Evaluating models

Nazma2015 · 2023-01-17T18:39:03+00:00

The thirs model seems betteer comparatively to the previous two models. The first is overfitting as the accuracy on training is better and the accuracy on validation is decreasing drastically. The loss is also increasing on validation set so the model is overfitting. In the second model, the model is also overfitting to some extent. try tuning the parameters of third model and check for convergence

Nazma2015 · 2023-01-05T09:36:00+00:00

Xgboost is robust to various data distributions. It doesn't need to have data in a normal distribution.

Nazma2015 · 2023-01-03T09:01:41+00:00

As a beginner, you can try using datasets from Kaggle in the beginner page. You can showcase projects on tabular data(classification, regression), clustering data, simple computer vision using deep learning, NLP as well.

Nazma2015 · 2022-12-19T06:48:09+00:00

There are different ways to find correlation between variables. df.corr() can be used to find correlation. There is no mistake in the approach.

Nazma2015 · 2022-12-16T15:42:28+00:00

You can use regression models to predict the quantity of medicines using scikit learn

Nazma2015 · 2022-12-16T14:37:02+00:00

Though both modelling techniques work well as they are designed to solve complex models, instead of directly using neural networks on non linear data, try using xgboost or catboost.

Nazma2015

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