[["user", "hi"], ["bot", "I don't know. Here's a FAQ similar to yours that you might try: \"Yes.\". "], ["user", "yes"], ["bot", "You seem to be quite positive."], ["user", "Hi"], ["bot", "Hello!"], ["user", "Hi"], ["bot", "Hello!"], ["user", "Hi"], ["bot", "Hello!"], ["user", "Hi"], ["bot", "Hello!"], ["user", "Hi"], ["bot", "Hello!"], ["user", "Hi"], ["bot", "Hello!"], ["user", "Hi"], ["bot", "Hello!"], ["user", "Hi"], ["bot", "Hello!"], ["user", "hi"], ["bot", "Sorry, something went wrong. Not sure what to say..."], ["user", " "], ["bot", "I'm not sure I understand you fully."], ["user", "none"], ["bot", "I don't know. Here's a FAQ similar to yours that you might try: \"What are some other Regression models besides LinearRegression? \". "], ["user", "horizontalRegression"], ["bot", "Please go on."], ["user", " "], ["bot", "I don't know. Here's a FAQ similar to yours that you might try: \"How to use chi squared test in feature selection?\". "], ["user", "hi"], ["bot", "I'm not sure I understand you fully."], ["user", "NONE"], ["bot", "Please go on."], ["user", "go on with what?"], ["bot", "GridSearchCV trains 3 different models on the data, by default, because it uses KFolds cross-validation with K=3. In addition, your implementation of your `create_model()` function trains and validates a Keras neural net model every time you call it. So the GridSearchCV is retraining and revalidating your model 3 times, for a total of 6 trainings.  Never use the black-box functions that do the work of a data scientist, like hyperparameter optimization or cross-validation. Training and evaluating model performance usually requires a lot of computation and memory and time, and you want to minimize that time by inserting your DS intuition into the optimization process. If you do want to automate a grid search use `itertools.product()` to manually iterate through your hyperparameter combinations and evaluate your model performance."], ["user", "awesome thanx"], ["bot", "Do you feel strongly about discussing such things ?"], ["user", "nope"], ["bot", "I'm not sure I understand you fully."], ["user", "i am also sure of this"], ["bot", "Both. Start with the dataset, like at the google dataset search tool. Look for interesting datasets where the data is arranged in tables that you can easily import and manipulate. CSVs with numerical values are usually the easiest. Think about the dimensionality of the features and the number of samples/examples/rows and whether you think the problem will be solvable. Examine the relationship between possible target variables and the other features to make sure the target is something that would be interesting to \"predict\" if you only had access to the features."], ["user", " "], ["bot", "I'm not sure I understand you fully."], ["user", "hi"], ["bot", "Please go on."], ["user", "hi"], ["bot", "What does that suggest to you ?"], ["user", "hi"], ["bot", "Do you feel strongly about discussing such things ?"], ["user", "hi"], ["bot", "I'm not sure I understand you fully."], ["user", "hi"], ["bot", "Please go on."], ["user", "hi"], ["bot", "What does that suggest to you ?"], ["user", "hi"], ["bot", "Do you feel strongly about discussing such things ?"], ["user", "hi"], ["bot", "I'm not sure I understand you fully."], ["user", "hello"], ["bot", "I don't know. Here's a FAQ similar to yours that you might try: \"What is Google Colab?\". "], ["user", ""], ["bot", 0.675], ["user", ""], ["bot", 0.675], ["user", "yes"], ["bot", 0.675], ["user", "2"], ["bot", 0.675], ["user", ""], ["bot", 0.675], ["user", "yes"], ["bot", 0.675]]