This question is very big – it is similar to asking a trader which we can invest. Years of experience analyze the insights to make a gradual graph to see what is going on in the market.

A lot of data analysts focus on the dataset and ML models. Beginners use the hit & the trial technique to predict future results. The model that has the best score will win but this is not a good approach all the time. At some times, the dataset would work out of the mark. While on the other hand ML model consider an overfit due to imbalanced data. Perhaps it is too good for beginners, avoiding is far better in first-priority cases.

Considering you are doing binary classification, first in mind will be Naive Bayesian, so why not think about SVM? Traverse the dataset, and look up positive and negative aspects. Try to find out the patterns to find the major differences. Jump into columns and rows, and know the worth of the problem. A lot of problems really matter in real life and then implement operations for preprocessing e.g. fraud analysis.

**How to Choose The Best Machine Learning Algorithm**

**Make Sense Of the Problem**

Start with very simple steps (tiny steps) to see how the much difficult the problem is. It will take time & years of experience to see the patterns in data. For example, if Support Vector Machine or Linear Regression is the best Classification then the presented algorithms perhaps work better. Once you get inside the dataset, you are experienced then you are free to go.

**Find Out Optimized Solution**

Understanding data is just like a story where you might focus on the dataset’s origin. If are you unable to find out the solution to a problem, it means the problem is much harder. If you need new techniques, go to some senior data scientists for expert advice. Let’s cover one example, a dataset that is highly non-linear, it’s hard to find out the features. In this position, you should use techniques of deep learning for rich features. Sometimes traditional machine learning works well like SVM.

Another problem to face in the dataset is the infinite number of features. Your ML model is ambiguous about which features shout not choose, remember it depends upon requirements. Anyways, a linear sparse regression algorithm will help you out in this situation.

**Hyperparameters Changes**

Let’s consider one model CNN (Convolutional Neural Network) widely used for image classification. Many problems related to video analysis are solved by converting video into frames (pictures). Batch normalization is a method (hyperparameters) where you drop out some layers to avoid overfitting the model. Hyperparameters are handles to tune the model for more accuracy of datasets.

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