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Practical Predictive Analytics 无水印pdf
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2017-09-29
15:42:34
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Contents
1: Getting Started with Predictive Analytics
b'Chapter 1: Getting Started with Predictive Analytics'
b'Predictive analytics are in so many industries'
b'Skills and roles that are important in Predictive Analytics'
b'Predictive analytics software'
b'Other helpful tools'
b'R'
b'How is a predictive analytics project organized?'
b'GUIs'
b'Getting started with RStudio'
b'The R console'
b'The source window'
b'Our first predictive model'
b'Your second script'
b'R packages'
b'References'
b'Summary'
2: The Modeling Process
b'Chapter 2: The Modeling Process'
b'Advantages of a structured approach'
b'Analytic process methodologies'
b'An analytics methodology outline \xc3\xa2\xc2\x80\xc2\x93
specific steps'
b'Step 2 data understanding'
b'Step 3 data preparation'
b'Step 4 modeling'
b'Step 5 evaluation'
b'Step 6 deployment'
b'References'
b'Summary'
3: Inputting and Exploring Data
b'Chapter 3: Inputting and Exploring Data'
b'Data input'
b'Joining data'
b'Exploring the hospital dataset'
b'Transposing a dataframe'
b'Missing values'
b'Imputing categorical variables'
b'Outliers'
b'Data transformations'
b'Variable reduction/variable importance'
b'References'
b'Summary'
4: Introduction to Regression Algorithms
b'Chapter 4: Introduction to Regression Algorithms'
b'Supervised versus unsupervised learning models'
b'Regression techniques'
b'Generalized linear models'
b'Logistic regression'
b'Summary'
5: Introduction to Decision Trees, Clustering, and SVM
b'Chapter 5: Introduction to Decision Trees, Clustering, and SVM'
b'Decision tree algorithms'
b'Cluster analysis'
b'Support vector machines'
b'References'
b'Summary'
6: Using Survival Analysis to Predict and Analyze Customer Churn
b'Chapter 6: Using Survival Analysis to Predict and Analyze
Customer Churn'
b'What is survival analysis?'
b'Our customer satisfaction dataset'
b'Partitioning into training and test data'
b'Setting the stage by creating survival objects'
b'Examining survival curves'
b'Cox regression modeling'
b'Time-based variables'
b'Comparing the models'
b'Variable selection'
b'Summary'
7: Using Market Basket Analysis as a Recommender Engine
b'Chapter 7: Using Market Basket Analysis as a Recommender
Engine'
b'What is market basket analysis?'
b'Examining the groceries transaction file'
b'The sample market basket'
b'Association rule algorithms'
b'Antecedents and descendants'
b'Evaluating the accuracy of a rule'
b'Preparing the raw data file for analysis'
b'Analyzing the input file'
b'Scrubbing and cleaning the data'
b'Removing colors automatically'
b'Filtering out single item transactions'
b'Merging the results back into the original data'
b'Compressing descriptions using camelcase'
b'Creating the test and training datasets'
b'Creating the market basket transaction file'
b'Method two \xc3\xa2\xc2\x80\xc2\x93 Creating a physical
transactions file'
b'Converting to a document term matrix'
b'K-means clustering of terms'
b'Predicting cluster assignments'
b'Running the apriori algorithm on the clusters'
b'Summarizing the metrics'
b'References'
b'Summary'
8: Exploring Health Care Enrollment Data as a Time Series
b'Chapter 8: Exploring Health Care Enrollment Data as a Time
Series'
b'Time series data'
b'Health insurance coverage dataset'
b'Housekeeping'
b'Read the data in'
b'Subsetting the columns'
b'Description of the data'
b'Target time series variable'
b'Saving the data'
b'Determining all of the subset groups'
b'Merging the aggregate data back into the original data'
b'Checking the time intervals'
b'Picking out the top groups in terms of average population size'
b'Plotting the data using lattice'
b'Plotting the data using ggplot'
b'Sending output to an external file'
b'Examining the output'
b'Detecting linear trends'
b'Automating the regressions'
b'Ranking the coefficients'
b'Merging scores back into the original dataframe'
b'Plotting the data with the trend lines'
b'Plotting all the categories on one graph'
b'Performing some automated forecasting using the ets function'
b'Smoothing the data using moving averages'
b'Simple moving average'
b'Verifying the SMA calculation'
b'Exponential moving average'
b'Using the ets function'
b'Forecasting using ALL AGES'
b'Plotting the predicted and actual values'
b'The forecast (fit) method'
b'Plotting future values with confidence bands'
b'Modifying the model to include a trend component'
b'Running the ets function iteratively over all of the categories'
b'Accuracy measures produced by onestep'
b'Comparing the Test and Training for the "UNDER 18
YEARS" group'
b'Accuracy measures'
b'References'
b'Summary'
9: Introduction to Spark Using R
b'Chapter 9: Introduction to Spark Using R'
b'About Spark'
b'Spark environments'
b'SparkR'
b'Building our first Spark dataframe'
b'Importing the sample notebook'
b'Creating a new notebook'
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