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The is a Microsoft Azure Web App project that helps the user to identify whether someone is suffering from Heart disease by simply inputting certain values like Chest Pain Type, Exercise Induced Angina, Thalassemia etc. with the help of kaggle database.

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stuti404/Heart-Disease-Prediction

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Heart-Disease-Prediction

The data science lifecycle is designed for big data issues and the data science projects. Generally, the data science project consists of seven steps which are problem definition, data collection, data preparation, data exploration, data modeling, model evaluation and model deployment.

The goal of this project is to go through the data science lifecycle steps in order to build a heart disease classification web application by using kanggle heart disease dataset. This project uses Flask API to deploy the model and build the web application.

Installation All libraries are available in Anaconda distribution of Python.

Dataset The dataset has these attributes:

age - age in years sex - (1 = male; 0 = female) cp - chest pain type 0: Typical angina: chest pain related decrease blood supply to the heart 1: Atypical angina: chest pain not related to heart 2: Non-anginal pain: typically esophageal spasms (non heart related) 3: Asymptomatic: chest pain not showing signs of disease trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern chol - serum cholestoral in mg/dl serum = LDL + HDL + .2 * triglycerides above 200 is cause for concern fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) '>126' mg/dL signals diabetes restecg - resting electrocardiographic results 0: Nothing to note 1: ST-T Wave abnormality can range from mild symptoms to severe problems signals non-normal heart beat 2: Possible or definite left ventricular hypertrophy Enlarged heart's main pumping chamber thalach - maximum heart rate achieved exang - exercise induced angina (1 = yes; 0 = no) oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more slope - the slope of the peak exercise ST segment 0: Upsloping: better heart rate with excercise (uncommon) 1: Flatsloping: minimal change (typical healthy heart) 2: Downslopins: signs of unhealthy heart ca - number of major vessels (0-3) colored by flourosopy colored vessel means the doctor can see the blood passing through the more blood movement the better (no clots) thal - thalium stress result 1,3: normal 6: fixed defect: used to be defect but ok now 7: reversable defect: no proper blood movement when excercising target - have disease or not (1=yes, 0=no) (= the predicted attribute)

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The is a Microsoft Azure Web App project that helps the user to identify whether someone is suffering from Heart disease by simply inputting certain values like Chest Pain Type, Exercise Induced Angina, Thalassemia etc. with the help of kaggle database.

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