Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data. Dimensionality reduction – Handling multidimensional data and standardizing the features for easier computation.K-means – The K-means algorithm that can be used for clustering problems in an unsupervised learning approach.Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.K-Nearest Neighbors – A simple algorithm that can be used for classification problems.Gradient Descent – Gradient descent algorithm is an iterative optimization approach to finding the local minimum and maximum of a given function.Support Vector Machine – SVM or support vector machines for regression and classification problems.Random Forest – Creating random forest models for classification problems in a supervised learning approach.Decision Tree – Creating decision tree models on classification problems in a tree-like format with optimal solutions.Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.How to optimize the efficiency of the clustering model.How to evaluate the model for a clustering problem.
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