replace hard coded values with inline code

report/content/_analysis.qmd

@@ -17,19 +17,22 @@ if 'Unnamed: 0' in dst_cv_results.columns:
 
 dt_test_accuracy_cv = round(dst_cv_results.loc[dst_cv_results[''] == 'test_accuracy', 'mean'].values[0] * 100, 2)
 dt_train_accuracy_cv = round(dst_cv_results.loc[dst_cv_results[''] == 'train_accuracy', 'mean'].values[0] * 100, 2)
+dt_test_recall_cv =  round(dst_cv_results.loc[dst_cv_results[''] == 'test_recall', 'mean'].values[0] * 100, 2)
+dt_test_precision_cv =  round(dst_cv_results.loc[dst_cv_results[''] == 'test_precision', 'mean'].values[0] * 100, 2)
+dt_test_f1_cv =  round(dst_cv_results.loc[dst_cv_results[''] == 'test_f1', 'mean'].values[0] * 100, 2)
 
 Markdown(dst_cv_results.to_markdown(index = False))
 ```
 
-Cross-validation results show a high train accuracy of 1, indicating perfect fit on the training data, with a test accuracy of 0.74, 
-suggesting good generalization. The model exhibits a precision of 0.709, recall of 0.737, and an F1-score of 0.718 on the test set, 
-with relatively low variability (standard deviations of 0.059, 0.069, and 0.079, respectively). 
-These results indicate a reasonable trade-off between precision and recall.
+Cross-validation results show a high train accuracy of 1, indicating perfect fit on the training data, with 
+a test accuracy of `{python} dt_test_accuracy_cv`%, suggesting good generalization. The model exhibits a precision of `{python} dt_test_precision_cv`%, 
+recall of `{python} dt_test_recall_cv`%, and an F1-score of `{python} dt_test_f1_cv`% on the test set, 
+with relatively low variability. These results indicate a reasonable trade-off between precision and recall.
 
 ![Confusion Matrix of Decision Tree Model](../results/tables/decision_tree/decision_tree_confusion_matrix.png){#fig-conf-m-dt}
 
-Confusion matrix reveals a higher number of false positives (29) compared to false negatives (25), 
-indicating some misclassification of the negative class (0).
+Confusion matrix reveals a higher number of false positives compared to false negatives, 
+indicating some misclassification of the negative class (target = 0).
 
 ### Decision Tree: Test Results
 
@@ -38,14 +41,20 @@ indicating some misclassification of the negative class (0).
 #| tbl-cap: "Test results of Decision Tree Model"
 dt_precision_class_0 = round(dst_model_results.loc[dst_model_results[''] == '0', 'precision'].values[0] * 100, 2)
 dt_recall_class_0 = round(dst_model_results.loc[dst_model_results[''] == '0', 'recall'].values[0] * 100, 2)
+dt_f1_class_0 = round(dst_model_results.loc[dst_model_results[''] == '0', 'f1-score'].values[0] * 100, 2)
+
 dt_precision_class_1 = round(dst_model_results.loc[dst_model_results[''] == '1', 'precision'].values[0] * 100, 2)
 dt_recall_class_1 = round(dst_model_results.loc[dst_model_results[''] == '1', 'recall'].values[0] * 100, 2)
+dt_f1_class_1 = round(dst_model_results.loc[dst_model_results[''] == '1', 'f1-score'].values[0] * 100, 2)
+
+dt_accuracy_test = round(dst_model_results.loc[dst_model_results[''] == 'accuracy', 'precision'].values[0] * 100, 2)
 
 Markdown(dst_model_results.to_markdown(index = False))
 ```
 
-Test results show balanced performance across classes, with precision for class 0 (0.727) and class 1 (0.771), 
-and F1-scores of 0.777 and 0.701 for class 0 and 1, respectively. The model's overall accuracy is 0.744.
+Test results show balanced performance across classes, with precision for class 0 (`{python} dt_precision_class_0`%) 
+and class 1 (`{python} dt_precision_class_1`%), and F1-scores of `{python} dt_f1_class_0`% and `{python} dt_f1_class_1`% 
+for class 0 and 1, respectively. The model's overall accuracy is `{python} dt_accuracy_test`%.
 
 ### Logistic Regression: Cross-Validation Results
 ```{python}
@@ -58,28 +67,40 @@ if 'Unnamed: 0' in lg_cv_results.columns:
 
 lg_test_accuracy_cv = round(lg_cv_results.loc[lg_cv_results[''] == 'test_accuracy', 'mean'].values[0] * 100, 2)
 lg_train_accuracy_cv = round(lg_cv_results.loc[lg_cv_results[''] == 'train_accuracy', 'mean'].values[0] * 100, 2)
+lg_test_recall_cv =  round(lg_cv_results.loc[lg_cv_results[''] == 'test_recall', 'mean'].values[0] * 100, 2)
+lg_test_precision_cv =  round(lg_cv_results.loc[lg_cv_results[''] == 'test_precision', 'mean'].values[0] * 100, 2)
+lg_test_f1_cv =  round(lg_cv_results.loc[lg_cv_results[''] == 'test_f1', 'mean'].values[0] * 100, 2)
 
 Markdown(lg_cv_results.to_markdown(index = False))
 ```
 
-Cross-validation results show strong performance with a test accuracy of 0.826 and train accuracy of 0.873. 
-The model's test precision (0.822), recall (0.8), and F1-score (0.809) demonstrate a balance between precision and recall, 
-with a slightly higher train performance. The standard deviations indicate minimal variability in performance across the folds.
+Cross-validation results show strong performance with a test accuracy of `{python} lg_test_accuracy_cv`% 
+and train accuracy of `{python} lg_train_accuracy_cv`%. 
+The model's test precision (`{python} lg_test_precision_cv`%), recall (`{python} lg_test_recall_cv`%), and F1-score (`{python} lg_test_f1_cv`%) 
+demonstrate a balance between precision and recall, with a slightly higher train performance. 
+The standard deviations indicate minimal variability in performance across the folds.
 
 ![Confusion Matrix of Logistic Regression Model](../results/tables/logistic_regression/logistic_regression_confusion_matrix.png){#fig-conf-m-lg}
 
-Confusion matrix indicates good performance with fewer false positives (17) compared to false negatives (19) for the positive class.
+Confusion matrix indicates good performance with fewer false positives compared to false negatives for the positive class.
 
 ### Logistic Regression: Coefficients
 
+```{python}
+lg_coeff = pd.read_csv("../results/tables/logistic_regression/logreg_coefficients.csv")
+
+chest_pain_type_asymptomatic_coef = round(lg_coeff.loc[lg_coeff['Feature'] == 'onehotencoder__chest_pain_type_asymptomatic', 'Coefficient'].values[0], 2)
+num_of_vessels_0_coef = round(lg_coeff.loc[lg_coeff['Feature'] == 'onehotencoder__num_of_vessels_0.0', 'Coefficient'].values[0], 2)
+```
+
 ![Coefficients of Logistic Regression Model](../results/tables/logistic_regression/logreg_coefficients.png){#fig-coef-lg}
 
 The coefficients of the Logistic Regression model reflect the impact of each feature on the likelihood of a positive outcome, 
 with positive coefficients indicating an increased likelihood and negative coefficients indicating a decreased likelihood. 
-For example, features like `chest_pain_type_asymptomatic` (1.18), `thalassemia_reversable defect` (0.97), and `num_of_vessels_2.0` (0.91)
- are positively associated with the likelihood of heart disease, meaning that higher values of these features increase the chances of 
- a positive outcome. In contrast, features such as `num_of_vessels_0.0` (-1.26), `chest_pain_type_non-anginal pain` (-0.93), 
- and `thalassemia_normal` (-0.58) show a negative relationship, meaning they decrease the likelihood of heart disease. The coefficients for scaled features like `age`, `cholesterol`, and `resting_blood_pressure` (e.g., 0.34 for `cholesterol`) indicate their contribution to the prediction, with the magnitude of the coefficient reflecting the strength of their influence on the model's outcome. Features with larger absolute coefficient values, such as `num_of_vessels_0.0` and `chest_pain_type_asymptomatic`, have a more significant impact on the model’s prediction.
+For example, features like `chest_pain_type_asymptomatic` (`{python} chest_pain_type_asymptomatic_coef`) 
+are positively associated with the likelihood of heart disease, meaning that higher values of these features increase the chances of 
+a positive outcome (presence of hear disease). In contrast, features such as `num_of_vessels_0.0`% (`{python} num_of_vessels_0_coef`)
+show a negative relationship, meaning they decrease the likelihood of heart disease. 
 
 ### Logistic Regression: Test Results
 
@@ -89,12 +110,19 @@ For example, features like `chest_pain_type_asymptomatic` (1.18), `thalassemia_r
 #| 
 lg_precision_class_0 = round(lg_model_results.loc[lg_model_results[''] == '0', 'precision'].values[0] * 100, 2)
 lg_recall_class_0 = round(lg_model_results.loc[lg_model_results[''] == '0', 'recall'].values[0] * 100, 2)
+lg_f1_class_0 = round(lg_model_results.loc[lg_model_results[''] == '1', 'f1-score'].values[0] * 100, 2)
+
 lg_precision_class_1 = round(lg_model_results.loc[lg_model_results[''] == '1', 'precision'].values[0] * 100, 2)
 lg_recall_class_1 = round(lg_model_results.loc[lg_model_results[''] == '1', 'recall'].values[0] * 100, 2)
+lg_f1_class_1 = round(lg_model_results.loc[lg_model_results[''] == '1', 'f1-score'].values[0] * 100, 2)
+
+lg_accuracy_test = round(lg_model_results.loc[lg_model_results[''] == 'accuracy', 'precision'].values[0] * 100, 2)
+
 
 Markdown(lg_model_results.to_markdown(index = False))
 ```
 
-Test results show that Logistic Regression outperforms the Decision Tree in terms of overall accuracy (0.844). 
-For precision and recall, class 0 achieves precision of 0.827 and recall of 0.896, while class 1 has precision of 
-0.868 and recall of 0.786, leading to an F1-score of 0.86 for class 0 and 0.825 for class 1.
+Test results show that Logistic Regression outperforms the Decision Tree in terms of overall accuracy (`{python} lg_accuracy_test`%). 
+For precision and recall, class 0 achieves precision of `{python} lg_precision_class_0`% and recall of `{python} lg_recall_class_0`%, 
+while class 1 has precision of `{python} lg_precision_class_1`% and recall of `{python} lg_recall_class_1`%, 
+leading to an F1-score of `{python} lg_f1_class_0`% for class 0 and `{python} lg_f1_class_1`% for class 1.

report/content/_discussion.qmd

@@ -10,15 +10,17 @@ and demographic factors.
 #### Overall Accuracy of the Classification Models
 
 Both the logistic regression and decision tree models demonstrated strong predictive capabilities. 
-The decision tree model achieved an accuracy of 73.33%, with a precision of 72.22% and recall of 81.25% 
-for class 0 (absence of heart disease) and a precision of 75.0% and recall of 64.29% for class 1 (presence of heart disease). 
-However, the decision tree model exhibited signs of potential overfitting, as indicated by the 100% training accuracy versus 
-a test accuracy of 72.5%, suggesting that it might not generalize well to unseen data.
-
-In comparison, the logistic regression model outperformed the decision tree model, achieving an overall accuracy of 81.11%. 
-It exhibited balanced precision (78.18% for class 0 and 85.71% for class 1) and recall (89.58% for class 0 and 71.43% for class 1), 
-with a test accuracy of 87.5%, and a training accuracy of 89.6%. This higher performance suggests that logistic regression provides 
-a more reliable model for predicting heart disease, with fewer issues related to overfitting and better generalization to new data.
+The decision tree model achieved an accuracy of `{python} dt_accuracy_test`%, with a precision of `{python} dt_precision_class_0`% and recall of `{python} dt_recall_class_0`% 
+for class 0 (absence of heart disease) and a precision of `{python} dt_precision_class_1`% and recall of `{python} dt_recall_class_1`% for class 1 (presence of heart disease). 
+However, the decision tree model exhibited signs of potential overfitting, as indicated by the `{python} dt_train_accuracy_cv`% training accuracy versus 
+a test accuracy of `{python} dt_accuracy_test`%, suggesting that it might not generalize well to unseen data.
+
+In comparison, the logistic regression model outperformed the decision tree model, achieving an overall accuracy of `{python} lg_accuracy_test`%. 
+It exhibited balanced precision (`{python} lg_precision_class_0`% for class 0 and `{python} lg_precision_class_1`% for class 1) 
+and recall (`{python} lg_recall_class_0`% for class 0 and `{python} lg_recall_class_1`% for class 1), 
+with a test accuracy of `{python} lg_accuracy_test`%, and a training accuracy of `{python} lg_train_accuracy_cv`%. 
+This higher performance suggests that logistic regression provides a more reliable model for predicting heart disease, 
+with fewer issues related to overfitting and better generalization to new data.
 
 #### Key Predictive Features for Heart Disease
 
@@ -36,9 +38,9 @@ confirming their relevance in the prediction of heart disease.
 
 The ability to predict whether an individual might develop heart disease based on health indicators and demographic factors was 
 evaluated through both models. Logistic regression demonstrated a strong capability to predict the likelihood of heart disease, 
-with particularly high recall for the absence of heart disease (89.58%), meaning the model was effective in identifying healthy 
+with particularly high recall for the absence of heart disease (`{python} lg_recall_class_1`%), meaning the model was effective in identifying healthy 
 individuals. Conversely, the decision tree model showed a more balanced performance but struggled to consistently predict class 1 
-(heart disease), as reflected by its lower recall (64.29%) for this group.
+(heart disease), as reflected by its lower recall (`{python} dt_recall_class_1`%) for this group.
 
 Overall, the findings suggest that machine learning models, particularly logistic regression, hold promise for accurately 
 predicting heart disease diagnoses. However, there is still room for improvement in refining these models to reduce misclassifications, 

results/tables/decision_tree/decision_tree_cv_results.csv

@@ -1,6 +1,6 @@
 ,mean,std
-fit_time,0.001,0.0
-score_time,0.002,0.0
+fit_time,0.002,0.002
+score_time,0.006,0.002
 test_accuracy,0.74,0.059
 train_accuracy,1.0,0.0
 test_precision,0.709,0.069

results/tables/logistic_regression/logistic_regression_cv_results.csv

@@ -1,6 +1,6 @@
 ,mean,std
-fit_time,0.002,0.001
-score_time,0.002,0.0
+fit_time,0.007,0.003
+score_time,0.004,0.0
 test_accuracy,0.826,0.021
 train_accuracy,0.873,0.009
 test_precision,0.822,0.054

results/tables/logistic_regression/logreg_coefficients.csv

@@ -1,26 +1,26 @@
 Feature,Coefficient
-onehotencoder__chest_pain_type_asymptomatic,1.1838672562061838
-onehotencoder__thalassemia_reversable defect,0.9657968582775411
-onehotencoder__num_of_vessels_2.0,0.914273306990384
-onehotencoder__exercise_induced_angina_yes,0.6288076002699492
-standardscaler__sex,0.6045607179898266
-onehotencoder__num_of_vessels_1.0,0.5644600036491417
-onehotencoder__slope_flat,0.35035154466635776
-standardscaler__resting_blood_pressure,0.340495546509579
-standardscaler__cholesterol,0.3244733216396428
-standardscaler__st_depression,0.304389572995754
-onehotencoder__slope_downsloping,0.2405322386242731
-onehotencoder__rest_ecg_left ventricular hypertrophy,0.19607845057825268
-onehotencoder__chest_pain_type_atypical angina,0.1904812312471063
-onehotencoder__rest_ecg_ST-T wave abnormality,0.17303560432627466
-standardscaler__age,0.05633345043789224
-standardscaler__fasting_blood_sugar,-0.16843768707612017
-standardscaler__max_heart_rate,-0.18641022684835776
-onehotencoder__num_of_vessels_3.0,-0.22288813387853396
-onehotencoder__rest_ecg_normal,-0.3690988378526316
-onehotencoder__thalassemia_fixed defect,-0.388411089249939
-onehotencoder__chest_pain_type_typical angina,-0.442410277986553
-onehotencoder__thalassemia_normal,-0.5773705519757063
-onehotencoder__slope_upsloping,-0.590868566238734
-onehotencoder__chest_pain_type_non-anginal pain,-0.9319229924148412
-onehotencoder__num_of_vessels_0.0,-1.255829959709096
+onehotencoder__chest_pain_type_asymptomatic,1.1838672562033263
+onehotencoder__thalassemia_reversable defect,0.9657968582786678
+onehotencoder__num_of_vessels_2.0,0.9142733069914617
+onehotencoder__exercise_induced_angina_yes,0.6288076002703239
+standardscaler__sex,0.6045607179889861
+onehotencoder__num_of_vessels_1.0,0.5644600036470457
+onehotencoder__slope_flat,0.3503515446664692
+standardscaler__resting_blood_pressure,0.3404955465111282
+standardscaler__cholesterol,0.32447332163941606
+standardscaler__st_depression,0.3043895729937989
+onehotencoder__slope_downsloping,0.24053223862408943
+onehotencoder__rest_ecg_left ventricular hypertrophy,0.1960784505781805
+onehotencoder__chest_pain_type_atypical angina,0.19048123124637503
+onehotencoder__rest_ecg_ST-T wave abnormality,0.17303560432690512
+standardscaler__age,0.05633345043781977
+standardscaler__fasting_blood_sugar,-0.16843768707539558
+standardscaler__max_heart_rate,-0.1864102268478972
+onehotencoder__num_of_vessels_3.0,-0.22288813387665835
+onehotencoder__rest_ecg_normal,-0.3690988378536457
+onehotencoder__thalassemia_fixed defect,-0.38841108925053175
+onehotencoder__chest_pain_type_typical angina,-0.4424102779839873
+onehotencoder__thalassemia_normal,-0.5773705519766958
+onehotencoder__slope_upsloping,-0.590868566239119
+onehotencoder__chest_pain_type_non-anginal pain,-0.9319229924142742
+onehotencoder__num_of_vessels_0.0,-1.2558299597104094