Training data and models in Intro to Computing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load training data from a CSV file using pandas.

Intro to Computing

import pandas as pd
train_data = pd.read_csv([1])

Drag options to blanks, or click blank then click option'

A'train.csv'

Btrain.csv

Ccsv.read('train.csv')

Dpd.load('train.csv')

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename.

Using incorrect function names like pd.load or csv.read.

The filename must be a string inside quotes when using pd.read_csv().

2fill in blank

medium

Complete the code to split data into features and labels.

Intro to Computing

X = data.drop(columns=[1])
y = data['label']

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A'label'

Blabel

C['label']

Dlabel_column

Attempts:

3 left

💡 Hint

Common Mistakes

Not using quotes around the column name.

Using a variable name instead of the string.

The column name must be a string inside quotes when dropping it.

3fill in blank

hard

Fix the error in the code to train a model using scikit-learn.

Intro to Computing

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.[1](X, y)

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Apredict

Btrain

Ctransform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using train() instead of fit().

Using predict() before training the model.

The method to train a model in scikit-learn is fit().

4fill in blank

hard

Fill both blanks to create a dictionary of feature means for training data where values are greater than 0.

Intro to Computing

feature_means = {col: data[col].[1]() for col in data.columns if (data[col][2] 0).any()}

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Amean

Bmax

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using max() instead of mean().

Using < instead of > in the condition.

We calculate the mean of each column and filter columns where values are greater than 0.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps feature names to their max values if max is greater than 10.

Intro to Computing

max_values = { [1] : data[[2]].[3]() for [2] in data.columns if data[[2]].max() > 10 }

Drag options to blanks, or click blank then click option'

Afeature

Bcol

Cmax

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of max().

Using an undefined variable like 'feature' for the key.

We use 'col' as key, iterate with 'col', and use max() to get max values.