How to use Huber loss for robust time series forecasting

Day 22/30 — Regression evaluation: MAE & MSE | FP continues 📐 Today ML (sklearn): Studied regression evaluation: MAE and MSE. Reviewed error distributions to understand bias/variance behavior. Python: Continued Functional Programming (pure functions, map/filter/reduce, small refactors). Takeaway: MAE is more robust to outliers; MSE penalizes large errors heavily—useful when big mistakes must be minimized. #90DaysOfCode #Python #FunctionalProgramming #MachineLearning #Regression #MAE #MSE #DataScience #LearningInPublic

Day 68 of #75DaysOfDataAnalysisChallenge: Cross-Validation and Model Evaluation Techniques! Today, I learned how cross-validation helps evaluate models by testing them on multiple subsets of data, ensuring they are robust and not just overfitted to training samples. I also explored different model evaluation metrics like Accuracy, Precision, Recall, F1-Score for classification, and MSE, RMSE, MAE, and R² for regression. These tools are critical for selecting the right model and making sure it performs well in real-world scenarios. 📊 Check out my full work on GitHub: https://lnkd.in/em9_zFNW #DataAnalysis #Python #MachineLearning #CrossValidation #ModelEvaluation #PredictiveModeling #DataScience #Analytics #DataDriven #75DaysOfDataAnalysis #EntriElevateProgram

🚀 **Python Learning - Day 10** In Chapter 4 of my Python journey, I explored some key data structures: 🔹 **Lists** – creation, indexing, slicing, and manipulation 🔹 **List Methods** – like `append()`, `sort()`, `insert()`, and more 🔹 **Tuples** – understanding immutability and usage 🔹 **Tuple Methods** – like `count()` and `index()` Also solved several practice problems to strengthen my understanding 💪 Learning one step at a time — consistency is the key! 🔥 #Python #CodingJourney #LearningInPublic #Programming #Developer

Stop waiting for the perfect dataset. Too many beginners waste months searching for that “ideal” dataset. Clean, structured, exciting. Guess what? It doesn’t exist. Every messy dataset is an opportunity to learn. Real-world data is incomplete, noisy, and far from perfect. That’s exactly why it teaches you more than a polished one ever could. Your first project doesn’t need to be groundbreaking. It just needs to be finished. Progress beats perfection, every single time. #ProgressOverPerfection #CareerAdvice #datascience #Python #machinelearning #sairani

I just built a Python visualizer for sorting algorithms! This program helps students learn sorting algorithms through real-time visualization. There are 9 different sorting algorithms (Bubble Sort, Merge Sort, Quick Sort, and more) that show how each algorithm processes data step-by-step. Each comparison and swap can be seen in real time, with color-coded highlights to help distinguish events. Another feature is the performance analysis that can run multiple algorithms side-by-side to see how their execution times scale with data size. This allows us to make Big O notation tangible and showcasing the difference in speed between algorithms. I'd like to thank Yair Diaz and Brandon Li for helping out! #CSUF

⚙️Python Generators — Smart Way to Handle Data Efficiently! 💡 While learning Python, I discovered Generators, and they completely changed how I think about loops and memory management. A Generator is a special type of function that lets you iterate through data one item at a time, instead of storing everything in memory at once. It’s created using the yield keyword instead of return. 🔍 Why it’s useful: • Saves memory and time • Great for working with large datasets • Produces values on the go (lazy evaluation) • Makes code cleaner and more efficient. 💬 Generators are one of those Python features that make big data feel small! #Python #Programming #Developer #Coding #Learning #PythonTips #DataScience #TechJourney

Data normalization is essential for algorithms that are sensitive to the magnitudes of the feature values. Normalizing your data helps the algorithm in converging, i.e., to find the local/ global minimum efficiently. Here are some common techniques for data normalization in Python using the scikit-learn library. Moreover, we are offering a Free Certification Course on Machine Learning: https://lnkd.in/gEyZ-ZpS #AnalyticsVidhya #DataScience #MachineLearning

In this experiment, I explored one of the most important steps in data preprocessing — handling missing values. Missing data can affect the accuracy of any analysis, so understanding how to detect and treat it is crucial. 💻 Using Python’s pandas library, I practiced different techniques such as: Identifying missing values Handling them using dropna() and fillna() Replacing missing values with mean, median, or mode for better consistency This experiment helped me understand how data cleaning improves model performance and ensures reliable results in data-driven projects. Ashish Sawant https://lnkd.in/egMdjgKa #DataScience #Python #Pandas #DataCleaning #MachineLearning #DataAnalysis

Propensity Score Matching (PSM) isn’t about algorithms; it’s about balance. When two groups appear comparable across key variables, you can finally measure the causal impact using observational data. Here’s what that looks like 👇 In causality, “balance” is the difference between guessing and understanding. #CausalInference #Analytics #DataScience #Experimentation #Python

𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐌𝐞𝐫𝐠𝐢𝐧𝐠 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 In this comprehensive guide, we explore: ✅ Traditional methods like update() ✅ Modern techniques using dictionary unpacking (**) ✅ The intuitive union operator (|) introduced in Python 3.9 ✅ Deep merging strategies for nested dictionaries 💡 Whether you're working with configuration settings, API responses, or data processing, understanding these methods will enhance your Python proficiency. Read full article: https://lnkd.in/gaNEnd_E #Python #Programming #PythonTips #CodeOptimization #TechTutorial #PythonDevelopment #SoftwareEngineering #DataScience #PythonProgramming #TechCommunity