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. Deployed the Web App on Heroku Cloud Platform. The Web App receives the data from the User Input, makes predictions with the saved ML model and sends a Prediction text indicating whether the order will reach on time or not.

Results from the Project :

Check out the Detail Project Overview on GitHub Repository

Check out the Detail Project Notebook on kaggle notebook

Technologies Used | Python | Sci-kit Learn | Flask | Gunicorn | Seaborn | | Matplotlib | Pandas | Numpy | Pickle