Diabetes Prediction (Web App)

In healthcare field diagnose a problem early offer more chance for traitement and guerison in this project we apply machine learning techniques to predict whether a patient will develop diabetes within the next five years. Early detection and diagnosis of diabetes is that the early stages of diabetes are often non-symptomatic. People who are on the path to diabetes (also known as prediabetes) often do not know that they have diabetes until it is too late.

Contents

1. Project Structure
2. Prosess
3. How to run
4. Deployment (Real world Use)
5. To improve
6. About Me

1. Project Structure

Data

• ├── diabetes.csv
• ├── cleaned_data.csv
• ├── scaled_data.csv
• ├── data_documentation.pdf

analysis_and_training

• ├── py_files
• ├── 1_data_preprocessing
• ├── 2_ML_model
• ├── 3_DL_model
• ├── helpers.py

figures

• ├── contains graph and figures

models

• ├── contains trained models

images

• ├── contains images used in this images

environment

requirements.txt

gitignore

report (pdf & ppt)

2. Process

• step1 : Importing Packages
• step2 : Loading the data
• step3 : Exploratory Data Analysis (EDA)
• step4 : Data Preparation
• step5 : Build and Train the model
• step6 : Model prediction and Evaluation
• step7 : Model Improvement

• Hyperparameter Tuning, Features Selection and Features Ingeneering

• step8 : Model Deployment

3. How to run

N.B : python 3.7 is recommended

3.1. CLONE PROJECT DIRECTORY

• $ git clonehttps://github.com/RekidiangData-S/p01ml_diabetes_prediction.git
• $ cd p01ml_diabetes_prediction

3.2. CREATE & ACTIVATE VIRTUAL ENVIRONMENT

3.2.1. WITH PIP and VENV

(Windows)

• $ python -m venv p01ml_venv
• $ p01ml_venv\Scripts\activate (<= Activate virtual Environment)
• $ deactivate (<= Deactivate virtual Environment)
• $ pip install -r requirements.txt
• Set VIRTUAL ENVIRONMENT as KERNEL :
  • $ python -m ipykernel install --user --name p01ml_venv --display-name "p01ml_kernel"
• $ jupyter notebook

(MasOS || LINUX)

• $ python3 -m venv p01ml_venv
• $ source p01ml_venv/bin/activate (<= Activate virtual Environment)
• $ deactivate (<= Deactivate virtual Environment)
• $ pip install -r requirements.txt
• Set VIRTUAL ENVIRONMENT as KERNEL :
  • $ python -m ipykernel install --user --name p01ml_venv --display-name "p01ml_kernel"
• $ jupyter notebook

3.2.2. WITH CONDA

• Verify if you have conda installed ($conda --version) if not go to anconda or miniconda to download and install it

• $ conda create -n p01ml_venv python=3.7

• $ conda activate p01ml_venv (<= Activate virtual Environment)

• $ conda deactivate (<= Deactivate virtual Environment)

• Set VIRTUAL ENVIRONMENT as KERNEL :

  • $ python -m ipykernel install --user --name p01ml_venv --display-name "p01ml_kernel"

• $ jupyter notebook

• Go to Kernel -> Change kernel -> p01ml_kernel

• $ jupyter kernelspec list (<= list all ipykernel in your system)

• $ jupyter kernelspec uninstall p01ml_venv (<= Delete the ipykernel in your system)

Manage kernel

• $ jupyter kernelspec list (<= list all ipykernel in your system)
• $ jupyter kernelspec uninstall p01ml_venv (<= Delete the ipykernel in your system)

4. Deployment (Real world Use)

• Pre-diabetique prediction (WebApp with streamlit)
• Pre-diabetique stage prediction (WebApp with flask)

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Kiese Diangebeni Reagan : Blockchain & Web Developer | Data Scientist

I'm technology passionate person, Artificial Intelligence and blockchain enthusiast, lifelong learner.