Overview

In this project, we explore a dataset containing health-related features to predict whether a person has diabetes. Multiple models such as Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Trees, Random Forests, and Gradient Boosting are trained and evaluated.

The pipeline involves:

• Data exploration and visualization
• Handling class imbalance using RandomOverSampler
• Model training and evaluation
• Hyperparameter tuning using GridSearchCV
• Performance comparison of models

Dataset

The dataset used is a diabetes dataset, which can be found here. It contains the following features:

• Pregnancies: Number of pregnancies
• Glucose: Plasma glucose concentration after 2 hours in an oral glucose tolerance test
• BloodPressure: Diastolic blood pressure (mm Hg)
• SkinThickness: Triceps skinfold thickness (mm)
• Insulin: 2-Hour serum insulin (mu U/ml)
• BMI: Body mass index (weight in kg/(height in m)^2)
• DiabetesPedigreeFunction: A function which scores likelihood of diabetes based on family history
• Age: Age of the patient
• Outcome: The target variable (1 for diabetic, 0 for non-diabetic)

Model Evaluation

Six machine learning models are evaluated:

1. Logistic Regression
2. K-Nearest Neighbors (KNN)
3. Support Vector Machine (SVM)
4. Decision Tree
5. Random Forest
6. Gradient Boosting

The models are tuned using GridSearchCV, and performance is evaluated based on:

• Accuracy: The proportion of correctly classified instances.
• F1 Score: A balance between precision and recall.

Additionally, confusion matrices are plotted for each model.

• Feature Scaling is performed using StandardScaler to ensure that all features are on the same scale. This is particularly important for algorithms such as KNN and SVM.
• Cross-validation is used during model training to ensure that the model's performance is evaluated across different subsets of the data, providing a more reliable estimate of its accuracy and robustness.
• ROC curves are plotted for each model to visualize the trade-off between true positive and false positive rates. The Area Under the Curve (AUC) is also calculated to measure overall model performance.
• Confusion matrices are used to visualize the performance of each model by showing the counts of true positives, false positives, true negatives, and false negatives. This helps in understanding how well the model distinguishes between diabetic and non-diabetic cases.

Results

The results are compared using a bar chart of model performance based on accuracy and F1 score. The confusion matrix for each model is also displayed to visualize the performance on both classes (diabetic and non-diabetic).