Project I : E-commerce Shipping

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

  1. Collected the dataset from Kaggle
  2. Preprocessed the data well and built an ML model by tuning the Hyperparameters of RandomForest Classifier using RandomizedSearchCV
  3. Saved the Best Performing model in a Pickle file
  4. Built a Web App using Python at the Backend with Flask API and HTML & Bootstrap serving at the Frontend.
  5. Deployed the Web App on 2 Cloud Platforms: Heroku Cloud and Amazon Web Services (AWS) Cloud
  6. 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

  • If the order will reach on time
  • If the order won’t reach on time

Technologies Used

| Python | Sci-kit Learn | Flask | AWS | Heroku | Gunicorn |

| Pickle | Pandas | Numpy | Matplotlib | Seaborn |

Created a Custom Transformer to handle the Outliers

Simplified the code wherever needed

Heroku Web App

AWS Web App

GitHub Repository