ml-model-comparison's Introduction

Model evaluation mini-project

Aim: Develop a program to evaluate the performance of several supervised models on regression datasets

In this repo we look at applying a variety of SupervisedLearning models to predict a variety of regression data-sets.

Setup

I used this dataset from Kaggle for car prices: https://www.kaggle.com/CooperUnion/cardataset (save it in the root directory as "car_data.csv")

I used this dataset from Kaggle for test_scprse: https://www.kaggle.com/kwadwoofosu/predict-test-scores-of-students (save it in the root directory as "test_scores.csv")

Run the project by running:

$ python3 main.py

Install dependencies with:

$ pip3 install -r requirements.txt

The following models were run on the dataset:

Evaluates the performance of 5 key models:

it should evaluate the performance on the validation set ✅
it should return a train, val and test loss value and R-squared score for that hyperparameterisation ✅
it should return the hyperparameters which resulted in that score ✅
the time taken to fit the model ✅
evaluate them on all of sklearn's toy regression datasets available in sklearn.datasets ✅
create a main.py file which loops through each dataset and each model, printing the results ✅
graphical visualisations of
time to fit each of the best models ✅
final train, validation and test set loss/mse scores ✅
final train, validation and test set R-squared scores (= model.score) ✅

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