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Mastering Python for Data Science

By : Samir Madhavan
Book Image

Mastering Python for Data Science

By: Samir Madhavan

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Mastering Python for Data Science
Samir Madhavan
Aug, 2015 | 294 pages
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Table of Contents (19 chapters)
Mastering Python for Data Science
Mastering Python for Data Science
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with Raw Data
Getting Started with Raw Data
The world of arrays with NumPy
Empowering data analysis with pandas
Data cleansing
Data operations
Summary
2
Inferential Statistics
Inferential Statistics
Various forms of distribution
A z-score
A p-value
One-tailed and two-tailed tests
Type 1 and Type 2 errors
A confidence interval
Correlation
Z-test vs T-test
The F distribution
The chi-square distribution
The chi-square test of independence
ANOVA
Summary
3
Finding a Needle in a Haystack
Finding a Needle in a Haystack
What is data mining?
Presenting an analysis
Studying the Titanic
Summary
4
Making Sense of Data through Advanced Visualization
Making Sense of Data through Advanced Visualization
Controlling the line properties of a chart
Creating multiple plots
Playing with text
Styling your plots
Box plots
Heatmaps
Scatter plots with histograms
A scatter plot matrix
Area plots
Bubble charts
Hexagon bin plots
Trellis plots
A 3D plot of a surface
Summary
5
Uncovering Machine Learning
Uncovering Machine Learning
Different types of machine learning
Decision trees
Linear regression
Logistic regression
The naive Bayes classifier
The k-means clustering
Hierarchical clustering
Summary
6
Performing Predictions with a Linear Regression
Performing Predictions with a Linear Regression
Simple linear regression
Multiple regression
Training and testing a model
Summary
7
Estimating the Likelihood of Events
Estimating the Likelihood of Events
Logistic regression
Summary
8
Generating Recommendations with Collaborative Filtering
Generating Recommendations with Collaborative Filtering
Recommendation data
User-based collaborative filtering
Item-based collaborative filtering
Summary
9
Pushing Boundaries with Ensemble Models
Pushing Boundaries with Ensemble Models
The census income dataset
Decision trees
Random forests
Summary
10
Applying Segmentation with k-means Clustering
Applying Segmentation with k-means Clustering
The k-means algorithm and its working
The k-means clustering with countries
Clustering the countries
Summary
11
Analyzing Unstructured Data with Text Mining
Analyzing Unstructured Data with Text Mining
Preprocessing data
Creating a wordcloud
Word and sentence tokenization
Parts of speech tagging
Stemming and lemmatization
The Stanford Named Entity Recognizer
Performing sentiment analysis on world leaders using Twitter
Summary
12
Leveraging Python in the World of Big Data
Leveraging Python in the World of Big Data
What is Hadoop?
Python MapReduce
File handling with Hadoopy
Pig
Python with Apache Spark
Summary
Index
Index

Chapter 7. Estimating the Likelihood of Events

Logistic regression is a type of regression analysis that helps in estimating the likelihood of an event to occur based on some given parameters. It is used as a classification technique with a binary outcome. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory (predictor) variables, using a logistic function.

You have been already introduced to Logisitc regression in Chapter 5, Uncovering Machine Learning. In this chapter, you'll learn to:

  • Build a logistic regression model with statsmodels

  • Build a logistic regression model with SciKit

  • Evaluate and test the model

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