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Preprocessing for Machine Learning in Python

Selecting features using text vectors

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Cet exercice fait partie du cours

Preprocessing for Machine Learning in Python

IntermédiaireNiveau de compétence

4.8+

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In this chapter you'll learn exactly what it means to preprocess data. You'll take the first steps in any preprocessing journey, including exploring data types and dealing with missing data.

Exercise 1: Introduction to preprocessing Exercise 2: Exploring missing data Exercise 3: Dropping missing data Exercise 4: Working with data types Exercise 5: Exploring data types Exercise 6: Converting a column type Exercise 7: Training and test sets Exercise 8: Class imbalance Exercise 9: Stratified sampling

This chapter is all about standardizing data. Often a model will make some assumptions about the distribution or scale of your features. Standardization is a way to make your data fit these assumptions and improve the algorithm's performance.

Exercise 1: Standardization Exercise 2: When to standardize Exercise 3: Modeling without normalizing Exercise 4: Log normalization Exercise 5: Checking the variance Exercise 6: Log normalization in Python Exercise 7: Scaling data for feature comparison Exercise 8: Scaling data - investigating columns Exercise 9: Scaling data - standardizing columns Exercise 10: Standardized data and modeling Exercise 11: KNN on non-scaled data Exercise 12: KNN on scaled data

In this section you'll learn about feature engineering. You'll explore different ways to create new, more useful, features from the ones already in your dataset. You'll see how to encode, aggregate, and extract information from both numerical and textual features.

Exercise 1: Feature engineering Exercise 2: Feature engineering knowledge test Exercise 3: Identifying areas for feature engineering Exercise 4: Encoding categorical variables Exercise 5: Encoding categorical variables - binary Exercise 6: Encoding categorical variables - one-hot Exercise 7: Engineering numerical features Exercise 8: Aggregating numerical features Exercise 9: Extracting datetime components Exercise 10: Engineering text features Exercise 11: Extracting string patterns Exercise 12: Vectorizing text Exercise 13: Text classification using tf/idf vectors

This chapter goes over a few different techniques for selecting the most important features from your dataset. You'll learn how to drop redundant features, work with text vectors, and reduce the number of features in your dataset using principal component analysis (PCA).

Exercise 1: Feature selection Exercise 2: When to use feature selection Exercise 3: Identifying areas for feature selection Exercise 4: Removing redundant features Exercise 5: Selecting relevant features Exercise 6: Checking for correlated features Exercise 7: Selecting features using text vectors

Exercice en cours

Exercise 8: Exploring text vectors, part 1 Exercise 9: Exploring text vectors, part 2 Exercise 10: Training Naive Bayes with feature selection Exercise 11: Dimensionality reduction Exercise 12: Using PCA Exercise 13: Training a model with PCA

Now that you've learned all about preprocessing you'll try these techniques out on a dataset that records information on UFO sightings.

Exercise 1: UFOs and preprocessing Exercise 2: Checking column types Exercise 3: Dropping missing data Exercise 4: Categorical variables and standardization Exercise 5: Extracting numbers from strings Exercise 6: Identifying features for standardization Exercise 7: Engineering new features Exercise 8: Encoding categorical variables Exercise 9: Features from dates Exercise 10: Text vectorization Exercise 11: Feature selection and modeling Exercise 12: Selecting the ideal dataset Exercise 13: Modeling the UFO dataset, part 1 Exercise 14: Modeling the UFO dataset, part 2 Exercise 15: Congratulations!