Index

A

• algorithms
  • best practices / Best practices for algorithms
• Anaconda
  • about / Anaconda
• ANOVA / Best practices for statistics
• applications and examples, predictive modelling
  • about / Applications and examples of predictive modelling
  • People also viewed feature, LinkedIn / LinkedIn's "People also viewed" feature, What it does?
  • online ads, correct targeting / Correct targeting of online ads, How is it done?
  • Santa Cruz predictive policing / Santa Cruz predictive policing
  • smartphone user activity, determining / Determining the activity of a smartphone user using accelerometer data
  • sport and fantasy leagues / Sport and fantasy leagues

B

• Bagging
  • about / Understanding and implementing random forests
• Bell Curve
  • about / Cumulative density function
• best practices
  • for coding / Best practices for coding
  • for data handling / Best practices for data handling
  • for algorithms / Best practices for algorithms
  • for statistics / Best practices for statistics
  • for business context / Best practices for business contexts
• best practices, for coding
  • about / Best practices for coding
  • codes, commenting / Commenting the codes
  • functions, defining for substantial individual tasks / Defining functions for substantial individual tasks
  • examples, of functions / Defining functions for substantial individual tasks, Example 3
  • hard-coding of variables, avoiding / Avoid hard-coding of variables as much as possible
  • version control / Version control
  • standard libraries / Using standard libraries, methods, and formulas
  • methods / Using standard libraries, methods, and formulas
  • formulas / Using standard libraries, methods, and formulas
• boxplots
  • about / Boxplots
  • plotting / Boxplots
• business context
  • best practices / Best practices for business contexts

C

• chi-square test
  • about / Chi-square tests, Chi-square test
  • usage / Chi-square tests
  / Best practices for statistics
• clustering
  • about / What is clustering?
  • using / How is clustering used?
  • cases / Why do we do clustering?
• clustering, fine-tuning
  • about / Fine-tuning the clustering
  • elbow method / The elbow method
  • Silhouette Coefficient / Silhouette Coefficient
• clustering, implementing with Python
  • about / Implementing clustering using Python
  • dataset, importing / Importing and exploring the dataset
  • dataset, exporting / Importing and exploring the dataset
  • values in dataset, normalizing / Normalizing the values in the dataset
  • hierarchical clustering, using scikit-learn / Hierarchical clustering using scikit-learn
  • k-Means clustering, using scikit-learn / K-Means clustering using scikit-learn
  • cluster, interpreting / Interpreting the cluster
• coding
  • best practices / Best practices for coding
• contingency table
  • about / Contingency tables
  • creating / Contingency tables
• correlation
  • about / Correlation
• correlation coefficient
  • about / Correlation
• Correlation Matrix
  • about / Correlation
• Cumulative Density Function
  • about / Cumulative density function
• Customer Churn Model
  • using / Method 1 – using the Customer Churn Model

D

• data
  • versus oil / Introducing predictive modelling
  • reading / Reading the data – variations and examples
  • summary / Basics – summary, dimensions, and structure
  • structure / Basics – summary, dimensions, and structure
  • dimensions / Basics – summary, dimensions, and structure
  • concatenating / Concatenating and appending data
  • appending / Concatenating and appending data
• data collection
  • about / How missing values are generated and propagated
• data extraction
  • about / How missing values are generated and propagated
• Data frame
  • about / Data frames
• data grouping
  • about / Grouping the data – aggregation, filtering, and transformation
  • illustration / Grouping the data – aggregation, filtering, and transformation
  • aggregation / Aggregation
  • filtering / Filtering
  • transformation / Transformation
  • miscellaneous operations / Miscellaneous operations
• data handling
  • best practices / Best practices for data handling
• data importing, in Python
  • about / Various methods of importing data in Python
  • dataset, reading with read_csv method / Case 1 – reading a dataset using the read_csv method
  • dataset, reading with open method / Case 2 – reading a dataset using the open method of Python
  • dataset, reading from URL / Case 3 – reading data from a URL
  • miscellaneous cases / Case 4 – miscellaneous cases
• dataset
  • visualizing, by basic plotting / Visualizing a dataset by basic plotting
  • sub-setting / Subsetting a dataset
  • columns, selecting / Selecting columns
  • rows, selecting / Selecting rows
  • combination of rows and columns, selecting / Selecting a combination of rows and columns
  • new columns, creating / Creating new columns
  • merging/joining / Merging/joining datasets
• dataset, reading with open method
  • about / Case 2 – reading a dataset using the open method of Python
  • reading line by line / Reading a dataset line by line
  • delimiter, changing / Changing the delimiter of a dataset
• decision tree
  • about / Introducing decision trees, A decision tree
  • using / A decision tree
  • mathematics / Understanding the mathematics behind decision trees
• decision tree, implementing with scikit-learn
  • about / Implementing a decision tree with scikit-learn
  • tree, visualizing / Visualizing the tree
  • decision tree, cross-validating / Cross-validating and pruning the decision tree
  • decision tree, pruning / Cross-validating and pruning the decision tree
• delimiter
  • about / Delimiters
• distance matrix
  • about / The distance matrix
• distances, between two observations
  • Euclidean distance / Euclidean distance
  • Manhattan distance / Manhattan distance
  • Minkowski distance / Minkowski distance
• dummy data frame
  • generating / Generating a dummy data frame
• dummy variables
  • creating / Creating dummy variables

E

• elbow method / The elbow method
• Euclidean distance
  • about / Euclidean distance

F

• F-statistics
  • about / F-statistics
  • significance / F-statistics

G

• guidelines, for selecting predictor variables
  • R2 / Summary of models
  • p-values / Summary of models
  • F-statistic / Summary of models
  • RSE / Summary of models
  • VIF / Summary of models

H

• Harvard Business Review (HBR)
  • about / Introducing predictive modelling
• heteroscedasticity / Other considerations and assumptions for linear regression
• hierarchical clustering
  • about / Hierarchical clustering
• histograms
  • about / Histograms
  • plotting / Histograms
• hypothesis testing
  • about / Hypothesis testing
  • null hypothesis, versus alternate hypothesis / Null versus alternate hypothesis
  • Z-statistic / Z-statistic and t-statistic
  • t-statistic / Z-statistic and t-statistic
  • confidence intervals / Confidence intervals, significance levels, and p-values
  • significance levels / Confidence intervals, significance levels, and p-values
  • p-values / Confidence intervals, significance levels, and p-values
  • types / Different kinds of hypothesis test
  • step-by-step guide / A step-by-step guide to do a hypothesis test
  • example / An example of a hypothesis test
• hypothesis tests
  • left-tailed / Different kinds of hypothesis test
  • right-tailed / Different kinds of hypothesis test
  • two-tailed / Different kinds of hypothesis test

I

• IDEs, for Python
  • about / IDEs for Python
  • IDLE / IDEs for Python
  • IPython Notebook / IDEs for Python
  • Spyder / IDEs for Python
• IDLE
  • about / IDEs for Python
  • features / IDEs for Python
• Inner Join
  • characteristics / Inner Join
  • about / Inner Join
  • example / An example of the Inner Join
• Inter Quartile Range(IQR) / Handling outliers
• intra-cluster distance / The elbow method
• IPython
  • about / Python and its packages for predictive modelling
  • URL / Python and its packages for predictive modelling
• IPython Notebook
  • about / IDEs for Python
  • features / IDEs for Python
• issues handling, in linear regression
  • about / Handling other issues in linear regression
  • categorical variables, handling / Handling categorical variables
  • variable, transforming to fit non-linear relations / Transforming a variable to fit non-linear relations
  • outliers, handling / Handling outliers

J

• joins
  • summarizing / Summary of Joins in terms of their length

K

• k-Means clustering
  • about / K-means clustering
• knowledge matrix, predictive modelling
  • about / Knowledge matrix for predictive modelling

L

• left-tailed test
  • about / Different kinds of hypothesis test
• Left Join
  • characteristics / Left Join
  • about / Left Join
  • example / An example of the Left Join
• Likelihood Ratio Test statistic
  • about / Likelihood Ratio Test statistic
• linear regression
  • issues, handling / Handling other issues in linear regression
  • considerations / Other considerations and assumptions for linear regression
  • assumptions / Other considerations and assumptions for linear regression
  • versus logistic regression / Linear regression versus logistic regression
• linear regression, implementing with Python
  • about / Implementing linear regression with Python
  • statsmodel library, using / Linear regression using the statsmodel library
  • multiple linear regression / Multiple linear regression
  • multi-collinearity / Multi-collinearity
  • Variance Inflation Factor (VIF) / Variance Inflation Factor
• linkage methods
  • about / Linkage methods
  • single linkage / Single linkage
  • compete linkage / Compete linkage
  • average linkage / Average linkage
  • centroid linkage / Centroid linkage
  • Ward's method / Ward's method
• logistic regression
  • scenarios / Linear regression versus logistic regression
  • math / Understanding the math behind logistic regression
• logistic regression, with Python
  • implementing / Implementing logistic regression with Python
  • data, processing / Processing the data
  • data exploration / Data exploration
  • data visualization / Data visualization
  • dummy variables, creating for categorical variables / Creating dummy variables for categorical variables
  • feature selection / Feature selection
  • model, implementing / Implementing the model
• logistic regression model
  • validation / Model validation and evaluation
  • evaluation / Model validation and evaluation
  • cross validation / Cross validation
• logistic regression parameters
  • about / Making sense of logistic regression parameters
  • Wald test / Wald test
  • Likelihood Ratio Test statistic / Likelihood Ratio Test statistic
  • chi-square test / Chi-square test

M

• Manhattan distance
  • about / Manhattan distance
• math, behind logistic regression
  • about / Understanding the math behind logistic regression
  • contingency tables / Contingency tables
  • conditional probability / Conditional probability
  • odds ratio / Odds ratio
  • moving to logistic regression / Moving on to logistic regression from linear regression
  • estimation, using Maximum Likelihood Method / Estimation using the Maximum Likelihood Method, Log likelihood function:
  • logistic regression model, building from scratch / Building the logistic regression model from scratch
• mathematics, behind clustering
  • about / Mathematics behind clustering
  • distances, between two observations / Distances between two observations
  • distance matrix / The distance matrix
  • distances, normalizing / Normalizing the distances
  • linkage methods / Linkage methods
  • hierarchical clustering / Hierarchical clustering
  • k-Means clustering / K-means clustering
• mathematics, decision tree
  • homogeneity / Homogeneity
  • entropy / Entropy
  • information gain / Information gain
  • ID3 algorithm / ID3 algorithm to create a decision tree
  • Gini index / Gini index
  • Reduction in Variance / Reduction in Variance
  • tree, puring / Pruning a tree
  • continuous numerical variable, handling / Handling a continuous numerical variable
  • missing value of attribute, handling / Handling a missing value of an attribute
• maths, behind linear regression
  • about / Understanding the maths behind linear regression
  • simulated data, using / Linear regression using simulated data
  • linear regression model, fitting / Fitting a linear regression model and checking its efficacy
  • linear regression model efficacy, checking / Fitting a linear regression model and checking its efficacy
  • optimum value of variable coefficients, finding / Finding the optimum value of variable coefficients
• matplotlib
  • about / Python and its packages for predictive modelling
  • URL / Python and its packages for predictive modelling
• miles per gallon (mpg) / Transforming a variable to fit non-linear relations
• Minkowski distance
  • about / Minkowski distance
• miscellaneous cases, data reading
  • reading, from .xls or .xlsx file / Reading from an .xls or .xlsx file
  • CSV or Excel file, writing to / Writing to a CSV or Excel file
• missing values
  • handling / Handling missing values
  • checking for / Checking for missing values
  • about / What constitutes missing data?
  • generating / How missing values are generated and propagated
  • propagating / How missing values are generated and propagated
  • treating / Treating missing values
  • deletion / Deletion
  • imputation / Imputation
• model validation
  • about / Model validation, Model validation
  • data split, training / Training and testing data split
  • data split, testing / Training and testing data split
  • models, summarizing / Summary of models
  • guidelines, for selecting variables / Summary of models
  • linear regression with scikit-learn / Linear regression with scikit-learn
  • feature selection, with scikit-learn / Feature selection with scikit-learn
• Monte-Carlo simulation
  • for finding value of pi / Using the Monte-Carlo simulation to find the value of pi
• multi-collinearity
  • about / Multi-collinearity

N

• normal distribution
  • about / Normal distribution
• null hypothesis
  • versus alternate hypothesis / Null versus alternate hypothesis
• NumPy
  • about / Python and its packages for predictive modelling
  • URL / Python and its packages for predictive modelling

O

• outliers
  • about / Handling outliers
  • handling / Handling outliers

P

• p-values
  • about / p-values
• pandas
  • about / Python and its packages for predictive modelling
  • URL / Python and its packages for predictive modelling
• parameters, random forest
  • node size / Important parameters for random forests
  • number of trees / Important parameters for random forests
  • number of predictors sampled / Important parameters for random forests
• pip
  • installing / Installing pip
• predictive analytics
  • about / Introducing predictive modelling
• predictive modelling
  • about / Introducing predictive modelling
  • scope / Scope of predictive modelling
  • statistical algorithms / Ensemble of statistical algorithms
  • statistical tools / Statistical tools
  • historical data / Historical data
  • mathematical function / Mathematical function
  • business context / Business context
  • knowledge matrix / Knowledge matrix for predictive modelling
  • task matrix / Task matrix for predictive modelling
  • applications and examples / Applications and examples of predictive modelling
• predictor variables
  • about / Multiple linear regression
  • forward selection approach / Multiple linear regression
  • backward selection approach / Multiple linear regression
• Probability Density Function
  • about / Probability density function
• probability distributions
  • about / Generating random numbers following probability distributions
  • Probability Density Function / Probability density function
  • Cumulative Density Function / Cumulative density function
• Python packages
  • about / Python and its packages – download and installation
  • Anaconda / Anaconda
  • Standalone Python / Standalone Python
  • installing / Installing a Python package
  • installing, with pip / Installing Python packages with pip
• Python packages, for predictive modelling
  • about / Python and its packages for predictive modelling
  • pandas / Python and its packages for predictive modelling
  • NumPy / Python and its packages for predictive modelling
  • matplotlib / Python and its packages for predictive modelling
  • IPython / Python and its packages for predictive modelling
  • scikit-learn / Python and its packages for predictive modelling

R

• random forest
  • implementing, using Python / Implementing a random forest using Python
  • features / Why do random forests work?
  • parameters / Important parameters for random forests
• random forest algorithm
  • about / The random forest algorithm
• random forests
  • about / Understanding and implementing random forests
• random numbers
  • about / Generating random numbers and their usage
  • generating / Generating random numbers and their usage
  • usage / Generating random numbers and their usage
  • methods, for generating / Various methods for generating random numbers
  • seeding / Seeding a random number
  • generating, following probability distributions / Generating random numbers following probability distributions
• random sampling
  • about / Random sampling – splitting a dataset in training and testing datasets
  • dataset, testing / Random sampling – splitting a dataset in training and testing datasets
  • dataset, splitting / Random sampling – splitting a dataset in training and testing datasets
  • Customer Churn Model, using / Method 1 – using the Customer Churn Model
  • sklearn, using / Method 2 – using sklearn
  • shuffle function, using / Method 3 – using the shuffle function
  • and central limit theorem / Random sampling and the central limit theorem
• read_csv method
  • about / Case 1 – reading a dataset using the read_csv method, The read_csv method
  • filepath / The read_csv method
  • sep / The read_csv method
  • dtype / The read_csv method
  • header / The read_csv method
  • names / The read_csv method
  • skiprows / The read_csv method
  • index_col / The read_csv method
  • skip_blank_lines / The read_csv method
  • na-filter / The read_csv method
  • use cases / Use cases of the read_csv method
• Receiver Operating Characteristic (ROC) curve
  • about / Model validation
• Recursive Feature Elimination (RFE) / Feature selection with scikit-learn
• regression tree algorithm
  • about / Regression tree algorithm
• regression trees
  • about / Understanding and implementing regression trees
  • advantages / Regression tree algorithm
  • implementing, with Python / Implementing a regression tree using Python
• Residual Standard Error (RSE)
  • about / Residual Standard Error
• result parameters
  • about / Making sense of result parameters
  • p-values / p-values
  • F-statistics / F-statistics
  • Residual Standard Error (RSE) / Residual Standard Error
• retrospective analytics
  • about / Introducing predictive modelling
• right-tailed test
  • about / Different kinds of hypothesis test
• Right Join
  • about / Right Join
  • characteristics / Right Join
  • example / An example of the Right Join
• ROC curve
  • about / The ROC curve
  • confusion matrix / Confusion matrix

S

• scatter plot
  • about / Scatter plots
  • plotting / Scatter plots
• scikit-learn
  • about / Python and its packages for predictive modelling
  • features / Python and its packages for predictive modelling
  • URL / Python and its packages for predictive modelling
• Sensitivity (True Positive Rate) / The ROC curve
• shuffle function
  • using / Method 3 – using the shuffle function
• Silhouette Coefficient / Silhouette Coefficient
• sklearn
  • using / Method 2 – using sklearn
• Specificity (True Negative Rate) / The ROC curve
• Spyder
  • about / IDEs for Python
  • features / IDEs for Python
• Standalone Python
  • about / Standalone Python
• statistical algorithms, predictive modelling
  • about / Ensemble of statistical algorithms
  • supervised algorithms / Ensemble of statistical algorithms
  • un-supervised algorithms / Ensemble of statistical algorithms
• statistics
  • best practices / Best practices for statistics

T

• t-statistic
  • about / Z-statistic and t-statistic
• t-test / Best practices for statistics
• t-test (Student-t distribution)
  • about / Z-statistic and t-statistic
• task matrix, predictive modelling
  • about / Task matrix for predictive modelling
• two-tailed test
  • about / Different kinds of hypothesis test

U

• uniform distribution
  • about / Uniform distribution
• use cases, read_csv method
  • about / Use cases of the read_csv method
  • directory address and filename, passing as variables / Passing the directory address and filename as variables
  • .txt dataset, reading with comma delimiter / Reading a .txt dataset with a comma delimiter
  • dataset column names, specifying from list / Specifying the column names of a dataset from a list

V

• value of pi
  • calculating / Geometry and mathematics behind the calculation of pi
• Variance Inflation Factor (VIF)
  • about / Variance Inflation Factor

W

• Wald test / Wald test

Z

• Z-statistic
  • about / Z-statistic and t-statistic
• Z-test / Best practices for statistics
• Z- test (normal distribution)
  • about / Z-statistic and t-statistic