Diabetes-Prediction-WebApp-using-Streamlit

Steps to run the project:

Step 1: Download the ipynb file and load the dataset in it.

Step 2: Run the file to get 'trained_model.sav' on your colab directory.

Step 3: Create a folder named 'Diabetes Prediction' and store the trained_model.sav file in it.

Step 4: Open Anaconda and create new environment named as 'MachineLearning'

Step 5: In the MachineLearning environment, open terminal and download streamlit and sklearn using:

    streamlit:
    i) pip install streamlit
    ii) streamlit hello
    
    sklearn:
    i) pip install sklearn

Step 6: After installing the libraries, open 'Diabetes-prediction-webapp.py' on Spyder and store it in 'Diabetes Prediction' folder

Step 7: Copy the path of 'Diabetes-prediction-webapp.py'

Step 8: Open terminal of MachineLearning environment and run the command:

    streamlit run "..[path]..\Diabetes-prediction-webapp.py"

Step 9: Enter all the user inputs required

Step 10: Click on Predict button inorder to predict whether a person is 'Diabetic' or 'Non Diabetic'

Important links:

1. Link to download Anaconda: https://www.anaconda.com/products/distribution
2. About streamlit: https://docs.streamlit.io/library/get-started/installation
3. About sklearn: https://pypi.org/project/scikit-learn/
4. Download dataset from: https://www.kaggle.com/datasets/mathchi/diabetes-data-set