https://github.com/allanotieno254/supervised-method--decision-trees--algorithms-diabetes-prediction-

Supervised-method--Decision-trees--algorithms-Diabetes-prediction if either one has diabetes or not
https://github.com/allanotieno254/supervised-method--decision-trees--algorithms-diabetes-prediction-

decision-tree-classifier googlecolab jupyter-notebook pandas-dataframe pandas-python supervised-machine-learning visualization

Last synced: about 2 months ago
JSON representation

Supervised-method--Decision-trees--algorithms-Diabetes-prediction if either one has diabetes or not

• Host: GitHub
• URL: https://github.com/allanotieno254/supervised-method--decision-trees--algorithms-diabetes-prediction-
• Owner: AllanOtieno254
• License: apache-2.0
• Created: 2024-04-25T14:36:20.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-04-25T14:38:52.000Z (10 months ago)
• Last Synced: 2024-11-06T20:05:12.368Z (3 months ago)
• Topics: decision-tree-classifier, googlecolab, jupyter-notebook, pandas-dataframe, pandas-python, supervised-machine-learning, visualization
• Language: Jupyter Notebook
• Homepage: https://colab.research.google.com/drive/1mtoRb_vHmiDlSjYq6tVZCMk0OtfwWdmA#scrollTo=8FevcATRpXsr
• Size: 124 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Supervised-method--Decision-trees--algorithms-Diabetes-prediction-

Supervised-method--Decision-trees--algorithms-Diabetes-prediction if either one has diabetes or not

diabetes dataset https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database This data frame contains following columns:

 1.Pregnancies: This column typically represents the number of times a person has been pregnant. It's a numeric (integer) attribute.

2.Glucose: This column represents the glucose concentration in the blood, often measured in milligrams per deciliter (mg/dL). It's a numeric (continuous) attribute and is an important indicator in diabetes diagnosis.

3.BloodPressure: This column represents the blood pressure of the individual. It's also a numeric (continuous) attribute and is typically measured in millimeters of mercury (mm Hg).

4.SkinThickness: This column represents the thickness of the skinfold at a certain location on the body. It's another numeric (continuous) attribute, which might be used in body composition measurements.

5.Insulin: This column represents the insulin level in the blood, typically measured in microunits per milliliter (µU/mL). It's a numeric (continuous) attribute and is related to glucose metabolism.

6.BMI (Body Mass Index): BMI is a measure of body fat based on an individual's weight in relation to their height. It's a numeric (continuous) attribute, and it's calculated as weight in kilograms divided by the square of height in meters.

7.DiabetesPedigreeFunction: This column is a numeric (continuous) attribute that represents a function designed to measure the likelihood of diabetes based on family hitory. It often accounts for the genetic component of diabetes risk.

8.Age: Age is a numeric (integer) attribute that represents the age of the individual.

9.Outcome: This column is a binary (categorical) attribute that typically represents the target variable or the label in a diabetes prediction task. It often takes two values, such as 0 for "No Diabetes" and 1 for "Diabetes," indicating the presence or absence of diabetes in the individual.