Shawn McMichael’s Post

Introducing Daggr - a new #opensource #Python library that simplifies building & debugging multi-step AI workflows. Developers can define workflows in Python, and Daggr automatically generates a visual canvas showing intermediate states, inputs, and outputs at every step. Learn more on #InfoQ ⇨ https://bit.ly/3ZhjKDm #AI

Machine Learning Journey at Codveda Technologies The project involved training and tuning hyperparameters such as the number of trees and max depth, evaluating performance using cross-validation and classification metrics (precision, recall, and F1-score), and analyzing feature importance to understand what drives predictions. Tools used: Python, scikit-learn, pandas, matplotlib. #CodvedaInternship #CodvedaTech #MachineLearning #python

Exploring Ensemble Learning: BaggingClassifier vs RandomForestClassifier (Python) Recently, I experimented with BaggingClassifier and RandomForestClassifier using Scikit-learn. ·      Created a dataset with make_classification ·      Trained both models ·      Visualized decision trees using plot_tree ·      Compared how Bagging uses random data while Random Forest uses random data + random features Key takeaway: Random Forest provides better generalization by reducing overfitting more effectively. This hands-on practice helped me understand ensemble learning and model interpretability at a deeper level. Always learning, always building #MachineLearning #Python #DataAnalytics #ScikitLearn #RandomForest #Bagging #LearningByDoing

Start strong: XGBoost's latest patches refine categorical handling and performance for predictive tasks. Changes: https://lnkd.in/gWiAbMEc In ML tasks, fine-tuning yields strong results. Following XGBoost evolutions? Views? #XGBoost #MachineLearning #Python #DataScience #AIProgress

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

🚢 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

🚀 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

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

Start strong: XGBoost 3.1.3 patches refine performance and fixes, supporting scalable predictive modeling. Recent release: https://lnkd.in/gydrEyN2 In ML tasks, these updates boost efficiency for tabular data. Noticing changes in recent XGBoost patches? Views? #XGBoost #MachineLearning #Python #DataScience #AIProgress

Built a machine learning regression model to predict California housing prices using XGBoost and evaluated its performance through an actual vs predicted comparison. The project focuses on model training, performance evaluation, and a simple Streamlit interface for making predictions. Tech stack: Python, Pandas, NumPy, Scikit-learn, XGBoost, Streamlit. #MachineLearning #DataScience #Python #XGBoost #Regression #ScikitLearn #Streamlit #LearningByDoing #BuildInPublic