The project, Diabetes Prediction Analysis Using Machine Learning in R, utilized the Pima Indians Diabetes dataset from Kaggle. This dataset consists of 765 rows and 9 features: pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, BMI, diabetes pedigree function, age, and the outcome variable indicating diabetes presence. The goal was to evaluate and compare the performance of various machine learning models for diabetes prediction and identify key predictive features.

This interactive Shiny application enables an in-depth exploration of drug effectiveness and side effects using the UCI Drug Review dataset. It provides a range of analytical features, such as sentiment analysis of user reviews, visualization of drug ratings distribution, and a comparison of side effects via word clouds. Users can filter data by conditions and drugs, offering insights into the drug’s effectiveness, common side effects, and statistical trends across various conditions. The application aims to support healthcare professionals and researchers in evaluating drug performance based on real-world user feedback.

Diabetes is a chronic disease that poses significant health challenges worldwide, affecting millions of individuals each year. Early detection and effective management of diabetes are crucial for preventing severe complications such as cardiovascular diseases, kidney failure, and neuropathy. With the increasing availability of medical data, machine learning techniques offer a powerful approach to predict diabetes and assist healthcare professionals in decision-making. In this project, we leverage various machine learning models to analyze a diabetes dataset. The goal is to predict the likelihood of diabetes occurrence based on health-related features, such as glucose levels, BMI, and blood pressure. We implement the following steps: 1. *Data Preprocessing*: Handling missing values, normalizing numerical features, and splitting the data into training and testing sets. 2. *Exploratory Data Analysis (EDA)*: Understanding the relationships between features and the target variable using visualizations. 3. *Model Training*: Applying multiple machine learning techniques, including: - Logistic Regression - Random Forest - Support Vector Machine (SVM) - Extreme Gradient Boosting (XGBoost) 4. *Model Evaluation*: Comparing models based on performance metrics such as accuracy, confusion matrix, and AUC (Area Under the Curve). 5. *Insights and Conclusion*: Identifying the most effective model for diabetes prediction. This analysis not only demonstrates the application of machine learning techniques in R but also provides valuable insights for healthcare professionals to make data-driven decisions. The results of this study highlight the power of advanced analytics in addressing real-world healthcare challenges.