- 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 skimmed through the basic concepts of statistics. Here is a brief summary of the concepts we learned:
- Hypothesis testing is used to test the statistical significance of a hypothesis. The one which already exists or is assumed to be true is a null hypothesis, the one which someone is not sure about or is being proposed as an alternate premise is an alternate hypothesis.
- One needs to calculate a statistic and the associated p-value to conduct the test.
- Hypothesis testing (p-values) is used to test the significance of the estimates of the coefficients calculated by the model.
- The chi-square test is used to test the causal relationship between a predictor and an input variable. It can also be used to check whether the data is fair or fake.
- The correlation coefficient can range from -1 to 1. The closer it is to the extremes, the stronger is the relationship between the two variables.
Linear regression is part of the family of algorithms called supervised algorithms as the dataset on which they are built has an output variable. In a sense, one can say that this output variable governs or supervises the development of the model and hence the name. More on this is covered in the next chapter.
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.