FEATURE ENGINEERING

FEATURE ENGINEERING
it is the process of transforming raw data into relevant information for use by machine learning models i.e creating predictive models features.

FEATURE ENGINEERING PROCESS
1.) FEATURE CREATION
it is the process of generating new features based on domain knowledge or by observing patterns in data.

TYPES OF FEATURE CREATION
a) Domain-Specific,creates new features based on domain knowledge like business rules or standards.
b)Data-Driven,creates new features by observing patterns in the data i.e calculating aggregations or creating interaction features.
c)Synthectic,generating new features by combining existing features or synthesizing new data points.

IMPORTANCE OF FEATURE CREATION
improves model performance by adding additional information to the model.
it increases model robustness.
it improves model interpretability,it makes it easier to understand the model predictions.
it increases model flexibility,in handling different types of data.

2.)FEATURE TRANSFORMATION
it is the process of transforming the features into a more suitable representation for the machine learning model.

TYPES OF FEATURE TRANSFORMATION
a)Normalization,they are rescaling features at a specific range i.e 0 and 1.
b)Scaling,it is used to transform numerical variables to have a similar scale i.e for easy comparison.
c)Encoding,transforming categorical features into a numerical representation like one-hot encoding and label encoding.
d)Transformation,it uses mathematical operations to change the distribution or scale of the features i.e logarithmic,square root,reciprocal transformations.

SIGNIFICANCE OF FEATURE TRANSFORMATION
-improves model performance.
-increases model robustness.
-improves computational efficiency.
-improves model interpretability

3.)FEATURE EXTRACTION
It is the process of creating new features from existing ones to provide more relevant information to the machine model.
TYPES OF FEATURE EXTRACTION
a)Dimensionality Reduction,reducing the number of features by transforming the data into lower-dimensional space while retaining important information e.g PCA and t-SNE.
b)Feature Combination,combines two or more existing features to create a new one.
c)Feature Aggregation,it aggregates features to create a new one.like calculating mean,sum,count of set of features.
d)Feature Transformation,transforming existing features into new representaion.

SIGNIFICANCE OF FEATURE EXTRACTION
-improves model performance.
-reduces overfitting
-improves computational efficiency.
-improves model interpretability.

4)FEATURE SELECTION
it is the process of selecting a subset of relevant features from the dataset to be used in a machine-learning model.

TYPES OF FEATURE SELECTION
a)Filter Method,Based on statistical measure of relationship between the feature and target variable.
b)Wrapper Method,based on the evaluation of the feature subset using a specific machine learning algorithim.
c)Embedded Method,based on the feature selection as part of the training process of the machine learning algorithim.

SIGNIFICANCE OF FEATURE SELECTION
-reduces overfitting.
-improves model performance.
-decreases computational costs.
-improves interpretability

5)FEATURE SCALING
it is the process of transforming the features so that they may have a similar scale.

TYPES OF FEATURE SCALING
a)Min-Max Scaling,it rescales features to a specific range i.e 0 and 1 by subtracting minimum value and diving by the range.
b)Standard Scaling,it rescales the features to have a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation.
c)Robust Scaling,it rescales the features to be robust to ouliers by diving them by interquartile range.

SIGNIFICANCE OF FEATURE SCALING
-improves model performance.
-increases model robustness.
-improves computational efficiency.
-improves model interpretability

STEPS IN FEATURE ENGINEERING
a) Data Cleansing(data cleaning/scrubbing),involes identifying and removing or correcting any errors or inconsistensis in the dataset.
b)Data Transformation,it converts and structures data into a usable format that can be easily analyzed.
c)Feature Extraction,it involves pattern recognition and identifying common themes among a large collection of documents.
d)Feature Selection,involves selecting the most relevant features from the dataset using techniques like corretional analysis,mutual information ,stepwisw regression.
e)Feature Iteration,involves refining and improving the features based on the performance of machine learning.it uses techniques like adding new features,removing redundant features,transforming features.

TECHNIQUES USED IN FEATURE ENGINEERING
1)ONE-HOT ENCODING
it is a technique used to transform categorical variables into numerical values that can be used by machine learning models.
-every category is transformed into a binary value indicating its presence or absence.

2)BINNING
it is a technique used to transform continous variables into categorical variables.
-range of values of the continous variable is divided into several bins and each bin is assigned a categorical value
-18-80 can be binned into 18-25,26,35,36-50 and 51-80

3)FEATURE SPLIT
it involves diving single features into multiple sub-features or groups based on specific criteria.
-the process unlocks valuable insights and enhances the model ability to capture complex relationships and patterns within the data.

4)TEXT DATA preprocessing
it involves removing stopwords,stemming,lemmatization and vectorization.
i)Stop Words,they are words that do not add much meaning to the text i.e 'the' and 'and.
ii)Stemming,it involves reducing words to their root forms,such as converting 'running' to 'run'
iii)Lemmatization,it reduces words to their ase form i.e vonverting 'running' to 'run'.
iv)Vectoriztion,it involves transforming text data into numerical vectors that can be used by machine learning models.

FEATURE ENGINEERING TOOLS
1)FEATURE TOOLS
it is a python library that enables automatic feature engineering for structured data.
it extracts features from multiple tables i.e relational dataases,csv files and generate new features based on user-defined primitives.

FEATURES OF FEATURE TOOLS
-Automated feature engineering using machine learning algorithims.
-support for handling time-dependent data.
-Intergration with popular python libraries i.e pandas and sciki-learn.
-Visualization tools for exploring and analyzing the generated features.
-Extensive documentation and tutorials for getting started.

2)TPOT(Tree-based Pipeline Optimization Tool)
it uses genetic programming to search for the best combination of features and machine learning algorithims for a given dataset.

FEATURES OF TPOT
-Autmatic feature selection and transformation.
-Support for multiple types of machine learning models i.e regression,classification,clustering.
-Ability to handle missing data and categorical variables.
-Intergration with popular libraries like scikit-learn and pandas.
-interactive visualizaions of generated pipelines.

3)DATA ROBOT
it uses automated machine learning techniques to generate new features and select the best combination of features and models for a given dataset.
FEATURES OF DATA ROBOT
-Automated feature engineering using machine learning algorithims.
-Support for handling time-dependant and text data.
-Intergration with popular python libraries like pandas,scikit-learn
-interactive visualization of the generated models and features.
-Collaboration tools for teams working on machine learning projects.

4)ALTERYX
it provides a visual interface for creating data pipelines that can extract,transform and generate features from multiple data sources.

FEATURES OF ALTERYX
-support for handling structured and unstructured data.
-Intergration with popular data sources like excel and databases.
-Pre-built tools for feature extraction and transformation.
-Support for custom scripting and code intergration.
-Collaboration and sharing tools for teams working on data projects.

5.)H20.ai
it providesa range of automated feature engineering techniques like feature scaling,imputation,encoding and feature engineering capabilities for more advanced users.

FEATURES OF H20.ai
-Automatic and manual feature engineering options
-Support for structured and unstructured data including text and image data.
-Intergration with popular data sources like csv files and databases.
-Interactive visualizaion of generated features and models.
-Collaboration and sharing tools for teams working on machine learning projects.