Client Report - The war with the Star Wars

Course DS 250

Author

Juan Zurita

Elevator pitch

It is really interesting to build a machine learning model which can predict if a person makes more or less than $ 50K using data provided from a survey about Star Wars. Yes, a sruvey about Star Wars could technically predict this. This is really interesting, and I think really cool to try at least.

Read and format project data 
df = pd.read_csv('https://github.com/fivethirtyeight/data/raw/master/star-wars-survey/StarWars.csv', encoding='ISO-8859-1')

QUESTION|TASK 1

Shorten the column names and clean them up for easier use with pandas. Provide a table or list that exemplifies how you fixed the names.

Show the code 
column_mapping = {
    'RespondentID': 'RespondentID',
    'Have you seen any of the 6 films in the Star Wars franchise?': 'SeenAnyFilm',
    'Do you consider yourself to be a fan of the Star Wars film franchise?': 'FanOfFranchise',
    'Which of the following Star Wars films have you seen? Please select all that apply.': 'SeenFilm1',
    'Unnamed: 4': 'SeenFilm2',
    'Unnamed: 5': 'SeenFilm3',
    'Unnamed: 6': 'SeenFilm4',
    'Unnamed: 7': 'SeenFilm5',
    'Unnamed: 8': 'SeenFilm6',
    'Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.': 'RankFilm1',
    'Unnamed: 10': 'RankFilm2',
    'Unnamed: 11': 'RankFilm3',
    'Unnamed: 12': 'RankFilm4',
    'Unnamed: 13': 'RankFilm5',
    'Unnamed: 14': 'RankFilm6',
    'Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.': 'HanSoloOpinion',
    'Unnamed: 16': 'LukeSkywalkerOpinion',
    'Unnamed: 17': 'PrincessLeiaOrganaOpinion',
    'Unnamed: 18': 'AnakinSkywalkerOpinion',
    'Unnamed: 19': 'ObiWanKenobiOpinion',
    'Unnamed: 20': 'EmperorPalpatineOpinion',
    'Unnamed: 21': 'DarthVaderOpinion',
    'Unnamed: 22': 'LandoCalrissianOpinion',
    'Unnamed: 23': 'BobaFettOpinion',
    'Unnamed: 24': 'C-3P0Opinion',
    'Unnamed: 25': 'R2D2Opinion',
    'Unnamed: 26': 'JarJarBinksOpinion',
    'Unnamed: 27': 'PadmeAmidalaOpinion',
    'Unnamed: 28': 'YodaOpinion',
    'Which character shot first?': 'WhoShotFirst',
    'Are you familiar with the Expanded Universe?': 'KnowsExpandedUniverse',
    'Do you consider yourself to be a fan of the Expanded Universe?ξ': 'FanOfExpandedUniverse',
    'Do you consider yourself to be a fan of the Star Trek franchise?': 'FanOfStarTrek',
    'Household Income': 'Income',
    'Location (Census Region)': 'Location'
}

df_cleaned = df.rename(columns=column_mapping)

df_cleaned.columns
Index(['RespondentID', 'SeenAnyFilm', 'FanOfFranchise', 'SeenFilm1',
       'SeenFilm2', 'SeenFilm3', 'SeenFilm4', 'SeenFilm5', 'SeenFilm6',
       'RankFilm1', 'RankFilm2', 'RankFilm3', 'RankFilm4', 'RankFilm5',
       'RankFilm6', 'HanSoloOpinion', 'LukeSkywalkerOpinion',
       'PrincessLeiaOrganaOpinion', 'AnakinSkywalkerOpinion',
       'ObiWanKenobiOpinion', 'EmperorPalpatineOpinion', 'DarthVaderOpinion',
       'LandoCalrissianOpinion', 'BobaFettOpinion', 'C-3P0Opinion',
       'R2D2Opinion', 'JarJarBinksOpinion', 'PadmeAmidalaOpinion',
       'YodaOpinion', 'WhoShotFirst', 'KnowsExpandedUniverse',
       'FanOfExpandedUniverse', 'FanOfStarTrek', 'Gender', 'Age', 'Income',
       'Education', 'Location'],
      dtype='object')

QUESTION|TASK 2

Clean and format the data so that it can be used in a machine learning model. As you format the data, you should complete each item listed below. In your final report provide example(s) of the reformatted data with a short description of the changes made

Filter the dataset to respondents that have seen at least one film.

Show the code 
df_filtered = df_cleaned.query('SeenAnyFilm == "Yes"')

df_filtered.head()
RespondentID SeenAnyFilm FanOfFranchise SeenFilm1 SeenFilm2 SeenFilm3 SeenFilm4 SeenFilm5 SeenFilm6 RankFilm1 ... YodaOpinion WhoShotFirst KnowsExpandedUniverse FanOfExpandedUniverse FanOfStarTrek Gender Age Income Education Location
1 3.292880e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 3 ... Very favorably I don't understand this question Yes No No Male 18-29 NaN High school degree South Atlantic
3 3.292765e+09 Yes No Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith NaN NaN NaN 1 ... Unfamiliar (N/A) I don't understand this question No NaN No Male 18-29 $0 - $24,999 High school degree West North Central
4 3.292763e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 5 ... Very favorably I don't understand this question No NaN Yes Male 18-29 $100,000 - $149,999 Some college or Associate degree West North Central
5 3.292731e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 5 ... Somewhat favorably Greedo Yes No No Male 18-29 $100,000 - $149,999 Some college or Associate degree West North Central
6 3.292719e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 1 ... Very favorably Han Yes No Yes Male 18-29 $25,000 - $49,999 Bachelor degree Middle Atlantic

5 rows × 38 columns

Create a new column that converts the age ranges to a single number. Drop the age range categorical column.

Show the code 
age_mapping = {
    '18-29': 1,
    '30-44': 2,
    '45-60': 3,
    '> 60': 4
}
df_filtered['AgeGroup'] = df_filtered['Age'].map(age_mapping)
df_filtered = df_filtered.drop(columns=['Age'])

df_filtered.columns
Index(['RespondentID', 'SeenAnyFilm', 'FanOfFranchise', 'SeenFilm1',
       'SeenFilm2', 'SeenFilm3', 'SeenFilm4', 'SeenFilm5', 'SeenFilm6',
       'RankFilm1', 'RankFilm2', 'RankFilm3', 'RankFilm4', 'RankFilm5',
       'RankFilm6', 'HanSoloOpinion', 'LukeSkywalkerOpinion',
       'PrincessLeiaOrganaOpinion', 'AnakinSkywalkerOpinion',
       'ObiWanKenobiOpinion', 'EmperorPalpatineOpinion', 'DarthVaderOpinion',
       'LandoCalrissianOpinion', 'BobaFettOpinion', 'C-3P0Opinion',
       'R2D2Opinion', 'JarJarBinksOpinion', 'PadmeAmidalaOpinion',
       'YodaOpinion', 'WhoShotFirst', 'KnowsExpandedUniverse',
       'FanOfExpandedUniverse', 'FanOfStarTrek', 'Gender', 'Income',
       'Education', 'Location', 'AgeGroup'],
      dtype='object')

Create a new column that converts the education groupings to a single number. Drop the school categorical column

Show the code 
education_mapping = {
    'Less than high school degree': 1,
    'High school degree': 2,
    'Some college or Associate degree': 3,
    'Bachelor degree': 4,
    'Graduate degree': 5
}
df_filtered['EducationGroup'] = df_filtered['Education'].map(education_mapping)
df_filtered = df_filtered.drop(columns=['Education'])

df_filtered.columns
Index(['RespondentID', 'SeenAnyFilm', 'FanOfFranchise', 'SeenFilm1',
       'SeenFilm2', 'SeenFilm3', 'SeenFilm4', 'SeenFilm5', 'SeenFilm6',
       'RankFilm1', 'RankFilm2', 'RankFilm3', 'RankFilm4', 'RankFilm5',
       'RankFilm6', 'HanSoloOpinion', 'LukeSkywalkerOpinion',
       'PrincessLeiaOrganaOpinion', 'AnakinSkywalkerOpinion',
       'ObiWanKenobiOpinion', 'EmperorPalpatineOpinion', 'DarthVaderOpinion',
       'LandoCalrissianOpinion', 'BobaFettOpinion', 'C-3P0Opinion',
       'R2D2Opinion', 'JarJarBinksOpinion', 'PadmeAmidalaOpinion',
       'YodaOpinion', 'WhoShotFirst', 'KnowsExpandedUniverse',
       'FanOfExpandedUniverse', 'FanOfStarTrek', 'Gender', 'Income',
       'Location', 'AgeGroup', 'EducationGroup'],
      dtype='object')

Create a new column that converts the income ranges to a single number. Drop the income range categorical column.

Show the code 
income_mapping = {
    '$0 - $24,999': 1,
    '$25,000 - $49,999': 2,
    '$50,000 - $99,999': 3,
    '$100,000 - $149,999': 4,
    '$150,000+': 5
}
df_filtered['IncomeGroup'] = df_filtered['Income'].map(income_mapping)
df_filtered = df_filtered.drop(columns=['Income'])

df_filtered.columns
Index(['RespondentID', 'SeenAnyFilm', 'FanOfFranchise', 'SeenFilm1',
       'SeenFilm2', 'SeenFilm3', 'SeenFilm4', 'SeenFilm5', 'SeenFilm6',
       'RankFilm1', 'RankFilm2', 'RankFilm3', 'RankFilm4', 'RankFilm5',
       'RankFilm6', 'HanSoloOpinion', 'LukeSkywalkerOpinion',
       'PrincessLeiaOrganaOpinion', 'AnakinSkywalkerOpinion',
       'ObiWanKenobiOpinion', 'EmperorPalpatineOpinion', 'DarthVaderOpinion',
       'LandoCalrissianOpinion', 'BobaFettOpinion', 'C-3P0Opinion',
       'R2D2Opinion', 'JarJarBinksOpinion', 'PadmeAmidalaOpinion',
       'YodaOpinion', 'WhoShotFirst', 'KnowsExpandedUniverse',
       'FanOfExpandedUniverse', 'FanOfStarTrek', 'Gender', 'Location',
       'AgeGroup', 'EducationGroup', 'IncomeGroup'],
      dtype='object')

Create your target (also known as “y” or “label”) column based on the new income range column.

Show the code 
df_filtered['Target'] = (df_filtered['IncomeGroup'] > 2)

df_filtered.columns

df_filtered.head()
RespondentID SeenAnyFilm FanOfFranchise SeenFilm1 SeenFilm2 SeenFilm3 SeenFilm4 SeenFilm5 SeenFilm6 RankFilm1 ... WhoShotFirst KnowsExpandedUniverse FanOfExpandedUniverse FanOfStarTrek Gender Location AgeGroup EducationGroup IncomeGroup Target
1 3.292880e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 3 ... I don't understand this question Yes No No Male South Atlantic 1.0 2.0 NaN False
3 3.292765e+09 Yes No Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith NaN NaN NaN 1 ... I don't understand this question No NaN No Male West North Central 1.0 2.0 1.0 False
4 3.292763e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 5 ... I don't understand this question No NaN Yes Male West North Central 1.0 3.0 4.0 True
5 3.292731e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 5 ... Greedo Yes No No Male West North Central 1.0 3.0 4.0 True
6 3.292719e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 1 ... Han Yes No Yes Male Middle Atlantic 1.0 4.0 2.0 False

5 rows × 39 columns

One-hot encode all remaining categorical columns.

::: {#cell-Q2f Get Dummies .cell execution_count=9}

Show the code 
df_filtered_ohe = pd.get_dummies(df_filtered)
df_filtered_ohe.columns.tolist()
['RespondentID',
 'AgeGroup',
 'EducationGroup',
 'IncomeGroup',
 'Target',
 'SeenAnyFilm_Yes',
 'FanOfFranchise_No',
 'FanOfFranchise_Yes',
 'SeenFilm1_Star Wars: Episode I  The Phantom Menace',
 'SeenFilm2_Star Wars: Episode II  Attack of the Clones',
 'SeenFilm3_Star Wars: Episode III  Revenge of the Sith',
 'SeenFilm4_Star Wars: Episode IV  A New Hope',
 'SeenFilm5_Star Wars: Episode V The Empire Strikes Back',
 'SeenFilm6_Star Wars: Episode VI Return of the Jedi',
 'RankFilm1_1',
 'RankFilm1_2',
 'RankFilm1_3',
 'RankFilm1_4',
 'RankFilm1_5',
 'RankFilm1_6',
 'RankFilm2_1',
 'RankFilm2_2',
 'RankFilm2_3',
 'RankFilm2_4',
 'RankFilm2_5',
 'RankFilm2_6',
 'RankFilm3_1',
 'RankFilm3_2',
 'RankFilm3_3',
 'RankFilm3_4',
 'RankFilm3_5',
 'RankFilm3_6',
 'RankFilm4_1',
 'RankFilm4_2',
 'RankFilm4_3',
 'RankFilm4_4',
 'RankFilm4_5',
 'RankFilm4_6',
 'RankFilm5_1',
 'RankFilm5_2',
 'RankFilm5_3',
 'RankFilm5_4',
 'RankFilm5_5',
 'RankFilm5_6',
 'RankFilm6_1',
 'RankFilm6_2',
 'RankFilm6_3',
 'RankFilm6_4',
 'RankFilm6_5',
 'RankFilm6_6',
 'HanSoloOpinion_Neither favorably nor unfavorably (neutral)',
 'HanSoloOpinion_Somewhat favorably',
 'HanSoloOpinion_Somewhat unfavorably',
 'HanSoloOpinion_Unfamiliar (N/A)',
 'HanSoloOpinion_Very favorably',
 'HanSoloOpinion_Very unfavorably',
 'LukeSkywalkerOpinion_Neither favorably nor unfavorably (neutral)',
 'LukeSkywalkerOpinion_Somewhat favorably',
 'LukeSkywalkerOpinion_Somewhat unfavorably',
 'LukeSkywalkerOpinion_Unfamiliar (N/A)',
 'LukeSkywalkerOpinion_Very favorably',
 'LukeSkywalkerOpinion_Very unfavorably',
 'PrincessLeiaOrganaOpinion_Neither favorably nor unfavorably (neutral)',
 'PrincessLeiaOrganaOpinion_Somewhat favorably',
 'PrincessLeiaOrganaOpinion_Somewhat unfavorably',
 'PrincessLeiaOrganaOpinion_Unfamiliar (N/A)',
 'PrincessLeiaOrganaOpinion_Very favorably',
 'PrincessLeiaOrganaOpinion_Very unfavorably',
 'AnakinSkywalkerOpinion_Neither favorably nor unfavorably (neutral)',
 'AnakinSkywalkerOpinion_Somewhat favorably',
 'AnakinSkywalkerOpinion_Somewhat unfavorably',
 'AnakinSkywalkerOpinion_Unfamiliar (N/A)',
 'AnakinSkywalkerOpinion_Very favorably',
 'AnakinSkywalkerOpinion_Very unfavorably',
 'ObiWanKenobiOpinion_Neither favorably nor unfavorably (neutral)',
 'ObiWanKenobiOpinion_Somewhat favorably',
 'ObiWanKenobiOpinion_Somewhat unfavorably',
 'ObiWanKenobiOpinion_Unfamiliar (N/A)',
 'ObiWanKenobiOpinion_Very favorably',
 'ObiWanKenobiOpinion_Very unfavorably',
 'EmperorPalpatineOpinion_Neither favorably nor unfavorably (neutral)',
 'EmperorPalpatineOpinion_Somewhat favorably',
 'EmperorPalpatineOpinion_Somewhat unfavorably',
 'EmperorPalpatineOpinion_Unfamiliar (N/A)',
 'EmperorPalpatineOpinion_Very favorably',
 'EmperorPalpatineOpinion_Very unfavorably',
 'DarthVaderOpinion_Neither favorably nor unfavorably (neutral)',
 'DarthVaderOpinion_Somewhat favorably',
 'DarthVaderOpinion_Somewhat unfavorably',
 'DarthVaderOpinion_Unfamiliar (N/A)',
 'DarthVaderOpinion_Very favorably',
 'DarthVaderOpinion_Very unfavorably',
 'LandoCalrissianOpinion_Neither favorably nor unfavorably (neutral)',
 'LandoCalrissianOpinion_Somewhat favorably',
 'LandoCalrissianOpinion_Somewhat unfavorably',
 'LandoCalrissianOpinion_Unfamiliar (N/A)',
 'LandoCalrissianOpinion_Very favorably',
 'LandoCalrissianOpinion_Very unfavorably',
 'BobaFettOpinion_Neither favorably nor unfavorably (neutral)',
 'BobaFettOpinion_Somewhat favorably',
 'BobaFettOpinion_Somewhat unfavorably',
 'BobaFettOpinion_Unfamiliar (N/A)',
 'BobaFettOpinion_Very favorably',
 'BobaFettOpinion_Very unfavorably',
 'C-3P0Opinion_Neither favorably nor unfavorably (neutral)',
 'C-3P0Opinion_Somewhat favorably',
 'C-3P0Opinion_Somewhat unfavorably',
 'C-3P0Opinion_Unfamiliar (N/A)',
 'C-3P0Opinion_Very favorably',
 'C-3P0Opinion_Very unfavorably',
 'R2D2Opinion_Neither favorably nor unfavorably (neutral)',
 'R2D2Opinion_Somewhat favorably',
 'R2D2Opinion_Somewhat unfavorably',
 'R2D2Opinion_Unfamiliar (N/A)',
 'R2D2Opinion_Very favorably',
 'R2D2Opinion_Very unfavorably',
 'JarJarBinksOpinion_Neither favorably nor unfavorably (neutral)',
 'JarJarBinksOpinion_Somewhat favorably',
 'JarJarBinksOpinion_Somewhat unfavorably',
 'JarJarBinksOpinion_Unfamiliar (N/A)',
 'JarJarBinksOpinion_Very favorably',
 'JarJarBinksOpinion_Very unfavorably',
 'PadmeAmidalaOpinion_Neither favorably nor unfavorably (neutral)',
 'PadmeAmidalaOpinion_Somewhat favorably',
 'PadmeAmidalaOpinion_Somewhat unfavorably',
 'PadmeAmidalaOpinion_Unfamiliar (N/A)',
 'PadmeAmidalaOpinion_Very favorably',
 'PadmeAmidalaOpinion_Very unfavorably',
 'YodaOpinion_Neither favorably nor unfavorably (neutral)',
 'YodaOpinion_Somewhat favorably',
 'YodaOpinion_Somewhat unfavorably',
 'YodaOpinion_Unfamiliar (N/A)',
 'YodaOpinion_Very favorably',
 'YodaOpinion_Very unfavorably',
 'WhoShotFirst_Greedo',
 'WhoShotFirst_Han',
 "WhoShotFirst_I don't understand this question",
 'KnowsExpandedUniverse_No',
 'KnowsExpandedUniverse_Yes',
 'FanOfExpandedUniverse_No',
 'FanOfExpandedUniverse_Yes',
 'FanOfStarTrek_No',
 'FanOfStarTrek_Yes',
 'Gender_Female',
 'Gender_Male',
 'Location_East North Central',
 'Location_East South Central',
 'Location_Middle Atlantic',
 'Location_Mountain',
 'Location_New England',
 'Location_Pacific',
 'Location_South Atlantic',
 'Location_West North Central',
 'Location_West South Central']

:::

QUESTION|TASK 3

Validate that the data provided on GitHub lines up with the article by recreating 2 of the visuals from the article.

Show the code 
#SeenFilm
HasSeenMoviesPercent = [round(df_filtered['SeenFilm1'].count() / 835, 2) * 100, 
                        round(df_filtered['SeenFilm2'].count() / 835, 2) * 100, 
                        round(df_filtered['SeenFilm3'].count() / 835, 2) * 100, 
                        round(df_filtered['SeenFilm4'].count() / 835, 2) * 100, 
                        round(df_filtered['SeenFilm5'].count() / 835, 2) * 100, 
                        round(df_filtered['SeenFilm6'].count() / 835, 2) * 100]

fig_1 = px.bar(x=HasSeenMoviesPercent,
               y=['Star Wars: Episode I The Phantom Menace',
                'Star Wars: Episode II Attack of the Clone',
                'Star Wars: Episode III Revenge of the Sith',
                'Star Wars: Episode IV A New Hope',
                'Star Wars: Episode V The Empire Strikes Back',
                'Star Wars: Episode VI Return of the Jedi'],
                  text=HasSeenMoviesPercent,
                  title="Which 'Star Wars' Movies Have You Seen? (Of 835 respondents)",
               )
fig_1.show()


# WhoShotFirst
hanShotFirst = [round(df_filtered_ohe['WhoShotFirst_Han'].sum() / 834, 2) * 100,
                round(df_filtered_ohe['WhoShotFirst_Greedo'].sum() / 834, 2) * 100,
                round(df_filtered_ohe["WhoShotFirst_I don't understand this question"].sum() / 834, 2) * 100]

fig_2 = px.bar(x=hanShotFirst, 
               y=['Han', 'Greedo', 'I dont understand this question'],
               title="Who Shot First? (According to 834 respondents)",
               text=hanShotFirst)

fig_2.show()

QUESTION|TASK 4

Build a machine learning model that predicts whether a person makes more than $50k. Describe your model and report the accuracy.

I used a DTC and a RFC to try to predict if a person taking the survey earns more than $50K. Unfortunately, the accuracy scores were not high enough to confirm that any of the model can predict this information. The accuracy score for the DTC is 0.60 or 60%, and the score for the RTC is 0.59 or 59%. These scores show that the models can predict right only that percentage of cases. Even though it is more than 50% and someone could say it can work, I also came to the conclusion that the data provided is not sufficently related with the income.

Show the code 
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

features = df_filtered_ohe.columns

features = features.drop('Target')
features = features.drop('IncomeGroup')

X = df_filtered_ohe[features]
y = df_filtered_ohe['Target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Decision Tree Classifier
dt_classifier = DecisionTreeClassifier()

dt_classifier = dt_classifier.fit(X_train, y_train)

y_pred = dt_classifier.predict(X_test)

dt_classifier_accuracy = accuracy_score(y_test, y_pred)

# Random Forest Classifier
rf_classifier = RandomForestClassifier()

rf_classifier.fit(X_train, y_train)

y_pred_rf = rf_classifier.predict(X_test)

rf_classifier_accuracy = accuracy_score(y_test, y_pred_rf)

(dt_classifier_accuracy, rf_classifier_accuracy)
(0.5444839857651246, 0.6014234875444839)
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