• In machine learning, classification is a supervised learning concept which basically categorizes a set of data into classes. The most common classification problems are – speech recognition, face detection, handwriting recognition, document classification, etc.

• It can be either a binary classification problem or a multi-class problem too. There are a bunch of machine learning algorithms for classification in machine learning. Let us take a look at those classification algorithms in machine learning.

Titanic Data-set

• The training set (train.csv) should be used to build your machinelearning models. For the training set, it provides the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class.

• The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.

Data-Visualization

Results

• Accuracy Score of the test data are very close i.e. 0.807 and 0.782, therefore the test and the training data performed quite accurately, giving as the predication on the survival rate of the passengers

• The result of the clarification the train.csv data leads to a prediction of survival of a particular passenger on the bases of his gender, class, embark and other given properties, as the accuracy of the data is almost equal a result with very low margin error will produce.

• The Test data of any kind with the given properties can be given to a predication result of a survival, 0 or 1, with non-survived and survived respectively.