Fundamentals of Python for Financial Econometrics

Chapter 48: Applied Economic Prediction and Classification Projects

How can an end-to-end workflow turn housing and passenger data into models that are evaluated honestly and interpreted carefully?

Download .ipynb Download .py

Loading notebook...Build a regression workflow with the Tehran housing dataBuild a classification workflow with Titanic datapipelines, cross-validation, and multiple metricsAnalyze residuals, confusion matrices, and subgroup performance

Notebook lab guide

Related dataset

Ceteris Lab teaching sample

Estimated time

35 to 55 min

Packages

pandas, numpy, matplotlib, statsmodels, patsy

Expected output

A regression or inference table with coefficients, uncertainty, and short interpretation notes.

Learning goals

Load and inspect Ceteris Lab teaching sample.
Run the Python cells connected to Applied Economic Prediction and Classification Projects.
Interpret the output using Build a regression workflow with the Tehran housing data and Build a classification workflow with Titanic data.

Common errors

File not found: check that wage_sample.csv is installed or use the course data folder.
Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
Column name error: compare your variable names with the dataset variables listed for this notebook.

Dataset path helper

import pandas as pd

df = pd.read_csv("/data/wage_sample.csv")
df.head()

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