STAT 361 Data Analysis - Yale University WORKSHEET 3.8: SUPERVISED LEARNING IN PYTHON

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APPLIED DATA SCIENCE

Jian Karlo R. Sta. Maria

WORKSHEET 3.8: SUPERVISED LEARNING IN PYTHON

1

Date:

Import house-votes-84 (edited).csv. Write codes necessary to import and examine this dataset. Which of the following statements is not true? The target variable in this DataFrame is ‘party’.

A. The DataFrame has a total of 232 rows and 17 columns.

B. Except for party, all of the columns are of type int64.

D C. The first row of the DataFrame consists of votes by a Democrat and the second row consists of votes by a Republican.

D. There are 17 predictor variables, or features, in this DataFrame.

Output

2

Date:

Perform graphical exploratory data analysis on the house votes dataset. Use Seaborn’s countplot to visualize the votes to the satellite testing bill, grouped by party. Include the following line before the show function: plt.xticks([0,1], [‘No’, ‘Yes’]). Do the same for the missile bill. Of the two bills, which one/s do Democrats vote resoundingly in favor of, compared to Republicans?

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APPLIED DATA SCIENCE

Jian Karlo R. Sta. Maria

WORKSHEET 3.8: SUPERVISED LEARNING IN PYTHON

A. Missile bill B. Satellite bill C. Both Missile and Satellite bills D. Neither Missile nor Satellite bill Output

3

C

Date:

Predict the party affiliation of the House member whose votes have been recorded in the file named x_new.csv. Write the code here to achieve the following output: Party Prediction: [‘democrat’/’republican’] Code import pandas as pd from sklearn.neighbors import KNeighborsClassifier hv84=pd.read_csv('house-votes-84 (edited).csv') x_new=pd.read_csv('x_new.csv') y=hv84['party']. values x=hv84.drop('party', axis=1).values knn=KNeighborsClassifier(n_neighbors=16) # n_neighbors = no. of categories knn.fit(x,y) prediction=knn.predict(x_new) print("Prediction: {}".format(prediction)) Output Prediction: ['democrat'] 4

Date:

Use train_test_split from sklearn on your House votes data. Use 70% of the data for training and the rest for testing.

Add the following arguments to train_test_split: random_state = 21, stratify = y. Print out the predictions for the test set and the model score.

Code from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split import pandas as pd hv84=pd.read_csv('house-votes-84 (edited).csv') y=hv84['party']. values x=hv84.drop('party', axis=1).values x_train, x_test, y_train, y_test=train_test_split(x,y, test_size=0.7, random_state=21, stratify=y) knn=KNeighborsClassifier(n_neighbors=16) # n_neighbors = no. of categories knn.fit(x_train, y_train) print(knn.score(x_test,y_test)) Output Test Set Predictions:['democrat' 'democrat' 'republican' 'republican' 'democrat' 'republican' 'democrat' 'republican' 'democrat' 2 of 4 This study source was downloaded by 100000861003072 from CourseHero.comPage on 04-27-2023 09:03:54 GMT -05:00 https://www.coursehero.com/file/83165998/WS38STAMARIAdocx/

APPLIED DATA SCIENCE

Jian Karlo R. Sta. Maria

WORKSHEET 3.8: SUPERVISED LEARNING IN PYTHON

'republican' 'democrat' 'republican' 'democrat' 'democrat' 'democrat' 'republican' 'republican' 'republican' 'republican' 'republican' 'republican' 'republican' 'republican' 'republican' 'democrat' 'democrat' 'democrat' 'republican' 'republican' 'democrat' 'democrat' 'democrat' 'democrat' 'republican' 'democrat' 'republican' 'democrat' 'republican' 'democrat' 'democrat' 'republican' 'republican' 'democrat' 'republican' 'democrat' 'democrat' 'republican' 'republican' 'democrat' 'republican' 'democrat' 'democrat' 'democrat' 'democrat' 'republican' 'democrat' 'republican' 'democrat' 'democrat' 'democrat' 'republican' 'democrat' 'democrat' 'republican' 'republican' 'republican' 'republican' 'democrat' 'democrat' 'democrat' 'democrat' 'democrat' 'democrat' 'republican' 'republican' 'republican' 'republican' 'democrat' 'democrat' 'democrat' 'democrat' 'democrat' 'democrat' 'republican' 'republican' 'democrat' 'democrat' 'democrat' 'democrat' 'democrat' 'republican' 'republican' 'republican' 'democrat' 'republican' 'democrat' 'republican' 'republican' 'republican' 'democrat' 'democrat' 'democrat' 'republican' 'democrat' 'democrat' 'democrat' 'democrat' 'republican' 'democrat' 'democrat' 'democrat' 'republican' 'republican' 'democrat' 'republican' 'republican' 'republican' 'republican' 'democrat' 'democrat' 'democrat' 'democrat' 'republican' 'democrat' 'democrat' 'democrat' 'republican' 'democrat' 'republican' 'democrat' 'republican' 'republican' 'republican' 'republican' 'republican' 'democrat' 'democrat' 'republican' 'republican' 'democrat' 'democrat' 'republican' 'democrat' 'democrat' 'republican' 'republican' 'republican' 'republican' 'republican' 'republican' 'democrat' 'democrat' 'democrat' 'republican' 'democrat' 'republican' 'republican' 'republican' 'republican' 'republican' 'republican' 'republican' 'democrat'] 0.9202453987730062

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APPLIED DATA SCIENCE

Jian Karlo R. Sta. Maria

WORKSHEET 3.8: SUPERVISED LEARNING IN PYTHON

5

Date:

Import the gapminder file. Perform regression on the data (life expectancy as a function of fertility). Prepare a plot showing the data points (in blue) and the linear model (in red). Print out the regression score.

Code Output

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