Delivery Time Prediction

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• I have developed a state-of-the-art machine learning model that is capable of accurately predicting the delivery time of the delivery person. Additionally, I have implemented modular coding techniques to streamline the pipelines, allowing the system to be executed using a single python file. Furthermore, The code is able to generate artifacts and logs, providing the valuable insights into its performance.

logs

Check Logs: Link

Run

1. Initialize git

git init

2. Clone the project

git clone https://github.com/dev-hack95/delivery_time_prediction

3. enter the project directory

cd delivery_time_prediction

4. install the requriments

pip install -r requirements.txt

5. run(By running this file artifacts will automatically generated)

python src/pipeline/training_pipeline.py

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
│
├── artifacts          <- For Saving model and processor pipeline pickle files
│
├── notebooks          <- Jupyter notebooks
│                     
│                        
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── data_ingestion <- Scripts to turn raw data into features for modeling and data transformation
|   |   ├── data_ingestion.py
│   │   └── data_transform.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
|   ├── pipeline       <- Pipelines to train train and predict
│   │   │
│   │   ├── prediction_pipeline.py
│   │   └── training_pipeline.py
|   |
│   ├── visualization  <- Scripts to create exploratory and results oriented visualizations
│   |    └── visualize.py
│   |
|   ├── exception.py   <- Script handle sys exceptions
|   |
|   ├── logger.py      <- Script handle logging data to logs
|   |                  
|   └── utils.py
|
|
|
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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