CSV analysis, dataset profiling, regression, correlation, confusion matrix, normalization, Z-scores, and probability calculators.
Analyze CSV text or uploads to inspect row count, column names, data types, and preview tables.
Generate summary statistics, memory footprint, and categorical vs numerical column breakdowns.
Detect null/empty cells, calculate missing value percentages per column, and recommend imputation strategies.
Identify duplicate data rows and calculate percentage duplication across tabular datasets.
Calculate Pearson correlation coefficient (r) between two numeric series.
Calculate linear regression equation (y = mx + c), slope (m), y-intercept (c), and R-squared (R^2).
Input True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN) for model evaluation.
Calculate Accuracy, Precision, Recall, Sensitivity, Specificity, and F1-Score metrics.
Scale numeric series into range [0, 1] using Min-Max normalization.
Standardize dataset values to zero mean and unit variance (Z-score transformation).
Identify statistical outliers using the 1.5x Interquartile Range (IQR) rule.
Calculate row count splits for Machine Learning Train/Validation/Test ratios (e.g. 80/10/10).
Compare Min-Max vs Robust Scaling vs MaxAbs scaling side-by-side.
Calculate event probability P(A), union P(A U B), intersection P(A n B), and conditional probability P(A|B).
Calculate Gaussian Normal Distribution Probability Density Function (PDF) and Cumulative Distribution Function (CDF).
Calculate Z-Score from raw value, mean, and standard deviation, with p-value probability lookup.
Calculate Principal Component Analysis (PCA) 2D covariance matrix and explained variance percentages.
Calculate Shannon Information Entropy H(X) in bits for probability distributions.
Calculate Decision Tree Information Gain IG(T, a) = H(T) - H(T|a).
Calculate required statistical sample size based on population size, confidence level (95%, 99%), and margin of error.