get_dummies.py

# %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

# %%
# Load the credit data.
df = pd.read_csv('credit.csv')
df.head()

# %%
# The response variable will be 'Balance.'
x = df.drop('Balance', axis=1)
y = df['Balance']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

# %%
# Trying to fit on all features in their current representation throws an error.
try:
    test_model = LinearRegression().fit(x_train, y_train)
except Exception as e:
    print('Error!:', e)

# %% [markdown]
# ⏸️ Given this error and what you've seen of the data so far, what do you think the problem is?
#
# A. We are trying to fit on too many features/columns\
# B. Some columns are strings\
# C. The column names contain capital letters\
# D. The features are on different scales

# %%
### edTest(test_chow1) ###
# Submit an answer choice as a string below.
answer1 = 'B'

# %%
# Inspect the data types of the DataFrame's columns.
df.dtypes

# %%
### edTest(test_model1) ###
# Fit a linear model using only the numeric features in the dataframe.
numeric_features = df.drop(df.columns[-5:], axis=1).columns
model1 = LinearRegression().fit(x_train[numeric_features], y_train)

# Report train and test R2 scores.
train_score = model1.score(x_train[numeric_features], y_train)
test_score = model1.score(x_test[numeric_features], y_test)
print('Train R2:', train_score)
print('Test R2:', test_score)

# %%
# Look at unique values of Ethnicity feature.
print('In the train data, Ethnicity takes on the values:', list(x_train['Ethnicity'].unique()))

# %% [markdown]
# ⏸️ From the output above, how many binary variables will be required to encode the `Ethnicity` feature?
#
# A. 1\
# B. 2\
# C. 3\
# D. 4

# %%
### edTest(test_chow2) ###
# Submit an answer choice as a string below.
answer2 = 'B'

# %%
### edTest(test_design) ###
# Create x train and test design matrices creating dummy variables for the categorical.
# hint: use pd.get_dummies() with the drop_first hyperparameter for this
x_train_design = pd.get_dummies(x_train, drop_first=True)
x_test_design = pd.get_dummies(x_test, drop_first=True)
x_train_design.head()

# %%
# Confirm that all data types are now numeric.
x_train_design.dtypes

# %%
### edTest(test_model2) ###
# Fit model2 on design matrix
model2 = LinearRegression().fit(x_train_design, y_train)

# Report train and test R2 scores
train_score = model2.score(x_train_design, y_train)
test_score = model2.score(x_test_design, y_test)
print('Train R2:', train_score)
print('Test R2:', test_score)

# %% [markdown]
# ⏸️ How do the $R^2$ scores of the two models compare?
#
# A. numeric only model performs better on both train and test.\
# B. numeric only model performs better on train but worse on test.\
# C. full model performs better on both train and test.\
# D. full model performs better on train but worse on test.

# %%
### edTest(test_chow3) ###
# Submit an answer choice as a string below.
answer3 = 'C'

# %%
# Note that the intercept is not a part of .coef_ but is instead stored in .intercept_.
coefs = pd.DataFrame(model2.coef_, index=x_train_design.columns, columns=['beta_value'])
coefs

# %%
# Visualize crude measure of feature importance.
sns.barplot(data=coefs.T, orient='h').set(title='Model Coefficients');

# %% [markdown]
# ⏸️ Base, on the plot above, which categorical feature has the strongest relationship with `Balance`?
#
# A. Cards\
# B. Gender_Female\
# C. Student_Yes\
# D. Ethnicity_Caucasian

# %%
### edTest(test_chow4) ###
# Submit an answer choice as a string below.
answer4 = 'C'

# %% [markdown]
# Fit a model to predict `Balance` from 2 predictors: `Income` and the best categorical feature from your answer.

# %%
### edTest(test_model3) ###
# Specify best categorical feature
best_cat_feature ='Student_Yes'

# Define the model.
features = ['Income', best_cat_feature]
model3 = LinearRegression()
model3.fit(x_train_design[features], y_train)

# Collect betas from fitted model.
beta0 = model3.intercept_
beta1 = model3.coef_[features.index('Income')]
beta2 = model3.coef_[features.index(best_cat_feature)]

# Display betas in a DataFrame.
coefs = pd.DataFrame([beta0, beta1, beta2], index=['Intercept']+features, columns=['beta_value'])
coefs

# %%
# Visualize crude measure of feature importance.
sns.barplot(data=coefs.T, orient='h').set(title='Model Coefficients');

# %%
### edTest(test_prediction_lines) ###
# Create space of x values to predict on.
x_space = np.linspace(x['Income'].min(), x['Income'].max(), 1000)

# Generate 2 sets of predictions based on best categorical feature value.
# When categorical feature is true/present (1)
y_hat_yes = beta0 + beta1 * x_space + beta2 * 1
# When categorical feature is false/absent (0)
y_hat_no = beta0 + beta1 * x_space

# Plot the 2 prediction lines for students and non-students.
ax = sns.scatterplot(data=pd.concat([x_train_design, y_train], axis=1), x='Income', y='Balance', hue=best_cat_feature, alpha=0.8)
ax.plot(x_space, y_hat_no)
ax.plot(x_space, y_hat_yes);

# %% [markdown]
# ⏸️ What is the effect of student status on the regression line?
#
# A. Non-students' balances increase faster with raising income (i.e., steeper slope)\
# B. Students' balances increase faster with raising income (i.e., steeper slope)\
# C. Non-students' higher balances on average (i.e., higher intercept)\
# D. students' higher balances on average (i.e., higher intercept)

# %%
### edTest(test_chow5) ###
# Submit an answer choice as a string below.
answer5 = 'D'