Welcome to our project repository for the ASAN Service Community Prediction Competition. 🌐

Növbə tutan vətəndaşın növbədən imtina etmə ehtimalını proqnozlaşdıra bilərsinizmi?

The competition tasked participants with predicting service cancellation instances during the first three months of 2020, leveraging historical data from 2018 and 2019. Find more about the competition here.

• data_vis.ipynb – Jupyter notebook used for exploratory data analysis and visualization of the datasets.
• model.py – Script where the predictive model is defined and trained using the historical data.
• requirements.txt – A list of Python packages necessary to run the scripts.
• submission.csv – The file used to submit our predictions in the competition.
• data/ – Folder containing all the datasets required for the project.

Ensure you have Python 3.8+ and Jupyter Notebook installed on your system. You can download Python from the official website and find Jupyter Notebook installation instructions on the official website.

1. Clone this repository to your local machine.
2. Navigate to the project's root directory in your terminal or command prompt.
3. Install the necessary Python packages using the following command:

   pip install -r requirements.txt

1. Explore data_vis.ipynb for data analysis and visualizations, helping to understand the datasets in depth.
2. Run model.py to train the predictive model using the historical data. This script will also generate predictions saved in submission.csv.

The datasets used in this project are stored in the data folder in the project's root directory. Feel free to explore them and utilize them while going through the notebooks and scripts.