Wine Quality Prediction using Random Forest | Machine Learning

In this video, we are going to learn about Random Forest Algorithm implementation in Machine learning using Python. Random Forest is a supervised machine learning algorithm that is used in the classification and regression kinds of problems. It is basically a set of decision trees from a randomly selected subset of the training set and then it collects the votes from different decision trees to decide the final prediction. A decision tree is the most powerful and popular tool for classification and prediction.

The decision tree has high variance, but combined together in the same models then the result will be low as each decision tree gets perfectly trained on particular sample data, and hence the output doesn't depend on one decision tree.

Importing Dataset- https://www.geeksforgeeks.org/different-ways-to-import-csv-file-in-pandas/ Missing Data Handing- https://www.geeksforgeeks.org/ml-handling-missing-values/ Correlation matrix- https://www.geeksforgeeks.org/create-a-correlation-matrix-using-python/ Outliers detection- https://www.geeksforgeeks.org/detect-and-remove-the-outliers-using-python/ Z - Score outliers - https://www.geeksforgeeks.org/z-score-for-outlier-detection-python/ Model Training - https://www.geeksforgeeks.org/learning-model-building-scikit-learn-python-machine-learning-library/ Data Modeling using random forest classifier - https://www.geeksforgeeks.org/random-forest-classifier-using-scikit-learn/