Machine Learning is not just about choosing an algorithm and fitting a model.
In real-world applications, ML is a structured workflow that transforms a vague business problem into a deployed, maintainable solution.
This tutorial walks you through the full Machine Learning project lifecycle, from problem definition to deployment mindset, with a mini end-to-end Python project.
A poorly defined problem leads to:
Rule #1: ML does not solve business problems directly — it solves well-defined prediction tasks.
Before touching any data, answer:
| Question | Example |
|---|---|
| What is the objective? | Predict house prices |
| What type of ML problem? | Regression |
| What is the target variable? | price |
| What is the success metric? | RMSE |
| What are constraints? | Interpretability, latency |
Business goal:
Help a real estate agency estimate house prices automatically.
ML formulation:
EDA helps you:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("housing.csv")
df.head()
df.info()
df.describe()
df.isnull().sum()
plt.figure(figsize=(8,6))
sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.show()
Feature engineering is the process of transforming raw data into meaningful inputs for ML models.
Better features > better algorithms
| Technique | Example |
|---|---|
| Handling missing values | Mean / median imputation |
| Encoding | One-Hot Encoding |
| Scaling | StandardScaler |
| Feature creation | Price per square meter |
| Feature selection | Drop low-importance features |
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
X = df.drop("price", axis=1)
y = df["price"]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
Model choice depends on:
For regression:
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train_scaled, y_train)
Model training is never one-shot:
A model that performs well on training data but poorly on new data is overfitting.
| Metric | Meaning |
|---|---|
| MAE | Average absolute error |
| RMSE | Penalizes large errors |
| R² | Explained variance |
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np
y_pred = model.predict(X_test_scaled)
mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
print("MAE:", mae)
print("RMSE:", rmse)
print("R²:", r2)
plt.scatter(y_test, y_pred)
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual vs Predicted")
plt.show()
A real ML system must be:
| Aspect | Question |
|---|---|
| Data drift | Will input data change over time? |
| Model updates | How often retrain? |
| Latency | Real-time or batch? |
| Monitoring | Detect performance drop |
| Versioning | Track models & datasets |
import joblib
joblib.dump(model, "house_price_model.pkl")
joblib.dump(scaler, "scaler.pkl")
Later used in:
1️⃣ Problem Definition
Predict house prices → Regression
2️⃣ Data Exploration
Understand distributions & correlations
3️⃣ Feature Engineering
Scaling, selection, cleaning
4️⃣ Model Training
Linear Regression baseline
5️⃣ Evaluation
MAE, RMSE, R²
6️⃣ Deployment Mindset
Save model, plan monitoring & retraining