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Diabetes Classification and Prediction

This project aims to classify and predict diabetes in patients using a Support Vector Machine (SVM) algorithm. SVM is a supervised machine learning model that can be used for both classification and regression challenges. It works by finding the hyperplane that best divides a dataset into classes.

How the Code Works

  1. Data Preprocessing: The dataset is first preprocessed to handle missing values, normalize features, and split into training and testing sets.
  2. Model Training: An SVM model is trained on the training dataset. The SVM algorithm tries to find the optimal hyperplane that separates the data points of different classes (diabetic and non-diabetic) with the maximum margin.
  3. Prediction: The trained SVM model is then used to predict the class labels of the test dataset.
  4. Evaluation: The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score.

The SVM algorithm is particularly effective for high-dimensional spaces and is robust against overfitting, especially in cases where the number of dimensions exceeds the number of samples.

Example Code Snippet

Here is a simplified example of how an SVM might be used in Python with the scikit-learn library:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import classification_report

Load dataset

data = pd.read_csv('diabetes.csv')

Preprocess data

X = data.drop('Outcome', axis=1)
y = data['Outcome']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train SVM model
svm_model = SVC(kernel='linear')
svm_model.fit(X_train, y_train)

Dependencies

The following libraries are required to run the notebook:

  • numpy
  • pandas
  • scikit-learn

You can install these dependencies using pip:

pip install numpy pandas scikit-learn

Data Collection

The dataset used in this project is the PIMA Diabetes Dataset. It contains several medical predictor variables and one target variable indicating the presence of diabetes.

Data Preprocessing

The data preprocessing steps include:

  • Standardizing the features using StandardScaler
  • Splitting the dataset into training and testing sets using train_test_split

Model Training

The Support Vector Machine (SVM) algorithm is used for training the model. The following steps are performed:

  1. Importing the SVM classifier from scikit-learn
  2. Training the model on the training data
  3. Making predictions on the test data

Model Evaluation

The accuracy of the model is evaluated using the accuracy_score metric.

Usage

To run the notebook, open Diabetes_Classification.ipynb in Jupyter Notebook or Google Colab and execute the cells sequentially.

Acknowledgements

  • The PIMA Diabetes Dataset is provided by the National Institute of Diabetes and Digestive and Kidney Diseases.