bizlens 2.3.3

Integrated Analytics Platform - Descriptive, Diagnostic & Predictive Analytics with Sample vs Population Distinction

BizLens 📊

Business analytics, statistical inference, process mining, and machine learning — all in one package

BizLens is a comprehensive Python library designed for business analysts, data scientists, educators, and students. It combines professional statistical analysis, beautiful Rich tables, interactive visualizations, and built-in business process mining capabilities — all accessible with a simple `pip install`.

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🚀 Open in Google Colab — No Installation Needed

Click any badge below to launch the notebook instantly:

Core Analytics

Notebook	Colab Link	What You'll Learn
Quick Start		Overview, frequency tables, outlier detection
Descriptive Analytics		Frequency, percentile, contingency, data profile
Statistical Inference		Confidence intervals, t-tests, ANOVA, correlation
Chi-Square & Association		Chi-square, contingency tables, Cramér's V
Probability & Distributions		Distribution fitting, simulation, sampling

Machine Learning

Notebook	Colab Link	What You'll Learn
Linear & Multiple Regression		OLS regression, diagnostics, predictions
Logistic Regression		Binary classification, ROC, confusion matrix
Decision Trees & Random Forests		Tree models, feature importance, ensembles
PCA & Clustering		Dimensionality reduction, K-Means, DBSCAN
Conjoint Analysis		Preference modeling, attribute utilities
Q-Learning		Reinforcement learning basics, Q-table

Advanced Analytics & Process Mining

Notebook	Colab Link	What You'll Learn
Master Process Mining		Case metrics, bottlenecks, variants, resources, workflow
Time Series & Anomaly Detection		Temporal patterns, trend analysis, anomaly detection

All notebooks automatically install the latest `bizlens` on first run. Just click the badge and run the first cell.

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💾 Installation

```bash pip install bizlens ```

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✨ Features

• Descriptive Analytics: Frequency tables, percentiles, contingency tables, data profiling
• Statistical Inference: Confidence intervals, t-tests, ANOVA, correlation, chi-square
• Machine Learning: Linear/Logistic regression, Decision Trees, Random Forests, PCA, Clustering, Conjoint Analysis, Q-Learning
• Process Mining: Case metrics, bottleneck analysis, variant discovery, resource analysis
• Time Series & Quality: Anomaly detection, completeness reports, dual pandas + polars support
• Beautiful Rich tables and interactive visualizations out of the box

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Made with ❤️ for analysts, educators, and students