import pandas as pd
import numpy as np
import os

import plotly.express as px
import plotly.graph_objects as go
pd.options.plotting.backend = 'plotly'
TEMPLATE = 'seaborn'

import seaborn as sns

from sklearn.linear_model import LinearRegression

Lecture 21 – Feature Engineering

DSC 80, Winter 2023

📣 Announcements

• Project 4 is due on Thursday, March 9th at 11:59PM.
• Lab 8 (modeling) is due on Monday, March 6th at 11:59PM.
• RSVP to the Senior Capstone Showcase on March 15th at hdsishowcase.com.
  • There is no live lecture for DSC 80 on the day of the showcase.

Agenda

• Case study: Restaurant tips 🧑‍🍳.
  • Other methods for evaluating regression models.
• Feature engineering.
  • One hot encoding.
  • Encoding categorical features, both nominal and ordinal.
  • Quantitative scaling.

Case study: Restaurant tips 🧑‍🍳

# The dataset is built into plotly (and seaborn)!
# We shuffle here so that the head of the DataFrame contains rows where smoker is Yes and smoker is No,
# purely for illustration purposes (it doesn't change any of the math).
np.random.seed(1)
tips = px.data.tips().sample(frac=1).reset_index(drop=True)
tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	3.07	1.00	Female	Yes	Sat	Dinner	1
1	18.78	3.00	Female	No	Thur	Dinner	2
2	26.59	3.41	Male	Yes	Sat	Dinner	3
3	14.26	2.50	Male	No	Thur	Lunch	2
4	21.16	3.00	Male	No	Thur	Lunch	2

Model #1: Constant

Let's suppose we choose squared loss, meaning that $h^* = \text{mean}(y)$.

mean_tip = tips['tip'].mean()
mean_tip

2.99827868852459

# Unfortunately, the code to visualize a scatter plot and a line
# in plotly is not all that concise.
fig = go.Figure()

fig.add_trace(go.Scatter(
    x=tips['total_bill'], 
    y=tips['tip'], 
    mode='markers',
    name='Original Data')
)

fig.add_trace(go.Scatter(
    x=[0, 60],
    y=[mean_tip, mean_tip],
    mode='lines',
    name='Constant Prediction (Mean)'
))

fig.update_layout(showlegend=True, title='Tip vs. Total Bill',
                  xaxis_title='Total Bill', yaxis_title='Tip',
                  template=TEMPLATE)
fig.update_xaxes(range=[0, 60])