Importing Data from Web with Pandas

🚀 New Video is Out: NumPy for Machine Learning (Part 1) If you're starting your journey in Data Science or Machine Learning, mastering NumPy is not optional… it’s essential. In this video, I break down the fundamentals of NumPy in a simple and practical way, including: 📌 What is NumPy and why it matters 📌 Creating and working with arrays 📌 Shape, dimensions, and indexing 📌 Mathematical operations 📌 Why NumPy is faster than Python lists 🎯 The goal is not just to learn concepts, but to actually understand how to work with data efficiently — which is the foundation of any ML project. 📂 Resources & Dataset: https://lnkd.in/dute-G9K 💻 GitHub Repo: https://lnkd.in/grVdMPr7 🎥 Full video link is in the comments 👇 Would love to hear your feedback 🙌 #MachineLearning #NumPy #DataScience #Python #AI

Theory is great, but building is better. 🛠️ Let’s code some machine learning models! Class 2 of my AI & ML series is officially live on YouTube. In this session, we tackle the bread and butter of data science: Python, Pandas, Scikit-Learn, and Google Colab. 🔗 Link to the video in the comments below! By the end of the video, you won't just know the syntax you will have built two real-world projects: 🌸 An Iris Flower Classifier using K-Nearest Neighbors (KNN) 📈 A Study Hours predictor using Linear Regression If you want to transition into AI or just want to understand how these models actually work under the hood, check out the full session below! Don't forget to grab the free Colab notebook in the description so you can code along with me. #MachineLearning #Python #DataScience #ArtificialIntelligence #GoogleColab #LearnToCode

Ever feel like something as simple as a scatter plot shouldn’t be this stressful? I built this visualization using Matplotlib, and honestly, it took more effort than I expected. Not because it’s complex but because I’m still getting comfortable with the tool. What I’m learning is this: Data Science isn’t just about concepts. It’s about translating ideas into code and that part takes practice. This plot shows the relationship between property area and price, and even though it looks simple, it represents progress. Small wins matter. If you’re learning too and feel stuck sometimes, you’re not alone. Keep building. #DataScience #Python #Matplotlib #LearningInPublic #AnalyticsJourney

🚀 Launching a new series: #Daily_DataScience_Code – From Data to Insight In this series, I’ll share daily coding tasks in data science, starting from the basics (data importing and exploration) and gradually moving toward machine learning and real-world applications. 🎯 The goal is to make data science simple, practical, and consistent. If you’re interested in building your skills step by step — feel free to follow along! Let’s code and learn together 👩💻 #DataScience #MachineLearning #Python #AI #learn_by_doing #DataScienceWithDrGehad

The best way to learn ML? Stop using libraries. I challenged myself to build linear regression using only NumPy and pandas. No sklearn. No model.fit(). No shortcuts. The result: 3 days of debugging, 4 major bugs, and one working model. I documented everything in a new Medium article: The math behind gradient descent (explained simply) Why feature scaling saved my model from exploding The dummy variable trap I almost fell into How I fixed R² = -6660 (yes, negative six thousand) If you're learning data science, this will save you hours of frustration. Read the full story: [https://lnkd.in/gvEu6-fM] Code on GitHub: [https://lnkd.in/gQUsAfzD] #DataScience #MachineLearning #Python #100DaysOfCode

📅 Day 3 – AI/ML Journey (Pandas Basics) Today I started working with Pandas, one of the most important libraries in Python for data analysis. 🔹 What I learned: • Reading datasets using read_csv() and read_excel() • Understanding the difference between CSV and Excel formats • Viewing data using .head() • Handling real-world messy data (missing values, wrong headers) • Debugging common errors while loading datasets ⚠️ Biggest lesson today: Data is never clean in real projects — most of the work is in understanding and preparing it. Still learning and improving step by step 🚀 #Day3 #AI #MachineLearning #Pandas #Python #DataScience #LearningInPublic #DeveloperJourney

20 ML algorithms and their real-world use cases. One cheat sheet i wish i had when i started. I spent months confusing random forest with decision trees and had no clue when to use xgboost vs lightgbm. So i made this for myself. Save this and share this with someone who's into data analytics. #machinelearning #datascience #algorithms #python #dataanalyst

🚀 Learning update: Visualization Today felt different, less about models, more about seeing the data. 🌳 Hierarchical Clustering Instead of forcing k clusters, it builds a tree of clusters 🔍 How It Works - Start with each point as its own cluster - Merge closest clusters step by step - End with one big cluster 📊 Dendrogram A tree-like diagram that shows: - How clusters are formed - Distance between clusters We can “cut” the tree at any level to get clusters. 🗺️ t-SNE (Visualization) This one blew my mind a bit. t-SNE converts high-dimensional data into 2D or 3D so we can see it. ⚠️ Important Insight - Points close together → similar - Clusters matter - Axes don’t mean anything 💡 My Takeaway Some tools are not for prediction, they’re for understanding and explaining data. And honestly, this is where things start to feel visual and intuitive. #DataVisualization #MachineLearning #DataScience #Python #DataCamp #DataCampAfrica

🚀 Day 11 - Pandas Series Mastered (90 Days GenAI Engineer Revision) Today’s focus was on Pandas Series - the foundation of data analysis in Python. 📊 What I learned: Creating Series (from lists, dictionaries, with custom index & name) Key attributes: size, dtype, name, index, values Useful methods: head(), tail(), sample(), value_counts() Statistical analysis: mean, median, mode, std, describe() Indexing techniques: slicing, label-based, fancy indexing Real-world operations: boolean filtering, arithmetic operations, apply() 🏏 Real Example: Analyzed a cricket player’s score data using Series: Calculated average score Identified highest performance Filtered out ducks (0 runs) using boolean indexing Used value_counts() to check consistency 💡 Key Insight: "You truly understand Pandas when you work with real data, not just theory." 📂 GitHub:https://lnkd.in/gDJHGieS Uploaded a complete, well-structured file → day11_pandas_series/series_complete.py On to the next concept tomorrow 🚀 #Day11 #Python #Pandas #DataAnalysis #GenAIEngineer #90DaysChallenge #LearningByDoing

Project Title: Creepypasta Data Analysis & Prediction 🚀 I'm excited to share my latest Machine Learning project! In this project, I performed data cleaning and applied ML algorithms to analyze Creepypasta data. Key Highlights: Data Cleaning: Processed messy data into a structured format. Models Used: Explored algorithms like Linear Regression and Random Forest. Tools: Python, Google Colab, and Pandas. You can check out the full code and dataset on my GitHub here: [https://lnkd.in/dFASwtJh] #MachineLearning #DataScience #Python #GitHub #MLProjects