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# Data Quality Assessment

This involves examining the overall quality of the data, including checking for missing values, duplicate records, and inconsistencies. It helps identify data quality issues that need to be addressed before further analysis.

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# Data Type Analysis

Understanding the data types of different columns in the dataset is crucial for proper data processing. Data profiling involves identifying the data types (e.g., numerical, categorical, date/time) of each column and ensuring they are correctly interpreted.

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# Summary Statistics

Calculating summary statistics such as mean, median, mode, standard deviation, minimum, and maximum values provides a high-level overview of the dataset's distribution and central tendencies. It helps identify outliers and anomalies in the data.

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# Data Distribution Analysis

Analyzing the distribution of numerical and categorical variables helps understand their underlying patterns and relationships. Visualization techniques such as histograms, box plots, and bar charts are commonly used to visualize data distributions.

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# Cardinality Assessment

Cardinality refers to the number of unique values in a column. Analyzing the cardinality of categorical variables helps understand their diversity and potential impact on analysis tasks such as grouping and aggregation.

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# Data Relationship Analysis

Exploring relationships between different variables in the dataset helps uncover correlations, dependencies, and patterns. Techniques such as correlation analysis, scatter plots, and heatmap visualizations are used to analyze relationships between numerical variables.

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# Data Skewness and Kurtosis

Skewness and kurtosis are measures of the shape of the distribution of numerical variables. Analyzing skewness and kurtosis helps understand the symmetry and tail heaviness of the distributions, which is important for modeling assumptions.

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