Heart Disease Predictive analytics

Understand the Historical data and predict the Patients who are having the high risk to getting Heart attack and This Analysis will helps doctors to take the early decision and Risk Classification and Scoring both helps to understand the patient Condition Analytics Goals : Since this project is classification problem, So we used below ML Algorithms to predict the “Heart Attack (Target)” for Unseen data Logistic regression Analytics Outcomes: - chest_pain_type - num_major_vessels - thalassemia - age - cholesterol - st_depression Correlaction Analysis 1. Looks like Data is high Correlated. It leads to Multicollinearity problem. 2. Exercise_induced_angina , chest_pain_type, st_depression, st_slope , num_major_vessels and thalassemia these variables are high correlated with dependent (Target) Variable 3. max_heart_rate_achieved , fasting_blood_sugar there variables are shows negative correlation with Dependent (Target) variable. 4. Age , Sex, resting_blood_pressure, cholesterol, rest_ecg is moderate correlations with Dependent (Target) variable https://ibb.co/kD2RB0X