Notes: Session 4: Regression I

Note

Preparation: start regression demo notebook:

cd ~/repos/analytics-and-big-data/_site/exercises jupyter lab TODO: open XY

TODO

examples and contents: https://chatgpt.com/c/698d8ac8-a0a0-8394-8432-e644441ed348

Example: https://github.com/collinprather/ISLR-Python/blob/master/Chapter%203%20Linear%20Regression.ipynb MAFS6010u_Regression see pairwise scatter and multiple linear regression.ipynb Check: https://github.com/lmarti/machine-learning/blob/master/02.%20Linear%20regression.ipynb

Process:

… in this session, we apply the CRISP-DM, taking a foundational analytical technique as an example -> modeling: selecting a model and refining it; or comparing different models

Model choice

Clustering (session 2): possible choice (use average price in each cluster as the prediction)

Using the Ames Housing dataset, students explore the relationship between living area and price, estimate a simple regression model, and then extend it to multiple predictors. The case illustrates how regression helps quantify marginal effects under ceteris paribus assumptions and highlights the difference between explanation and prediction in observational data.

Dataset: - mention Kaggle as a great source. Different datasets are available. It offers descriptive overviews (EDA), and links existing analyses (tab: code)

Notebook demonstration: run regression without NA-filling and correct afterwards.

“Solution”


import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Load dataset (download from Kaggle and place train.csv locally)
df = pd.read_csv("data/ames.csv")

df.columns


# Select numeric predictors
features = [
    "area",
    "Overall.Qual",
]

X = df[features]
y = df["price"]


# Fit linear regression
model = LinearRegression()
model.fit(X, y)


# Run, read error, add fillna before model
X = X.fillna(X.median())

# Show coefficients
coef_df = pd.DataFrame({
    "Feature": features,
    "Coefficient": model.coef_
})
print(coef_df)

Explicitly discuss “why do we focus on/start with regression? (boring/want ML/AI)” -> simplest form of a model, where we can see principles (like fitting/overfitting/…) -> sometimes, we may try to extract structured data from big data to run regression/ML models