Data Science Workflow: From Problem to Solution – A Complete Step-by-Step Journey for Beginners

📗 Chapter 6: Model Building and Training

From Clean Data to Predictive Power — Training Machine Learning Models with Confidence

🧠 Introduction

After cleaning your data, engineering meaningful features, and selecting the most important ones, it’s finally time to build your model.

Model building is where data science transforms into intelligent automation.
But it's not just about running .fit() — it’s about choosing the right model, training it properly, and validating it rigorously.

In this chapter, you’ll learn:

• How to split your dataset for training and testing
• Which algorithms to use for different tasks
• How to build, train, and interpret models in Python
• Best practices for cross-validation and performance tracking
• Hands-on examples using scikit-learn

Let’s dive into one of the most rewarding stages in the data science workflow.

🧩 1. Understanding Supervised Learning

Model training typically refers to supervised learning — where we provide both features (X) and a target (y), and the model learns to map inputs to outputs.

🔹 Two Main Types:

🧪 2. Train-Test Split

Before training, split your data to avoid data leakage and to measure how your model generalizes.

python

from sklearn.model_selection import train_test_split

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)

• 80% of data is used for training
• 20% is held back for testing

⚙️ 3. Choosing the Right Model

Use this table as a quick reference:

📌 Tip:

Start simple. Use a baseline model (like LogisticRegression) before trying complex models like XGBoost or Neural Networks.

✅ 4. Training a Classification Model: Logistic Regression

python

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

model = LogisticRegression()

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))

🌲 5. Training a Tree-Based Model: Random Forest

python

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_estimators=100)

rf.fit(X_train, y_train)

preds = rf.predict(X_test)

print("Accuracy:", accuracy_score(y_test, preds))

▶ Advantages:

• Handles non-linear data
• No need for feature scaling
• Works with both numerical and categorical data (if encoded)

📈 6. Training a Regression Model

▶ Linear Regression

python

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error

lr = LinearRegression()

lr.fit(X_train, y_train)

y_pred = lr.predict(X_test)

print("MSE:", mean_squared_error(y_test, y_pred))

🔄 7. Cross-Validation

Instead of just a train-test split, you can split into k folds and validate across all of them.

python

from sklearn.model_selection import cross_val_score

scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')

print("Cross-validated accuracy:", scores.mean())

• Reduces overfitting
• Provides a more stable estimate

🧠 8. Hyperparameter Tuning (Preview)

Every model has settings that control its behavior — called hyperparameters.

You can optimize them using GridSearchCV:

python

from sklearn.model_selection import GridSearchCV

params = {'max_depth': [3, 5, 10], 'min_samples_split': [2, 5]}

grid = GridSearchCV(RandomForestClassifier(), params, cv=3)

grid.fit(X_train, y_train)

print("Best params:", grid.best_params_)

📊 9. Comparing Models with Metrics

Use different metrics based on the task.

python

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

🧪 10. Model Evaluation Table (Example)

💾 11. Saving and Reusing Models

Once you’ve trained your model, you can save it for future predictions.

python

import joblib

joblib.dump(model, 'model.pkl')

model = joblib.load('model.pkl')

✅ Full Code Workflow Example

python

# Full workflow

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import classification_report

import pandas as pd

# Load data

df = pd.read_csv('titanic_clean.csv')

X = df.drop('Survived', axis=1)

y = df['Survived']

# Split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)

# Train

model = RandomForestClassifier()

model.fit(X_train, y_train)

# Predict & Evaluate

y_pred = model.predict(X_test)

print(classification_report(y_test, y_pred))

📋 Summary Table: Model Building Steps