Unlocking Longevity with Python Data Analysis

💡 Why Model Evaluation is Important in Machine Learning Building a model is only part of the process. Evaluating the model correctly is equally important. In my Machine Learning project, I used the following metrics: 📈 Accuracy 📈 Precision 📈 Recall 📈 F1 Score 📈 ROC-AUC These metrics help us understand how well the model performs and whether it can make reliable predictions. This experience helped me understand the importance of selecting the right evaluation metrics based on the problem. Continuing my journey in Data Science and Machine Learning! #MachineLearning #DataScience #ModelEvaluation #Python

💡 Why Model Evaluation is Important in Machine Learning Building a model is only part of the process. Evaluating the model correctly is equally important. In my Machine Learning project, I used the following metrics: 📈 Accuracy 📈 Precision 📈 Recall 📈 F1 Score 📈 ROC-AUC These metrics help us understand how well the model performs and whether it can make reliable predictions. This experience helped me understand the importance of selecting the right evaluation metrics based on the problem. Continuing my journey in Data Science and Machine Learning! #MachineLearning #DataScience #ModelEvaluation #Python

#Day83 of #100DaysOfLearning Today I focused on an important preprocessing step in Machine Learning: Feature Scaling. What I learned: • Why feature scaling is necessary for ML algorithms • Difference between Normalization (Min Max Scaling) and Standardization (Z score scaling) • How scaling affects distance based algorithms like KNN and K Means • Why some models are sensitive to feature magnitude while others are not Key insight: If features are not on the same scale, some algorithms get biased toward larger values and give incorrect results. Scaling is not optional, it directly impacts model performance. Day 83 completed. Improving how data is prepared before training models. #MachineLearning #DataScience #FeatureScaling #Python #100DaysOfLearning

🚀 **Tech Stack Update** Skeptical Science New Research for Week #17 2026 Technical note: new feature in New Research Every article we list here is eyeball-scanned by a real human but we do lean on bibliographic catalogs (publication databases) to supply article metadata for assembly of each edition of our weekly research surveilla… Read more: https://lnkd.in/gnhvT7cq #AI #Python #CSE #KSRCE #DharaniV

Day 1 BGSCET AI-DS - AI Techniques & Implementation Using Python We kicked off the 2-day workshop by building the right foundations: understanding AI in real systems, setting up Python for implementation, and working hands-on with core libraries like pandas, numpy, and matplotlib/seaborn. Students focused on problem framing, data preparation, and starting AI technique implementation with an engineer’s mindset. Excited for Day 2—more builds, more implementation, more outcomes Certisured NIDITH T T Tarun D #BGSCET #BGSCollegeOfEngineering #AIDS #ArtificialIntelligence #Python #AIWorkshop #DataScience #Pandas #NumPy #Matplotlib #Seaborn #MachineLearning #FutureSkills #StudentDevelopment #Certisured #Analogica

The exploration of aging is increasingly accessible, particularly through the utilization of Python. Researchers are employing advanced algorithms and data analysis techniques to uncover the complexities associated with lifespan and the potential pursuit of immortality. By leveraging the capabilities of Python, we can effectively model aging processes, analyze extensive biological datasets, and investigate interventions that may ultimately extend human longevity. This endeavor not only enhances our comprehension of the science of aging but also paves the way for novel pathways in biomedical research. Read more at https://lnkd.in/ea98rhXj. #AgingResearch #Python #DataAnalysis #BiomedicalResearch #Longevity

📊 Day 4 | Linear Regression 📈📉 Today, I learned about Linear Regression, one of the simplest and most widely used Machine Learning algorithms. It is used to predict a continuous value based on input data. The idea is to find a straight line (best fit line) that represents the relationship between variables. 📌 Example: Predicting product price based on cost or features. To understand this, I implemented a simple Linear Regression model using Python 💻 This helped me see how machines can learn patterns and make predictions. Linear Regression is often the first step into Machine Learning models 📊 #MachineLearning #LinearRegression #DataScience #LearningInPublic #Python

#PrincipalComponentAnalysis (PCA) is more than just a technique for dimensionality reduction - it’s one of the most powerful applications of eigenanalysis in data science. By identifying the directions of maximum variance, PCA simplifies complex datasets while preserving their essential structure. What’s inside this guide: * The math: Covariance matrices and Eigen-decomposition.  * The logic: From data centering to explained variance.  * The code: Python realizations using NumPy and scikit-learn. Swipe through the carousel below to explore the mechanics of PCA! The link to the full #Medium article with complete code is in the first comment. #DataScience #MachineLearning #Python #LinearAlgebra #AI #STEM

📊 Day 8 | Naive Bayes 📊🔍 Today, I learned about Naive Bayes, a probabilistic algorithm based on Bayes Theorem. It assumes that features are independent (which is a “naive” assumption). Despite this, it works very well in many real-world problems. Examples: ✔ Spam detection ✔ Text classification I implemented a Naive Bayes model in Python to see how probabilities are used for prediction 💻 Naive Bayes is simple, fast, and efficient for large datasets. #MachineLearning #NaiveBayes #DataScience #LearningInPublic #Python

Day 04 of Python + Data Science + GenAI 🚀 at @SkillVex.in from @Harshith V V sir 🧠 What I learned today: - for loop for repeating tasks efficiently - for in loop to iterate through strings, lists, and collections - range() function for generating sequences in loops - while loop for condition-based repetition - Useful string functions for text handling and manipulation 💡 Key Takeaway: Loops automate repetitive tasks, and string functions help process text data effectively. @Skillvex @Harshith V V #Python #DataScience #AI #GenerativeAI #LearningInPublic #Consistency #CareerGrowth