Question 1

Use the dataset found here for this question: [click for toy dataset].

Encode the toy1.csv dataset. Generate dummy variables for the shape and metal. Encode height, width and length as z-scores. Include, but do not encode the weight. If this encoding is performed in a function, named encode_toy_dataset, you will have an easier time reusing the code from question 1 in question 2.

Write the output to a CSV file that you will submit with this assignment. The CSV file will look similar to Listing 3.

Listing 3: Question 2 Output Sample

Question 2

Use the dataset found here for this question: [click for toy dataset].

Use the encoded dataset from question 1 and train a neural network to predict weight. Use 25% of the data as validation and 75% as training, make sure you shu e the data. Report the RMSE error for the validation set. No CSV file need be generated for this question.

Question 3

Use the dataset found here for this question: [click for toy dataset].

Using the toy1.csv dataset calculate and report the mean and standard deviation for height, width and length. Calculate the z-scores for the dataframe given by Listing 4. Make sure that you use the mean and standard deviations you reported for this question. Write the results to a CSV file.

Listing 4: Question 3 Input Data

• testDF = pd.DataFrame([
• {length:1, width:2, height: 3},
• {length:3, width:2, height: 5},
• {length:4, width:1, height: 3}
• ])

Your resulting CSV file should look almost exactly like Listing 5.

Listing 5: Question 3 Output Sample

• height,length,width
• -0.8822049883269626,-1.5819074849494659,-1.2356589865858818
• -0.18585564084337075,-0.8828608931337095,-1.2356589865858818
• -0.8822049883269626,-0.5333375972258314,-1.5853375067165896

Question 4

Use the dataset found here for this question: [click for iris dataset].

Usually the iris.csv dataset is used to classify the species. Not this time! Use the fields species, sepal-l, sepal-w, and petal-l to predict petal-w. Use a 5-fold cross validation and report ONLY out-of-sample predictions to a CSV file. Make sure to shu e the data. Your generated CSV file should look similar to Listing 6. Encode each of the inputs in a way that makes sense (e.g. dummies, z-scores).

Listing 6: Question 4 Output Sample

• sepal_l,sepal_w,petal_l,petal_w,species-Iris-setosa,species-Iris- versicolor,species-Iris-virginica,0,0
• 30995914214417364, -0.5903951331558184, 0.5336208818725668, 1.2,0.0,1.0,0.0,1.2,1.444551944732666
• -0.1730940663922016, 1.7038864723719687, -1.1658086782311483,

0.3,1.0,0.0,0.0,0.3,0.

Question 5

Use the dataset found here for this question: [click for auto mpg dataset].

Usually the auto-mpg.csv dataset is used to regress the mpg. Not this time! Use the fields to predict how many cylinders the car has. Treat this as a classification problem, where there is a class for each number of cylinders. Use a 5-fold cross validation and report ONLY out-ofsample predictions to a CSV file. Make sure to shu e the data. Your generated CSV file should look similar to Listing 7. Encode each of the inputs in a way that makes sense (e.g. dummies, z-scores). Report the final out of sample accuracy score.

Listing 7: Question 4 Output Sample

• mpg,cylinders,displacement,horsepower,weight,acceleration,year,origin,name ,ideal,predict
• -0.7055506566787514, 8, 1.0892327311042995, 0.6722714619460141, 6300768256149949, -1.2938698102195594, 70, -0.7142457922976494, chevrolet chevelle malibu,8,8
• -1.0893794720944747, 8, 1.5016242793620063, 1.5879594901955474,

0.8532590135498572, -1.4751810504376373, 70, -0.7142457922976494,buick skylark 320,8,8

• -0.7055506566787514, 8, 1.1947282434492943, 1.19552176380289,

0.5497784722839334, -1.6564922906557151, 70, -0.7142457922976494, plymouth satellite,8,8