Negative Indexing in NumPy: Accessing End Elements

Exploring clustering algorithms in Machine Learning! 📊 I recently implemented the **K-Medoids (PAM) clustering algorithm in Python** and visualized the clusters using Matplotlib. Unlike K-Means, K-Medoids selects actual data points as cluster centers, making it more robust to outliers. This small project helped me better understand how clustering works and how different algorithms group similar data points. Tools used: • Python • NumPy • Matplotlib Always exciting to turn theory into practical implementation! 🚀 #MachineLearning #DataScience #Python #Clustering #KMedoids #StudentProject

Task 3(Intermediate Level): Clustering Analysis (K Means) Description: Implement K-Means clustering to group similar data points together based on feature similarities. Tools: Python, scikit-learn, matplotlib, seaborn I standardized the dataset (using StandardScaler). I applied K-Means clustering and determined the optimal number of clusters using the elbow method. I visualized the clusters using 2D scatter plots. #CodvedaAchievements #CodvedaProjects #CodvedaJourney #CodvedaExperience #FutureWithCodveda Codveda Technologies

DSA Tip: Binary Trees (Part 2) Building a tree is one thing… traversing it is where the real power comes in. In Binary Trees, we don’t just store data, we visit nodes in a specific order. The 3 main ways: Inorder (Left -> Root -> Right) Preorder (Root -> Left -> Right) Postorder (Left -> Right -> Root) Each traversal gives a different perspective of the same data. Insight: How you traverse data can be just as important as how you store it. Quick Challenge: Given this tree: 5 / \ 3 7 What is the Inorder traversal? Drop your answer, I’ll review the best ones. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #BinaryTree #Python #CodingTips #LearnToCode

You can fit the most common Bayesian regression models in Python using a consistent syntax (similar to brms in R) using the bambi package. It utilizes PyMC to do the simulations. It's remarkably easy and straightforward to use - you just adjust the family name to the right model type. Here are a few examples. More instructions are available here: https://lnkd.in/eGSG3-Bk #statistics #datascience #analytics #rstats #python #peopleanalytics #technology #ai

The Outlier You Shouldn't Have Removed 🐍 The outlier looked wrong. So, you removed it. Clean data. Clean chart. Clean analysis. Except that outlier was the most important row in the dataset. The problem isn't the outlier. It's removing it without asking why it's there. 👇 See the visual below — when to remove, when to keep and real-world scenarios where blind removal backfires. #DataAnalytics #Python #AnalyticsThinking #LearningInPublic

Search Insert Position: Binary Search with Fallback to Left Pointer Standard binary search returns -1 when target not found. This variant returns the insertion index instead. The key insight: when the loop terminates without finding target, left pointer naturally points to where target should be inserted to maintain sorted order. Why Left Pointer Works: Binary search terminates when l > r. At this point, left has crossed past the position where target would fit. This property makes left pointer the correct insertion index without extra logic. Time: O(log n) | Space: O(1) #BinarySearch #InsertPosition #PointerProperties #SortedArrays #Python #AlgorithmDesign #SoftwareEngineering

Day 74 Sometimes a problem becomes clearer when you look at it from a different angle. #Day74 🧩 543. Diameter of Binary Tree Revisited this problem and got a better intuition. Key idea: • For every node, calculate the maximum depth of the left subtree • Calculate the maximum depth of the right subtree • The diameter passing through that node becomes: left_depth + right_depth Then track the maximum result globally while recursion returns the depth. What I realized today: Many tree problems become simple once you separate what you return vs what you track globally. Revision is making these patterns clearer. #LeetCode #DSA #Python #BinaryTree #Recursion #LearningInPublic #Consistency

In quant research, small inefficiencies compound. Most workflows still default to Pandas — not because it’s optimal, but because it’s familiar. We revisited Pandas vs Polars from a pipeline perspective, focusing on execution speed, memory behavior, and scalability. The takeaway is simple: This isn’t a library switch. It’s a shift in how data is processed. Speed, in quant systems, is not just performance — it’s edge. Sharing a short breakdown below. #QuantResearch #DataEngineering #Python #SystemDesign #FinanceTech

We just finished the first YouTube series on py-dss-toolkit. If you use OpenDSS with Python, this series shows practical ways to explore models, extract results, and visualize studies more efficiently. You can use py-dss-toolkit to: ✅ work with OpenDSS data in a more structured way 📊 move results into pandas DataFrames for analysis 💻 create visual workflows, including in Google Colab I’d love your feedback: what should the next series focus on? 🔎 Plotting? 🔎 QSTS? 🔎 Model exploration? 🔎 Automation? #OpenDSS #Python #PowerSystems