Steps

**Worked on the Dataset using Python**

**Steps:**
1. Analysis of the dataset through Exploratory Data Analysis
Using Visualization extracted some useful insights like females have a higher chance of getting heart disease, Older the patient is, the greater is the chance of getting heart disease..

2. Feature Engineering and Extraction:
This is the most important phase in machine learning. A model can work at its best only if the features provided to it are good enough.
  - Imputation of Missing Values: No values were missing so skipped this step.
  - Feature Extraction: No features were extracted.
  
3. Modelling using:
 - Phase 1:
   - Base Models: Used Logistic Regression, Decision Tree, Random Forest, Extra Trees Classifiers, SVM and KNN
   - Ensembles : Used Bagging with Logistic Regression, Decision Tree and Random Forest, Boosting like Adaptive Boosting, Extreme Gradient Boosting and Gradient Boosting
 - Phase 2:
   - Used hyperparameter tuning like GridSearchCV to fine tune the base models and ensembles.
   - Worked on a number of permutations and combinations with the base and ensembles
   - Finally also performed Stacking

4. Further Work that needs to be done:
  - Work on extracting some features
  - Work on feature specific models
  - Use deep learning techniques