I went for a deeper dive this time looking at building a decision tree... it ended up with:

Post 1 - data collection from ChEMBL database using web resource client (https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/1_data_col_prep.html)

Post 2 - more data preprocessing and transformation (https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/2_data_prep_tran.html)

Post 3 - building decision tree model using scikit-learn & estimating experimental errors (https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/3_model_build.html)

#ml #decisiontree #scikitlearn #pandas #rdkit #datamol #cheminformatics #seaborn #graphviz #dtreeviz #python