Vitor Domingos’ Post

🔧 Sensor Component Failure Prediction Project 🚚 In predictive maintenance, reducing false predictions is essential to prevent unnecessary repair costs and, most importantly, avoid potentially hazardous breakdowns. Project Highlights: ➡️ Objective: Minimize false predictions to reduce repair costs and enhance safety. ➡️ Data Prep: Conducted extensive Exploratory Data Analysis (EDA) and used multiple imputation techniques (KNN, Simple Imputer - Median/Constant/Mean, MICE, PCA) to handle missing values. Tackled class imbalance with SMOTETomek to ensure our models are well-balanced. ➡️ Modeling: Implemented various machine learning models, including: Random Forest Decision Tree Logistic Regression K-Neighbors Classifier Gradient Boosting XGBoost CatBoost AdaBoost ➡️ Outcome: Among all models, XGBClassifier stood out, delivering a cost of $1440. False negatives (FN), which incur a higher cost of $500 per FN, contributed the most to the overall expense, compared to false positives at $10 per FP. Minimizing FNs became our focus to avoid costly truck breakdowns and ensure safety on the road. Conclusion: Leveraging advanced imputation, class balancing, and a diverse set of models allowed us to hone in on a solution that supports safer and more cost-effective operations. Predictive maintenance in action! For more details, check out the full project on GitHub: https://lnkd.in/gwvj8k_u #PredictiveMaintenance #MachineLearning #DataScience #XGBoost #Logistics #Safety #CostReduction #LinkedInProjects #MaintenanceTech

Thrilled to share that our presentation on anomaly detection in machine maintenance is officially a wrap! 🚀 Gayathri Sasikumar , Shamina Raja Mohamad It's been an incredible journey delving into the intricacies of anomaly detection and its transformative impact on reliability engineering. We grabbed this opportunity on the very first day of class when the Professor asked us about doing an optional presentation. The dataset we got was not a great one, it was synthetic data about Machine Maintenance and we had to deal with imbalanced data which was quite a challenge. Nonetheless, we were able to detect the anomalies in the best possible ways. From exploring traditional methods like supervised decision trees to the cutting-edge applications of One-Class SVM, we've uncovered invaluable insights into detecting deviations and ensuring operational excellence. 💡 Special thanks to Professor Amir Zemoodeh, and all our classmates who joined us on the presentation, contributing their expertise and insights. This discussion had gave us wonderful insights into topics like AUC curve and its importance. Together, we've explored the vital role of anomaly detection in preventing costly breakdowns, optimizing maintenance schedules, and enhancing operational safety. As we reflect on our learnings, we're more inspired than ever to harness the power of data to keep our machines running smoothly and our operations thriving. 🛠 Here's to continued innovation, collaboration, and success in machine maintenance! #AnomalyDetection #MachineMaintenance #ReliabilityEngineering #DataAnalytics #OperationsExcellence #Industry40 #FraudAnalytics #MachineLearning #DataScience

Big Data Analytics (BDA) is another tool in the tech arsenal that’s making waves. It’s all about using the vast amounts of data generated daily to make smarter decisions. By analysing data from diverse sources, companies can identify the best locations for drilling, anticipate when machinery might need maintenance and much more. BDA streamlines operations and boosts safety and productivity. For instance, companies can predict when equipment might fail to prevent accidents and avoid costly downtime. “What is Big Data and Why is it important?” TechTarget #techtarget #bigdata #newage

summarise and group_by functions from dplyr package are used in pairs to get deep insights from the data. The summarize function takes many observations and turns them into one observation. group_by function divides all the rows in the dataset into pre-defined groups. In this video we will generate some interesting observations on the automobiles data set by grouping the observations into three different groups and generating statistical summaries. You can watch the video here: https://lnkd.in/dNMaXDCi This video is about 5 mins duration... 😀

Enhancing the reliability of predictive modeling: https://ow.ly/pxvm50StkJL . . #predictiveanalytics #models #reliability #development

🚀✨ Thrilled to Share My Latest Project: Predicting Fuel Type Using Logistic Regression! 🚗💨 In this exciting journey, I tackled the challenge of predicting fuel types based on features like 'name', 'company', 'year', 'Price', and 'kms_driven'. Here’s a quick rundown of the steps I took: 🧹 Data Cleaning: I kicked things off by cleaning the dataset, removing any empty cells to ensure accuracy! ✅ 🔠 Encoding: Used label encoding to transform categorical string values into numerical format, making the data model-ready! 📊 📏 Scaling: Applied Min-Max scaling to normalize the feature values, enhancing the model's performance! 📈 🔍 Data Splitting: Divided the dataset into an 80% training set and a 20% test set for effective evaluation! 🎯 🧠 Model Training: Trained a logistic regression model and evaluated its performance using precision, recall, F1-score, and accuracy! 📉 📊 Confusion Matrix: Visualized the results with a confusion matrix to see how well the model performed! 🥳 The potential applications of this model in the automotive industry! 🚘💡 I would love to hear your thoughts and feedback on this project! Let’s connect and discuss! 🤝💬 #MachineLearning #LogisticRegression #FuelTypePrediction #DataCleaning #ModelEvaluation

Bergen Engines to add ioCurrents analytics as optional extra https://lnkd.in/e3hnZRBu

Time to Event Analysis: An Introduction https://lnkd.in/g6J9xVu9 A Statistical Methods for Failure-Time Data article by Shishir Rao. In this article, we will analyze vehicle shock absorber failure time data. Failure time data is also known as survival data, life data, event-time data or reliability data, depending on the field of study. and estimate a few basic survival quantities.

Bergen Engines to add ioCurrents analytics as optional extra https://lnkd.in/eDCkEbGs

I am excited to share the results of my recent project on predicting vehicle miles per gallon (MPG) using linear regression! Through meticulous data preprocessing, in-depth exploratory data analysis, and rigorous model training, I achieved an impressive R^2 value of 0.821, indicating a strong correlation between predicted and actual MPG values. Key Highlights: - Comprehensive data preprocessing and outlier analysis - Insightful univariate, bivariate, and multivariate analyses - Robust model performance with a clear visualization of actual vs. predicted values This project not only demonstrates the predictive power of linear regression but also underscores the potential for enhancing fuel efficiency in the automotive industry. #DataScience #MachineLearning #LinearRegression #DataAnalysis