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Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. To obtain practical expertise, run the algorithms on datasets. It is critical for knowing how to work with huge data sets efficiently.
. · Machine Learning: R provides numerous packages for machine learning tasks, making it a popular choice for data scientists. Packages like caret, random Forest, glmnet, and xgboost offer implementations of various machine learning algorithms, including classification, regression, clustering, and dimensionality reduction.
Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes. Techniques like regression analysis, time series forecasting, and machine learning algorithms are used to predict customer behavior, sales trends, equipment failure, and more.
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