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How to Perform Outlier Detection Using Boxplots and Z-Scores
Outlier detection is a critical step in data preprocessing and analysis. Outliers can distort statistical analyses and machine learning models, leading to misleading results. Two widely used techniques for detecting outliers are boxplots and z-scores. Each method offers distinct advantages depending on the distribution and nature of the data. This article explores how to effectively…
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How to Perform Missing Data Analysis Using EDA
Exploratory Data Analysis (EDA) is a critical first step in understanding and analyzing data, especially when dealing with missing values. Missing data can significantly impact the performance of machine learning models and statistical analyses if not handled properly. Performing a comprehensive missing data analysis during EDA helps in identifying patterns, understanding the nature and extent…
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How to Perform Exploratory Data Analysis on Structured vs Unstructured Data
Exploratory Data Analysis (EDA) is a crucial process in the data science workflow, aimed at understanding the dataset and uncovering underlying patterns, anomalies, and relationships. It typically involves visualizing and summarizing the data to generate insights that can guide further modeling or decision-making processes. The approach to EDA can differ significantly when dealing with structured…
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How to Perform Exploratory Data Analysis on Image Data
Exploratory Data Analysis (EDA) on image data is a critical step in understanding the structure, patterns, and quality of image datasets before applying any machine learning or deep learning models. Unlike structured tabular data, image data presents unique challenges due to its high dimensionality, format complexity, and visual nature. Performing effective EDA on image data…
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How to Perform Exploratory Data Analysis on Geospatial Data
Exploratory Data Analysis (EDA) is a critical first step in any data science or analytics workflow, and this holds true for geospatial data as well. Geospatial data adds complexity with its spatial component, but it also unlocks powerful insights through spatial relationships and patterns. Performing EDA on geospatial data involves a blend of statistical analysis,…
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How to Perform Exploratory Data Analysis on Financial Data
Exploratory Data Analysis (EDA) is a crucial step in understanding financial data before diving into modeling or decision-making. It helps uncover underlying patterns, spot anomalies, test hypotheses, and check assumptions. Here’s a comprehensive guide on how to perform EDA on financial data, covering key steps, techniques, and best practices. Understanding the Nature of Financial Data…
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How to Perform Exploratory Data Analysis on Complex Networks
Exploratory Data Analysis (EDA) on complex networks is a critical first step in understanding the structure, behavior, and properties of networked systems. Complex networks appear in many domains such as social networks, biological systems, the internet, and transportation grids. EDA helps uncover patterns, anomalies, and essential characteristics of these networks, providing a strong foundation for…
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How to Perform Dimensionality Reduction Using PCA in EDA
Dimensionality reduction is a critical step in Exploratory Data Analysis (EDA), especially when dealing with high-dimensional datasets. Principal Component Analysis (PCA) is one of the most widely used techniques for reducing the number of features in a dataset while retaining as much of the variance (information) as possible. Here’s a detailed guide on how to…
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How to Perform an EDA Workflow for Data Science Projects
In data science, performing Exploratory Data Analysis (EDA) is a fundamental step in understanding the dataset, uncovering underlying patterns, and ensuring data quality. EDA is critical before any machine learning model or statistical analysis because it helps to grasp the context of the data, identify trends, and spot potential problems like missing values or outliers.…
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How to Investigate Relationships Between Multiple Variables Using EDA
Exploratory Data Analysis (EDA) is a crucial step in understanding and analyzing the relationships between multiple variables in a dataset. It helps uncover patterns, trends, correlations, and outliers, offering valuable insights for model development or further analysis. Here’s a comprehensive guide on how to investigate relationships between multiple variables using EDA. 1. Start with Descriptive…