaggle Preprocessed the data well and built an ML model by tuning the Hyperparameters of Random-Forest Classifier using Randomized-Search-CV Saved the Best Performing model in a Pickle file Built a Web App using Python at the Backend with Flask API and HTML & Bootstrap serving at the Frontend.

Aim of the Project To build a Web App using Deep Convolutional Neural Networks to predict whether a person is Wearing a Mask or Not Life Cycle of the Project Collected the dataset from Kaggle which contained 12,000 images in total for both the classes Used Data Augmentation to generate extra data from the existing dataset Applied Transfer Learning using Pre-trained VGG16 model and built a High Performance Custom CNN Model providing an Accuracy of 99.