Effortlessly reduce Iris dataset from 4 to 2 dimensions using PCA in Python ML project tutorial.

🎓 BCSL606 – Machine Learning Lab | VTU CSE 6th Semester 📊 Project: Principal Component Analysis (PCA) on Iris Dataset In this video, learn how to apply Principal Component Analysis (PCA) to reduce the Iris dataset from 4 features to 2 dimensions using Python and scikit-learn. This tutorial is part of the VTU Machine Learning Lab (BCSL606) for CSE 6th Semester and is perfect for students and beginners in data science and machine learning. 🔧 What You'll Learn: What is PCA and why it's used Step-by-step Python implementation Visualization of reduced dimensions Real-world application of dimensionality reduction 📁 Technologies Used: Python Scikit-learn (sklearn) Matplotlib / Seaborn Jupyter Notebook ✅ Great for: VTU students, Machine Learning Lab practicals, Python ML projects, academic submissions, and beginners. 📌 Download Code + Dataset: https://searchcreators.org/ 👨‍🏫 For more ML Lab Experiments: Subscribe + Hit 🔔 📥 Download Study Materials (PDF Notes & PPTs): 🔗 https://searchcreators.org/ 📌 Join our community for more updates: 🌐 Website: https://searchcreators.org/ 📩 WhatsApp: +917348878215 Stay Connected for Updates and Tips: Website: https://searchcreators.org/ 6th Semester Link: https://searchcreators.org/sixth_sem_22/ Telegram: https://t.me/SearchCreators whatsapp Group :https://chat.whatsapp.com/HCUc3WBEs0I046YzjHQGb7 Instagram: Follow us here: https://www.instagram.com/search.creators/ Facebook : https://www.facebook.com/profile.php?id=61573284870401&sk=followers #MachineLearningLab #VTU2022Scheme #BCSL606 #DataVisualization #pythonforbeginners #VTU #BCSL606 #MLLab #MachineLearning #DataVisualization #Python #Seaborn #CaliforniaHousing #BoxPlot #Histogram #Outliers #VTU6thSem 📌 Don't forget to like, share, and subscribe for more VTU-based tutorials and machine learning projects!