Census Income – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.MNIST Digit Data Case Study For Dimensionality Reduction.Recruitment and Factory Salary Case Study.Insurance Data And Scrap Price Regression Case Study.r2, adjusted r2, mean squared error, etc.Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.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 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 – The 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.How to train the model in a clustering problem.Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.How to optimize the efficiency of the classification model.How to evaluate the model for a classification problem. ![]() How to train the model in a classification problem.Introduction to classification problems, Identification of a classification problem, dependent and independent variables.How to optimize the efficiency of the regression model. ![]()
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