A Machine learning model to Predict students' dropout and academic success,
I built the model using LogisticsRegression, DecisionTreeClassifier ,RandomForestClassifier, at the end RandomForestClassifier and Support Vector Machine (SVM) AT the end RandomForestClassifier model appears to have an accuracy of 76.27%. The precision for class 0 is 0.85, meaning that 85% of the time when the model predicts class 0, it is correct. The recall for class 0 is 0.77, meaning that the model correctly identifies 77% of the total positive cases for class 0. The f1-score is a balance between precision and recall, and is calculated as 0.81 for class 0. The results for the other classes can be similarly analyzed. It's important to consider the confusion matrix, the classification report, and the accuracy together to get a comprehensive understanding of the model's performance.
Accuracy: 0.768361581920904
Confusion Matrix:
[[237 23 56]
[ 32 49 70]
[ 9 15 394]]
Classification Report:
precision recall f1-score support
0 0.85 0.75 0.80 316
1 0.56 0.32 0.41 151
2 0.76 0.94 0.84 418
accuracy 0.77 885
macro avg 0.72 0.67 0.68 885
macro avg 0.72 0.67 0.68 885
weighted avg 0.76 0.77 0.75 885