Capstone: Your Own Dataset, Start to Finish

The Pipeline, and What It's Demonstrated On

Palmer Penguins — real physical measurements (bill length, bill depth, flipper length, body mass) of three penguin species from Palmer Station, Antarctica, plus island and sex. It's used here specifically because, unlike Chapter 7's own squeaky-clean Iris dataset, it has real missing values and a real categorical column — giving this capstone a genuine reason to exercise every stage of the pipeline, not just the modeling step.

Stage 1 — Collect

import pandas as pd

# swap this line for your own dataset:
#   - a downloaded CSV (as here)
#   - a scraped page (Chapter 1's own pattern)
#   - a live API, single or combined (Chapter 3's / Chapter 6's own pattern)
raw = pd.read_csv("penguins.csv")
print(raw.shape)

Stage 2 — Clean

def clean_penguins(df, report):
    before = len(df)
    df = df.dropna(subset=["species", "bill_length_mm"])
    report.append(f"Dropped {before - len(df)} row(s) missing species or bill_length_mm.")

    n_sex_missing = df["sex"].isna().sum()
    df["sex"] = df["sex"].fillna("unknown")
    report.append(f"Filled {n_sex_missing} missing sex value(s) with 'unknown'.")

    return df

report = [f"Started with {len(raw)} rows."]
cleaned = clean_penguins(raw, report)
report.append(f"Finished cleaning with {len(cleaned)} rows.")

Directly reusing Chapter 2's own shape: a function that cleans and appends plain-English lines to a shared report list, with the same kind of real per-column judgment call that chapter's own warn-box named — rows missing a core measurement are dropped, a missing category is labeled rather than guessed.

Stage 3 — Explore

report.append("--- Exploration ---")
report.append(str(cleaned.groupby("species")["bill_length_mm"].describe()[["mean", "std"]]))
report.append(f"Species counts: {cleaned['species'].value_counts().to_dict()}")

Chapter 5's own flowing, narrative style rather than a numbered checklist — a couple of well-chosen summaries, not an exhaustive re-run of every EDA step, since the real goal here is a working pipeline end to end, not a second full exploration chapter.

Stage 4 — Visualize

import seaborn as sns
import matplotlib.pyplot as plt

sns.scatterplot(
    data=cleaned, x="bill_length_mm", y="flipper_length_mm",
    hue="species", palette=["#15803D", "#EAB308", "#4ade80"],
)
plt.title("Bill Length vs. Flipper Length by Species")
plt.savefig("capstone_dashboard.png")

Stage 5 — Model

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

features = ["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g"]
X = cleaned[features].dropna()
y = cleaned.loc[X.index, "species"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = KNeighborsClassifier(n_neighbors=5)
model.fit(X_train, y_train)
accuracy = model.score(X_test, y_test)

report.append(f"--- Model ---")
report.append(f"KNN classifier accuracy on held-out test data: {accuracy:.2%}")

Chapter 7's own exact template — same algorithm, same honest framing (a light touch, not a substitute for ml1's own full depth) — applied here to a dataset with genuinely more real-world texture than Iris had.

Stage 6 — Report

from pathlib import Path

Path("findings_report.txt").write_text("\n".join(report))
print("\n".join(report))

The same report-list pattern from Chapter 2, now spanning the entire pipeline rather than just the cleaning stage — one readable, saved artifact documenting exactly what happened to the data at every step, from raw file to trained model.

Chapter Attribution Table

Course Complete

That's all 8 projects of Data Science & ML Projects (Beginner) — a scraper, a cleaning pipeline, a live weather dashboard, a pandas-powered finance analyzer, a full EDA, a multi-source API aggregator, a first classifier, and this capstone — combining ds1's own data-handling toolkit with one light, honest taste of ml1. The still-unfleshed dsproj2 (Intermediate) remains as the final piece of the Data Science & ML subject's own six-course roadmap, and is exactly where NLP-shaped projects like a real sentiment analyzer belong.