This repository explores, processes, and analyzes diabetic patient data using machine learning techniques. It includes scripts for data preprocessing, exploratory analysis, predictive modeling, and visualization, aiming to extract meaningful insights and predictions to better understand diabetes-related trends and outcomes.
- Identify patterns in diabetic patient data
- Predict outcomes using machine learning models
- Visualize trends to enhance understanding of the data
- Create a reference workflow that can be reused or extended
- Contribute to research supporting diabetes awareness and medical advancements
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Data Preprocessing
- Scripts to clean and prepare the dataset
- Handle missing values and outliers
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Exploratory Data Analysis (EDA)
- Descriptive statistics and summary metrics
- Visualizations to uncover trends and patterns
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Machine Learning Models
- Train predictive models on patient metrics
- Evaluate outcomes and model performance
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Visualization
- Generate plots and charts for intuitive understanding of results
Mandatory:
pip install -r requirements.txtPython Version:
- Python 3.7+
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Clone the repository
git clone https://github.com/gholinasabmaryam/Diabetic-patient-data-analysis.git cd Diabetic-patient-data-analysis -
Install dependencies
pip install -r requirements.txt
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Run the notebooks or scripts
- Use Jupyter Notebook or any Python IDE to execute analysis and training scripts
- Comprehensive EDA: Gain insights into the dataset through descriptive statistics and visualizations
- Predictive Modeling: Train machine learning models to predict diabetic patient outcomes
- Data Cleaning: Preprocess datasets to improve reliability of analysis
- Add advanced machine learning algorithms for improved predictions
- Integrate deep learning models for complex analysis
- Explore real-time prediction capabilities
- Python Data Science Ecosystem – pandas, NumPy, Matplotlib, and scikit-learn
- Open-source community – for tools that power this analysis