Machine Learning Journey: Data Preprocessing with Encoding & Scaling

Day 2 of Machine Learning Journey 🚀 Today, I continued working on Exploratory Data Analysis (EDA) — but this time with a completely different dataset. Key Realization 💡 : 70–80% of Machine Learning is actually EDA, Data Cleaning and Extraction, Feature Engineering and Selection. Every dataset teaches something new. I’m focusing on building strong fundamentals before jumping into models. you can check my work here, ( https://lnkd.in/gEEwAvT9 ) Goal is Consistency 🚀 #MachineLearning #EDA #DataScience #Python #LearningInPublic #AI #Consistency #LearningJourney

As I continue learning Machine Learning, one thing I’m focusing on is not just how to implement algorithms—but when to use them effectively. Key Takeaways: Linear Regression → Strong baseline model for simple relationships Ridge Regression → Useful when dealing with multicollinearity Lasso Regression → Helps with feature selection by shrinking irrelevant coefficients to zero Understanding the intuition behind model selection is just as important as writing the code. Open to feedback from the data science community—always learning and improving 🚀 #MachineLearning #DataScience #LearningInPublic #Regression #Python #AI #Analytics #AspiringDataScientist

🚀 Project Demo: Anomaly Detection using Machine Learning I’m excited to share a demo video of my recent project on Anomaly Detection. In this project, I focused on identifying unusual patterns in transactional data using machine learning techniques. Such systems are highly useful in areas like fraud detection, system monitoring, and predictive maintenance. In this video, I demonstrate how the model works in practice, including how anomalies are detected and interpreted. I also deployed the whole application on the AWS instance the demo also includes accessing the application through it Tech stack: Python | Pandas | Scikit-learn | Streamlit | AWS I’d love to hear your thoughts and feedback! #MachineLearning #DataScience #AnomalyDetection #AI #Python #ProjectDemo

Mastering 📊 Linear Regression — from data to decisions. A complete view of how predictions are made: ✔️ Model equation & coefficients ✔️ Actual vs Predicted analysis ✔️ Error metrics (R², RMSE, MAE) ✔️ Residual insights for model validation Turn data into meaningful insights with the power of simple yet effective algorithms 🚀 #DataScience #MachineLearning #LinearRegression #Analytics #AI #Python #DataAnalytics #TechSkills Skillcure Academy

When I started Machine Learning, I focused only on models. Later I understood: Without good data, models don’t work properly In my blog, I shared: • Complete ML pipeline • Data cleaning basics • Feature engineering • Handling missing data and outliers 📖 Read here: https://lnkd.in/gWkJqCKR Grateful to my mentors and instructors at Innomatics Research Labs Research Labs for their continuous guidance and support 🙌 Special thanks to Ramkumar Eetakota for the insights and direction. hashtag #MachineLearning #DataScience #FeatureEngineering #MLPipeline #Python #DataAnalytics #LearningJourney

Breaking down Machine Learning models doesn’t have to be complicated. I created this carousel to simplify 11 key ML algorithms — from fundamentals to real-world applications, including the math and Python behind them. Whether you're preparing for interviews, building projects, or transitioning into data science, this is a solid reference to keep handy. From Linear Regression → XGBoost → PCA, everything in one place. Swipe through and tell me: 👉 Which model do you use the most? #MachineLearning #DataScience #AI #Python #Analytics #DeepLearning #MLModels #DataAnalytics #TechCareer #ComputationalChemistry #ChemistryAI #LearnInPublic #CareerGrowth #AIinScience #PortfolioProject

Most of us learned Z-scores in school. Very few of us actually understood what they were saying. A Z-score is not just a formula. It is a question your data is asking and once you hear it, you cannot unhear it. In my latest article on Towards AI, I break down: → What a Z-score is really measuring → Why raw numbers lie without context → Where Z-scores silently power ML pipelines, anomaly detection and fraud systems → And the mistake most people make when using them. >No textbook definitions. >No dry formulas. >Just the intuition that makes it click.🎯 Link in the comments 👇 #DataScience #Statistics #MachineLearning #Python #TowardsAI #Zscore #DataAnalytics

🚀 Excited to share my AI & ML Practicals Repository! I’ve uploaded my Artificial Intelligence and Machine Learning (AIML) lab practicals on GitHub, covering key concepts like data preprocessing, EDA, supervised & unsupervised learning algorithms, and model evaluation. 🔍 This repository reflects my hands-on learning in Data Science and helps strengthen my practical understanding of machine learning concepts :🔹 DataFrame Operations 🔹 Correlation Matrix 🔹 Normal Distribution 🔹 Simple Linear Regression 🔹 Logistic Regression 🔹 Decision Trees (ID3 Algorithm) 🔹 Confusion Matrix 🔹 Decision Tree Pruning A special thanks to my mentor Ashish Sawant for guiding us throughout this practical sessions !! 📂 Check it out here : https://lnkd.in/g7f3Q8vv I’d love your feedback and suggestions! 😊 #MachineLearning #ArtificialIntelligence #DataScience #Python #GitHub #LearningJourney

🏆Excited to share my latest work on Machine Learning & Al Practicals! I've created a collection of hands-on Jupyter Notebooks covering core ML concepts and algorithms as part of my academic learning journey. This project helped me strengthen my understanding by implementing models from scratch and analyzing real datasets. Key topics covered: DataFrame Operations Correlation Matrix Normal Distribution Simple Linear Regression Logistic Regression Decision Trees (ID3 Algorithm) Confusion Matrix Decision Tree Pruning Tools & Technologies: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Jupyter Notebook Through this project, I gained practical experience in: Data preprocessing Model building & evaluation Data visualization Understanding ML algorithms in depth Check out my GitHub repository: https://lnkd.in/gJCenmxd I'm continuously learning and exploring more in the field of AI & ML. Open to feedback and suggestions! #Machine Learning #ArtificialIntelligence #DataScience #Python #LearningJourney #GitHub #Students #AI #ML

🤖 Top 5 Scikit-learn Codes Every Data Scientist Should Know Building a Machine Learning model doesn’t have to be complicated—if you know the right steps. With Scikit-learn, you can go from raw data to predictions in just a few lines of code. 📌 What you’ll learn: • Loading datasets • Splitting data (train/test) • Training ML models • Making predictions • Evaluating performance 💡 Mastering these fundamentals is the first step toward becoming a confident Data Scientist. Start simple. Stay consistent. Build real projects. #MachineLearning #DataScience #Python #ScikitLearn #AI #Coding #LearnToCode #TechSkills