Notebook:

I learned about the difference between DecisionTree Classification, RandomForest Classifier and xGBoost Classifier and their working. When I practiced all these models, the best accuracy was given by the xGBoost Classifier.

may allah bless your father

https://www.kaggle.com/muhammadrameez242 , ye meri kaggle ki I'd hai yha par Ap mukamal assignments simple roman urdu mein mill jai gi codes mein

Done

Boosting algorithms comparison in Python coding.

I have done this lecture with 100% practice.

I have done this video with 100% practice.

AOA, shouldn't the prediction for each model be set in a different variable besides y_pred? y_pred is over written every time and has last xgboost model prediction values. Consequently bar plots are same for all three models. Thank you

AOA, I also learned in this lecture how to code a boosting algorithm for classification in Python.Steps of Boosting in Python1-Import libraries 2-Import and load the dataset of DIMOND 3-Split the data into X and y. 4-Encode the input variables 5-Encode the target variable 6-Split the data into train and test sets 7-Train the decision tree model 8-Predict the test data 9-Train the random forest model 10-Predict the test data 11-Train the XGBoost model 12-Predict the test data 13-Make a bar plot showing each matrix with respect to the modelALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey or ap k walid-e-mohtram ko karwat karwat jannat ata farmay,Ameen.