### What is the difference between Feature Engineering and Feature Selection?

Feature Engineering is the process of using domain knowledge to extract numerical representations from raw data.

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Feature Engineering is the process of using domain knowledge to extract numerical representations from raw data.

Discretization refers to the process of binning a continuous variable into a discrete number of buckets.

Timestamp attributes can be very informative for certain forecasting applications.

In some text fields, especially those that follow a consistent pattern throughout all of the observations, features can be extracted based on sub-components of the original text strings.

This is another way to convert a categorical variable into a numeric representation suitable for model training.

In order to represent a categorical variable in a machine learning model, it usually must be somehow coded numerically before it is used in the training of a model.

Some of the common feature engineering techniques are Dummy Encoding, Ordinal Encoding, Text Feature Extraction, Data Extraction, and Discretization

Feature engineering is the process of deriving informative input attributes that can be passed to a machine learning algorithm in order to learn their associations with the target variable.

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (114)
- Regression (42)
- Classification (46)
- Logistic Regression (10)
- Support Vector Machine (10)
- Naive Bayes (4)
- Discriminant Analysis (5)
- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (55)
- Clustering (28)
- Distance Measures (9)
- Dimensionality Reduction (9)

- Deep Learning (23)
- Data Preparation (34)
- General (5)
- Standardization (6)
- Missing data (7)
- Textual Data (16)