Data Pre-Processing using Scikit-learn

What is Data Pre-Processing?
Data preprocessing involves transforming raw data to well-formed data sets so that data mining analytics can be applied. Raw data is often incomplete and has inconsistent formatting. The adequacy or inadequacy of data preparation has a direct correlation with the success of any project that involve data analytics.
Preprocessing involves both data validation and data imputation. The goal of data validation is to assess whether the data in question is both complete and accurate. The goal of data imputation is to correct errors and input missing values — either manually or automatically through business process automation (BPA) programming.

Today, we will see some of the data pre-processing techniques namely: Standardization, Normalization, Encoding, Discretization and Imputation of missing values.

Dataset used:

Importing all the required libraries and reading the dataset.

Data standardization is the process of rescaling one or more attributes so that they have a mean value of 0 and a standard deviation of 1.
Standardization assumes that your data has a Gaussian (bell curve) distribution. This does not strictly have to be true, but the technique is more effective if your attribute distribution is Gaussian.

Encode categorical features as a one-hot numeric array.
The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are encoded using a one-hot (aka ‘one-of-K’ or ‘dummy’) encoding scheme. This creates a binary column for each category and returns a sparse matrix or dense array (depending on the sparse parameter)

Label encoding
It refers to converting the labels into a numeric form so as to convert them into the machine-readable form. Machine learning algorithms can then decide in a better way how those labels must be operated. It is an important pre-processing step for the structured dataset in supervised learning.

It is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values.

It scales all the data features in the range [0, 1] or else in the range [-1, 1] if there are negative values in the dataset. This scaling compresses all the inliers in the narrow range [0, 0.005].
In the presence of outliers, StandardScaler does not guarantee balanced feature scales, due to the influence of the outliers while computing the empirical mean and standard deviation. This leads to the shrinkage in the range of the feature values.

Imputing Missing Values
Missing data are values that are not recorded in a dataset. They can be a single value missing in a single cell or missing an entire observation (row). Missing data can occur both in a continuous variable (e.g. height of students) or a categorical variable (e.g. gender of a population).

Data discretization is the process of converting continuous data into discrete buckets by grouping them. by doing this we can limit the number of possible states. basically, we convert the numerical features into categorical columns.



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