- Python: Advanced Predictive Analytics
- Table of Contents
- Python: Advanced Predictive Analytics
- Credits
- Preface
- 1. Module 1
- 1. Getting Started with Predictive Modelling
- 2. Data Cleaning
- Reading the data – variations and examples
- Various methods of importing data in Python
- Basics – summary, dimensions, and structure
- Handling missing values
- Creating dummy variables
- Visualizing a dataset by basic plotting
- Summary
- 3. Data Wrangling
- Subsetting a dataset
- Generating random numbers and their usage
- Grouping the data – aggregation, filtering, and transformation
- Random sampling – splitting a dataset in training and testing datasets
- Concatenating and appending data
- Merging/joining datasets
- Summary
- 4. Statistical Concepts for Predictive Modelling
- 5. Linear Regression with Python
- 6. Logistic Regression with Python
- Linear regression versus logistic regression
- Understanding the math behind logistic regression
- Implementing logistic regression with Python
- Model validation and evaluation
- Model validation
- Summary
- 7. Clustering with Python
- 8. Trees and Random Forests with Python
- 9. Best Practices for Predictive Modelling
- A. A List of Links
- 2. Module 2
- 1. From Data to Decisions – Getting Started with Analytic Applications
- 2. Exploratory Data Analysis and Visualization in Python
- 3. Finding Patterns in the Noise – Clustering and Unsupervised Learning
- 4. Connecting the Dots with Models – Regression Methods
- 5. Putting Data in its Place – Classification Methods and Analysis
- 6. Words and Pixels – Working with Unstructured Data
- 7. Learning from the Bottom Up – Deep Networks and Unsupervised Features
- Learning patterns with neural networks
- A network of one – the perceptron
- Combining perceptrons – a single-layer neural network
- Parameter fitting with back-propagation
- Discriminative versus generative models
- Vanishing gradients and explaining away
- Pretraining belief networks
- Using dropout to regularize networks
- Convolutional networks and rectified units
- Compressing Data with autoencoder networks
- Optimizing the learning rate
- The TensorFlow library and digit recognition
- Summary
- Learning patterns with neural networks
- 8. Sharing Models with Prediction Services
- 9. Reporting and Testing – Iterating on Analytic Systems
- Bibliography
- Index
The main learning outcomes of this chapter are summarized as follows:
- Various methods and variations in importing a dataset using pandas:
read_csvand its variations, reading a dataset using open method in Python, reading a file in chunks using theopenmethod, reading directly from a URL, specifying the column names from a list, changing the delimiter of a dataset, and so on. - Basic exploratory analysis of data: observing a thumbnail of data, shape, column names, column types, and summary statistics for numerical variables
- Handling missing values: The reason for incorporation of missing values, why it is important to treat them properly, how to treat them properly by deletion and imputation, and various methods of imputing data.
- Creating dummy variables: creating dummy variables for categorical variables to be used in the predictive models.
- Basic plotting: scatter plotting, histograms and boxplots; their meaning and relevance; and how they are plotted.
This chapter is a head start into our journey to explore our data and wrangle it to make it modelling-worthy. The next chapter will go deeper in this pursuit whereby we will learn to aggregate values for categorical variables, sub-set the dataset, merge two datasets, generate random numbers, and sample a dataset.
Cleaning, as we have seen in the last chapter takes about 80% of the modelling time, so it's of critical importance and the methods we are learning will come in handy in the pursuit of that goal.
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