Diabetes Prediction using Machine Learning

Project Overview

This project explores a healthcare classification problem: predicting whether a patient is likely to have diabetes based on clinical measurements such as glucose level, blood pressure, BMI, age, and other features.[file:35]

The goal is to build and evaluate machine learning models that can help identify high‑risk patients early, so that doctors and healthcare providers can focus attention on the right cases.

Dataset

• Name: Pima Indians Diabetes dataset (commonly used for diabetes prediction tasks)
• Rows: 768
• Columns (features): Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, and Outcome (0 = no diabetes, 1 = diabetes).[file:35]
• Source: Public dataset (e.g., Kaggle / UCI repository).
• Target variable: Outcome (binary classification).

Note: The dataset is not included here for licensing reasons. You can download it from a public source and place diabetes.csv in the project folder, or update the path in the notebook accordingly.

Objectives

1. Understand the structure and distribution of the diabetes dataset.
2. Perform exploratory data analysis (EDA) to uncover basic patterns and relationships.
3. Build machine learning models to predict diabetes based on patient features.
4. Evaluate model performance using standard classification metrics and interpret the results.

Methods and Workflow

The notebook PROJECTDiabetesPrediction.ipynb walks through the following steps:[file:35]

1. Data loading and inspection

   • Load diabetes.csv into a pandas DataFrame.
   • Inspect data types, missing values, and basic statistics using df.info() and df.describe().

2. Exploratory Data Analysis (EDA)

   • View the first few rows to understand feature meanings.
   • Plot histograms for key variables such as Glucose, BMI, and Age to see their distributions.[file:35]
   • Optionally visualize relationships between features and the Outcome variable.

3. Preprocessing

   • Handle any zero or unusual values that may represent missing data (e.g., zero values in Glucose, BloodPressure, BMI, etc.).
   • Scale or normalize features if needed.
   • Split the dataset into training and test sets.

4. Modeling

   • Train one or more classification models (e.g., Logistic Regression, Random Forest, etc.).
   • Fit the model(s) on the training data and generate predictions on the test set.

5. Evaluation

   • Evaluate performance using metrics such as accuracy, confusion matrix, precision, recall, and F1‑score.
   • Compare different models (if multiple are used) and discuss trade‑offs.

6. Insights

   • Highlight which features are most influential for predicting diabetes.
   • Discuss how such a model could be used in a real healthcare setting (e.g., pre‑screening or decision support).

How to Run the Notebook(chnage the links and paths as required)

1. Clone the repository:

   git clone https://github.com/your-username/data-analytics-projects.git
   cd data-analytics-projects/diabetes-prediction

2. Create a virtual environment and install dependencies (optional but recommended):

   pip install -r ../requirements.txt

3. Obtain the diabetes dataset (diabetes.csv) from a public source (e.g., Kaggle / UCI) and place it in this folder.

4. Open the notebook locally or in Google Colab:

   • Locally: using Jupyter Notebook / JupyterLab.
   • Colab: upload the notebook or open it directly from GitHub in Colab.

5. Run all cells from top to bottom to reproduce the analysis and results.

Future Improvements

• Add more feature engineering and advanced models (e.g., XGBoost, ensemble methods).
• Address class imbalance if present (e.g., SMOTE, class weights).
• Deploy a simple API or web interface where users can input patient data and get predictions.