Pima Indians Diabetes Prediction

This project analyzes the Pima Indians Diabetes dataset to predict the onset of diabetes in individuals within 5 years based on various medical parameters. The problem is framed as a binary classification task, where the model predicts whether a person will develop diabetes (1) or not (0).

Dataset Overview

The dataset consists of 768 records, each with 8 medical features and 1 output variable indicating diabetes status. Some of the medical features include:

1. Number of times pregnant
2. Plasma glucose concentration (after a 2-hour oral glucose tolerance test)
3. Diastolic blood pressure (mm Hg)
4. Triceps skinfold thickness (mm)
5. 2-hour serum insulin (mu U/ml)
6. Body mass index (BMI) (weight in kg / (height in m)^2)
7. Diabetes pedigree function (family history)
8. Age (years)

The missing values in the dataset are coded as zero.

Key Tasks

1. Visualizing the Data:

   • Histograms for all features without considering the class variable.
   • Histograms of features 2, 3, 6, and 7, with separate plots for the two classes (diabetes or not).

2. Statistical Analysis:

   • Calculation of the mean and variance for feature 6 (BMI).

3. Scatter Plot:

   • Plotting a scatter plot between features 6 (BMI) and 8 (Age), with different colors representing the two classes.

4. Classification:

   • Building a decision tree classifier with a maximum depth of 4 to classify whether an individual has diabetes or not based on the provided features.

Conclusion

This project provides a comprehensive analysis of the Pima Indians Diabetes dataset, combining data visualization, statistical analysis, and machine learning techniques to predict diabetes onset.