Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python About This Book A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Get to grips with the basics of Predictive Analytics with Python Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Who This Book Is For If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this book will also help you. The book will be beneficial to and can be read by any Data Science enthusiasts. Some familiarity with Python will be useful to get the most out of this book, but it is certainly not a prerequisite. What You Will Learn Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries Analyze the result parameters arising from the implementation of Predictive Analytics algorithms Write Python modules/functions from scratch to execute segments or the whole of these algorithms Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries Understand the best practices while handling datasets in Python and creating predictive models out of them In Detail Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You'll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. Style and approach All the concepts in this book been explained and illustrated using a dataset, and in a step-by-step manner. The Python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. The statistical concepts are explained in detail wherever required."
Author(s): ashish kumar
Publisher: Packt Publishing
Year: 2016
Language: English
Pages: 354
Contents
Preface
1 Start with Predictive Modelling
Introducing predictive modelling
Scope of predictive modelling
Ensemble of statistical algorithms
Statistical tools
Historical data
Mathematical function
Business context
Knowledge matrix for predictive modelling
Task matrix for predictive modelling
Applications and examples of predictive modelling
LinkedIn's "People also viewed" feature
What it does?
How is it done?
Correct targeting of online ads
How is it done?
Santa Cruz predictive policing
How is it done?
Determining the activity of a smartphone user using accelerometer data
How is it done?
Sport and fantasy leagues
How was it done?
Python and its packages – download and installation
Anaconda
Standalone Python
Installing a Python package
Installing pip
Installing Python packages with pip
Python and its packages for predictive modelling
IDEs for Python
Summary
2 Data Cleaning
Reading the data – variations and examples
Data frames
Delimiters
Various methods of importing data in Python
Case 1 – reading a dataset using the read_csv method
The read_csv method
Use cases of the read_csv method
Case 2 – reading a dataset using the open method of Python
Reading a dataset line by line
Changing the delimiter of a dataset
Case 3 – reading data from a URL
Case 4 – miscellaneous cases
Reading from an .xls or .xlsx file
Writing to a CSV or Excel file
Basics – summary, dimensions, structure
Handling missing values
Checking for missing values
What constitutes missing data?
How missing values are generated and propagated
Treating missing values
Deletion
Imputation
Creating dummy variables
Visualizing a dataset by basic plotting
Scatter plots
Histograms
Boxplots
Summary
3 Data Wrangling
Subsetting a dataset
Selecting columns
Selecting rows
Selecting a combination of rows and columns
Creating new columns
Generating random numbers and their usage
Various methods for generating random numbers
Seeding a random number
Generating random numbers following probability distributions
Probability density function
Cumulative density function
Uniform distribution
Normal distribution
Using the Monte-Carlo simulation to find the value of pi
Geometry and mathematics behind the calculation of pi
Generating a dummy data frame
Grouping the data – aggregation, filtering, and transformation
Aggregation
Filtering
Transformation
Miscellaneous operations
Random sampling – splitting a dataset in training and testing datasets
Method 1 – using the Customer Churn Model
Method 2 – using sklearn
Method 3 – using the shuffle function
Concatenating and appending data
Merging/joining datasets
Inner Join
Left Join
Right Join
An example of the Inner Join
An example of the Left Join
An example of the Right Join
Summary of Joins in terms of their length
Summary
4 Statistical Concepts for Predictive Modelling
Random sampling and the central limit theorem
Hypothesis testing
Null versus alternate hypothesis
Z-statistic and t-statistic
Confidence intervals, significance levels, and p-values
Different kinds of hypothesis test
A step-by-step guide to do a hypothesis test
An example of a hypothesis test
Chi-square tests
Correlation
Summary
5 Linear Regression with Python
Understanding the maths behind linear regression
Linear regression using simulated data
Fitting a linear regression model and checking its efficacy
Finding the optimum value of variable coefficients
Making sense of result parameters
p-values
F-statistics
Residual Standard Error
Implementing linear regression with Python
Linear regression using the statsmodel library
Multiple linear regression
Multi-collinearity
Variance Inflation Factor
Model validation
Training and testing data split
Summary of models
Linear regression with scikit-learn
Feature selection with scikit-learn
Handling other issues in linear regression
Handling categorical variables
Transforming a variable to fit non-linear relations
Handling outliers
Other considerations and assumptions for linear regression
Summary
6 Logistic Regression with Python
Linear regression versus logistic regression
Understanding the math behind logistic regression
Contingency tables
Conditional probability
Odds ratio
Moving on to logistic regression from linear regression
Estimation using the Maximum Likelihood Method
Building the logistic regression model from scratch
Making sense of logistic regression parameters
Wald test
Likelihood Ratio Test statistic
Chi-square test
Implementing logistic regression with Python
Processing the data
Data exploration
Data visualization
Creating dummy variables for categorical variables
Feature selection
Implementing the model
Model validation and evaluation
Cross validation
Model validation
The ROC curve
Confusion matrix
Summary
7 Clustering with Python
Introduction to clustering – what, why, and how?
What is clustering?
How is clustering used?
Why do we do clustering?
Mathematics behind clustering
Distances between two observations
Euclidean distance
Manhattan distance
Minkowski distance
The distance matrix
Normalizing the distances
Linkage methods
Single linkage
Compete linkage
Average linkage
Centroid linkage
Ward's method
Hierarchical clustering
K-means clustering
Implementing clustering using Python
Importing and exploring the dataset
Normalizing the values in the dataset
Hierarchical clustering using scikit-learn
K-Means clustering using scikit-learn
Interpreting the cluster
Fine-tuning the clustering
The elbow method
Silhouette Coefficient
Summary
8 Trees & Random Forests with Python
Introducing decision trees
A decision tree
Understanding the mathematics behind decision trees
Homogeneity
Entropy
Information gain
ID3 algorithm to create a decision tree
Gini index
Reduction in Variance
Pruning a tree
Handling a continuous numerical variable
Handling a missing value of an attribute
Implementing a decision tree with scikit-learn
Visualizing the tree
Cross-validating and pruning the decision tree
Understanding and implementing regression trees
Regression tree algorithm
Implementing a regression tree using Python
Understanding and implementing random forests
The random forest algorithm
Implementing a random forest using Python
Why do random forests work?
Important parameters for random forests
Summary
9 Best Practices for Predictive Modeling
Best practices for coding
Commenting the codes
Defining functions for substantial individual tasks
Example 1
Example 2
Example 3
Avoid hard-coding of variables as much as possible
Version control
Using standard libraries, methods, and formulas
Best practices for data handling
Best practices for algorithms
Best practices for statistics
Best practices for business contexts
Summary
Links
Index
