Name: Iris Dataset: Machine Learning Basics | Kunal Yadav posted on the topic | LinkedIn
Uploaded: 2026-03-08T12:30:06.672Z
Duration: 3 min 16 s
Channel: Kunal Yadav

Kunal Yadav

1mo

Back to basics: The Iris dataset is the 'Hello World' of Machine Learning. I used it to demonstrate how clear-cut decision boundaries can be when features are perfectly separated. What was the first dataset that made you fall in love with Machine Learning? Tech Stack: Python | Scikit-Learn | Pandas | Matplotlib | Plotly | Machine Learning #DataScience #Python #MachineLearning #ArtificialIntelligence #Portfolio

Transcript

Good day everyone, I'm not the other one. Today I will be presenting my project on Iris Flower Classification, A cornerstone task in my data science internship with OSS InfoPath. The objective is to utilize their biological measurement of iris flowers, specifically the sepal and the petal length and width to accurately categorize them into their distinct species, Setosa, versicolor and virginica. So this is a classic multiclass classification problem where the aim is to build a robust model capable of identifying the patterns in botanical data. So now moving on to our Jupiter notebook on Google Colab. So here as you can see I have implemented a terminal command to install Cagle and. Mounting my drive, importing the necessary libraries for the exploratory data analysis and modeling. Providing the API key and the username for easy access of the data through Kaggle Hub data set download. So to begin I imported a data set using pandas read CSV as you can see here. We observe 150 instances with four numerical features. To understand the relationship between these features, I utilize the Cbonds pair plot. As we can see in the visualization, the Setosa species forms a completely separate cluster. Based on pattern measurements, while versicolor and virginica exhibit a slight overlap, this indicates that the Battle dimension will likely be our most. An influential feature during model training. So now let's move on to model implementation. That data is then partitioned into training and testing. Sets with a 7030 split. To ensure the models generalizability I chose to implement. SVC due to its efficiency with small to medium sized data sets. I perform feature scaling to ensure that no single feature dominates the models weight due to its magnitude. The model was then fitted into the. Training data. And here let's move on to the report that I have created evaluating the model using a confusion matrix. Getting kind of a little tricky, so I concluded it. With the help of a classification report, we achieve an accuracy of. 45 and one, the precision and recall scores remain consistently high across all three classes. In conclusion, this project successfully demonstrates how machine learning can automate the edification of biological species with high precision, bridging the gap between data science and botany. Thank you.

To view or add a comment, sign in

More Relevant Posts

Shahana Yoosaf
1mo
Report this post
🔥 While working with data, I noticed something interesting. The same dataset can lead to different conclusions depending on how it is visualized. 📊 Using Matplotlib and Seaborn in Python helped me see this clearly. Matplotlib gives more control to design charts the way we want. Seaborn helps create clean and structured visuals quickly. #DataAnalytics #Python #Matplotlib #Seaborn #DataVisualization
Like Comment
To view or add a comment, sign in
Ramswarup Suthar
1mo
Report this post
Student Performance Prediction Model using Python! I developed a Multiple Linear Regression model using Scikit-learn to predict marks based on study hours, sleep, and practice sessions. What's inside? Multiple Features: Used data like study hours & sleep to train the model. Performance: Evaluated using Train-Test split and Visualization: Insights plotted using Matplotlib. Score. Building this helped me understand how raw data can be turned into predictive insights. Excited to explore more in the world of Data Science! #MachineLearning #Python #DataScience #ScikitLearn #LinearRegression #DataAnalytics #Coding #Project
1 Comment
Like Comment
To view or add a comment, sign in
MunishKumar R
1mo
Report this post
Starting your ML journey? Begin with the fundamentals 🎯 Day 1 tip: Master these before diving into algorithms: ✅ Python basics (variables, loops, functions) ✅ NumPy & Pandas for data manipulation ✅ Linear algebra & calculus concepts ✅ Statistics & probability Remember: Strong foundations = Better ML models The quality of your features determines your model's ceiling. Garbage in, garbage out! #MachineLearning #LearningJourney #Python #DataScience
Like Comment
To view or add a comment, sign in
Vikas Singh
4w
Report this post
Python looks simple on the surface… but the real power runs deeper. Clean syntax outside. Powerful engine inside. ⚡ That’s why tools like NumPy, Pandas, and even AI libraries feel so powerful. Sometimes, the beauty you see is powered by something even stronger underneath. #Python #Programming #AI #cpython #MachineLearning #DataScience #Coding
Like Comment
To view or add a comment, sign in
Oluwapelumi Foluso
3w Edited
Report this post
Today, I started diving into the basics of Python, the programming language at the heart of AI and Machine Learning. I explored different data types like integers, floats, booleans, complex numbers, and strings, and learned the rules for using parentheses and other syntax essentials. My Key Takeaways: Choosing the right data type is critical for correct operations Understanding Python syntax ensures your code runs smoothly These foundational concepts make everything else in AI/ML easier to learn Python may seem simple at first glance, but mastering the basics is the first step to building complex AI solutions. #Python #AI #MachineLearning #DataScience #30DayChallenge #M4ACE
Like Comment
To view or add a comment, sign in
Abdullah Nabil
1mo
Report this post
🚀 Ridge Regression Visualized! Created an interactive dashboard with 9 visualizations that demystify L2 regularization - from 3D loss landscapes to real housing predictions. Built with Python, scikit-learn & Matplotlib. #DataScience #AI #Python
2 Comments
Like Comment
To view or add a comment, sign in
Sohail Abbas
1mo
Report this post
🚀 Ridge Regression Visualized! Created an interactive dashboard with 9 visualizations that demystify L2 regularization - from 3D loss landscapes to real housing predictions. Built with Python, scikit-learn & Matplotlib. Check out the coefficient paths in the carousel! 👇 #DataScience #AI #Python
Like Comment
To view or add a comment, sign in
Wasim K.
1mo
Report this post
What’s the biggest dataset you’ve ever scraped? Curious about this. A lot of scraping discussions focus on techniques, but not much on scale. What’s the largest dataset you’ve personally collected from scraping? Thousands of pages? Millions? Also curious what problems started appearing once things got bigger. #WebScraping #BigData #DataEngineering #Python #DataScience #WebData

2 Comments
Like Comment
To view or add a comment, sign in
Rheinwerk Computing

2,138 followers
1mo
Report this post
Mastering machine learning sounds cool until you're buried in math, lost in algorithms, and wondering what Python package you're supposed to install next. If you've ever: - Opened a tutorial and closed it 10 minutes later - Felt like everyone else already gets it - Wondered where you were supposed to start... This blog post can help you. It breaks down the real path to getting started with machine learning using Python. #MachineLearning #Python #AI #DataScience #RheinwerkComputingBlog #RheinwerkComputingInfographic Take your first (or next) step here: https://hubs.la/Q047Wntr0
Like Comment
To view or add a comment, sign in
Muhammad Zain
1mo
Report this post
Hitting 'Play' on the Python journey again! ▶️🐍 After a brief pause from my daily updates, I am back at the keyboard and ready to dive deeper into code. Moving forward, my ultimate focus is building a strong foundation for Artificial Intelligence and Machine Learning. Mastering these core Python mechanics is step one on that roadmap, and I am excited to get the momentum going again. We are picking right back up where we left off. Day 7 is loading! 💻 Question for my network: For those of you working in data or AI, what core Python concept do you find yourself using the absolute most on a daily basis? 👇 #Python #MachineLearning #ArtificialIntelligence #LearningInPublic #100DaysOfCode
Like Comment
To view or add a comment, sign in

3 followers

5 Posts

View Profile Follow

Transcript

More Relevant Posts

Explore content categories