Machine Learning Pipeline

title

Name : Your name Date​: DD/MM/YY

Data Overview

Independent Features

• describe the features​: Describe the features

Target Feature

• Target Feature: Describe the target score

Unique Identifying Features

• index/ID: Unique ID or index

Catergorical Features

• Categorical features?: types of categorical features

Binary Features

• Binary features?: Description of binary features

Numerical Features

• Numerical Features: Description of numerical features

Sypnopsis of the problem.

• Regression/Classification: For you to decide whether you want to use regression or classification

Overview of Submitted folder

. ├── eda.ipnyb ├── init.py ├── data │ └── score.db # removed ├── requirements.txt ├── results │ └── MAE.csv ├── run.sh └── src ├── init.py ├── ml_module │ ├── init.py │ ├── data_prep.py │ ├── eda_preprocessing.py │ ├── model_classification.py │ └── model_regression.py └── run.py

Executing the pipeline

Step 1) Data-preprocessing(eda_preprocessing.py) Imports the data from .db file, data is processed through the findings from EDA.ipnyb

Step 2) Data Preparation (data-prep.py) Data is prepared by one-hot encoding categorical features. Target encoding was done for ordinal features. Numerical features will be pre-processed as required. The overall dataset is also train-test split into 80/20 split.

Step 3 & 4) Hyper Parameter tuning for Regression & Classification All hyper parameters are tuned through gridsearchCV through a predefined range^1^. The evaluation criteria for regression and classification gridsearch are based on MAE of final test (numerical and categorical (1-10)).

^1^ In gridsearch CV the predefined hyperparameters has gone through multiple iterations previously to derive the optimal range. The grid can be expanded up to users discretion

Step 4 & 5) Results Taking the best hyperparams previously found in step 3 and 4. the best parameters are fed into the final model and the results saved as a txt file -> Since Classification is evaluated on an ordinal MAE, to calculate the true MAE, take ordinal MAE * 10.

Running of machine learning pipeline.

Machine learning model created with python 3 and bash script.

Installing dependencies

Paste the following command on your bash terminal to download dependencies

pip install -r requirements.txt

Running the Machine Learning Pipeline

Past the followin command on your bash terminal to grant permission to execute the 'run.sh' file

chmod +x run.sh

Paste the following command on the bash terminal to run the machine learning programme

./run.sh

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License

MIT