- 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
In this chapter, we learned the following:
- Clustering is an unsupervised predictive algorithm to club similar data points together and segregate the dissimilar points from each other. This algorithm finds the usage in marketing, taxonomy, seismology, public policy, and data mining.
- The distance between two observations is one of the criteria on which the observations can be clustered together.
- The distance between all the points in a dataset is best represented by an nxn symmetric matrix called a distance matrix.
- Hierarchical clustering is an agglomerative mode of clustering wherein we start with n clusters (equal to the number of points in the dataset) that are agglomerated into a lesser number of cluster based on the linkages developed over distance matrix.
- K-means clustering algorithm is a widely used mode of clustering wherein the number of clusters need to be stated in advance before performing the clustering. K-means clustering method outputs a label for each row of data depicting the cluster it belongs to. It also outputs the cluster centers. K-means method is easier to analyze and make sense of.
- Deciding the number of clusters (k) for the k-means clustering is an important task. The elbow method and silhouette coefficient method are some of the methods that can help us to decide the optimum number of k.
In the current chapter, we dealt with an unsupervised algorithm that is very widely used. Next, we will learn about a classification supervised algorithm. It is called a decision tree. It is a great set of algorithms to classify and predict data.
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