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Diabetes Detection using Machine Learning :

This project aims to detect diabetes in patients using machine learning algorithms. It is built using Python and leverages popular libraries such as numpy, pandas, and scikit-learn.

Features :

  • Data preprocessing and cleaning
  • Feature selection
  • Model training and evaluation using Support Vector Machine (SVM)
  • Predictions
  • Visualization of results

Prerequisites :

  • Anaconda (for managing Python packages and environments)
  • PyCharm (or any other Python IDE)
  • Basic knowledge of Python and machine learning

Libraries Used :

import numpy as np  
import pandas as pd  
from sklearn.preprocessing import StandardScaler  
from sklearn.model_selection import train_test_split  
from sklearn import svm  
from sklearn.metrics import accuracy_score 


## Dataset:
**The dataset used for this project is diabetes.csv, which should be placed in the data directory**.

## Visualization:
**The project includes various visualizations to help understand the data and the model's performance. These are created using matplotlib and seaborn and can be found in the notebooks or generated by running the scripts**