https://github.com/anyesh/predict-future-sales
This challenge serves as final project for the "How to win a data science competition" Coursera course done as a training at Fusemachines.
https://github.com/anyesh/predict-future-sales
Last synced: 8 months ago
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This challenge serves as final project for the "How to win a data science competition" Coursera course done as a training at Fusemachines.
- Host: GitHub
- URL: https://github.com/anyesh/predict-future-sales
- Owner: Anyesh
- Created: 2020-09-28T08:03:19.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-05-02T08:49:21.000Z (about 4 years ago)
- Last Synced: 2025-10-09T06:16:55.975Z (8 months ago)
- Language: Jupyter Notebook
- Size: 246 KB
- Stars: 0
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Predict Future Sales
This challenge serves as final project for the "How to win a data science competition" Coursera course.
In this competition you will work with a challenging time-series dataset consisting of daily sales data, kindly provided by one of the largest Russian software firms - 1C Company.
We have been asked to predict total sales for every product and store in the next month.
## Project structure
```
├── README.md
├── archives
├── checkpoints
│ ├── frozen_data.npz
│ └── xgboost
│ ├── model.pkl
│ └── model_0.pkl
├── data
│ ├── archive
│ │ └── competitive-data-science-predict-future-sales.zip
│ ├── item_categories.csv
│ ├── items.csv
│ ├── sales_train.csv
│ ├── sample_submission.csv
│ ├── shops.csv
│ └── test.csv
├── debug.log
├── environment.yml
├── mlruns
│ └── 0
├── notebooks
│ ├── 01-baseline-pre-processing.ipynb
│ └── Playground.ipynb
├── outputs
│ ├── xgboost_submission.csv
│ └── xgboost_submission_0.csv
├── pyproject.toml
└── src
├── config
│ └── model_params.py
├── dispatcher.py
├── feature_generator.py
├── main.py
├── mlruns
│ └── 0
│ └── meta.yaml
├── settings.py
└── utils.py
```
## Run the project
```
python -m src.main
```