External ML AI Projects - NenadBalaneskovic/NenadBalaneskovic GitHub Wiki

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Table of contents

  1. Introduction
  2. Project 1: Bank Marketing Campaign - Predictive Modeling
  3. Project 2: Boarder Crossing Forecasting - SARIMAX Modeling
  4. Project 3: Stock Price Forecasting - SARIMAX Modeling with Akima Interpolation
  5. Project 4: Sentiment Analysis Project - News Sentiment Evaluation
  6. Project 5: GAN vs OpenCV Chessboard Reconstruction
  7. Project 6: Real Estate Data Set Analysis in Flower Hill
  8. Project 7: Advanced Signal Denoising Framework
  9. Project 8: Quantum Optimization with Qiskit
  10. Project 9: Balanced Gauge Study Analysis

External Machine Learning and AI driven Data Analysis Projects Static Badge

Introduction

This section lists some of the external projects involving various algorithmic Machine Learning and AI driven methods within the realm of data analysis in technical, financial and physical domains which I had the opportunity to implement during my career. Most of them have been pursued with the intention of experimenting with numerous modern methods of mathematical physics, information and complexity theory in the context of their interaction with capabilities of advanced statistical inference used in the scope of contemporary data analysis.

Machine learning, while not exclusive to physics, has become an invaluable tool in the field. It involves the use of algorithms and statistical models that allow computers to learn patterns from data and make predictions or decisions without explicit programming. In physics, machine learning is applied to analyze complex systems, identify patterns in experimental data, and simulate phenomena that would be computationally expensive using traditional methods. It is accelerating advancements in areas such as quantum mechanics, cosmology, and material science, helping researchers uncover insights that were previously unattainable.

Machine learning is a transformative computational technique that enables systems to learn patterns and make decisions based on data, without being explicitly programmed for every task. It relies on algorithms and statistical models, such as neural networks, decision trees, and support vector machines, to analyze vast and complex datasets. In physics, where phenomena often involve intricate systems and massive amounts of data, machine learning has proven to be a powerful tool for accelerating research and discovery.

In experimental physics, machine learning is employed to process and analyze data from sophisticated instruments, such as particle detectors, telescopes, and quantum devices. It helps physicists detect patterns, anomalies, or rare events that might otherwise go unnoticed, such as identifying gravitational waves or isolating quantum states. In computational physics, machine learning is used to optimize simulations, solve partial differential equations, and reduce the computational cost of modeling complex systems, like fluid dynamics or astrophysical phenomena.

Furthermore, machine learning has paved the way for new approaches to understanding fundamental physical laws. By training models on data from experiments and simulations, physicists can uncover hidden relationships and generate predictions about systems that are difficult to study directly. For example, machine learning has been used to predict material properties, simulate high-energy physics events, and even design new experiments.

Another significant application of machine learning in physics is in quantum mechanics and quantum computing. Machine learning aids in analyzing and optimizing quantum algorithms, as well as interpreting the outcomes of quantum experiments. These methods are crucial for advancing quantum technologies and harnessing their potential.

In summary, machine learning is revolutionizing physics by enabling researchers to handle the complexity and scale of modern scientific challenges. Its applications span experimental, theoretical, and computational domains, making it a cornerstone in shaping the future of physics research and technology development.

The context of the project list displayed below will grow continuously as new accomplished ML-driven data analytical studies and their pdf reports become available.

Project list

Project 1 - Details

1. "Bank Marketing Campaign - Predictive Modeling"

Description

This project analyzes customer responses to a bank's term deposit marketing campaign, employing machine learning to optimize predictive accuracy and improve future campaign strategies.

Technologies and Python packages used in the project:

πŸ“Œ Technologies Used

  • Machine Learning Algorithms

    • Logistic Regression
    • Decision Tree
    • Random Forest
    • XGBoost
    • Ensemble Learning (Stacking, Bagging, Boosting)

  • Data Preprocessing Techniques

    • Handling Missing Values (Imputation & Removal)
    • One-Hot Encoding & Label Encoding
    • Scaling & Normalization
    • Class Balancing (SMOTE)

  • Evaluation & Metrics

    • Accuracy, Precision, Recall, F1-Score
    • Hyperparameter Tuning

  • Visualization & Interpretation

    • Feature Importance
    • Heatmaps & Correlation Analysis
    • Confusion Matrix

  • Deployment Methods

    • Pickle Storage for Trained Models

🐍 Python Packages Used

  • Core Libraries

    • pandas – Data manipulation
    • numpy – Numerical computations
    • scikit-learn – ML algorithms & preprocessing
    • xgboost – Gradient boosting

  • Data Preprocessing & Feature Engineering

    • sklearn.preprocessing – Scaling & encoding
    • imbalanced-learn – SMOTE for class balancing

  • Machine Learning & Model Evaluation

    • sklearn.linear_model – Logistic Regression
    • sklearn.tree – Decision Trees
    • sklearn.ensemble – Random Forest & StackingClassifier
    • sklearn.metrics – Evaluation metrics

  • Visualization

    • matplotlib – Plotting graphs
    • seaborn – Statistical visualizations

  • Deployment & Model Persistence

    • pickle – Saving models

This setup ensures efficient preprocessing, accurate ML modeling, proper evaluation, and reproducibility.

Project 2 - Details

2. "Boarder Crossing Forecasting - SARIMAX Modeling"

Description

This project attempts at forecasting the number of boarder crossings between USA and Canada based on the corresponding kaggle data set of The Bureau of Transportation Statistics (BTS) containing entries from 1996 to 2024, employing Python's SARIMAX forecasing scheme to optimize predictive accuracy and improve future security strategies.

Technologies and Python packages used in the project:

πŸ“Œ Technologies Used

  • Time Series Forecasting

    • SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous variables)
    • Fast Fourier Transform (FFT) for periodicity estimation
    • Custom & automated cross-validation
    • Grid search for hyperparameter tuning

  • Data Engineering & Processing

    • DuckDB for data extraction and querying
    • Pandas for data manipulation
    • Aggregation techniques for monthly entry volume analysis
    • Train/Test split for model validation

  • Ensemble Learning & Optimization

    • Automated SARIMAX grid search
    • Model parameter storage for reproducibility

  • Evaluation Metrics

    • RMSE (Root Mean Squared Error)
    • MAPE (Mean Absolute Percentage Error)
    • Execution duration tracking

  • Visualization & Interpretation

    • Forecast plots for historical trends and predictions
    • Performance comparison of different SARIMAX models

🐍 Python Packages Used

  • Core Libraries for Data Handling

    • pandas β†’ DataFrame operations & time-series manipulation
    • numpy β†’ Numerical computations

  • Database & Querying

    • duckdb β†’ SQL-style queries on structured datasets

  • Time Series Forecasting & Statistical Modeling

    • statsmodels β†’ SARIMAX model implementation
    • scipy.fftpack β†’ FFT-based periodicity detection

  • Machine Learning & Evaluation

    • sklearn.model_selection β†’ Train/Test split & cross-validation
    • sklearn.metrics β†’ RMSE & MAPE calculation

  • Visualization Tools

    • matplotlib β†’ Standard plotting for trends & comparisons
    • seaborn β†’ Advanced statistical visualizations

This setup provides strong predictive modeling capabilities, efficient data handling, and optimized forecasting methods.

Project 3 - Details

3. "Stock Price Forecasting - SARIMAX Modeling with Akima Interpolation"

Description

This project attempts at forecasting the temporal stock prices evolution based on a ficticious csv file containing daily stock prices by means of Akima interpolated stock price data subject to SARIMAX and critical point modeling implemented via the physical theory of critical phenomena.

Technologies and Python packages used in the project:

πŸ“Œ Technologies Used

βœ” Time Series Forecasting

  • SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous variables)
  • Akima Interpolation for trend smoothing
  • Critical Point Analysis for volatility estimation
  • Adaptive Trend Detection for stock price evolution
  • Grid Search Optimization for SARIMAX parameter tuning

βœ” Data Processing & Engineering

  • Data generation (synthetic stock prices)
  • Feature extraction (first & second derivatives of price trends)
  • Characterization of trend types (Bullish Surge, Sharp Decline)
  • Structured CSV-based data storage for analysis

βœ” Evaluation Metrics

  • RMSE (Root Mean Squared Error)
  • MAE (Mean Absolute Error)
  • $$R^2$$ (R-squared performance metric)
  • Execution time tracking

βœ” Visualization & Interpretation

  • Akima-interpolated stock prices
  • Trend detection graphs
  • SARIMAX-forecasted price plots

βœ” Storage & Documentation

  • Model-generated forecasts stored in critical_trends.csv
  • Markdown documentation with embedded references and research papers

🐍 Python Packages Used

βœ” Core Libraries for Data Handling

  • pandas β†’ Data manipulation, CSV reading/writing
  • numpy β†’ Numerical computations

βœ” Time Series Forecasting & Modeling

  • statsmodels.tsa.statespace.sarimax β†’ SARIMAX forecasting model
  • scipy.interpolate.Akima1DInterpolator β†’ Akima interpolation for trend analysis

βœ” Machine Learning & Optimization

  • sklearn.model_selection β†’ Grid search hyperparameter tuning
  • sklearn.metrics β†’ RMSE, MAE, R-squared calculations

βœ” Visualization Tools

  • matplotlib.pyplot β†’ Standard plotting for trends and comparisons
  • seaborn β†’ Advanced statistical visualizations

βœ” Storage & Model Persistence

  • pickle β†’ Saving critical data trends for reproducibility

This setup provides robust time series forecasting, efficient stock price trend analysis, and reliable volatility estimation using SARIMAX and Akima interpolation techniques.

Project 4 - Details

4. "Sentiment Analysis Project - News Sentiment Evaluation"

Description

This project analyzes sentiments of news headlines contained within the Groundnews website by means of a customized Pythonic GUI.

Technologies and Python packages used in the project:

πŸ“Œ Technologies Used

βœ” Natural Language Processing (NLP)

  • Sentiment analysis using NLTK VADER
  • Named Entity Recognition (NER) via SpaCy
  • Keyword extraction
  • Word Cloud visualization

βœ” Web Scraping & Data Fetching

  • Fetching news headlines from Ground News
  • BeautifulSoup for HTML parsing

βœ” Graphical User Interface (GUI)

  • PyQt5-based GUI for interactive analysis
  • Real-time sentiment trend visualization using PyQtGraph

βœ” Data Processing & Export

  • Pandas for structured data handling
  • CSV storage for analysis results
  • PNG export for graphical outputs

βœ” Visualization & Trend Analysis

  • Word Cloud for keyword representation
  • Sentiment trend plotting and tracking
  • Interactive graphs to display sentiment evolution

βœ” Performance Optimization

  • Asynchronous threading for news fetching
  • UI interaction improvements for seamless experience

🐍 Python Packages Used

βœ” Core Libraries for Data Handling

  • pandas β†’ Data manipulation, CSV exporting
  • numpy β†’ Numerical computations

βœ” Natural Language Processing (NLP) & Sentiment Analysis

  • nltk.sentiment.vader β†’ Sentiment polarity scoring
  • spacy β†’ Named Entity Recognition (NER)
  • wordcloud β†’ Generating keyword cloud visualizations

βœ” Web Scraping & Data Fetching

  • beautifulsoup4 β†’ Parsing HTML content for news headlines

βœ” Graphical User Interface (GUI) & Visualization

  • PyQt5 β†’ Interactive GUI elements
  • PyQtGraph β†’ Real-time sentiment trend visualization

βœ” Performance Optimization & Asynchronous Execution

  • threading β†’ Multi-threaded news fetching
  • requests β†’ Asynchronous HTTP requests (suggested future enhancement)

This setup provides a powerful NLP-driven sentiment analysis application, combining real-time news data, text processing, interactive visualization, and efficient data storage for valuable insights.

Project 5 - Details

5. "GAN vs OpenCV Chessboard Reconstruction"

Description

This project aims to compare traditional OpenCV-based methods for chessboard image reconstruction with Generative Adversarial Network (GAN)-driven approaches. The goal is to evaluate the effectiveness of deep learning in reconstructing secluded or obscured chessboard sections more accurately than conventional techniques.

Technologies and Python packages used in the project:

πŸ“Œ Technologies Used

βœ” Computer Vision Techniques

  • Edge Detection (cv2.Canny)
  • Contour Detection (cv2.findContours)
  • Perspective Transformation (cv2.getPerspectiveTransform)

βœ” Deep Learning (GAN)

  • Generative Adversarial Networks (GAN) for chessboard reconstruction
  • Adversarial loss optimization
  • TensorFlow/Keras-based model training

βœ” Image Preprocessing & Augmentation

  • Grayscale normalization
  • Image resizing (cv2.resize)
  • Dataset creation for GAN training

βœ” Performance Optimization & Deployment

  • GPU acceleration using Google Colab (T4 GPU)
  • Batch processing for faster training
  • Model persistence (gan_chessboard_model.h5)

βœ” Evaluation & Comparison

  • Reconstruction accuracy in obstructed images
  • Processing speed and computational efficiency
  • Comparison between GAN-based and OpenCV-based methods

🐍 Python Packages Used

βœ” Core Libraries for Data Handling & Computation

  • numpy β†’ Numerical operations
  • pandas β†’ Data handling

βœ” Computer Vision & Image Processing

  • opencv-python β†’ Edge detection, contour detection, perspective transformation
  • matplotlib β†’ Visualization of reconstructed images

βœ” Deep Learning & Neural Networks

  • tensorflow.keras β†’ GAN model implementation & training
  • tensorflow.keras.models β†’ Model saving & persistence (save_model)
  • tensorflow.keras.optimizers β†’ Loss function optimization

βœ” System & File Operations

  • os β†’ File management
  • shutil β†’ File copying/moving

βœ” Google Colab Integration

  • Google Colab GPU Acceleration β†’ Faster training execution
  • Interactive runtime configuration

This setup provides a powerful AI-driven chessboard reconstruction system, leveraging traditional computer vision (OpenCV) and deep learning-based GAN techniques for superior image completion.

Project 6 - Details

6. "Real Estate Data Set Analysis in Flower Hill"

Description

This project involves a large data set related to real estate sales for a fictional town of Flower Hill. The aim is to combine the analysis of this data set with PyMongo, MLflow, Python (SARIMAX times series forecasting, classification, Neural Networks, Kohonen Maps) and DAG-like process organization of ML-tasks. Thus, we are blending data engineering, machine learning, forecasting, and process automation into a well-structured framework.

Technologies and Python packages used in the project:

πŸ“Œ Technologies Used

βœ” Database & Data Storage

  • MongoDB (PyMongo) β†’ NoSQL database for storing real estate transactions.
  • Apache Airflow β†’ DAG-based automation for machine learning workflows.
  • MLflow β†’ Model tracking, logging, and versioning for ML experiments.

βœ” Machine Learning & Forecasting

  • SARIMAX β†’ Time series forecasting for property price trends.
  • Classification Models β†’ Predict buyer types, resale probability, and price ranges.
  • Neural Networks β†’ Deep learning models for pattern recognition.
  • Kohonen Maps β†’ Self-organizing neural networks for clustering districts.
  • Random Forest β†’ Classification & decision-making for district identification.

βœ” Data Engineering & Processing

  • Feature Engineering β†’ Extracting key property attributes for ML models.
  • Data Cleaning β†’ Handling missing values, formatting timestamps, and standardizing currency.
  • Automated Forecast Updates β†’ Using DAG scheduling in Airflow.

βœ” Visualization & Interpretation

  • Real Estate Price Trends β†’ Box plots, comparative district analyses.
  • Urban Expansion & Buyer Segmentation β†’ Cluster analysis with Kohonen maps.
  • Market Control & Economic Cycles β†’ Comparative analytics across districts.
  • Sentiment Analysis & Economic Growth Forecasts β†’ Exploring price evolution in different districts.

βœ” Performance Optimization & Deployment

  • Windows 10 (Anaconda Environment) β†’ Isolated ML/AI dependencies.
  • Cloud-Based Solution for Scaling β†’ Suggested AWS/GCP/Azure deployment options.
  • GPU Acceleration for Faster Computation β†’ Optimization for ML workloads.

🐍 Python Packages Used

βœ” Core Libraries for Data Handling & Computation

  • pandas β†’ DataFrame operations for structured transactions.
  • numpy β†’ Numerical computations for forecasting models.
  • pymongo β†’ MongoDB integration for transaction storage & retrieval.

βœ” Machine Learning & Forecasting Models

  • statsmodels.tsa.statespace.sarimax β†’ SARIMAX for property price forecasting.
  • sklearn.ensemble.RandomForestClassifier β†’ Random Forest model for classification.
  • kohonen β†’ Kohonen Maps for self-organizing district clustering.
  • tensorflow.keras β†’ Neural Networks for property price prediction.

βœ” Pipeline Automation & Model Tracking

  • apache-airflow β†’ DAG-based execution for ML tasks.
  • mlflow β†’ Model logging, tracking, and visualization.

βœ” Visualization & Interpretation

  • matplotlib β†’ Standard plotting for time series & district comparisons.
  • seaborn β†’ Advanced statistical visualizations.

βœ” System & Deployment Tools

  • pickle β†’ Model persistence & saving trained classifiers.
  • shutil β†’ File management for structured dataset handling.

This setup provides a structured ML pipeline for analyzing real estate trends, leveraging MongoDB, Airflow, MLflow, and various machine learning models for forecasting, classification, clustering, and trend analysis.

Project 7 - Details

"Advanced Signal Denoising Framework"

πŸ“ Description

This project focuses on adaptive noise mitigation techniques in signal processing, evaluating various approaches beyond deep learning models.
The goal is to establish an ensemble-based noise suppression framework, leveraging mathematical modeling, filtering strategies, and real-time adaptability.
Key methodologies include variance estimation, correlation-based denoising, hybrid statistical filtering, and multi-stage noise suppression techniques.

Technologies and Python packages used in the project:

πŸ“Œ Technologies Used

βœ” Signal Processing & Statistical Analysis

  • Median Filter Variance Estimation β†’ Adaptive smoothing and noise variance estimation.
  • Autocorrelation-Based Noise Reduction β†’ Detects and mitigates periodic noise disturbances.
  • Beta-Sigma Adaptive Resampling β†’ Enhances signal fidelity using dynamic resampling strategies.
  • Hybrid Multi-Pass Filtering β†’ Integrates multiple filtering steps for improved robustness.
  • Flexible Dynamic Denoising β†’ Automated selection of optimal denoising techniques based on real-time signal properties.

βœ” Mathematical Modeling & Optimization

  • Root Mean Square Error (RMSE) Analysis β†’ Performance benchmarking of denoising methods.
  • Variance Estimation Techniques β†’ Dynamic signal complexity adjustments.
  • Multi-Stage Fusion Frameworks β†’ Real-time adaptive optimization.

🐍 Python Packages Used

βœ” Core Libraries for Signal Processing

  • numpy β†’ Efficient numerical computations.
  • scipy.signal β†’ Advanced filtering methods.
  • statsmodels.tsa.stattools β†’ Autocorrelation function (ACF) for noise estimation.

βœ” Machine Learning & Statistical Modeling

  • sklearn.metrics β†’ RMSE computation for evaluating signal fidelity.
  • kohonen β†’ Self-organizing clustering for noise classification.

βœ” Visualization & Interpretation

  • matplotlib β†’ Signal waveform analysis.
  • seaborn β†’ Statistical visualization of performance results.

πŸ“Š Performance Evaluation & Optimization

βœ” Comparative RMSE Analysis β†’ Tracks signal accuracy before and after noise reduction.
βœ” Multi-Stage Adaptive Filtering β†’ Combines variance estimation, autocorrelation-based denoising, and dynamic resampling techniques.
βœ” Fusion-Based Optimization β†’ Uses adaptive weighting for selecting the best-performing denoising method dynamically.
βœ” Real-Time Signal Adaptation β†’ Ensures flexibility across different noise environments without prior deep learning training.

πŸš€ Deployment & System Considerations

βœ” Windows 10 (Anaconda Environment) β†’ Structured ML dependencies for implementation.
βœ” Cloud-Based Computational Scaling β†’ Recommended deployment via AWS, GCP, or Azure.
βœ” Parallel Computation β†’ Optimized multi-threaded processing for real-time noise suppression tasks.

This project provides a structured exploration of adaptive signal denoising, enhancing real-time processing with flexible ensemble-based strategies.
The final framework integrates variance estimation, multi-stage filtering, and fusion-based optimization, ensuring robust noise reduction while maintaining signal integrity.

πŸ“Œ Project 8 - Details

"Quantum Optimization with Qiskit (Kaggle introductory course)"

πŸ“ Description

This project explores quantum optimization techniques for solving complex combinatorial problems efficiently using Qiskit. The goal is to leverage quantum algorithms to enhance optimization processes beyond classical methods, enabling faster and more scalable solutions.
Key methodologies include variational quantum optimization, Grover’s search-based decision-making, and hybrid quantum-classical workflows for real-world applications.

πŸ“Œ Technologies Used

βœ” Quantum Computing & Optimization

  • Variational Quantum Eigensolver (VQE) β†’ Solves optimization problems by estimating the lowest energy state.
  • Quantum Approximate Optimization Algorithm (QAOA) β†’ Provides near-optimal solutions for combinatorial problems.
  • Grover’s Search β†’ Accelerates decision-making by reducing search complexity.
  • Quantum Fourier Transform (QFT) β†’ Extracts periodicity for structured problem-solving.

βœ” Hybrid Classical-Quantum Integration

  • Classical Preprocessing with NumPy β†’ Data preparation and matrix operations.
  • Quantum Execution with IBM Quantum β†’ Real-time execution on quantum hardware.
  • Optimization Refinement β†’ Hybrid workflows combining classical solvers with quantum approaches.

🐍 Python Packages Used

βœ” Core Quantum Libraries

  • qiskit β†’ Quantum circuit design and execution.
  • qiskit-aer β†’ High-performance quantum simulations.
  • qiskit-optimization β†’ Specialized optimization functions.

βœ” Mathematical Modeling & Analysis

  • numpy β†’ Efficient numerical computations for parameter tuning.
  • matplotlib β†’ Visualization of quantum optimization results.

πŸ“Š Performance Evaluation & Optimization

βœ” Benchmarking Quantum vs. Classical Optimization β†’ Performance comparison for scalability and efficiency.
βœ” Quantum Error Mitigation β†’ Techniques for improving accuracy and reducing noise interference.
βœ” Hybrid Workflow Enhancement β†’ Integrating quantum algorithms with classical optimization methods.
βœ” Scalability Testing β†’ Evaluating the effectiveness of QAOA and VQE on real-world datasets.

πŸš€ Deployment & System Considerations

βœ” IBM Quantum Platform β†’ Remote execution on quantum processors.
βœ” Local Quantum Simulation β†’ Running Qiskit circuits on qiskit-aer.
βœ” Cloud-Based Optimization Scaling β†’ Leveraging AWS, GCP, or Azure for quantum computing experiments.
βœ” Parallel Execution Strategies β†’ Optimized batch processing for complex optimization tasks.

This project offers a structured approach to quantum-enhanced optimization, leveraging Qiskit for real-world problem-solving.
By integrating classical and quantum techniques, the framework boosts computational efficiency, demonstrating the potential of quantum algorithms in combinatorial optimization.

πŸ“Œ Project 9 - Details

Balanced Gauge Study Analysis

πŸ“ Description

This project focuses on developing a PyQt-GUI for conducting Balanced Gauge Studies, assessing measurement system capability using ANOVA (Analysis of Variance) techniques. The objective is to evaluate repeatability (same operator/device) and reproducibility (different operators/devices), ensuring measurement accuracy across trials. The project applies statistical methodologies to analyze variance components, optimize measurement consistency, and enhance quality control in experimental processes.

πŸ“Œ Technologies Used

βœ” Measurement System Analysis & Statistical Modeling

  • One-Factor Gauge Study β†’ Evaluates repeatability (operator/device consistency).
  • Two-Factor Gauge Study β†’ Assesses reproducibility (operator and part variability).
  • ANOVA-Based Variance Decomposition β†’ Identifies sources of measurement error.

βœ” GUI-Based Data Processing & Visualization

  • PyQt β†’ Interactive GUI for CSV input, Gauge Study generation, and results visualization.
  • pandas β†’ Efficient handling of structured measurement datasets.
  • Matplotlib β†’ Graphical representation of statistical metrics.

🐍 Python Packages Used

βœ” Core Statistical & Data Science Libraries

  • scipy.stats β†’ ANOVA calculations and hypothesis testing.
  • statsmodels β†’ Generalized linear models for variance analysis.
  • numpy β†’ Efficient numerical computations for variance decomposition.
  • matplotlib & seaborn β†’ Data visualization of Gauge Study results.

πŸ“Š Performance Evaluation & Optimization

βœ” Gauge Precision Metrics

  • PTR (Precision-to-Tolerance Ratio) β†’ Evaluates measurement precision reliability.
  • SNR (Signal-to-Noise Ratio) β†’ Determines stability of measurement accuracy.
  • Cp (Process Capability Index) β†’ Ensures measurement system meets industrial standards.

βœ” Graphical & XAI-Based Classification

  • Variance Contribution Plots β†’ Breakdown of measurement variability sources.
  • Box Plots for Repeatability β†’ Identification of operator-specific inconsistencies.
  • Histogram for Measurement Distribution β†’ Evaluates bias and systematic errors.

πŸš€ Deployment & System Considerations

βœ” Interactive GUI for Data Handling β†’ User-friendly interface for importing and analyzing CSV datasets.
βœ” Automated Report Generation β†’ PDF-based summaries with statistical conclusions.
βœ” Real-Time Statistical Evaluations β†’ Immediate Gauge R&R calculations based on user input.
βœ” Scalability & Industrial Application β†’ Optimized for measurement system validation across industries like manufacturing, engineering, and quality control.

This project provides a structured and automated approach to Balanced Gauge Studies, ensuring measurement system validation while leveraging statistical modeling for repeatability and reproducibility assessments.