Welcome to the Project: Healthcare Data Analysis and Predictive Modeling! In this project, we aim to leverage data analysis and predictive modeling techniques to improve healthcare outcomes. Specifically, our goal is to predict patients at risk of diabetes infection based on a comprehensive healthcare dataset.

We are working with a healthcare dataset containing information on diabetes patients. The dataset includes various features such as age, BMI and glucose levels, among others. The target variable is binary, indicating whether a patient has a diabetes infection (1) or not (0).

Dataset Source: The dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether a patient has diabetes based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

• Exploratory Data Analysis (EDA): Conduct a thorough exploration of the healthcare dataset to gain insights into patient demographics, medical history, and risk factors associated with diabetes.

Exploratory Data Analysis is a crucial step in understanding the dataset and preparing it for modeling. During this phase, we perform the following tasks:

• Data cleaning and preprocessing.

• Descriptive statistics and data summary.

• Visualization of key features and distributions.

• Identification of correlations and patterns.

Our EDA findings will guide the feature selection and engineering process for the predictive modeling phase.

• Predictive Modeling: Build and evaluate predictive models that can accurately identify patients with potential diabetes infection based on the dataset's features.

In the predictive modeling phase, we aim to develop a machine learning model capable of predicting diabetes infections accurately. The key steps in this phase include:

• Data Preprocessing: Feature selection and engineering

• Model Selection, Training and Tuning: Selecting and training a Machine Learning Model on the dataset and fine-tuning hyperparameters for optimal performance.

• Model Evaluation: Assessing the model's performance using appropriate metrics like accuracy, precision, recall and F1-score.

• Interpretation: Gaining insights into which features are most influential for predicting diabetes infections.

• Insights and Recommendations: Provide actionable insights and recommendations to healthcare professionals to aid in early diagnosis and intervention for patients at risk of diabetes.

Our project's success will be measured by the predictive accuracy of our model and the actionable insights we provide to healthcare professionals. We will present our results through:

• Performance metrics and evaluation scores.

• Visualizations of key findings.

• Feature importance analysis.

• Recommendations for early diagnosis and intervention.

Click on the link below to view the full Project Repository.

  GitHub