Predicting Heart disease using Machine Learning This notebook looks into using various Python-based ML and Data Science libraries in an attempt to build a Machine Learning model capable of predicting whether or not someone has heart disease based on their medical attributes. We're going to take the following approach: Problem Definition Data Evaluation Features Modelling About Heart Disease Cardiovascular disease or heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease. From WHO statistics every year 17.9 million dying from heart disease. The medical study says that human life style is the main reason behind this heart problem. Apart from this there are many key factors which warns that the person may/maynot getting chance of heart disease. From the dataset if we create suitable machine learning technique which classify the heart disease more accurately, it is very helpful to the health organisation as well as patients. 1. Problem Defintion In a statement, Given clinical parameters about a patient, can we predict whether or not they have heart disease? 2. Data The original data came from the Cleavland data from the UCI Machine Learning Repository There is also a version of it available on Kaggle. Heart Disease Date 3. Evaluation Target to reach more than 85% If the model scored better than 85% we will select the model 4. Features age: displays the age of the individual. sex: displays the gender of the individual using the following format : 1 = male 0 = female cp (Chest-Pain Type): displays the type of chest-pain experienced by the individual using the following format : 0 = typical angina 1 = atypical angina 2= non — anginal pain 3 = asymptotic trestbps(Resting Blood Pressure): displays the resting blood pressure value of an individual in mmHg (unit) chol(Serum Cholestrol): displays the serum cholesterol in mg/dl (unit) fbs (Fasting Blood Sugar): compares the fasting blood sugar value of an individual with 120mg/dl. If fasting blood sugar > 120mg/dl then : 1 (true) else : 0 (false) restecg (Resting ECG): displays resting electrocardiographic results 0 = normal 1 = having ST-T wave abnormality 2 = left ventricular hyperthrophy thalach(Max Heart Rate Achieved): displays the max heart rate achieved by an individual. exang (Exercise induced angina): 1 = yes 0 = no oldpeak (ST depression induced by exercise relative to rest): displays the value which is an integer or float. slope (Peak exercise ST segment) : 0 = upsloping 1 = flat 2 = downsloping ca (Number of major vessels (0–3) colored by flourosopy): displays the value as integer or float. thal : displays the thalassemia (is an inherited blood disorder that causes your body to have less hemoglobin than normal) : 0 = normal 1 = fixed defect 2 = reversible defect target (Diagnosis of heart disease): Displays whether the individual is suffering from heart disease or not : 0 = absence 1 = present. Preparing the tools We are going to use :- Pandas & Numpy for Data Analysis and Manipulation Matplotlib and Seaborn for Data Visualisation Scikit-Learn for the Modeling building and Reports