We have to choose suitable Machine Learning algorithm depending on the problem statement and the dataset and no model is better algorithm compared to another.
- If it is a regression problem, then use Linear regression, Decision Trees, Random Forest, KNN, etc.
- If it is a classification problem, then use Logistic regression, Random forest, XGboost, AdaBoost, SVM, etc.
- If it is unsupervised learning, then use clustering algorithms like K-means algorithm.
Try out these models depending on the problem and use performance metrics to validate your model and find out which model is suitable for your problem statement. But, if you are confused about which one to choose then go for the Random Forest model.
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Also, watch this video on Machine Learning Algorithms:
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