Although I’m beginner to ML I’m facing few issues. To solve a particular problem what model should I use?
Any resources blog,article etc
The model selection completely depends on the data you are using. Whether you are working on a supervised or unsupervised problem (regression, classification, clustering).
For a regression problem, you can use linear regression, decision tree, random forest (and other ensemble models). Similarly, for classification, you can go for logistic regression, decision tree, random forest etc.
You might also want to check if your data is imbalanced, boosting techniques work well in such cases. If you have a very large dataset, go for LGBM and for a high number or categorical variables you can use CatBoost.
Thanks @AishwaryaSingh for the solution.
Can you provide few resources for ML beginner.
You can refer this article to learn about the basic machine learning techniques. There are multiple links provided within the article to help you understand each of these in detail.
Once you are able to build a model, this article will help you evaluate the model performance.
if you are beginner and facing problem with machine learning. first of all you learn machine learning in depth and than solve issues .