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\nPredictive Analytics with TensorFlow 4.2: From Disaster to Decision –Titanic Example Revisited\n

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\nIn this video, we will see how we could develop several predictive models for Titanic survival prediction and do some analytics using them. In particular, we will discuss logistic regression, random forest, and linear SVM. We start with...\nIn this video, we will see how we could develop several predictive models for Titanic survival prediction and do some analytics using them. In particular, we will discuss logistic regression, random forest, and linear SVM. We start with logistic regression. Then we go with SVM since the number of features is not that large. Finally, we will see how we could improve the performance using Random Forests. • Do exploratory analysis on titanic dataset • Use logistic and linear regression for survival prediction • Use random forest for survival prediction\n
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Resource Details

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Educator Rating
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Media Length
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14:33
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Grade
Higher Ed
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Subjects
Math
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Instructional Videos
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Source:
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\nPackt - Data\n\n
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Date
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2019
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Language
English
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For Teacher Use
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Fine Print: Educational Use
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Concepts

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linear, blockchain, coding, computer science, stem
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Additional Tags

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linear svm, machine learning, model, string variables, aws, predictive models, angular, null values, null, estimator, feature engineering, confusion matrix, feature, software, ratio visually, svm model, tensor flow, longer names, lr estimator, training set, count, dummies function, programming, background knowledge, required packages, c++, survival prediction, react, fit method, function, 1st letter, c#, rf model, test set, data set, python, cabin, lr model
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\n\" data-title=\"Get Full Access\" data-container=\"body\" rel=\"popover\" tabindex=\"0\" aria-label=\"Play video: Predictive Analytics with TensorFlow 4.2: From Disaster to Decision –Titanic Example Revisited\" href=\"/subscription/new\">\"Predictive\n\n
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