The following features have been provided to help us predict whether a person is Diabetic or not:

1. Pregnancies: Number of times pregnant.
2. Glucose: Plasma glucose concentration over 2 hours in an oral glucose tolerance test.
3. Blood Pressure: Diastolic blood pressure (mm Hg).
4. Insulin: 2-hour serum insulin (mu U/ml).
5. BMI: Body mass index (weight in Kg)/(height in M2).
6. Age: Age (years)
7. Outcome: Class variable (0 if non-diabetic, 1 if diabetic)

1. Data Exploration: Analyzing the dataset and understanding the features.
2. Data Preprocessing: Handling missing values and converting numerical data into categorical variables for improved interpretation.
3. Model Building: Developing predictive models using machine learning techniques.
4. Model Evaluation: Assessing model performance and selecting the most suitable model.
5. Conclusion: Recommendations and insights drawn from the analysis.

Diabetes Dataset Model Python File Model pdf file