🍀 Challenge: Hypertune Model Parameters

Problem statement

The titanic dataset classifies survival or not survival given the features. This is the same code explained here.

The training data train.csv and the testing data test.csv file are provided. A sample model architecture is specified along with the compile and the fit method. The model performance is evaluated using the training data and the test data. The task is to hypertune the model parameters 🔨 so that we can achieve at least 80% accuracy on training, validation, and test set.

📝 Note: There can be many solutions to the problem. We have provided one solution in the next lesson.

Sample input

A sample model configuration is given.

Sample output

Tuning of hyperparameters related to the network structure and during the network training to achieve at least 80% accuracy on the training, validation and test set.