PYTHON Tutorial
Feature engineering is the process of transforming raw data into features that are more suitable for machine learning models. It involves three key steps: feature extraction, feature selection, and dimensionality reduction.
Feature extraction is the process of creating new features from the raw data. This can be done through a variety of techniques, such as:
Feature selection is the process of selecting the most relevant features for the machine learning model. This can be done through a variety of techniques, such as:
Dimensionality reduction is the process of reducing the number of features in the dataset. This can be done through a variety of techniques, such as:
The following Python example demonstrates how to create new features from raw data to improve model performance:
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, StandardScaler
# Load the raw data
df = pd.read_csv('raw_data.csv')
# Create new features using one-hot encoding
encoder = OneHotEncoder()
df = pd.concat([df, pd.DataFrame(encoder.fit_transform(df['category']), columns=encoder.categories_[0])], axis=1)
# Normalize the continuous features
scaler = StandardScaler()
df['numeric_feature'] = scaler.fit_transform(df['numeric_feature'])
# Use the new features to train a machine learning model
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(df.drop('target', axis=1), df['target'])
By creating new features and selecting the most relevant ones, we can improve the performance of our machine learning model.