Scikit learn Models

Naive-Bayes

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable.

Approach

• Separate the dataset into training and testing datasets
• Take the training dataset and separate it by the target values
• Calculate statistical values such as mean, the standard deviation for the dataset
• Summarize the data by class
• Calculate the Gaussian Probability Density Function
• Estimate the class probabilities

Result

Accuracy: 73%

Logistic Regression

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, logistic regression is a predictive analysis.

Approach

• Separate the dataset into training and testing datasets
• Train the classifier
• Predict the values using the test dataset with the classifier

Result

Accuracy: 78%

Random Forest

Random forest is a type of supervised machine learning algorithm based on ensemble learning.

Approach

• Pick N random records from the dataset
• Build a decision tree based on N records
• Choose the number of trees you want to build and repeat the above two steps (More the trees, high the prediction accuracy)

Result

Accuracy: 80%