The MeriSkill data analysis internship spans 3-6 months, offering practical experience to students and recent grads in the data analysis realm. Participants engage in real-time projects, collaborate with seasoned professionals, and stay abreast of the latest trends and technologies. Key tasks include data collection, cleaning, statistical analysis, model building, data visualization, and presenting insights to stakeholders. Interns gain exposure to tools like Python, SQL, and Tableau, making this internship ideal for those aspiring to a successful career in data analysis. It equips participants with essential skills and experience for the rapidly growing field.

This project presents a predictive model for diagnosing diabetes based on the analysis of diagnostic measurements. The dataset utilized is sourced from the National Institute of Diabetes and Digestive and Kidney Diseases, specifically focusing on females aged at least 21 years old of Pima Indian heritage. The primary objective is to predict the likelihood of a patient having diabetes through the examination of various medical predictor variables.

The primary goal of this project is to develop an accurate and reliable prediction model that aids in the early diagnosis of diabetes. Leveraging machine learning techniques, the system processes the provided diagnostic measurements to classify patients into diabetic or non-diabetic categories.

The dataset encompasses critical health metrics, providing a comprehensive view for predictive analysis. The key variables include:

• Pregnancies
• Glucose
• Blood Pressure
• Skin Thickness
• Insulin
• BMI (Body Mass Index)
• Age
• Outcome: Target variable indicating diabetes presence (1) or absence (0)

The developed Diabetes Prediction System demonstrates promising accuracy and reliability in identifying individuals at risk of diabetes. The model's performance is assessed through rigorous evaluation metrics, providing insights into its strengths and areas for potential improvement.

• .ipynb Notebook
• Dataset