🩺 Diabetes Prediction Using Machine Learning

Welcome to the Diabetes Prediction project! 🎉 The goal of this project is to predict whether a person has diabetes using various machine learning algorithms. We focus on applying data cleaning, visualization, and modeling techniques to build accurate prediction models.

🎯 Objective

• 🧹 Clean the data to ensure high-quality inputs.
• 📊 Visualize the data to better understand patterns and correlations.
• 🤖 Train machine learning models to predict diabetes.
• 💡 Evaluate model performance using several evaluation metrics.

🛠️ Techniques Used

1. Data Cleaning 🧼: Removing missing values, handling outliers, and preparing the data for modeling.
2. Data Visualization 📊: Analyzing and visualizing the data to understand patterns and trends.
3. Machine Learning Modeling 🤖: Training multiple machine learning models to predict diabetes.

🧠 Algorithms Used

1. Logistic Regression 🧑‍💼
2. Support Vector Machine (SVM) 🔲
3. K-Nearest Neighbors (KNN) 🔍
4. Random Forest Classifier 🌳
5. Naive Bayes 🧑‍🔬
6. Gradient Boosting 🔥

📈 Model Evaluation Methods Used

1. Accuracy Score ✅: Measures how often the model makes correct predictions.
2. ROC AUC Curve 📉: Evaluates the trade-off between true positive rate and false positive rate.
3. Cross-Validation 🔄: Splitting the data into different subsets to ensure the model performs well on unseen data.
4. Confusion Matrix 📊: Provides a breakdown of prediction errors, including false positives, false negatives, true positives, and true negatives.

📦 Dependencies

To run this project, you will need the following libraries:

• pandas 📑
• numpy 🔢
• matplotlib 📊
• seaborn 🎨
• scikit-learn 🧑‍💻

You can install the dependencies by running:

pip install pandas numpy matplotlib seaborn scikit-learn