🚢 Titanic Survival Prediction – Machine Learning & Streamlit ✅ Developed and deployed a user-friendly Streamlit web application to predict Titanic survival using Machine Learning, with clear model comparison and performance insights GITHUB LINK : https://lnkd.in/gaptG8kv STREAMLIT.IO LINK : https://lnkd.in/g6R6TwAd #DataScience #MachineLearning #Streamlit #Python #MLProject #LearningJourney #CareerRestart

⌛ This was 8 years ago, and if you try Python in Excel it feels like a feature they are still "considering." The real way to integrate Python and Excel is to move your Excel work to Python environments -- NOT jam python functions into your workbook. Python environments can handle larger datasets, faster processing, and more sophisticated AI. This is what we are building at Mito AI. The Excel-user front end for Python/AI workflows 🚀 #AI #Excel #Python #Data #DataScience

Built a Movie Recommender System using Machine Learning to understand how large-scale platforms personalize content. . . . Worked on data preprocessing, feature representation, and similarity-based recommendations using Python and Scikit-learn. Developed in Colab, modularized and tested in PyCharm. #MachineLearning #Colab #MovieRecommenderSystems #DataScience #Python #MLProjects #LearningByBuilding

Every Tuesday and Thursday, I send 2 tips to help you discover useful Python tools for data and AI. Recent tips: • PydanticAI: Type-safe LLM outputs with auto-validation • Polars: Stream million-row exports without memory spikes • Narwhals: One function for pandas, Polars, and DuckDB • uv: Switch Python versions without rebuilding environments It's free on Substack. 📬 Subscribe here: https://bit.ly/46fdOPl #Python #DataEngineering #AI #OpenSource #PythonTips

I wish I had this roadmap when I started Machine Learning. So I built a simple 6-slide guide that shows: • Where to start • What to learn next • How Python fits into ML • How to avoid beginner mistakes If you’re learning ML in 2026, this is for you 👇 Swipe | Save | Share #MachineLearning #Python #SelfLearning #AIJourney #TechCareers#MLBeginner #PythonLearning #LearnMachineLearning #TechSkills #SelfLearning

🚀 House Price Prediction | Machine Learning Project Built a machine learning regression model to predict house prices using Python. Performed data cleaning, EDA, feature encoding, model training, and evaluation. Tech Stack: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Jupyter Notebook GitHub Project: https://lnkd.in/ggrBHjNM #MachineLearning #DataScience #Python #MLProject #LearningJourney

Why Python for AI? Python offers a powerful ecosystem for building intelligent systems. With NumPy for numerical computing, Pandas for data preparation, and Matplotlib for visualization, it enables a smooth transition from raw data to actionable insights. #ArtificialIntelligence #Python #AI #DataScience #FutureofAi

“Want to get into AI? 🚀 Start with Python: learn the basics, handle data with pandas and numpy, and try small projects with scikit-learn. 💡 Tip: Code a little every day, experiment, and track your progress. Consistency beats speed. #Python #AI #MachineLearning #DataScience”

Developed a data-driven real-time availability prediction system using Python, Random Forest, and Streamlit. This project focuses on transforming raw data into actionable insights by predicting availability and supporting smarter booking decisions through machine learning. ✅ Built ML model using Random Forest ✅ Created interactive dashboard using Streamlit ✅ Converted model outputs into real-time decision support Always learning and exploring ways to turn data into impactful solutions! 📊 #MachineLearning #Python #Streamlit #RandomForest #Projects #LearningInPublic

Let's jump on level 2 task list. 🚀 Task 1 : Regression Analysis Description: Perform a simple linear regression analysis to predict one variable based on another. Objectives : Split the dataset into training and testing sets. Fit a linear regression model using scikit-learn. Interpret the coefficients and evaluate the model using metrics such as R-squared and mean squared error. Tools: Python, scikit-learn, pandas. Codveda Technologies #CodvedaJourney #CodvedaExperience #FutureWithCodveda