What is Data Preparation and Feature Engineering

Data Preprocessing and Feature Selection Techniques

Contentsn of Data Preprocessing Techniques

• Data Preprocessing Techniques
• Feature Engineering Techniques
• Feature Selection Techniques
• Dimensionality Reduction Techniques

Data Preparation and Feature Engineering are crucial steps in the machine learning pipeline. In this step, we prepare and preprocess the raw data to make it suitable for machine learning algorithms. The act of turning unprocessed data into features that may be used in machine learning algorithms is known as feature engineering. Feature selection and dimensionality reduction are also part of feature engineering, where we select the most relevant features and reduce the dimensionality of the data to improve the model's performance.

Data Preprocessing Techniques:

Data preprocessing is the process of cleaning, transforming, and preparing raw data for machine learning algorithms. The following are some common data preprocessing techniques:

    Data cleaning: 

This involves removing irrelevant and inconsistent data, filling missing values, and correcting data errors.

    Data transformation: 

This involves scaling, normalization, and encoding categorical variables.

    Data integration: 

This involves combining data from multiple sources to create a single dataset.

Example code for data cleaning:

python code

import pandas as pd

Load data

data = pd.read_csv('data.csv')

Drop irrelevant columns

data = data.drop(['id', 'date'], axis=1)

Fill missing values with median

data = data.fillna(data.median())

Correct data errors

data['age'] = data['age'].apply(lambda x: max(x, 0))

Save cleaned data

data.to_csv('cleaned_data.csv', index=False)

Feature Engineering Techniques:

The act of turning unprocessed data into features that may be included into machine learning algorithms is known as feature engineering. The following are some common feature engineering techniques:

    Feature scaling: 

This involves scaling features to a common scale to prevent bias towards features with a larger scale.

    Feature extraction: 

This involves extracting new features from existing features using mathematical or statistical techniques.

    Feature encoding: 

This involves encoding categorical variables into numerical variables that can be used in machine learning algorithms.

Example code for feature scaling:

python Code

import pandas as pd

from sklearn.preprocessing import StandardScaler

Load data

data = pd.read_csv('data.csv')

Scale features

scaler = StandardScaler()

data[['age', 'income']] = scaler.fit_transform(data[['age', 'income']])

Save scaled data

data.to_csv('scaled_data.csv', index=False)

Feature Selection Techniques:

The process of choosing a subset of the dataset's most important characteristics for use in creating a machine learning model is known as feature selection. It helps to reduce the complexity of the model and improve its accuracy and performance. There are several feature selection techniques available, including:

    Filter methods: 

These methods use statistical tests to rank the features based on their correlation with the target variable. The most common filter methods include chi-squared test, mutual information, and correlation coefficient.

    Wrapper methods: 

These methods evaluate subsets of features using a machine learning algorithm and select the best subset that gives the highest accuracy. Forward selection, backward elimination, and recursive feature removal are some of the most used wrapper techniques.

Embedded methods: 

These methods combine the feature selection process with the model building process. They use regularization techniques like Lasso and Ridge regression to penalize the coefficients of irrelevant features and select the most important features.

Example code for feature selection:

Python code

import pandas as pd

from sklearn.feature_selection import SelectKBest, f_regression

Load data

data = pd.read_csv('data.csv')

Select top 5 features based on F-test

selector = SelectKBest(f_regression, k=5)

X = data.drop(['target'], axis=1)

y = data['target']

X_new = selector.fit_transform(X, y)

Save selected features

selected_features = X.columns[selector.get_support()]

selected_data = pd.DataFrame(X_new, columns=selected_features)

selected_data['target'] = y

selected_data.to_csv('selected_data.csv', index=False)

Example code for feature selection using chi-squared test:

Python code

import pandas as pd

from sklearn.feature_selection import SelectKBest, chi2

Load data

data = pd.read_csv('data.csv')

Define dependent variable and independent variables

Y = data['Target']

X = data.drop('Target', axis=1)

Apply feature selection using chi-squared test

selector = SelectKBest(chi2, k=5)

X_new = selector.fit_transform(X, Y)

Print selected features

print(X.columns[selector.get_support(indices=True)])

Dimensionality Reduction Techniques:

The practice of lowering the number of features in a dataset while maintaining the majority of the crucial data is known as "dimensionality reduction." The following are some common dimensionality reduction techniques:

Principal Component Analysis (PCA): 

This involves transforming the features into a lower-dimensional space while preserving the variance of the data.

Example code for PCA:

Python code

from sklearn.decomposition import PCA

from sklearn.datasets import load_iris

import pandas as pd

# load iris dataset

iris = load_iris()

X = iris.data

y = iris.target

# fit PCA with 2 components

pca = PCA(n_components=2)

X_pca = pca.fit_transform(X)

# convert to pandas dataframe for visualization

df = pd.DataFrame(X_pca, columns=['PC1', 'PC2'])

df['target'] = y

# plot the data

import matplotlib.pyplot as plt

import seaborn as sns

sns.scatterplot(x='PC1', y='PC2', hue='target', data=df)

plt.title('PCA with 2 components')

plt.show()

This code loads the iris dataset, performs PCA with 2 components, and visualizes the data in a scatterplot. The resulting plot shows how the different species of iris are separated in the reduced dimensional space.

To Main (Topics of Data Science)

                                            Continue to (Model Evaluation and Selection)