Riyansh Tanwar’s Post

📶 Experiment 9: K-Nearest Neighbors (KNN) Algorithm using Python 📊 In this lab, I explored the K-Nearest Neighbors (KNN) algorithm — a simple yet powerful instance-based learning technique used for both classification and regression tasks. 🔍 Key learning outcomes: • Understanding the concept of distance-based classification • Implementing KNN using scikit-learn • Choosing the optimal value of K for better accuracy • Evaluating model performance using various metrics • Visualizing decision boundaries and classification outcomes This experiment deepened my understanding of how KNN leverages similarity between data points to make accurate predictions, emphasizing the importance of feature scaling and data normalization. 📁 Explore the repository here :  👉 https://lnkd.in/epWys7e7 #DataScience #MachineLearning  #Python  #KNN  #ScikitLearn  #Classification  #DataAnalysis  #PredictiveModeling  #Statistics  #LearningJourney  #JupyterNotebook Ashish Sawant Sir

Python one-liners are powerful tools that can simplify and speed up common machine learning tasks such as loading data, encoding features, splitting datasets, training models, and extracting insights. By using concise, readable lines of code, practitioners can make their workflows more efficient and easier to maintain. These one-liners leverage popular libraries like Scikit-learn and Pandas to perform complex operations—like fitting a model or sorting feature importances—in just a single expression, helping both beginners and experts write clean, effective code with less effort. Find out more at: https://lnkd.in/gxepPNnH #MachineLearning #Python #DataScience #CodingTips #ScikitLearn #Programming

🧠 Day 78 — Scikit-learn Base Jupyter Notebook Today I learned how to build a simple Machine Learning model using Scikit-learn in Jupyter Notebook. From loading data to saving the trained model — this covered the full ML workflow. I used the “tips” dataset, prepared the data, trained a Linear Regression model, made predictions, and evaluated it using MAE and R² Score. Finally, I saved the model using pickle for future use. This practice helped me understand the complete process of creating, testing, and saving an ML model in Python. ✨ #Day78 #MachineLearning #ScikitLearn #Python #DataScienceJourney #LearningEveryday

Side-by-side comparison of 3D PCA visualization in Python and R. Python focuses on high-quality, presentation-ready visual output, while R offers lightweight, interactive 3D exploration with minimal code. Includes: dataset, Python script, R notebook, and visual examples. Full code available on my GitHub: https://lnkd.in/ghgTA75d #DataScience #PCA #DimensionalityReduction #3DPCA #MachineLearning #Python #Rstats #DataAnalytics #BusinessIntelligence #CustomerSegmentation #DataVisualization #ExploratoryDataAnalysis #Clustering #HighDimensionalData #Analytics #InsightDiscovery #RProgramming #PythonProgramming #GitHubProject

📘 Learning NumPy and Vectorization amazed me You know how in pure Python, say you want to square each number in a list, you have to loop through every element manually? That works — but it’s slow and repetitive. But with NumPy, you don’t loop over elements one by one. You apply the operation to the entire array at once as shown in the code snippet below ✅ Fewer lines of code ✅ Faster execution especially with large datasets ✅ More efficient and readable This simple concept really shows why NumPy is a foundation for data science and machine learning — performance matters when you're working with thousands or millions of values. Excited to keep learning 📈 #NumPy #Python #DataScience #Vectorization #MachineLearning #Day11 Moses O. Adewuyi. #15dayswritingconsistencywithmoses

🚀 Today, I explored some more about NumPy! NumPy is the backbone of numerical computing in Python, and it’s incredible how much we can achieve with just a few lines of code. 💻✨ Efficient array and matrix manipulations Powerful mathematical and statistical functions Essential for data science, ML, and AI projects Some more about what I tried: Calculated matrix determinants and inverses Practiced matrix multiplication and element-wise operations Explored reshaping and stacking arrays for better data handling Excited to keep building my Python and data skills with practical hands-on examples! #Python #NumPy #DataScience #MachineLearning #LearningJourney

🚀Experiment 4: Handling Missing Values in Data using Pandas 🐼 Missing data — one of the most common (and tricky!) challenges in any dataset. In this lab, I learned how to detect, understand, and treat missing values effectively using Python’s Pandas library. 🔍 What I explored:  • Identifying missing data using functions like isnull() & notnull()  • Cleaning data through imputation, removal, and replacement techniques  • Understanding the impact of missing data on model performance This hands-on exercise helped me grasp how data preprocessing lays the foundation for reliable analysis and better machine learning outcomes. 📁 Explore the repository here :  👉 https://lnkd.in/epWys7e7 #DataScience #Python  #Pandas  #DataCleaning  #MachineLearning  #Statistics  #JupyterNotebook Ashish Sawant sir

#Day52 of my fourth #100DaysOfCode Working on a collection of Python projects, including: 1. Scientific Calculator: A simple scientific calculator built with Python's tkinter library, featuring basic arithmetic operations, trigonometric functions, and more! 2. Scrabble Game: A multiplayer Scrabble game built with Python's tkinter library, featuring word validation, score tracking, and definition lookup. 3. Screen Capture Tool: A screen capture tool built with Python's OpenCV and PIL libraries, allowing for screen recording, screenshot capture, and delayed screenshot capture. 4. Search Bot: A search bot built with Python's requests and BeautifulSoup libraries, featuring search query input, search engine selection, summary generation, and word cloud generation. Check out the code on GitHub let me know what you think! Github Link:- https://lnkd.in/gsTcpcAw #Python #Coding #Projects #Scrabble #Calculator #ScreenCapture #SearchBot #OpenCV #tkinter #BeautifulSoup #requests #PIL #gensim #wordcloud #matplotlib #nltk #PyDictionary

Day 11 – PYTHON VARIABLES 🧠🐍 (My Techrise cohort 2 journal) Today in my TechRise Cohort 2 journey, I learned about Python Variables — the building blocks of every program! Variables are like containers that hold data, and I explored different data types such as integers, floats, strings, booleans, and even complex numbers. I also practiced data type conversion in Python using simple code examples. Here’s a quick snippet from my learning: a = 10 k = float(a) p = complex(a) print(k) print(p) Every new lesson makes Python more exciting and practical for real-world AI and Machine Learning applications. 🚀 #TechRiseCohort2 #Python #AI #MachineLearning #CodingJourney #DigitalSkills

Adding a touch of randomness to data can make testing and learning much more powerful. 🎯 Here’s a quick demo on using randint() with Pandas — a simple yet effective trick for creating dynamic datasets in Python. Whether you’re experimenting, building simulations, or just exploring data behavior, this approach keeps things flexible and fun. 💬 I’d love to hear how you use randomness in your data projects — share your thoughts below! #Python #Pandas #DataScience #MachineLearning #Coding #Learning #Analytics