Machine Learning project on predicting justifiable Salary for Private and Public Firm Employees
Directory Structure:
root
├── client_x generate (To produce data free of sensitive information)
| |
| └── anonymise_client.py, len_service_change.py
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├── data_cleaning (Preprocessing scripts for each data)
| └── payroll_data
│ └── CleaningClient1_4.py, CleaningClient1_5.py, clients_compile.py, data_cleansing_client_extra.py, data_cleansing_clienta.py
│
├── etl (Extract-Load-Transform data)
│ ├── client_1_4
| | └── initial_model.py, k_models.ipynb
| |
│ ├── client_1_5
| └── Client1_5_ASmethod.py, Gapsquare_EDA_Client1_5.pdf, Gapsquare_KNN_Kmeans_Client1_5.Rmd, Gapsquare_Ridge_Lasso_Client1_5.pdf, KNN_KMeans.ipynb, KNN_KMeans.py, RidgeLassoPCA.py
| |
│ ├── client_a
| | └── eda_stdifference.py, predict_salary_helper.py
| |
│ └── client_extra
│ └── client_extra_analysis_ola_model.ipynb
│ └── output
│ └── analysis1.png, analysis2.png, analysis3.png, analysis4.png, analysis5.png, analysis6.png
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├── eda (Exploratory Data Analysis of each data)
│ └── src
│ └── app.ini, etl.py
│
├── data_helper (Anonymous Data Transformation)
│ └── load_transform.py
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└─ model (Scripts related to feature selection, model creation)
├── feature_selection
| └── app_client_1_4.ini, app_client_1_5.ini, app_client_a.ini, app_client_extra.ini, fs_client1_5.py, fs_client_etra.ipynb, fs_generic.py
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├── model_selection
| └── app.ini, model_selection.py
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├── regressor
| └── data_adjustment.py, data_from_postgresql.py, outliers_treatment.py, regressor_model.py, regressor_nn.py
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└── rf_regressor
└── app_client_1_4.ini, app_client_a.ini, app_client_extra.ini, rf_regress.py