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EDA, or exploratory data analysis, is a method for examining and comprehending data collection. Statistical and visualization approaches must be used to find patterns, connections, and anomalies in the data.
E-commerce companies heavily rely on data in today’s data-driven world to make wise decisions and stay ahead of the competition. Exploratory Data Analysis (EDA) is a powerful technique that assists businesses in converting raw data into useful insights. Collecting data to improve your understanding of your clients and business is often not difficult. This blog will discuss how EDA can be useful for processing data for e-commerce enterprises.
EDA can assist online retailers in seeing patterns and trends in consumer behaviors. Each company with a website, a social media presence, and accepting any electronic payment gathers information about its clients, users, web traffic, demographics, and other factors. If you can figure out how to access it, all that data is loaded with promise. Businesses can utilize EDA, for instance, to identify the most well-liked products, the expanding product categories, and the channels consumers like to use to make purchases. With this knowledge, companies may adjust their product lineups and marketing initiatives to satisfy consumer demand and beat out the competitors.
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The practice of obtaining information or data from numerous sources is data collection. Surveys, experiments, observations, interviews, databases, web scraping, social media platforms, sensors, and a host of other sources can all be included in this. The data types that can be collected include categories, textual, audio, visual, and numerical data. Any research or analysis must start with the data gathering procedure since it is the foundation for all later modeling and analysis.
The process of locating and fixing or deleting flaws, inconsistencies, and inaccuracies in data is known as data cleaning, sometimes known as data cleansing or data scrubbing. Data cleaning is done to make the data more accurate and of higher quality for modeling and analysis. There are various steps involved in data cleaning, such as:
- Finding and dealing with missing or incomplete data entails finding any values absent from the data and either eliminating them or replacing them with statistical methods.
- Duplicate record handling: This entails finding and eliminating any duplicate records from the data.
- Making data errors right: This entails locating and fixing any data problems, such as misspellings, typos, and inaccurate values and making data errors right.
A crucial stage in data cleaning and analysis is locating outliers. The data analysis and modeling outcomes can be considerably impacted by outliers, which are data points markedly different from the other data points in the dataset. There are various techniques for finding outliers, such as:
- Visual inspection: In this step, the data points are plotted in a scatter or box plot. The plot is visually inspected for data points that deviate noticeably from the rest.
- Statistical techniques: This entails identifying data points significantly different from the other data points using z-scores or interquartile range (IQR).
- Machine learning techniques: At this step, outliers in the data are found using machine learning algorithms. A few popular machine learning techniques for outlier detection are isolation forest, local outlier factor, and one-class SVM.
Identification of a typical data
Data points that differ significantly from those in a dataset are called anomalous data or outliers. The identification of unusual data can impact the ability of statistical analysis and modelling to be accurate and valid. The following techniques can be used to spot anomalous data: –
- Visualization: Creating visualizations of the data, such as histograms, box plots, or scatterplots, is one of the simplest ways to spot anomalous data. Data points that are very different from the majority of the other data points will show up as abnormal.
- Statistical tests: Several statistical tests, including the z-score test, the interquartile range (IQR) test, and the Grubbs’ test can be used to spot anomalous data. These tests assess if a data point’s value significantly differs from the others by comparing it to the mean or median of the dataset.
Correlation of variables
Correlation is a statistical measure that measures the degree to which two or more variables are related. Correlation analysis is utilized to ascertain the nature, direction, and strength of the relationship between variables.
Exploratory data analysis (EDA) is a powerful method for examining and comprehending large, complicated data sets. EDA can offer insightful information that can guide additional research or decision-making by employing statistical and visualization approaches to find patterns, correlations, and anomalies in the data.
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1. What is Exploratory Data Analysis (EDA)?
ANS: – EDA is an approach to data analysis that involves summarizing and visualizing data to identify patterns, trends, relationships, and anomalies.
2. Why is EDA important?
ANS: – EDA is important because it helps identify patterns and trends in data, which can inform decision-making in various fields, including business, finance, healthcare, and social sciences. EDA can also help to identify outliers and anomalies in the data, which can be important for quality control and data cleaning.
3. What are some common EDA techniques?
ANS: – Some common EDA techniques include data cleaning, data visualization, summary statistics, correlation analysis, and hypothesis testing.
4. What are some common tools used for EDA?
ANS: – Some common tools used for EDA include Excel, Python, R, Tableau, SAS, Power BI, and Google Analytics.
5. What are some challenges of EDA?
ANS: – Some challenges of EDA include dealing with missing data, outliers, and anomalies and determining the appropriate statistical techniques for a given dataset.
WRITTEN BY Sunil H G
Sunil H G is a highly skilled and motivated Research Associate at CloudThat. He is an expert in working with popular data analysis and visualization libraries such as Pandas, Numpy, Matplotlib, and Seaborn. He has a strong background in data science and can effectively communicate complex data insights to both technical and non-technical audiences. Sunil's dedication to continuous learning, problem-solving skills, and passion for data-driven solutions make him a valuable asset to any team.