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How to Explore the Influence of External Factors Using EDA
Exploratory Data Analysis (EDA) is a fundamental step in the data science workflow, used to analyze datasets, summarize their main characteristics, and uncover relationships between variables before applying any modeling techniques. When aiming to explore the influence of external factors—such as economic conditions, weather, market trends, or government policies—EDA helps uncover patterns, outliers, and correlations…
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How to Explore Distribution Fitting for Better Data Understanding
Exploring distribution fitting is an essential process when analyzing data to better understand its underlying structure. By fitting a probability distribution to your dataset, you can gain insights into the nature of the data, identify patterns, and make informed decisions based on statistical models. Here’s a comprehensive look at how to explore distribution fitting for…
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How to Explore Data with Missing Values Using Multiple Imputation
Missing data is a common problem in real-world datasets, and improper handling of it can lead to biased estimates, reduced statistical power, and misleading conclusions. Among various techniques, multiple imputation (MI) stands out as a robust and statistically sound method to deal with missing data. It allows analysts to explore, analyze, and draw inferences from…
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How to Explore Data Using Summary Statistics in Python
Exploring data using summary statistics in Python is a crucial step in understanding the underlying patterns, distributions, and relationships within a dataset. Summary statistics provide concise information about the central tendency, spread, and shape of the data, making it easier to draw initial insights before performing more complex analyses. In Python, this can be efficiently…
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How to Explore Data Using Interactive Plots and Dashboards
Exploring data is a fundamental step in any data analysis or data science project. It allows you to gain insights, identify trends, and detect patterns or anomalies that might not be immediately visible through traditional summary statistics or raw numbers. One of the most effective ways to explore data is through interactive plots and dashboards.…
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How to Detect Trends and Patterns in Time Series Data with EDA
Exploratory Data Analysis (EDA) is a critical step in any data science workflow, especially when working with time series data. It provides insight into the structure, underlying patterns, and anomalies of the dataset before deploying more complex models. Time series data, by nature, captures observations sequentially over time, making trend and pattern detection an essential…
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How to Detect Seasonal Patterns in Time Series Data
Detecting seasonal patterns in time series data is an essential step in time series analysis, helping identify recurring patterns at regular intervals. These patterns can be daily, weekly, monthly, or even yearly, depending on the nature of the data. Detecting seasonality allows businesses and analysts to forecast future values more accurately and make informed decisions.…
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How to Detect Patterns in Data Using Rolling Window Analysis
Rolling window analysis is a versatile statistical technique used to detect patterns and trends in time series or sequential data. By evaluating subsets of data through a moving, fixed-size window, this method allows for real-time observation of localized behavior, smoothing of noise, and dynamic tracking of changes. It’s widely used in fields like finance, meteorology,…
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How to Detect Non-Linearity in Your Data
Detecting non-linearity in your data is a crucial step in understanding the underlying relationships between variables. Linear models, such as linear regression, assume that the relationship between the independent and dependent variables is linear. However, real-world data is often more complex, and understanding whether the relationships are non-linear can significantly improve the accuracy and insights…
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How to Detect Multivariate Outliers Using Scatterplot Matrices
Detecting multivariate outliers is a crucial step in data preprocessing, especially for tasks like regression analysis, clustering, and machine learning modeling. Outliers can significantly skew results and reduce model performance. One effective visualization method to identify such anomalies is using scatterplot matrices. This article explores how to detect multivariate outliers using scatterplot matrices, their advantages,…