bizlens 2.2.0

Integrated Analytics Platform — Descriptive, Diagnostic, Predictive, Prescriptive & Simulation Analytics

BizLens v2.0.0

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

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🎯 What is BizLens?

BizLens is a comprehensive analytics platform for business and educational use, featuring:

• Descriptive Analytics: What happened? (statistics, distributions, visualizations)
• Diagnostic Analytics: Why did it happen? (correlations, hypothesis testing, assumptions)
• Predictive Analytics: What will happen? (regression, forecasting, confidence intervals)
• Sample vs Population: Explicit distinction throughout (n-1 vs n denominator)

Designed for High School → Undergraduate → Postgraduate students and professionals.

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✨ Key Features

📊 Descriptive Analytics

• Central tendency: Mean, Median, Mode
• Dispersion: Range, Variance, Standard Deviation, IQR
• Distribution analysis: Skewness, Kurtosis
• 9+ visualization types (histogram, boxplot, violin, density, heatmap, etc.)
• Professional color schemes (Academic, Pastel, Vibrant)

🔍 Diagnostic Analytics

• Hypothesis testing (t-tests, ANOVA, chi-square)
• Correlation analysis (Pearson, Spearman)
• Assumption checking (normality, linearity, homoscedasticity)
• Segment analysis and comparisons
• Effect size and statistical significance

🔮 Predictive Analytics

• Linear regression (simple & multiple)
• Time series forecasting with seasonality
• Logistic regression (binary classification)
• Confidence intervals & uncertainty quantification
• Cross-validation and model evaluation
• Diagnostic plots (residuals, Q-Q plots)

📚 Educational Excellence

• Sample vs population distinction in all calculations
• Mathematical notation and formulas
• Real datasets with proper citations (Iris, Titanic, Gapminder, World Bank)
• Jupyter notebook templates (12-section standardized structure)
• Python fundamentals integrated throughout
• Skill-level progression (Basics → Intermediate → Advanced)

🎓 Real Data Integration

• Built-in sample datasets with citations
• World Bank API integration (with caching)
• Dataset metadata and quality reports
• Reproducibility and provenance tracking

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🚀 Quick Start

Installation

pip install bizlens==2.0.0

Basic Example

import bizlens as bl
import pandas as pd

# Load data
data = bl.load_dataset('iris')

# Describe (Sample-level statistics)
stats = bl.describe(data['sepal_length'], calculation_level='sample')
print(stats)

# Diagnose (Hypothesis test)
t_stat, p_value = bl.test.compare_groups(
    data[data['species']=='setosa']['sepal_length'],
    data[data['species']=='versicolor']['sepal_length']
)

# Predict (Linear regression)
prediction = bl.predict.regression.simple(
    x=data['sepal_length'],
    y=data['petal_length'],
    confidence_interval=0.95
)

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📚 Learning Pathways

For Business Analytics

• Sales forecasting with seasonal decomposition
• Customer segmentation and profiling
• Marketing effectiveness analysis
• Revenue prediction and ROI estimation

For Data Science

• Statistical foundations
• Hypothesis testing workflows
• Regression model development
• Time series analysis and forecasting

For Academic Research

• Rigorous statistical methods
• Publication-ready visualizations
• Assumption validation
• Effect size and confidence intervals

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🎯 Sample vs Population

A core pedagogical principle throughout BizLens:

# Sample (your dataset)
sample_stats = bl.describe(data, calculation_level='sample')  # Uses n-1

# Population (all possible values)
pop_stats = bl.describe(data, calculation_level='population')  # Uses n

# Compare both
bl.compare_sample_population(data)

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🔧 API Overview

Descriptive Analytics

bl.describe(data)                    # Comprehensive statistics
bl.visualize.histogram(data)         # 9+ visualization types
bl.datasets.load_dataset('iris')     # Real datasets with citations

Diagnostic Analytics

bl.test.hypothesis(data1, data2)     # Hypothesis testing
bl.correlation.pearson(data)         # Correlations
bl.assumptions.normality(data)       # Assumption checking

Predictive Analytics

bl.predict.regression.simple(x, y)   # Simple linear regression
bl.predict.forecast(timeseries)      # Time series forecasting
bl.predict.classify.logistic(X, y)   # Logistic regression

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📖 Documentation & Examples

• Quick Start: 15 minutes to first analysis
• Notebooks: 12-section templates for all use cases
• API Reference: Complete function documentation
• Roadmap: v2.1+ will add ML foundations (decision trees, random forests, clustering)

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🛣️ Roadmap

v2.0.0 (Current) - Integrated Analytics Platform

• ✅ Descriptive, diagnostic & predictive analytics
• ✅ Sample vs population distinction
• ✅ Real data integration
• ✅ Educational focus

v2.1 (Q3 2026) - ML Foundations

• Classification (decision trees, random forests, naive bayes)
• Clustering (K-means, hierarchical, DBSCAN)
• Dimensionality reduction (PCA, feature selection)
• AutoML basics

v2.2 (Q4 2026) - Advanced ML

• Ensemble methods (XGBoost, LightGBM, stacking)
• Advanced time series (ARIMA, SARIMA, Prophet)
• Anomaly detection (Isolation Forest, LOF)
• Explainability (SHAP, LIME)

v3.0 (2027) - Deep Learning

• Neural networks (MLPs, CNNs, RNNs)
• Transfer learning
• Reinforcement learning
• Foundation model integration

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📦 Requirements

• Python 3.8+
• pandas >= 1.3.0
• numpy >= 1.21.0
• scipy >= 1.7.0
• matplotlib >= 3.3.0
• seaborn >= 0.11.0

Optional:

• polars >= 0.14.0 (for performance)
• plotly >= 5.0.0 (for interactive plots)

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📄 License

MIT License - See LICENSE file for details

👨‍💻 Author

Sudhanshu Singh

• Email: [e-mail]
• GitHub: https://github.com/solutiongate-learn/bizlens

🤝 Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

📝 Citation

@software{bizlens2026,
  title={BizLens: Integrated Analytics Platform},
  author={Singh, Sudhanshu},
  year={2026},
  url={https://github.com/solutiongate-learn/bizlens}
}

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BizLens v2.0.0 - Making analytics accessible, rigorous, and educational.