Capstone Project
Time to put everything together. A capstone project is your chance to tackle a real-world problem from start to finish — data collection, cleaning, exploration, modeling, and presentation. This is what hiring managers want to see.
Project Structure
Every solid data science project follows a similar structure. Here is a template you can adapt for any project:
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
df = pd.read_csv('data.csv')
print(f"Dataset shape: {df.shape}")
print(df.head())
df = df.dropna()
df = pd.get_dummies(df, drop_first=True)
X = df.drop('target', axis=1)
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, predictions):.2%}")
print(classification_report(y_test, predictions))
Project Ideas
Here are some great capstone project ideas:
- Predict house prices using the Ames Housing dataset
- Analyze customer churn for a telecom company
- Build a recommendation system for movies or books
- Sentiment analysis on product reviews
- Predict customer lifetime value for an e-commerce store
Presenting Your Results
A great project with poor presentation is invisible. Always include:
- A clear problem statement
- Data source and collection method
- Exploratory data analysis with visualizations
- Model selection and evaluation
- Conclusions and next steps
Key Takeaways
- Follow a consistent project structure from start to finish
- Document your process thoroughly
- Choose projects that interest you — passion shows in quality
- Presentation matters as much as the analysis itself