Before you start data analysis or run your data through a machine learning algorithm, you must clean your data and make sure it is in a suitable form. Further, it is essential to know any recurring patterns and significant correlations that might be present in your data. The process of getting to know your data in depth is called Exploratory Data Analysis.
Exploratory Data Analysis is an integral part of working with data. In this tutorial titled âAll the ins and outs of exploratory data analysis,â you will explore how to perform exploratory data analysis on different data types.
Exploratory Data Analysis (EDA) is a critical initial phase in the data analysis process. EDA enables you to better understand your dataset, uncover insights, detect anomalies, and validate assumptions before diving into modeling or visualization. Conducting thorough EDA is foundational for extracting maximum value from your data.
In this comprehensive guide we’ll explain what exploratory data analysis entails why it matters, and provide a step-by-step methodology you can follow to perform effective EDA. Let’s get started!
What is Exploratory Data Analysis?
Exploratory data analysis refers to techniques data scientists use to analyze and investigate datasets to discover patterns, anomalies, and important relationships. The goal of EDA is to learn as much as possible about the structure, contents, and characteristics of the data.
Key exploratory data analysis activities include:
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Estimating summary statistics like mean median, max min, etc.
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Finding missing values and considering how to handle them
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Identifying univariate and multivariate outliers
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Assessing the data distribution and transformations needed
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Validating assumptions by calculating statistical tests
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Creating visualizations to spot trends and patterns
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Detecting correlations between variables
Thorough EDA enables you to ensure your data is suitable for planned analyses and reveals opportunities to generate further insights.
Why Perform Exploratory Data Analysis?
Conducting EDA provides many benefits that enhance downstream analytics and modeling including:
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Uncovering hidden insights – EDA often reveals unexpected trends, relationships and anomalies analysts can further explore.
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Understanding the data landscape – Get to know your data’s structure, contents, variables etc. to guide analysis.
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Identifying problems – Locate issues like missing values, errors, outliers etc. needed to clean and transform the data.
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Validating assumptions – Use statistics and visuals to test assumptions about distributions, variance, correlations etc.
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Building intuition – Explore and interact with the data to build familiarity and intuition for deeper analysis.
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Determining modeling approaches – EDA indicates suitable data preprocessing steps and modeling algorithms to pursue.
Investing time upfront in thorough EDA almost always leads to more impactful and valid data insights further on.
Step-by-Step EDA Methodology
Follow this robust methodology to perform effective exploratory data analysis:
Step 1: Import and Observe the Raw Data
- Import the dataset into your analysis environment.
- Print out a sample to check validity.
- Verify data types for each variable.
- Scan for any obvious issues.
Step 2: Study Structure and Summary Statistics
- Check number of rows and columns.
- Calculate summary stats like mean, min, max for numeric columns.
- Review value ranges and counts for categorical columns.
Step 3: Handle Missing Values
- Identify columns with missing values.
- Consider amount of missing values and pattern.
- Choose how to handle like dropping rows/columns or imputation.
Step 4: Detect Outliers
- Find univariate outliers using techniques like boxplots and z-scores.
- Identify multivariate outliers with methods like linear regression.
- Assess severity and handle appropriately.
Step 5: Evaluate Distributions
- Check data distributions – Gaussian, skewed, uniform etc.
- Consider transformations like log scaling for skewed data.
-Normalize or standardize values as needed.
Step 6: Visualize Key Relationships
- Create visualizations like histograms, scatter plots, heat maps to find insights.
- Identify trends, correlations, clusters, anomalies, etc.
- Determine interesting relationships for further analysis.
Step 7: Perform Statistical Analysis
- Calculate correlation coefficients between variables.
- Conduct hypothesis testing relevant to project goals.
- Fit exploratory models to estimate effects and relationships.
Step 8: Record EDA Tasks and Results
- Document process and record important findings.
- Track transformations made and rationales.
- Create visual summaries and reports to share outcomes.
Following this rigorous EDA methodology will ensure you thoroughly analyze and understand your dataset prior to further modeling and visualization.
EDA Tips and Best Practices
Keep these tips in mind as you conduct exploratory data analysis to maximize effectiveness:
- Automate repetitive EDA tasks whenever possible for efficiency.
- Create reusable code templates and functions for EDA you can apply to new data.
- Visualize relationships between many variable pairs to uncover insights.
- Stratify analyses by different segments or subgroups within data.
- Validate assumptions and hypotheses relevant to project goals.
- Brainstorm additional questions and angles to explore further during EDA.
- Maintain thorough notes on steps taken, findings and conclusions.
Key Takeaways for Exploratory Data Analysis
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EDA is critical for inspecting, cleaning, and understanding data before further analysis.
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Key EDA tasks include assessing distributions, outliers, missing data, statistical relationships and visualizations.
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Follow a methodical EDA process to extract maximum insights from data.
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Automate repetitive checks and record findings comprehensively.
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Let data observations guide additional exploratory avenues to pursue.
Performing thorough exploratory data analysis establishes a strong analytical foundation that leads to more valid conclusions and impactful data insights further on in the project lifecycle. All successful data scientists are masters of exploratory analysis.
What Is Exploratory Data Analysis?
Exploratory Data Analysis is a data analytics process to understand the data in depth and learn the different data characteristics, often with visual means. This allows you to get a better feel of your data and find useful patterns in it.            Â
Figure 1: Exploratory Data Analysis
It is crucial to understand it in depth before you perform data analysis and run your data through an algorithm. You need to know the patterns in your data and determine which variables are important and which do not play a significant role in the output. Further, some variables may have correlations with other variables. You also need to recognize errors in your data.Â
All of this can be done with Exploratory Data Analysis. It helps you gather insights and make better sense of the data, and removes irregularities and unnecessary values from data.Â
- Helps you prepare your dataset for analysis.
- Allows a machine learning model to predict our dataset better.
- Gives you more accurate results.
- It also helps us to choose a better machine learning model.
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Figure 2: Exploratory Data Analysis uses
Steps Involved in Exploratory Data Analysis
Data collection is an essential part of exploratory data analysis. It refers to the process of finding and loading data into our system. Good, reliable data can be found on various public sites or bought from private organizations. Some reliable sites for data collection are Kaggle, Github, Machine Learning Repository, etc.
The data depicted below represents the housing dataset that is available on Kaggle. It contains information on houses and the price that they were sold for.Â
Figure 3: Housing dataset
Data cleaning refers to the process of removing unwanted variables and values from your dataset and getting rid of any irregularities in it. Such anomalies can disproportionately skew the data and hence adversely affect the results. Some steps that can be done to clean data are:
- Removing missing values, outliers, and unnecessary rows/ columns.
- Re-indexing and reformatting our data.
Now, itâs time to clean the housing dataset. You first need to check to see the number of missing values in each column and the percentage of missing values they contribute to.
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          Figure 4: Finding Missing Values             Â
To do so, drop the columns which are missing more than 15% of the data. Further, some variables are missing a significant chunk of the data, like PoolQC , MiscFeature, Alley, etc., seem to be outliers.Â
Figure 5: Dropping Missing Values
Your final dataset after cleaning looks as shown below. You now have only 63 columns of importance.
Figure 6: Final Dataset
In Univariate Analysis, you analyze data of just one variable. A variable in your dataset refers to a single feature/ column. You can do this either with graphical or non-graphical means by finding specific mathematical values in the data. Some visual methods include:
- Histograms: Bar plots in which the frequency of data is represented with rectangle bars.
- Box-plots: Here the information is represented in the form of boxes.
Lets make a histogram out of our SalePrice column.              Â
Figure 7: Data Distribution in our Dataset
From the above graph, you can say that the graph deviates from the normal and is positively skewed. Now, find the Skewness and Kurtosis of the graph.                Â
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Figure 8: Skewness and Kurtosis in your data
To understand exactly which variables are outliers, you need to establish a threshold. To do this, you have to standardize the data. Hence, the data should have a mean of 1 and a standard deviation of 0.Â
Figure 9: Standardising data
The above figure shows that the lower range values fall in a similar range and are too far from 0. Meanwhile, all the higher range values have a range far from 0. You cannot consider that all of them are outliers, but you have to be careful with the last two variables that are above 7.
Here, you use two variables and compare them. This way, you can find how one feature affects the other. It is done with scatter plots, which plot individual data points or correlation matrices that plot the correlation in hues. You can also use boxplots.
Lets plot a scatter plot of the greater living area and Sales prices. Here, you can see that most of the values follow the same trend and are concentrated around one point, except for two isolated values at the very top. These are probably the data points with values above 7.               Â
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Figure 10: Scatterplot
Now, delete the last two values as they are outliers.           Â
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Figure 11: Deleting Outliers
Now, plot a scatter plot of the Basement area vs. the Sales Price and see their relationship. Again, you can see that the greater the basement area, the more the sales price.       Â
Figure 12: Scatterplot
Moving ahead, plot a boxplot of the Sales Price with Overall Quality. The overall quality feature is categorical here. It falls in the range of 1 to 10. Here, you can see the increase in sales price as the quality increases. The rise looks a bit like an exponential curve.Â
Figure 13: Boxplot
Exploratory Data Analysis
How to conduct exploratory data analysis?
Here are six key steps that you can follow to conduct EDA: 1. Observe your dataset The first step to conducting exploratory data analysis is to observe your dataset at a high level. Start by determining the size of your dataset, including how many rows and columns it has. This can help you predict any future issues you might have with your data. 2.
What is exploratory data analysis (EDA)?
Photo by Devon Divine on Unsplash Exploratory Data Analysis (EDA) is the single most important task to conduct at the beginning of every data science project. In essence, it involves thoroughly examining and characterizing your data in order to find its underlying characteristics, possible anomalies, and hidden patterns and relationships.
Why is exploratory data analysis important?
However, many EDA techniques can remedy some common problems that are present in every dataset. Exploratory Data Analysis does two main things: 1. It helps clean up a dataset. 2. It gives you a better understanding of the variables and the relationships between them.
What is exploratory data analysis in data mining?
In data mining, Exploratory Data Analysis (EDA) is an approach to analyzing datasets to summarize their main characteristics, often with visual methods. EDA is used for seeing what the data can tell us before the modeling task. It is not easy to look at a column of numbers or a whole spreadsheet and determine important characteristics of the data.
