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The penguins dataset is a 2D list. The inner lists at the following index positions contain:

  • 0 - Penguin Species
  • 1 - Island
  • 2 - Penguin Bill Length (in mm)
  • 3 - Penguin Bill Depth (in mm)
  • 4 - Penguin Flipper Length (in mm)
  • 5 - Penguin Body Mass (in grams)
  • 6 - Penguin Sex

We will fit a machine learning model to this dataset by:

  1. Removing observations with excessive missing data.
  2. Imputing missing data for observations with small amounts of missing data.
  3. Categorically encode any string data so that the entire dataset is made up of numeric values.
  4. Split the data into training and test datasets.
  5. Fit a Decision Tree model using the training data.
  6. Assess the model's accuracy using the "test" dataset.

Check the "shape" of the data

Let's check to make sure that all measured penguin attributes have the same number of values. Inner list at index position 0 has 344 values Inner list at index position 1 has 344 values Inner list at index position 2 has 344 values Inner list at index position 3 has 344 values Inner list at index position 4 has 344 values Inner list at index position 5 has 344 values Inner list at index position 6 has 344 values

Check for missing data: 'NA' values

We have good data for the species and island of each penguin.

We're missing values for two penguins for each of the body measurements.

We're missing sex data for 11 penguins.

Inner list at index position 0 has 0 "NA" values Inner list at index position 1 has 0 "NA" values Inner list at index position 2 has 2 "NA" values Inner list at index position 3 has 2 "NA" values Inner list at index position 4 has 2 "NA" values Inner list at index position 5 has 2 "NA" values Inner list at index position 6 has 11 "NA" values

Are the penguins that have 2 missing values the same two penguins or are these missing values spread throughout the penguins?

[['Adelie', 'Torgersen', 39.1, 18.7, 181.0, 3750.0, 'male'], ['Adelie', 'Torgersen', 39.5, 17.4, 186.0, 3800.0, 'female'], ['Adelie', 'Torgersen', 40.3, 18.0, 195.0, 3250.0, 'female'], ['Adelie', 'Torgersen', 'NA', 'NA', 'NA', 'NA', 'NA'], ['Adelie', 'Torgersen', 36.7, 19.3, 193.0, 3450.0, 'female']]
['Adelie', 'Torgersen', 'NA', 'NA', 'NA', 'NA', 'NA'] ['Adelie', 'Torgersen', 34.1, 18.1, 193.0, 3475.0, 'NA'] ['Adelie', 'Torgersen', 42.0, 20.2, 190.0, 4250.0, 'NA'] ['Adelie', 'Torgersen', 37.8, 17.1, 186.0, 3300.0, 'NA'] ['Adelie', 'Torgersen', 37.8, 17.3, 180.0, 3700.0, 'NA'] ['Adelie', 'Dream', 37.5, 18.9, 179.0, 2975.0, 'NA'] ['Gentoo', 'Biscoe', 44.5, 14.3, 216.0, 4100.0, 'NA'] ['Gentoo', 'Biscoe', 46.2, 14.4, 214.0, 4650.0, 'NA'] ['Gentoo', 'Biscoe', 47.3, 13.8, 216.0, 4725.0, 'NA'] ['Gentoo', 'Biscoe', 44.5, 15.7, 217.0, 4875.0, 'NA'] ['Gentoo', 'Biscoe', 'NA', 'NA', 'NA', 'NA', 'NA']

Remove the rows with 5 NaNs from the dataset

We now have 342 data points. [['Adelie', 'Torgersen', 39.1, 18.7, 181.0, 3750.0, 'male'], ['Adelie', 'Torgersen', 39.5, 17.4, 186.0, 3800.0, 'female'], ['Adelie', 'Torgersen', 40.3, 18.0, 195.0, 3250.0, 'female'], ['Adelie', 'Torgersen', 36.7, 19.3, 193.0, 3450.0, 'female'], ['Adelie', 'Torgersen', 39.3, 20.6, 190.0, 3650.0, 'male']]

Impute the remaining NaNs with the mode if a categorical variable, or the mean if a continuous variable.

['Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap'] ['Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 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'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 'Dream', 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16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7] [181.0, 186.0, 195.0, 193.0, 190.0, 181.0, 195.0, 193.0, 190.0, 186.0, 180.0, 182.0, 191.0, 198.0, 185.0, 195.0, 197.0, 184.0, 194.0, 174.0, 180.0, 189.0, 185.0, 180.0, 187.0, 183.0, 187.0, 172.0, 180.0, 178.0, 178.0, 188.0, 184.0, 195.0, 196.0, 190.0, 180.0, 181.0, 184.0, 182.0, 195.0, 186.0, 196.0, 185.0, 190.0, 182.0, 179.0, 190.0, 191.0, 186.0, 188.0, 190.0, 200.0, 187.0, 191.0, 186.0, 193.0, 181.0, 194.0, 185.0, 195.0, 185.0, 192.0, 184.0, 192.0, 195.0, 188.0, 190.0, 198.0, 190.0, 190.0, 196.0, 197.0, 190.0, 195.0, 191.0, 184.0, 187.0, 195.0, 189.0, 196.0, 187.0, 193.0, 191.0, 194.0, 190.0, 189.0, 189.0, 190.0, 202.0, 205.0, 185.0, 186.0, 187.0, 208.0, 190.0, 196.0, 178.0, 192.0, 192.0, 203.0, 183.0, 190.0, 193.0, 184.0, 199.0, 190.0, 181.0, 197.0, 198.0, 191.0, 193.0, 197.0, 191.0, 196.0, 188.0, 199.0, 189.0, 189.0, 187.0, 198.0, 176.0, 202.0, 186.0, 199.0, 191.0, 195.0, 191.0, 210.0, 190.0, 197.0, 193.0, 199.0, 187.0, 190.0, 191.0, 200.0, 185.0, 193.0, 193.0, 187.0, 188.0, 190.0, 192.0, 185.0, 190.0, 184.0, 195.0, 193.0, 187.0, 201.0, 211.0, 230.0, 210.0, 218.0, 215.0, 210.0, 211.0, 219.0, 209.0, 215.0, 214.0, 216.0, 214.0, 213.0, 210.0, 217.0, 210.0, 221.0, 209.0, 222.0, 218.0, 215.0, 213.0, 215.0, 215.0, 215.0, 216.0, 215.0, 210.0, 220.0, 222.0, 209.0, 207.0, 230.0, 220.0, 220.0, 213.0, 219.0, 208.0, 208.0, 208.0, 225.0, 210.0, 216.0, 222.0, 217.0, 210.0, 225.0, 213.0, 215.0, 210.0, 220.0, 210.0, 225.0, 217.0, 220.0, 208.0, 220.0, 208.0, 224.0, 208.0, 221.0, 214.0, 231.0, 219.0, 230.0, 214.0, 229.0, 220.0, 223.0, 216.0, 221.0, 221.0, 217.0, 216.0, 230.0, 209.0, 220.0, 215.0, 223.0, 212.0, 221.0, 212.0, 224.0, 212.0, 228.0, 218.0, 218.0, 212.0, 230.0, 218.0, 228.0, 212.0, 224.0, 214.0, 226.0, 216.0, 222.0, 203.0, 225.0, 219.0, 228.0, 215.0, 228.0, 216.0, 215.0, 210.0, 219.0, 208.0, 209.0, 216.0, 229.0, 213.0, 230.0, 217.0, 230.0, 217.0, 222.0, 214.0, 215.0, 222.0, 212.0, 213.0, 192.0, 196.0, 193.0, 188.0, 197.0, 198.0, 178.0, 197.0, 195.0, 198.0, 193.0, 194.0, 185.0, 201.0, 190.0, 201.0, 197.0, 181.0, 190.0, 195.0, 181.0, 191.0, 187.0, 193.0, 195.0, 197.0, 200.0, 200.0, 191.0, 205.0, 187.0, 201.0, 187.0, 203.0, 195.0, 199.0, 195.0, 210.0, 192.0, 205.0, 210.0, 187.0, 196.0, 196.0, 196.0, 201.0, 190.0, 212.0, 187.0, 198.0, 199.0, 201.0, 193.0, 203.0, 187.0, 197.0, 191.0, 203.0, 202.0, 194.0, 206.0, 189.0, 195.0, 207.0, 202.0, 193.0, 210.0, 198.0] [3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3475.0, 4250.0, 3300.0, 3700.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 2975.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 4100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 4650.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 4725.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 4875.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0] ['male', 'female', 'female', 'female', 'male', 'female', 'male', 'NA', 'NA', 'NA', 'NA', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'NA', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'NA', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'NA', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'NA', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'NA', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'male', 'female', 'male', 'female', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'male', 'female', 'female', 'male', 'female', 'male', 'male', 'female']
168 penguins are "male". 165 penguins are "female".

Replace "NA" values in 7th column with "male"

['Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap'] ['Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Torgersen', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Biscoe', 'Dream', 'Dream', 'Dream', 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'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'male', 'female', 'male', 'female', 'female', 'male', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'female', 'male', 'female', 'female', 'male', 'female', 'male', 'male', 'female']

Categorical Encoding of categorical columns containing strings

Which columns contain non-numeric values? AKA – values other than integers and floats?

342 342 0 0 0 0 342
342 342 0 0 0 0 342

What categorical values are we dealing with?

{'Chinstrap', 'Adelie', 'Gentoo'} {'Biscoe', 'Dream', 'Torgersen'} {'female', 'male'}

How many of each do we have?

151 Adelie penguins 123 Gentoo penguins 68 Chinstrap penguins
167 penguins from Biscoe island. 51 penguins from Torgersen island. 124 penguins from Dream island.
177 male penguins 165 female penguins
[0, 0, 0, 0, 0] [1, 1, 1, 1, 1] [2, 2, 2, 2, 2]
[0, 1, 1, 1, 0] [0, 1, 0, 1, 0] [0, 1, 0, 0, 1]

Summary Statistics

  • 0 - Penguin Species
  • 1 - Island
  • 2 - Penguin Bill Length (in mm)
  • 3 - Penguin Bill Depth (in mm)
  • 4 - Penguin Flipper Length (in mm)
  • 5 - Penguin Body Mass (in grams)
  • 6 - Penguin Sex

Mean

average bill length in mm is: 43.9219298245614 average bill depth in mm is: 17.151169590643274 average flipper length in mm is: 200.91520467836258 average body mass in grams is: 4201.754385964912

Median

median bill length in mm is: 44.45 median bill depth in mm is: 17.3 median flipper length in mm is: 197.0 median body mass in grams is: 4050.0

Min

min bill length in mm is: 32.1 min bill depth in mm is: 13.1 min flipper length in mm is: 172.0 min body mass in grams is: 2700.0

Max

max bill length in mm is: 59.6 max bill depth in mm is: 21.5 max flipper length in mm is: 231.0 max body mass in grams is: 6300.0

Stratified Summary Statistics

[[0, 2, 39.1, 18.7, 181.0, 3750.0, 0], [0, 2, 39.5, 17.4, 186.0, 3800.0, 1], [0, 2, 40.3, 18.0, 195.0, 3250.0, 1], [0, 2, 36.7, 19.3, 193.0, 3450.0, 1], [0, 2, 39.3, 20.6, 190.0, 3650.0, 0]]
[[0, 2, 39.1, 18.7, 181.0, 3750.0, 0], [0, 2, 39.5, 17.4, 186.0, 3800.0, 1], [0, 2, 40.3, 18.0, 195.0, 3250.0, 1], [0, 2, 36.7, 19.3, 193.0, 3450.0, 1], [0, 2, 39.3, 20.6, 190.0, 3650.0, 0], [0, 2, 38.9, 17.8, 181.0, 3625.0, 1], [0, 2, 39.2, 19.6, 195.0, 4675.0, 0], [0, 2, 34.1, 18.1, 193.0, 3475.0, 0], [0, 2, 42.0, 20.2, 190.0, 4250.0, 0], [0, 2, 37.8, 17.1, 186.0, 3300.0, 0], [0, 2, 37.8, 17.3, 180.0, 3700.0, 0], [0, 2, 41.1, 17.6, 182.0, 3200.0, 1], [0, 2, 38.6, 21.2, 191.0, 3800.0, 0], [0, 2, 34.6, 21.1, 198.0, 4400.0, 0], [0, 2, 36.6, 17.8, 185.0, 3700.0, 1], [0, 2, 38.7, 19.0, 195.0, 3450.0, 1], [0, 2, 42.5, 20.7, 197.0, 4500.0, 0], [0, 2, 34.4, 18.4, 184.0, 3325.0, 1], [0, 2, 46.0, 21.5, 194.0, 4200.0, 0], [0, 0, 37.8, 18.3, 174.0, 3400.0, 1], [0, 0, 37.7, 18.7, 180.0, 3600.0, 0], [0, 0, 35.9, 19.2, 189.0, 3800.0, 1], [0, 0, 38.2, 18.1, 185.0, 3950.0, 0], [0, 0, 38.8, 17.2, 180.0, 3800.0, 0], [0, 0, 35.3, 18.9, 187.0, 3800.0, 1], [0, 0, 40.6, 18.6, 183.0, 3550.0, 0], [0, 0, 40.5, 17.9, 187.0, 3200.0, 1], [0, 0, 37.9, 18.6, 172.0, 3150.0, 1], [0, 0, 40.5, 18.9, 180.0, 3950.0, 0], [0, 1, 39.5, 16.7, 178.0, 3250.0, 1], [0, 1, 37.2, 18.1, 178.0, 3900.0, 0], [0, 1, 39.5, 17.8, 188.0, 3300.0, 1], [0, 1, 40.9, 18.9, 184.0, 3900.0, 0], [0, 1, 36.4, 17.0, 195.0, 3325.0, 1], [0, 1, 39.2, 21.1, 196.0, 4150.0, 0], [0, 1, 38.8, 20.0, 190.0, 3950.0, 0], [0, 1, 42.2, 18.5, 180.0, 3550.0, 1], [0, 1, 37.6, 19.3, 181.0, 3300.0, 1], [0, 1, 39.8, 19.1, 184.0, 4650.0, 0], [0, 1, 36.5, 18.0, 182.0, 3150.0, 1], [0, 1, 40.8, 18.4, 195.0, 3900.0, 0], [0, 1, 36.0, 18.5, 186.0, 3100.0, 1], [0, 1, 44.1, 19.7, 196.0, 4400.0, 0], [0, 1, 37.0, 16.9, 185.0, 3000.0, 1], [0, 1, 39.6, 18.8, 190.0, 4600.0, 0], [0, 1, 41.1, 19.0, 182.0, 3425.0, 0], [0, 1, 37.5, 18.9, 179.0, 2975.0, 0], [0, 1, 36.0, 17.9, 190.0, 3450.0, 1], [0, 1, 42.3, 21.2, 191.0, 4150.0, 0], [0, 0, 39.6, 17.7, 186.0, 3500.0, 1], [0, 0, 40.1, 18.9, 188.0, 4300.0, 0], [0, 0, 35.0, 17.9, 190.0, 3450.0, 1], [0, 0, 42.0, 19.5, 200.0, 4050.0, 0], [0, 0, 34.5, 18.1, 187.0, 2900.0, 1], [0, 0, 41.4, 18.6, 191.0, 3700.0, 0], [0, 0, 39.0, 17.5, 186.0, 3550.0, 1], [0, 0, 40.6, 18.8, 193.0, 3800.0, 0], [0, 0, 36.5, 16.6, 181.0, 2850.0, 1], [0, 0, 37.6, 19.1, 194.0, 3750.0, 0], [0, 0, 35.7, 16.9, 185.0, 3150.0, 1], [0, 0, 41.3, 21.1, 195.0, 4400.0, 0], [0, 0, 37.6, 17.0, 185.0, 3600.0, 1], [0, 0, 41.1, 18.2, 192.0, 4050.0, 0], [0, 0, 36.4, 17.1, 184.0, 2850.0, 1], [0, 0, 41.6, 18.0, 192.0, 3950.0, 0], [0, 0, 35.5, 16.2, 195.0, 3350.0, 1], [0, 0, 41.1, 19.1, 188.0, 4100.0, 0], [0, 2, 35.9, 16.6, 190.0, 3050.0, 1], [0, 2, 41.8, 19.4, 198.0, 4450.0, 0], [0, 2, 33.5, 19.0, 190.0, 3600.0, 1], [0, 2, 39.7, 18.4, 190.0, 3900.0, 0], [0, 2, 39.6, 17.2, 196.0, 3550.0, 1], [0, 2, 45.8, 18.9, 197.0, 4150.0, 0], [0, 2, 35.5, 17.5, 190.0, 3700.0, 1], [0, 2, 42.8, 18.5, 195.0, 4250.0, 0], [0, 2, 40.9, 16.8, 191.0, 3700.0, 1], [0, 2, 37.2, 19.4, 184.0, 3900.0, 0], [0, 2, 36.2, 16.1, 187.0, 3550.0, 1], [0, 2, 42.1, 19.1, 195.0, 4000.0, 0], [0, 2, 34.6, 17.2, 189.0, 3200.0, 1], [0, 2, 42.9, 17.6, 196.0, 4700.0, 0], [0, 2, 36.7, 18.8, 187.0, 3800.0, 1], [0, 2, 35.1, 19.4, 193.0, 4200.0, 0], [0, 1, 37.3, 17.8, 191.0, 3350.0, 1], [0, 1, 41.3, 20.3, 194.0, 3550.0, 0], [0, 1, 36.3, 19.5, 190.0, 3800.0, 0], [0, 1, 36.9, 18.6, 189.0, 3500.0, 1], [0, 1, 38.3, 19.2, 189.0, 3950.0, 0], [0, 1, 38.9, 18.8, 190.0, 3600.0, 1], [0, 1, 35.7, 18.0, 202.0, 3550.0, 1], [0, 1, 41.1, 18.1, 205.0, 4300.0, 0], [0, 1, 34.0, 17.1, 185.0, 3400.0, 1], [0, 1, 39.6, 18.1, 186.0, 4450.0, 0], [0, 1, 36.2, 17.3, 187.0, 3300.0, 1], [0, 1, 40.8, 18.9, 208.0, 4300.0, 0], [0, 1, 38.1, 18.6, 190.0, 3700.0, 1], [0, 1, 40.3, 18.5, 196.0, 4350.0, 0], [0, 1, 33.1, 16.1, 178.0, 2900.0, 1], [0, 1, 43.2, 18.5, 192.0, 4100.0, 0], [0, 0, 35.0, 17.9, 192.0, 3725.0, 1], [0, 0, 41.0, 20.0, 203.0, 4725.0, 0], [0, 0, 37.7, 16.0, 183.0, 3075.0, 1], [0, 0, 37.8, 20.0, 190.0, 4250.0, 0], [0, 0, 37.9, 18.6, 193.0, 2925.0, 1], [0, 0, 39.7, 18.9, 184.0, 3550.0, 0], [0, 0, 38.6, 17.2, 199.0, 3750.0, 1], [0, 0, 38.2, 20.0, 190.0, 3900.0, 0], [0, 0, 38.1, 17.0, 181.0, 3175.0, 1], [0, 0, 43.2, 19.0, 197.0, 4775.0, 0], [0, 0, 38.1, 16.5, 198.0, 3825.0, 1], [0, 0, 45.6, 20.3, 191.0, 4600.0, 0], [0, 0, 39.7, 17.7, 193.0, 3200.0, 1], [0, 0, 42.2, 19.5, 197.0, 4275.0, 0], [0, 0, 39.6, 20.7, 191.0, 3900.0, 1], [0, 0, 42.7, 18.3, 196.0, 4075.0, 0], [0, 2, 38.6, 17.0, 188.0, 2900.0, 1], [0, 2, 37.3, 20.5, 199.0, 3775.0, 0], [0, 2, 35.7, 17.0, 189.0, 3350.0, 1], [0, 2, 41.1, 18.6, 189.0, 3325.0, 0], [0, 2, 36.2, 17.2, 187.0, 3150.0, 1], [0, 2, 37.7, 19.8, 198.0, 3500.0, 0], [0, 2, 40.2, 17.0, 176.0, 3450.0, 1], [0, 2, 41.4, 18.5, 202.0, 3875.0, 0], [0, 2, 35.2, 15.9, 186.0, 3050.0, 1], [0, 2, 40.6, 19.0, 199.0, 4000.0, 0], [0, 2, 38.8, 17.6, 191.0, 3275.0, 1], [0, 2, 41.5, 18.3, 195.0, 4300.0, 0], [0, 2, 39.0, 17.1, 191.0, 3050.0, 1], [0, 2, 44.1, 18.0, 210.0, 4000.0, 0], [0, 2, 38.5, 17.9, 190.0, 3325.0, 1], [0, 2, 43.1, 19.2, 197.0, 3500.0, 0], [0, 1, 36.8, 18.5, 193.0, 3500.0, 1], [0, 1, 37.5, 18.5, 199.0, 4475.0, 0], [0, 1, 38.1, 17.6, 187.0, 3425.0, 1], [0, 1, 41.1, 17.5, 190.0, 3900.0, 0], [0, 1, 35.6, 17.5, 191.0, 3175.0, 1], [0, 1, 40.2, 20.1, 200.0, 3975.0, 0], [0, 1, 37.0, 16.5, 185.0, 3400.0, 1], [0, 1, 39.7, 17.9, 193.0, 4250.0, 0], [0, 1, 40.2, 17.1, 193.0, 3400.0, 1], [0, 1, 40.6, 17.2, 187.0, 3475.0, 0], [0, 1, 32.1, 15.5, 188.0, 3050.0, 1], [0, 1, 40.7, 17.0, 190.0, 3725.0, 0], [0, 1, 37.3, 16.8, 192.0, 3000.0, 1], [0, 1, 39.0, 18.7, 185.0, 3650.0, 0], [0, 1, 39.2, 18.6, 190.0, 4250.0, 0], [0, 1, 36.6, 18.4, 184.0, 3475.0, 1], [0, 1, 36.0, 17.8, 195.0, 3450.0, 1], [0, 1, 37.8, 18.1, 193.0, 3750.0, 0], [0, 1, 36.0, 17.1, 187.0, 3700.0, 1], [0, 1, 41.5, 18.5, 201.0, 4000.0, 0]]
average bill length in mm is: 38.79139072847682 median bill length in mm is: 38.8 min bill length in mm is: 32.1 max bill length in mm is: 46.0 -------------------------------------------------- average bill depth in mm is: 18.34635761589404 median bill depth in mm is: 18.4 min bill depth in mm is: 15.5 max bill depth in mm is: 21.5 -------------------------------------------------- average flipper length in mm is: 189.95364238410596 median flipper length in mm is: 190.0 min flipper length in mm is: 172.0 max flipper length in mm is: 210.0 -------------------------------------------------- average mass in g is: 3700.662251655629 median mass in g is: 3700.0 min mass in g is: 2850.0 max mass in g is: 4775.0
average bill length in mm is: 47.50487804878049 median bill length in mm is: 47.3 min bill length in mm is: 40.9 max bill length in mm is: 59.6 -------------------------------------------------- average bill depth in mm is: 14.982113821138212 median bill depth in mm is: 15.0 min bill depth in mm is: 13.1 max bill depth in mm is: 17.3 -------------------------------------------------- average flipper length in mm is: 217.1869918699187 median flipper length in mm is: 216.0 min flipper length in mm is: 203.0 max flipper length in mm is: 231.0 -------------------------------------------------- average mass in g is: 5076.016260162602 median mass in g is: 5000.0 min mass in g is: 3950.0 max mass in g is: 6300.0
average bill length in mm is: 48.83382352941177 median bill length in mm is: 49.55 min bill length in mm is: 40.9 max bill length in mm is: 58.0 -------------------------------------------------- average bill depth in mm is: 18.42058823529412 median bill depth in mm is: 18.45 min bill depth in mm is: 16.4 max bill depth in mm is: 20.8 -------------------------------------------------- average flipper length in mm is: 195.8235294117647 median flipper length in mm is: 196.0 min flipper length in mm is: 178.0 max flipper length in mm is: 212.0 -------------------------------------------------- average mass in g is: 3733.0882352941176 median mass in g is: 3700.0 min mass in g is: 2700.0 max mass in g is: 4800.0

Split into train and test datasets

  • randomly select 2/3 of the observations for training data
  • randomly select 1/3 of the observations for test data
228
[[0, 2, 39.1, 18.7, 181.0, 3750.0, 0], [0, 2, 39.5, 17.4, 186.0, 3800.0, 1], [0, 2, 40.3, 18.0, 195.0, 3250.0, 1], [0, 2, 36.7, 19.3, 193.0, 3450.0, 1], [0, 2, 39.3, 20.6, 190.0, 3650.0, 0]]
[[0, 0, 40.5, 18.9, 180.0, 3950.0, 0], [2, 1, 50.2, 18.7, 198.0, 3775.0, 1], [1, 0, 50.8, 17.3, 228.0, 5600.0, 0], [1, 0, 47.7, 15.0, 216.0, 4750.0, 1], [2, 1, 49.8, 17.3, 198.0, 3675.0, 1]]
228
114

Fit a Decision Tree Model

  • 0 - Penguin Species
  • 1 - Island
  • 2 - Penguin Bill Length (in mm)
  • 3 - Penguin Bill Depth (in mm)
  • 4 - Penguin Flipper Length (in mm)
  • 5 - Penguin Body Mass (in grams)
  • 6 - Penguin Sex

Penguin Category Labels:

  • Adelie: 0
  • Gentoo: 1
  • Chinstrap: 2

Baseline Accuracy

[1, 0, 2, 1, 1, 1, 2, 0, 2, 0, 0, 2, 0, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 0, 2, 0, 1, 0, 2, 0, 0, 0, 1, 0, 1, 0, 1, 0, 2, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 2, 2, 0, 0, 1, 2, 0, 0, 2, 2, 2, 1, 0, 1, 2, 1, 2, 0, 0, 2, 1, 0, 0, 0, 0, 1, 0, 2, 2, 0, 0, 1, 2, 1, 1, 1, 0, 0, 0, 1, 1, 1, 2, 2, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
0.5

Use a heuristic to identify chinstrap penguins

[0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0, 2]
0.6403508771929824

Use a heuristic to identify chinstrap and gentoo penguins.

[1, 0, 2, 1, 1, 1, 2, 0, 0, 0, 0, 1, 0, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2, 1, 1, 0, 0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 0, 2, 0, 1, 0, 2, 0, 0, 0, 1, 0, 1, 0, 1, 0, 2, 0, 0, 0, 0, 2, 0, 1, 1, 0, 0, 0, 1, 0, 2, 2, 0, 2, 1, 2, 0, 0, 0, 2, 2, 1, 0, 1, 1, 1, 0, 0, 0, 2, 1, 0, 0, 0, 0, 1, 0, 2, 2, 0, 2, 1, 2, 1, 1, 1, 0, 0, 2, 1, 1, 1, 2, 2, 0, 2]
0.9035087719298246
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