Team: Good To Go
- Gurami KERETCHASHVILI
- Guillaume BARTHE
- Temur MALISHAVA
The goal of this data challenge is to study and apply machine learning/artificial intelligence techniques to a real-world regression problem. In this regression problem, each sample corresponds to a researcher (i. e., an author of research papers), and the goal is to build a model that can predict accurately the h-index of each author.
To install the requirements, run at the root folder:
pip install -r requirements.txt
All the original data files must be located in the same directory as the coding files which is in the code folder.
The data can be found on the website: https://www.kaggle.com/c/inf554-2021/data.
First, with the code in Part1_GTG.ipynb notebook you can run the data preprocessing of the data, feature extraction and feature selection. As a result this notebook creates two files, train_data_cleaned.csv and test_data_cleaned.csv, which will be used later in the Part2_GTG.ipynb and Part3_GTG.ipynb.
In the Part2_GTG.ipynb notebook we perform an analysis of the data and build baseline models for the simplest regressors to see the differences between them.
Finally, the Part3_GTG.ipynb notebook builds our neural network model, trains it, and validates the results. This notebook uses already cleaned versions of the datasets, train_data_cleaned.csv and test_data_cleaned.csv, and it performs a prediction to upload on kaggle.