📊 Experiment 12 – Random Forest Classifier In this experiment, I explored the Random I explored how the model splits data into branches based on key features and how it reaches a final decision at each leaf node. It was exciting to visualize the tree structure and learn how pruning can help improve model accuracy. This practical gave me a better understanding of how decision trees handle classification problems in a simple yet powerful way. 📁 GitHub: https://lnkd.in/eTtC53qu 🎓 Guided by: Ashish Sawant #MachineLearning #RandomForest #DataScience #Python #AI #Coding #Learning #JupyterNotebook #CSE #PRMCEAM

🎩 One week later… and the Bayesian model still teaches lessons! This piece made me reflect on how evidence-based decisions reshape our way of thinking — especially when uncertainty tries to take over. With Python and Bayes, we turn guesses into probabilities, intuition into parameters, and raw data into logical management. 🔗 Full article and charts in the comments 👇 If you believe great management begins with reasoning, this one’s for you. 🎯 Hashtags #Python #Bayes #DecisionScience #BayesianStatistics #DataDriven #EvidenceBased #ZVPython #RetroTech #MachineLearning #Analytics #DecisionMaking #LinkedInCreator

📊 Experiment 12: Random Forest Classifier I implemented Random Forest, an ensemble learning technique that combines multiple decision trees. By training and testing the model, I learned how aggregating predictions improves accuracy and reduces errors. This practical gave me a deep understanding of ensemble methods in real-world machine learning applications. 🌲 📁 GitHub: [https://lnkd.in/dFff8cPb] 🎓 Guided by: Ashish Sawant #MachineLearning #RandomForest #Python #AI #DataScience #EnsembleLearning #CSE #PRMCEAM

📊 Experiment 9: K-Nearest Neighbors (KNN) Classifier In this practical, I implemented the K-Nearest Neighbors (KNN) algorithm for classification tasks. I understood how the model predicts new data points by checking their closest neighbors in the dataset. It was an insightful hands-on experience to understand distance-based learning in action. 📁 GitHub: [https://lnkd.in/dFff8cPb] 🎓 Guided by: Ashish Sawant #MachineLearning #KNN #Python #AI #DataScience #Analytics #StudentLearning #CSE #PRMCEAM

I am sharing here the full materials for the workshop on Explanatory Statistical Modeling which I delivered at the SIOP conference last Thursday. The online presentations, interactive assignments, solutions and take home project can be found at https://lnkd.in/eWvtHyb7. This workshop tried to condense a full day program into 3.5 hours, so we did not cover the fourth module on advanced methods. Ideally this content would be delivered over a full day, or over several sessions akin to a multi-week short lecture course.. It is free for you to use as long as you credit me as the source. #statistics #analytics #datascience #peopleanalytics #rstats #python #ai #technology

🚀 Day 17: Stats Behind Univariate & Bivariate Plots Today’s learning combined statistics with visualization to uncover insights more effectively. 📊 Lesson 1: Univariate Plots Focused on analyzing a single variable. Used histograms, bar charts, and boxplots to study distributions, frequencies, and detect outliers. 📊 Lesson 2: Bivariate Plots Explored relationships between two variables. Used scatter plots and correlation to understand how one variable changes with another. 👉 Visualization isn’t just about pretty graphs — it’s about telling a clear story from data. These methods form the base for deeper exploratory analysis. #Day17 #Statistics #Python #DataVisualization #Seaborn #Matplotlib #DataScience #100DaysOfCode

🚀 Day 18: Stats Behind Multivariate Plots & Advanced Charts Today I moved beyond single-variable and two-variable plots, diving into multivariate analysis — where multiple variables are studied together to find deeper insights. 📊 Lesson 1: Multivariate Plots Learned how to visualize relationships among 3+ variables. Used techniques like pair plots, heatmaps, and 3D plots to uncover complex interactions. 📊 Lesson 2: Advanced Charts (Bonus) Explored advanced visualizations such as violin plots, swarm plots, and joint plots. Understood how they give a clearer picture of data distributions and relationships. 👉 Multivariate plots are especially powerful for feature analysis in machine learning and for drawing insights from real-world datasets. #Day18 #Statistics #Python #DataVisualization #DataScience #100DaysOfCode

🚀 DSA Challenge – Day 90 Problem: Single Element in a Sorted Array ⚙️🔍 This problem was a clever application of binary search where understanding the index patterns of paired elements was the key to achieving logarithmic efficiency. 🧠 Problem Summary: You are given a sorted array where every element appears exactly twice, except for one element that appears only once. Your task: return that single element in O(log n) time and O(1) space. ⚙️ My Approach: 1️⃣ Used binary search to narrow down the segment containing the single element. 2️⃣ Checked if the middle element breaks the pairing rule — if so, that’s our unique number. 3️⃣ Observed the pattern: Before the single element, pairs start at even indices. After it, pairs start at odd indices. 4️⃣ Used this parity observation to adjust the search boundaries efficiently. 📈 Complexity: Time: O(log n) → Binary search halves the search space each step. Space: O(1) → Constant space used. ✨ Key Takeaway: Sometimes, the structure of sorted pairs reveals more than meets the eye — a small parity trick transforms a linear scan into a logarithmic search. ⚡ 🔖 #DSA #100DaysOfCode #LeetCode #ProblemSolving #BinarySearch #Algorithms #CodingChallenge #Python #Optimization #EfficientCode #TechCommunity #InterviewPrep #LearningByBuilding #CodeEveryday

#Week3 | Sorting Algorithms: The Foundation of Efficient Data Handling This week, I dove deep into the world of sorting algorithms, exploring how different approaches tackle the fundamental problem of arranging data. Here’s what I covered: * Implemented Merge Sort, a classic divide-and-conquer algorithm. * Implemented Quick Sort, discovering the importance of pivot selection. * Implemented Heap Sort, leveraging the power of heap data structures. Tech Stack / Tools Used: Python, Jupyter Notebook Key Insights / Learnings: Understanding the trade-offs between these algorithms in terms of time complexity, space complexity, and stability was the key takeaway. Merge sort is stable but needs extra space, Quick sort is fast but unstable, and Heap sort is in-place. This Week’s Plan: Next, I'll be exploring another fundamental concept: search algorithms. Reflections / Takeaway: Mastering these sorting algorithms is a crucial step in writing efficient and scalable code. Project / Repo Link: https://lnkd.in/gTaqbQKc #AIJourney #MachineLearning #Python #DataStructures #Algorithms #SortingAlgorithms #LearningInPublic #12WeeksAIReset