10 Basic Feature Engineering Techniques to Prepare Your Data

Author(s): Muhammad Ihsan

Originally published on Towards AI.

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When we have data, we will certainly think about extracting valuable values ​​from it; the goal may be to help decision-making or predict future trends.

So how do we get that?

At times like this, we need feature engineering. Feature Engineering is the process of modifying raw data into more informative features. In this article, we will learn ten basic feature engineering techniques, a brief intuition, and implementation examples.

Before we get started, we need to import additional libraries to ensure our code works well. Here are the libraries used in this article:

import pandas as pd #data manipulationimport numpy as np #numerical computationimport matplotlib.pyplot as plt #data visualizationimport gensim.downloader as api #to download corpus provided in gensimfrom gensim.models import Word2Vec #word embeddingfrom sklearn.pipeline import Pipeline #chaining multiple data processing stepsfrom sklearn.decomposition import PCA #pca implementationfrom sklearn.datasets import load_iris # load iris datasetfrom sklearn.impute import SimpleImputer # imputationfrom sklearn.compose import ColumnTransformer #applying transformation to datasetfrom sklearn.feature_extraction.text import TfidfVectorizer #tf-idf implementation classfrom sklearn.preprocessing import MinMaxScaler, StandardScaler #scaling data

Let’s start by learning about Imputation. This term refers to the process of… Read the full blog for free on Medium.

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Published via Towards AI