- In this project, I have predicted that a person has diabetes or not on the basis of the dataset. Before prediction, some data cleaning, feature engineering, feature selection, exploratory data analysis, data visualization I performed.
- I used libraries like sklearn, pandas, numpy, matplotlib, seaborn, tensorflow, and keras
- I made Logistic Regression model, Decision Tree Model, AdaBoost Classifier model, Random Forest model, kNN model, SVM model, XGBoost model. Among these I found Logistic Regression model is perfect for our project.
- I also builded Dense Neural Network for prediction. But still I found Logistic Regression model is best fitted for our project.
Context: 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.
Content: The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.
This .csv file is taken from kaggle and also I submitted my project on kaggle as well. Here I am providing kaggle link to see all about .csv file that was I provided in this repository: https://www.kaggle.com/uciml/pima-indians-diabetes-database