Creating example datasets has never been this easy. With the drawdata library in Python, you can sketch your data and turn it into a dataset in seconds. You can create clusters, trends, and outliers exactly the way you need. I just released a new module on this in the Statistics Globe Hub: https://lnkd.in/exBRgHh2 #datascience #python #machinelearning #statistics #dataanalysis #datavisualization #programming #ai #statisticsglobehub

Today, I learned how to take user input in Python using the input() function. This allows programs to interact with users and collect data such as name, age, and city. I also learned how to convert input into numbers using int() and float(), which is very important for calculations and data processing. #Day2 #Python #LearningJourney #DataScience #MachineLearning #Consistency

🎥 Project Explanation Video Here is my explanation for Iris Flower Classification project using Machine Learning. 🔗 GitHub Link: https://lnkd.in/gKwJNFrr #DataScience #MachineLearning #Python #CodeAlpha

(Open Access) An Introduction to R and Python for Data Analysis: https://lnkd.in/ePKAz3bM Look for "Read and Download Links" section to download. Follow me if you like this post. #Python #programming #DataAnalysis #DataScience #LLMs #GenAI #GenerativeAI

Get started with machine learning using Python and discover how to build intelligent systems that can learn from data and improve their performance over time with this comprehensive guide https://lnkd.in/gDJ28K-Y #MachineLearningWithPython Read the full article https://lnkd.in/gDJ28K-Y

🚀 Day 11/30 – Python Challenge Exploring sets in Python and how they handle unique data! 🐍 🔹 Key Concepts Covered: * Creating sets * Understanding that sets store only unique values * Adding elements using add() * Iterating through set elements 💻 Mini Task: Created a set of numbers, observed how duplicate values are automatically removed, added a new element, and displayed all values using a loop. 🎯 Learning Outcome: Learned how sets are useful for storing unique data and performing operations where duplicates are not needed. Understanding different data structures step by step 🚀 #Python #CodingChallenge #LearningJourney #DataStructures #StudentDeveloper #Day11

Ever struggled to find the right dataset? With the drawdata library in Python, you can sketch your own data and turn it into a dataset in seconds. In this example, I analyze it in R using k-means clustering, all within one Positron workflow. I just released a new module on this in the Statistics Globe Hub: https://lnkd.in/exBRgHh2 #datascience #python #rstats #machinelearning #kmeans #statisticsglobehub

Task 3 ✅ Built an IPL Winner Predictor 🏏 using Python & ML to predict match results from historical data. Learning, building, and growing every day 📈 #Python #MachineLearning #IPL #DataScience InternPe

Day 2 of learning Pandas Today was all about cleaning data handled missing values, dropped unnecessary columns, and did some basic filtering. Starting to see how messy data becomes usable with the right steps #Python #Pandas #DataScience #LearningJourney

Built Linear Regression from scratch using Python (no libraries) Wanted to understand what’s happening under the hood before moving to sklearn. So I implemented a simple model to predict marks based on hours studied using Gradient Descent. 🔹 What I did: Implemented the prediction function (y = wx + b) Calculated Mean Squared Error (MSE) manually Computed gradients and updated parameters over 1000 epochs 🔹 What I learned: How gradient descent updates weights step by step Why learning rate plays a critical role How loss decreases as the model learns 🔹 Result: The model successfully learned the relationship. Example: If a student studies 9 hours → predicted marks ≈ 89.3 🔗 Code: https://lnkd.in/gPHCenhB Next step: implementing this using NumPy and then sklearn. #MachineLearning #Python #LearningInPublic