Python Machine Learning Test

Implement the desired_marketing_expenditure function, which returns the required amount of money that needs to be invested in a new marketing campaign to sell the desired number of units.

Use the data from previous marketing campaigns to evaluate how the number of units sold grows linearly as the amount of money invested increases.

For example, for the desired number of 60,000 units sold and previous campaign data from the table below, the function should return the float 250,000.

Previous campaigns

As a part of an application for iris enthusiasts, implement the train_and_predict function which should be able to classify three types of irises based on four features.

The train_and_predict function accepts three parameters:

• train_input_features - a two-dimensional NumPy array where each element is an array that contains: sepal length, sepal width, petal length, and petal width.
• train_outputs - a one-dimensional NumPy array where each element is a number representing the species of iris which is described in the same row of train_input_features. 0 represents Iris setosa, 1 represents Iris versicolor, and 2 represents Iris virginica.
• prediction_features - two-dimensional NumPy array where each element is an array that contains: sepal length, sepal width, petal length, and petal width.

The function should train a classifier using train_input_features as input data and train_outputs as the expected result. After that, the function should use the trained classifier to predict labels for prediction_features and return them as an iterable (like list or numpy.ndarray). The nth position in the result should be the classification of the nth row of the prediction_features parameter.

Your team has decided to use a k-nearest neighbors model for fraud detection model, but nobody has tuned the hyperparameters yet. You have been asked to implement tune_knn to perform an exhaustive search over a given list of neighbor counts and return the best value.

Use accuracy as the scoring metric. If multiple values achieve the same accuracy, return the first value that achieves the best score.

For example, the following code:

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(
    n_samples=100,
    n_features=8,
    n_informative=4,
    random_state=42
)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
print(tune_knn(X_train.tolist(), y_train.tolist(), X_test.tolist(), y_test.tolist(), [11, 1, 6]))

Should print:

6