Diabetes-Prediction---ML-Classification-Algorithms---Python

The goal of this project is to build a machine learning model that can predict whether or not a patient has diabetes based on certain medical factors.

Data:

The dataset I used for this project is the Pima Indians Diabetes Dataset, available on Kaggle. The dataset consists of 768 observations and 8 features, including:

Pregnancies: Number of times pregnant Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test BloodPressure: Diastolic blood pressure (mm Hg) SkinThickness: Triceps skin fold thickness (mm) Insulin: 2-Hour serum insulin (mu U/ml) BMI: Body mass index (weight in kg/(height in m)^2) DiabetesPedigreeFunction: Diabetes pedigree function Age: Age (years)

The target variable is a binary variable indicating whether or not the patient has diabetes.

Steps:

1. Load the Pima Indians Diabetes Dataset (available on https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database).
2. Explore the dataset to gain insights and visualize the data.
3. Preprocess the data by handling missing values, scaling the features, and splitting the data into training and testing sets.
4. Build and train several machine learning models, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machines (SVM).
5. Evaluate the performance of each model using appropriate evaluation metrics, such as accuracy, precision, recall, and F1 score.
6. Select the best-performing model.