This project uses Data Science and AI technologies to create a diagnosis process for chronic kidney disease from a dataset of patients. I has been realized at UTBM during the semester of spring 2023 as a part of the UV DS50 taught by J.Gaber and S.Iovleff.
BEN SALAH Youssef BOUKHTAM Mohamed CHENG Ling Yi HOU Xinyu LAATAR Nisrine AUBRY Youri
This project consists in a machine learning prediction pipeline. Each step of the pipeline is represented by one notebook. Steps generate a some files, which are used in the following steps.
| File name | Description |
|---|---|
| EDA | Exploratory Data Analysis of the dataset. Specific to CKD |
| data_preparation | Preprocess the dataset into a clean dataset |
| hyperparameter_tuning | For a set of learning models, optimize its parameters for the dataset |
| feature_selection | Select the best features to use for training the dataset |
| hand_feature_selection | Computation of a prediction model made from topic research. Specific to CKD |
| model_comparison | Compare the models. Creates various comparison graphs |
The pipeline can be adapted to any dataset. To do so, the file has to be placed in a csv file at path Data/{dataset_name}/original_dataset.csv. All of the files generated by the pipeline will then be generated in the same directory. By default, the project contains 2 datasets with all of their generated files :
| File name | Generated during step | Used for steps |
|---|---|---|
| original_dataset.csv | not generated by the pipeline | data_preparation, EDA, feature_selection |
| clean_dataset.csv | data_preparation | hyperparameter_tuning, model_comparison |
| tuned_hyperparameters.json | hyperparameter_tuning | model_comparison |
| important_features.json | feature_selection | model_comparison |
| lda_dataset.csv | feature_selection | model_comparison |
| pca_dataset.csv | feature_selection | model_comparison |
| feat_selec_dataset.csv | feature_selection | model_comparison |
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Facebook
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Alibaba
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Tencent