Machine Learning Projects in Python - Binary Classification - End-to-End Jupyter Notebook
In this video, I explained the important points of classification(specifically binary classification - heart attack detection) in ML. If you are beginner in ML, this can be a reference notebook for you. I also highlighted some tips and covers the sections such as data analysis, data visualization, outliers check, feature scaling, building ML models, evaluating them, choosing the final model and saving/loading it. For more details, check the below:
#machinelearning #classification #python #jupyternotebook
Chapters:
0:00 introduction and understanding the problem
1:46 exploring the data, import libraries and load the data
3:24 inspect the data with pandas methods
6:30 check missing values
8:11 check class distribution
10:09 visualise more and check the outliers
17:18 pre-processing, split and scale the data
18:40 build ML models, MLP
22:51 build SVM
23:50 build XGBoost
24:26 which metrics is the most important, why?
25:10 build random forest
25:28 choose the final model and build a pipeline
27:56 save and load the model with joblib
The notebook and the dataset:
https://github.com/emre-kocyigit/clas...
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