It is a data science project for predicting the lowest ticket price in a certain period of time.
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Scraped from tcharter.ir
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Pre-processing data that I gathered.
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Training model
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Predicting phase.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── development <- The deployment files like requirements.txt or docker-compose.yml can be here.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── source_codes <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate dataset
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
How to run this project:
1- Create virtual environment
2- Go to development dir and install dependencies with:
$ pip install -r requirements.txt
3- Go to data/common_utils and install it with:
$ pip install .
Note
this is my common utils between flight_ticket_preprocessing and flight_tickets_scraper
Project based on the cookiecutter data science project template. #cookiecutterdatascience