Beginner Projects Quick Reference Guide

Quick reference for building ML projects.

Table of Contents

• Project Workflow
• Code Snippets
• Common Tasks
• Best Practices Checklist

Project Workflow

1. Problem Definition
2. Data Collection
3. EDA
4. Preprocessing
5. Model Training
6. Evaluation
7. Improvement

Code Snippets

Load Data

df = pd.read_csv('data.csv')
print(df.info())

Handle Missing Values

df['column'].fillna(df['column'].median(), inplace=True)

Train Model

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
score = model.score(X_test, y_test)

Common Tasks

Classification

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()

Regression

from sklearn.linear_model import LinearRegression
model = LinearRegression()

Best Practices Checklist

• Do EDA first
• Handle missing values
• Encode categorical variables
• Split train/test properly
• Try multiple models
• Evaluate with appropriate metrics
• Document your work

Try next: Ship a baseline, then change one thing. Keep a short changelog of score deltas.