Data Science Interview Questions

A classifier that predicts if an image contains only a cat, a dog, or a llama produced the following confusion matrix:

What is the accuracy of the model, in percentages?

Two bacteria cultures, A and B, were set up in two different dishes, each covering 50% of its dish. Over 20 days, bacteria A's percentage of coverage increased to 70% and bacteria B's percentage of coverage reduced to 40%:

A company stores login data and password hashes in two different containers:

• DataFrame with columns: Id, Login, Verified.
• Two-dimensional NumPy array where each element is an array that contains: Id and Password.

Elements on the same row/index have the same Id.

Implement the function login_table that accepts these two containers and modifies id_name_verified DataFrame in-place, so that:

• The Verified column should be removed.
• The password from NumPy array should be added as the last column with the name "Password" to DataFrame.

For example, the following code snippet:

id_name_verified = pd.DataFrame([[1, "JohnDoe", True], [2, "AnnFranklin", False]], columns=["Id", "Login", "Verified"])
id_password = np.array([[1, 987340123], [2, 187031122]], np.int32)
login_table(id_name_verified, id_password)
print(id_name_verified)

Should print:

   Id        Login   Password
0   1      JohnDoe  987340123
1   2  AnnFranklin  187031122

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 company is running a test that is designed to compare two different versions of the company’s website.

Version A of the website is shown to 60% of users, while version B of the website is shown to the remaining 40%. The test shows that 8% of users who are presented with version A sign up for the company’s services, as compared to 4% of users who are presented with version B.

If a user signs up for the company’s services, what is the probability that she/he was presented with version A of the website?

The following .csv file contains the data from a classifier model that predicts if an image contains a dog: predictions.csv 

The first column contains information if the dog is in the image or not. The second column contains the classifier prediction, which is in the interval 0-100, with higher values meaning that the classifier is more confident that image contains a dog.

What is the value of the decision boundary that will maximize the accuracy of the model? Values greater than or equal to the decision boundary will be treated as positive.

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

You are given a list of tickers and their daily closing prices for a given period.

Implement the most_corr function that, when given each ticker's daily closing prices, returns the pair of tickers that are the most highly (linearly) correlated by daily percentage change.