How to Analyze Academic Profiles with Python

🚀 Excited to share my latest Python NumPy projects! 🐍 Over the past few weeks, I’ve been diving deep into NumPy, exploring a wide range of concepts including: Array creation, manipulation, and reshaping Matrix operations and broadcasting Element-wise computations and conditional operations Advanced indexing and slicing These assignments helped me strengthen my problem-solving skills and gain hands-on experience in efficient numerical computing—a key skill for data analysis, machine learning, and scientific computing. A special thanks to KSR Datavizon for structured learning support and practical assignments that made the concepts crystal clear. You can explore my full Python NumPy programs here 👉:https://lnkd.in/gXXRjKnM #Python #NumPy #DataScience #MachineLearning #CodingSkills KSR Datavizon Mallikarjuna R

📶 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

🧠 Fun fact: “NaN” isn’t just a random error, it actually means something! If you’ve ever worked with datasets in Python, you’ve definitely seen NaN values pop up. But did you know it stands for “Not a Number”? It’s a special floating-point value defined by the IEEE 754 standard, used to represent undefined or unrepresentable results, like dividing 0 by 0. In other words, NaN is Python’s way of saying “I tried, but this doesn’t make sense.” Behind that simple error message hides decades of mathematical logic designed to keep computers from crashing every time we ask them impossible questions. So next time you see a column full of NaNs, smile, your code isn’t broken, it’s just being philosophically honest 😄 #Python #DataScience #MachineLearning #Pandas #FunFact #LearningInPublic #Statistics #AI #CodingHumor

A mini project about Supervised Learning, applied it by predicting house prices using the California Housing Dataset from Kaggle. Tools: Python, Pandas, Scikit-learn, Matplotlib Steps: Cleaned and visualized the dataset Trained a Linear Regression model Evaluated using mean squared error and r2 score Achieved an RMSE of 69,297.72 and visualized predictions vs actual prices. GitHub: https://lnkd.in/d8CkpV_b #MachineLearning #DataScience #Python #LearningJourney #AI

Unlocking the power of data with NumPy! From mean and median to standard deviation and correlation — understanding basic statistics in NumPy is the first step to mastering data analysis in Python. Using functions like np.mean(), np.median(), np.std(), and np.corrcoef() makes statistical computation fast and reliable. Data-driven decisions start with understanding the basics. #NumPy #Python #MachineLearning #Statistics #DataScience #CodingJourney #CodeNewbie #LearningJourney #DataScienceJourney #AI #DataAnalytics #ArrayinNumpy #ArrayManipulation

🧠 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

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

AI-Based Study Planner I built a smart study planner using Python and Machine Learning that: Predicts the number of study hours needed for each subject based on your target scores Generates a daily study plan with prioritized subjects Highlights the subject that needs the most focus Provides visual insights with charts Check out the project on GitHub: https://lnkd.in/dymhkZ63 I also made a short video demo of the project running in Google Colab to show it in action. This project is perfect for students and anyone looking to organize their study schedule efficiently. #MachineLearning #Python #AI #StudyPlanner #DataScience #GitHub