This is Pandas Which of the following would return the correlation between the variables "height" and "weight" from the data frame df?   df["height", "weight"].corr()   df["height", "weight"].compare()   df[["height", "weight"]].correlation()   df[["height", "weight"]].corr()

Implement

def get_nearest_station(my_latitude: float, my_longitude: float, stations: List['Station']) -> int: "''Return the id of the station from stations that is nearest to the location given by my_latidute and my_longitude. In the case of a tie, return the ID of the last station in stations with that distance. Preconditions: len(stations) > 1 » get_nearest_station(43.671134, -79.325164, SAMPLE_STATIONS) 7571 » get_nearest_station(43.674312, -79.299221, SAMPLE_STATIONS) 7486

You will be using on RV related review comments to train a Sentence completion model. You could use the code below. The code is written in Jupyter notebook python.  please follow the following instructions: 1. Build a working sentence completion model. Training data is provided in train.csv. This model should take partial sentences (e.g., "hey how ar," "hello, please") and provide completion for that sentence. Not every input needs to be completed into a correct sentence (e.g., "asdfauaef" does not need to have a sentence completion). For this model's purposes, a sentence is considered completed if an "end of sentence" character (like a full stop, question mark, etc.) occurs within the next five words OR. We predict up to 5 words from the input. 2. BONUS: If you use word vectors or other techniques from what we shared in class using character encoding and have a working model. 3. The test data (test.csv) will be used to evaluate the model. The test file contains partial sentences. Use…

Numpy

Use the sacramento.csv file to complete the following assignment. Create a file, sacramento.py, that loads the .csv file and runs a logistic regression. The regression should predict whether or not a house has 1 or more than one bathroom based on beds, sqft, and price, in that order.You will need to create a new variable from baths, and it should make it such that those observations of 1 bath correspond to a value of 0, and those with more than 1 bath correspond to a 1. Make sure to add a constant using sm.add_constant(X) Your file should print the results in this way: print(mod.params.round(2)) print(mod.pvalues.round(2)) print('The smallest p-value is for sqft')

Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set 1. Required Model (100 pts): Implement and train a logistic regression as your classification model. • You have to use Sklearn deep learning library. • You may want to refer to this tutorial: https://bit.ly/37anOxi

If we want to filter out the results and shows only the values of certain properties, such as Name, Status of Get-Service cmdlet, we can use this cmdlet: Group of answer choices Remove-Item Select-String Select-Object Sort-Object

You are given a list of projects and a list of dependencies (which is a list of pairs ofprojects, where the second project is dependent on the first project). All of a project's dependenciesmust be built before the project is. Find a build order that will allow the projects to be built. If thereis no valid build order, return an error.EXAMPLEInput:projects: a, b, c, d, e, fdependencies: (a, d), (f, b), (b, d), (f, a), (d, c)Output: f, e, a, b, d, c

def get_nearest_station(my_latitude: float, my_longitude: float, stations: List['Station']) -> int:    """Return the id of the station from stations that is nearest to the    location given by my_latidute and my_longitude.     In the case of a tie, return the ID of the last station in stations with    that distance.     Preconditions: len(stations) > 1     >>> get_nearest_station(43.671134, -79.325164, SAMPLE_STATIONS)    7571    >>> get_nearest_station(43.674312, -79.299221, SAMPLE_STATIONS)    7486    """