3 o’clock |Exploratory Data Analysis (EDA)
In other words, exploring data before building models is an essential step in any data science or machine learning workflow. This phase – known as exploratory data analysis (EDA) – allows you to understand your data’s characteristics, detect potential issues, and uncover insights that inform model selection and feature engineering.
Understanding Distributions: You examine how values in a dataset are distributed – whether they follow normal distribution, skewed distribution, or have outliers. Common methods include histograms, box plots, and density plots.
Detecting Correlations: Understanding relationships between variables helps in feature selection. Pearson’s correlation coefficient, scatterplots, and heatmaps are used to determine if variables are positively or negatively correlated.
Identifying Patterns & Trends: Line graphs, bar charts, and time-series plots reveal hidden trends or seasonality in data. Clustering techniques can help detect naturally occurring groups.
Turning Numbers into Action: Leveraging Analytical Techniques
Organizations across industries use statistical analysis to optimize decisions and improve efficiency. Banks assess credit score distributions to gauge lending risks, while hospitals analyze patient wait times to enhance staff allocation. Companies study purchase behavior to refine marketing strategies.
Retailers examine the link between advertising spend and sales to optimize budgets. Researchers explore correlations between lifestyle choices and disease occurrence to guide public health initiatives. Sports teams analyze training intensity and injury rates to improve conditioning programs.
Supermarkets track seasonal sales trends to manage inventory efficiently. Digital platforms identify trending topics through keyword spikes for personalized content recommendations. Utility providers study electricity consumption to enhance grid efficiency and demand-response systems.
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