SONAR Rock vs Mine Prediction with 9 SkLearn & XGBoost Algorithms + Bayesian Hyperparameters [2024]

#scikitlearn #xgboost #datascienceprojects #hyperparametertuning
This is the 1st video in a series on End to End Data Science Projects with Machine Learning and Deep Learning. Transform your data science skills with this end to end data science project on Kaggle dataset 60-feature SONAR Submarine data (mine vs Rock), A supervised Learning classification project, featuring 9 algorithms from Python Libraries ScikitLearn and XGBoost including ADABoost, and XGBoost 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.
===========================
Get Access to 20+ Years Experience in AI:
===========================
⚡️Free guide: www.maryammiradi.com/free-guide
⚡️AI Training: www.maryammiradi.com/training
===========================
Connect with Me!
=============================
Linkedin ➡︎ / maryammiradi
===========================
Mentioned in this Video
=============================
📚 Link to Python Code ➡︎
colab.research.google.com/dri...
📺 Data Science Projects ➡︎ • Data Science Projects ...
⏰ Timecodes ⏰
0:00 Introduction
0:09 Start of Hands-on Tutorial in Google Colab - Data Exploration
8:12 Data Preparation including data cleaning and data formatting and Visualizations using Seaborn and Matplotlib
14:11 Feature Engineering including Feature Transformation, Encoding and Custom Scikit Learn Pipeline
26:00 Building 9 Classification Models with ScikitLearn and XGBoost and Model Comparison using
34:33 Hyperparameter Search for ADABoost and Logistic Regression with Grid Search
39:03 Improving Area under precision and recall plot with Savitzky-Golay filter from Scipy Library
40:25 Model Selection using auc pr metric and k fold cross validation and Results in Confusion Matrix
44:20 Model Fine Tuning: XGBoost Hyperparameter Tuning with Hyperopt using Bayesian Optimization
51:16 Building Final XGBoost Model in a Sklearn Pipeline
51:30 Testing XGBoost Results on Test Set using Conusion Matrix and the classification metrics area under precision and recall (auc pr)
53:51 Outro
====================
✍️ Please Leave any questions you have about AI & Data Science in the comments!
#ai #machinelearning #python

[@maryammiradi]

Hello everyone👋Thank you for joining me! Let me know in the comments which steps of the End to End project was most useful to you!

[@nathandouieb]

Just Wow.....I think you're the only one I know who takes the time to go over each point in detail (like hyperparameters) that others skip...

[@maryammiradi]

Thanks alot Nathan!! Really glad to hear that it adds value 👋

[@adilmajeed8439]

Thanks a lot for sharing. It would be good if you take the machine learning from the ground up along with some projects. Keep up the good work

[@maryammiradi]

@adilmajeed8439 Thank you for the suggestion. Would you please explain what do you mean? Perhaps give an example. Because this channel is about projects and all the videos are actually projects. Do you mean more explanation or slower on coding?

[@Emadamx]

Great video 🙌

[@maryammiradi]

Glad you liked it!

[@LASIGAF]

very interesting

[@atanu78]

Thank you for making this channel❤

[@maryammiradi]

Glad to hear it adds value

[@manvendrasingh934]

thank you for this

[@maryammiradi]

You're very welcome!

[@chigbookafor]

Thank you Dr. Maryam. Please send me the guide.

[@maryammiradi]

Sure! Here you can download My free guide with data science roadmap and 100 Python libraries: www.maryammiradi.com/free-guide

[@user-ni4gu3qs3u]

EXTROADINAIRY🎉🎉🎉

[@maryammiradi]

Thank you!

[@sunone5]

Thank you Dr.Maryam for this generous work. Please send me the guide!

[@maryammiradi]

Of course! Here you can download my free guide with 10 steps and 100 libraries: www.maryammiradi.com/free-guide

[@ZatoichiRCS]

This is fantastic material. Please send me the guide!

[@maryammiradi]

Thank you very much! You can donbwload the free guide here: www.maryammiradi.com/free-guide

[@pardhasaradhichukka5825]

Please send me the guide!

[@maryammiradi]

Sure! Here you can download My free guide with data science roadmap and 100 Python libraries: www.maryammiradi.com/free-guide

[@sketchytv1321]

Please add code filles

[@maryammiradi]

The link is in the description.

[@HUMANBRAINEXPO]

Please, send me the guide

[@maryammiradi]

Of course! Here you can download my free guide with 10 steps and 100 libraries: www.maryammiradi.com/free-guide

[@sandeepgill2693]

Please send me the guide.

[@maryammiradi]

Of course! You can grab the guid here: www.maryammiradi.com/free-guide

[@artokokkinen8122]

Please send me the guide

[@maryammiradi]

Of course! Here you can download my free guide with 10 steps and 100 libraries: www.maryammiradi.com/free-guide

[@ayanbasak5167]

please send me the guide

[@maryammiradi]

Sure! Please go to this link: www.maryammiradi.com/free-guide

[@gauravbhattacharya7788]

Please send me the guide

[@maryammiradi]

of course! Here you go! Just gram it here: www.maryammiradi.com/free-guide

[@delali1900]

Please send me the guide

[@maryammiradi]

Sure! Here you can download the free guide incl 100 Python Libraries: www.maryammiradi.com/free-guide