- 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
We have now examined many of the tasks needed to start building analytical applications. Using the IPython notebook, we have covered how to load data in a file into a DataFrame in Pandas, rename columns in the dataset, filter unwanted rows, convert column data types, and create new columns. In addition, we have joined data from different sources and performed some basic statistical analyses using aggregations and pivots. We have visualized the data using histograms, scatter plots, and density plots as well as autocorrelation and log plots for time series. We also visualized geospatial data, using coordinate files to overlay data on maps. In addition, we processed the movies dataset using PySpark, creating both an RDD and a PySpark DataFrame, and performed some basic operations on these datatypes.
We will build on these tools in future sections, manipulating the raw input to develop features for building predictive analytics pipelines. We will later utilize similar tools to visualize and understand the features and performance of the predictive models we develop, as well as reporting the insights that they may deliver.
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