Diabetes Prediction

Objective

Original dataset : https://archive.ics.uci.edu/ml/datasets/diabetes

Kaggle Competitions : https://www.kaggle.com/uciml/pima-indians-diabetes-database

Overview

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not 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.

Techniques Used

• Data Cleaning
• Data Visualization
• Machine Learning Modeling

Algortihms Used

1. Logistic Regression
2. KNN
3. Support Vector Machine
4. Naivye Bayes
5. Random Forest Classifier
6. Decision Tree
7. XGboost

Accuracy We got

1. Logistic Regression : 77.92%
2. KNN : 74.92%
3. Support Vector Machine : 78.57%
4. Naivye Bayes : 77.27%
5. Random Forest Classifier : 80.52%
6. Decision Tree : 79.22%
7. XGboost : 75.32%

Screenshot

Installation

• Clone this repository and unzip it.

• After downloading, cd into the Deployment directory.

• Begin a new virtual environment with Python 3 and activate it.

• Install the required packages using pip install -r requirements.txt

• Execute the command: python manage.py runserver

• Open http://127.0.0.1:8000/ in your browser.

Guide Lines

Packages and Tools Required:

Pandas 
Matplotlib
Seaborn
Scikit Learn
Jupyter Notebook
Django

Package Installation

pip3 install -r requirements.txt