Hi guys, Welcome to the Advanced Video Search Engine, and you are watching Data Science Project End to End: 9 SONAR Prediction ScikitLearn & XGBoost Models | FULL Code [2024]. That video is uploaded by Dr. Maryam Miradi at 2024-06-28T14:08:36-07:00. We are promoting this video only for entertainment and educational purpose only. So, I hope you like & enjoy it. [i1L7qAV-_rY]
| Name | Data Science Project End to End: 9 SONAR Prediction ScikitLearn & XGBoost Models | FULL Code [2024] |
| Video Uploader | Video From Dr. Maryam Miradi |
| Upload Date | This Video Uploaded At 28-06-2024 21:08:36 |
| Video Discription | Transform your data science skills with this end to end machine learning project on 60-feature SONAR data, featuring 9 ScikitLearn and XGBoost classification models including ADABoost, and hyperparameter tuning using Hyperopt. Learn essential techniques like feature engineering, k-fold cross-validation for model selection, and area under precision and recall for imbalanced data. Master Python libraries such as Scikit learn, XGBoost, and Seaborn, and build a strong data science portfolio with full code included. ⏱️⏱️VIDEO CHAPTERS⏱️⏱️ 0:00 Why this hands on tutorial helps you to improve your data science 0:48 Business Understanding about SONAR 1:57 Hands-on Tutorial in Google Colab for Setting UP libraries, Data Understanding 14:15 Data Preparation including data cleaning and data formatting with visualizations using Seaborn and Matplotlib 20:15 Feature Engineering including Feature Transformation, Encoding and Custom Scikit Learn Pipeline 32:06 Building 9 Classification Models with ScikitLearn and XGBoost and Model Comparison using 40:40 Hyperparameter Search for ADABoost and Logistic Regression with Grid Search 45:48 Improving Area under precision and recall plot with Savitzky-Golay filter from Scipy Library 47:10 Model Selection using auc pr metric and k fold cross validation and Visualization of Test Results in Confusion Matrix 51:04 XGBoost Hyperparameter Tuning with Hyperopt using Bayesian Optimization 58:00 Building Final Model __________ ✍️ Leave any questions you have about AI & Data Science in the comments! __________ 📚 Link to Python Code in Google Colab: https://colab.research.google.com/drive/1ItzTyk7tKvQGEn8jK5br9mdovtT8aZ-L?usp=sharing __________ 🏆Do you want to become a (End to End) Data Scientist? I have two options to help: ▪ FREE Data Science Guide - 10X Your AI Solutions for Real-World ((Data Science Roadmap to Excellence) including 100 Python Libraries for Impactful Machine Learning and Deep Learning ⭆ Grab your Free guide here: https://www.maryammiradi.com/free-guide ▪ READY TO JOIN MY END TO END Data Science TRAINING PROGRAM? ⭆ Check out AI Practitioner Plus 4-Weeks Program here: https://www.maryammiradi.com/ai-practitioner-plus i1L7qAV-_rY |
| Category | Science & Technology |
| Tags | area under precision and recall | scikitlearn | xgboost | xgboost classification | hyperparameter tuning | xgboost hyperparameter tuning | feature engineering | feature engineering in machine learning | cross validation | k fold cross validation | adaboost | ensemble learning in machine learning | confusion matrix in machine learning | python libraries for data science | end to end machine learning project | seaborn | data science project end to end | sklearn | auc | random forest | data science projects |
