Matplotlib Tick Labels Troubleshooting with Cameron Riddell

150 flowers. 4 measurements. 3 species. 1 algorithm that just... gets it. I built a KNN classifier on the Iris dataset — and while the dataset is classic, the process taught me something that no tutorial spells out: The model doesn't "think." It just remembers. K-Nearest Neighbors works by asking "who are your closest neighbors?" — and classifying based on majority vote. No equations being solved. No weights being learned. Just proximity. And yet — it achieves high accuracy on a real classification task. That gap between simplicity and power is what keeps pulling me deeper into ML. What I built: → Loaded & explored the Iris dataset with pandas → Trained a KNN classifier (k=3) using scikit-learn → Evaluated performance with accuracy score + confusion matrix → Built prediction for new, unseen flower samples Another project in the books. Each one teaches me something the last one didn't. 🔗 GitHub: https://lnkd.in/eybDDsdY #MachineLearning #Python #ScikitLearn #KNN #DataScience #BuildingInPublic

𝗪𝗲𝗲𝗸 𝟰 of my 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 & 𝘔𝘓 journey with ParoCyber Here's what I explored: ✅ String Formatting: three ways to combine text and variables; concatenation, f-strings, and the .format() method ✅ Control Flow: using if, elif, else, loops, and ternary operators to make code think and decide ✅ Functions: writing reusable logic with def, lambda, and built-in functions like map(), filter(), and reduce() ✅ Pandas: working with Series and DataFrames to store, access, and combine real data Biggest lesson? Python doesn't use brackets to organize code; it uses indentation. One misplaced space can break everything. I also realized that what I learned in Week 2 (dictionaries) was quietly preparing me for Pandas DataFrames all along. The dots are connecting. 4 weeks in. Still showing up. Still learning. To see the full breakdown, it's all documented on my GitHub. Link below. 🔗https://lnkd.in/d_v-C3mW #DataScience #Python #MachineLearning #ParoCyber #LearningInPublic #CareerGrowth #WomenInTech

🚀 Day-54 of #100DaysOfCode 📊 NumPy Practice – Filtering Even Numbers Today I practiced generating random arrays and filtering values using NumPy. 🔹 Concepts Practiced: ✔ np.random.randint() ✔ Boolean indexing ✔ Modulo operation ✔ Vectorized filtering 🔹 Key Learning: NumPy allows powerful filtering operations without using loops, making code cleaner and computationally efficient. Step by step moving deeper into NumPy & Data Analysis fundamentals 💡🔥 #Python #NumPy #DataScience #ArrayFiltering #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

🚀 Day-56 of #100DaysOfCode 📊 NumPy Practice – Finding Unique Values & Frequency Today I practiced identifying unique elements and counting their occurrences using NumPy. 🔹 Concepts Practiced: ✔ np.unique() ✔ Frequency counting ✔ Handling duplicate values ✔ Efficient array analysis 🔹 Key Learning: Using return_counts=True makes frequency analysis simple and efficient without loops — very useful in data preprocessing. Slowly stepping into data analysis concepts using NumPy 💡🔥 #Python #NumPy #DataAnalysis #ArrayOperations #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

Spent today exploring pandas while starting work with the MovieLens dataset for a recommendation systems project. A few small observations from the process: • pandas makes it incredibly easy to move from raw CSV files to structured data exploration • building a user–movie matrix is just a pivot operation away • debugging environments in VS Code can be surprisingly tricky when working with virtual environments The most interesting part for me was realizing how quickly you can move from: raw rating logs → structured dataset → matrices suitable for recommendation algorithms. Next step: experimenting with similarity-based recommendations using the dataset. Small progress today, but the foundation for something much bigger. Challenge : what pandas method gave the output in the terminal 🙃 🙃 #MachineLearning #DataScience #Python #RecommenderSystems

🚀 Day-66 of #100DaysOfCode 📊 NumPy Practice – Image Matrix Manipulation Today I simulated a grayscale image using NumPy and performed a simple brightness adjustment. 🔹 Concepts Practiced ✔ Random matrix generation ✔ Array arithmetic operations ✔ Pixel value clipping using np.clip() ✔ Understanding image data as matrices 🔹 Key Learning Images in computer vision are essentially NumPy matrices, where each element represents a pixel intensity. NumPy makes it easy to manipulate these values efficiently. Exploring how NumPy connects with image processing and computer vision 📸✨ #Python #NumPy #DataScience #ComputerVision #MachineLearning #100DaysOfCode

⚠️ Pandas trap: groupby() silently drops NaN keys by default, groupby() excludes rows where grouping columns contain NaN (dropna=True). This means: • Your training population may shrink • Group sizes may be biased • Downstream thresholds may fail Always define explicitly 💪 : Which rows you learn from. Whether NaN groups should be included (dropna=False). Your data quality assumptions before aggregation 🙅♀️ Silent defaults create silent bias. #Python #Pandas #DataScience #DataEngineering #DataQuality

Time series forecasting in Python can help you predict future trends. In this course, you'll learn what time series data is and how to break it down into its key components. Then you'll build baseline models, learn important forecasting techniques like ARIMA and seasonal ARIMA, evaluate your models, & more. https://lnkd.in/gzrtnBdV

New day — new good-to-know-and-repeat. Today I revisited a tutorial on ARIMA models for time-series forecasting. Not the newest method — but still one of the most useful to understand. A quick reminder for anyone working with data: • Fancy models don’t replace strong fundamentals. • Forecasting starts with understanding your data — trends, seasonality, stationarity. • Sometimes simple and interpretable models outperform complex ones. In a world full of deep learning hype, it’s good to occasionally come back to the classics. ARIMA is one of them. What forecasting methods do you keep coming back to? 📊

Raw data is just noise until you give it a shape. 🌦️📉 Fetched the live API with `requests`. Cleaned the chaos with `pandas`. Painted the story with `matplotlib`. We aren't just looking at the weather app anymore; we’re extracting the data and building our own. When you know Python, the whole internet is just a database waiting to be visualized. 🐍✨ What’s your go-to visualization library: Matplotlib, Seaborn, or something else? 👇 #Python #DataScience #Matplotlib #100DaysOfCode