How to Explore Data in Machine Learning

When working on a machine learning problem, the first step that comes is exploring the data. This allows us to uncover patterns, identify anomalies, and determine the best approach for data preparation. If you ignore this step, then the machine learning model will fail due to—

• Incorrect assumptions made around feature distributions

• Missing values

• Irrelevant variables

This article introduces the concept of data exploration and outlines the steps to help you achieve it effectively.

Understanding the Data

To improve the effectiveness of the machine learning models, it is very essential to know about the data used in Analysis.

The following are some methods for data understanding that can be carried out:

A. Looking at Raw Data

By using the “head()” function, one gets a quick view of the data we are dealing with, as shown below—

      B. Checking Data Dimensions

The total runtime for a machine learning model depends on the amount of data in terms of total rows and columns.

      C. Obtaining the Column Datatypes

In order to identify the datatype for each column present in the data, one can use the “dtypes” function as shown below—

D. Describing the Data

The "describe()" function helps in understanding the characteristics of data by the below details--

• Count
• Mean
• Standard Deviation
• Min Value
• Max value
• 25%
• 50% (Median)
• 75%

E. Reviewing Class Distribution

In the classification problem of machine learning, it is recommended to check the class distribution. If the classes are imbalanced, special handling may be required during data preparation.

F. Checking Correlation between Attributes

The performance of the machine learning model also depends upon how the attributes are correlated with each other. Pearson’s correlation coefficient is the most common method for calculating correlation containing categories—

• Positive correlation if Coefficient value = 1
• Negative correlation if Coefficient value = -1
• No correlation if Coefficient value = 0

Determining Data Quality

Determining the quality of data also matters a lot when it comes to preparing the data for model development. If the data quality is high, then the chances of getting the highly accurate results increases.

The most common issues that need to be addressed when handling data quality are—

• Missing values
• Duplicates
• Wrong datatypes
• Outliers

Important Concepts Involved

When going through the data, there are various variables that we come across when performing the analysis in machine learning. This section talks about the concepts in Exploratory data analysis that show the involvement of variables

A. Univariate Analysis

• Involves exploring a single variable at a time and it is used in summarization, dispersion and central tendency.
• Univariate Analysis has following categories:

Ø  Frequency Distribution Analysis

Ø  Histogram

Ø  Pie Chart

Ø  Boxplot

Ø  Bar Chart

B. Bivariate Analysis

• Examines the relationship between 2 variables that can be either numerical or categorical.

• The techniques used in this analysis come under the following scenarios:

Ø  Numerical vs. Numerical (Scatterplot used)

Ø  Categorical vs. Categorical (Chi-Square Test Used)

Ø  Numerical vs. Categorical (ANOVA used)

C. Multivariate Analysis

• This concept involves analyzing data containing more than 2 variables. Alternatively, it is used for analyzing the relationship between dependent and independent variables.

• Techniques used in Multivariate analysis are as follows:

Ø  Clustering Analysis

Ø  Principal Component Analysis (PCA)

Ø  Multiple Correspondence Analysis (MCA)

    

Python Libraries for Data Exploration

When we start with exploring the data for machine learning process, it is also important to know packages being used. This section brings out the list of packages when the machine learning is carried out using python tool.

Pandas:
This library is built for data manipulation and analysis when working with structured and tabular data. The core functionality behind this package is to inspect, summarize and manipulate datasets.

Numpy:
This package handles large datasets by performing mathematical and statistical operations.

Matplotlib:
This library is used for visualizing the data in various formats. Also, it helps in converting numerical data into meaningful visual representations.

Seaborn:
Built on top of Matplotlib, this package focuses on statistical visualizations. It provides a high-level interface for creating informative statistical graphics.

 

Conclusion

Having reached the end of the article, we covered most of the points around why exploring the data is essential for machine learning. We also got to know about the concepts involved around data exploration in detail and what are the prerequisites are for carrying out the tasks effectively.