Neeraj Karande - Quality Engineer
MATLAB Predictive Modeling & Statistical Analysis
This section showcases data-driven solutions using MATLAB for real-world industrial challenges.Through statistical modeling, visualization, and machine learning techniques, it highlights how predictive analytics can uncover insights, optimize performance, and enhance decision-making in manufacturing, operations, and beyond.
Exploratory Data Analysis with MATLAB
In this course, I learned how to explore and interpret data using MATLAB’s interactive tools and Live Scripts. I worked with large tabular datasets, applying group-based statistics and visual summaries to extract trends and outliers. I also used automated code generation to turn visual exploration into reproducible scripts. One of my key projects was analyzing weather event damages using maps and sliders to dynamically filter and present time-based data — giving me the skills to create data-driven stories with minimal manual coding.
Data Processing and Feature Engineering with MATLAB
Here I focused on cleaning messy, real-world data and crafting useful features for predictive modeling. I combined datasets from different sources, handled missing values, identified outliers, and normalized variables to bring everything onto a common scale. I also created datetime variables and worked with unstructured formats like text and images. One of the most interesting parts was transforming raw sensor and text data into engineered features ready for machine learning — a process that gave me a solid understanding of what really makes a model work.
Predictive Modeling and Machine Learning with MATLAB
This final course tied everything together by walking through the complete machine learning pipeline. I trained and evaluated models like decision trees, SVMs, and ensembles using the Classification Learner App. I applied cost-sensitive learning to real data like cancer diagnosis, where recall was more critical than accuracy. I also learned to tune hyperparameters and reduce model complexity using feature selection techniques.
Driving Quality Excellence Through Data-Driven Decision Making with MATLAB
This portfolio demonstrates how MATLAB’s statistical, machine learning, and predictive modeling capabilities. By identifying sources of variation and visualizing critical relationships between process inputs and outcomes, these analyses conform to ISO 9001 standards and other quality management systems that promote evidence-based decision-making and continuous improvement. MATLAB dashboards, predictive models, and interactive visualizations enable data-driven containment, corrective actions, and preventive strategies. Heatmaps, neural network architectures, and multivariate analytics not only uncover hidden patterns and trends but also empower cross-functional teams to collaborate with clarity. Ultimately, this approach transforms raw operational data into actionable insights that enhance product consistency, process capability, and long-term customer satisfaction.