Machine Learning — Complete Visual Guide

Supervised Learning Flow

The Business Problem

Imagine you're a real estate company. A client asks: "What should I price my 2,000 sq ft house?" You have data on thousands of past sales. Linear regression finds the mathematical relationship between house features and price, then uses it to predict prices for new houses.

Linear regression answers the question: "How does a change in X affect Y?"

The Equation

ŷ = predicted value  |  β₀ = intercept (y value when x = 0)  |  β₁ = slope (change in y per unit change in x)  |  x = feature

From Simple to Multiple

Simple linear regression uses one feature. Multiple linear regression uses two or more features. Real-world predictions almost always involve multiple factors.

p = number of features  |  each β_j = change in y for 1-unit change in x_j, holding other features constant

R-Squared (R²) — Coefficient of Determination

SS_res = Σ(y_i - ŷ_i)² (residual sum of squares)  |  SS_tot = Σ(y_i - ȳ)² (total sum of squares)

• Interpretation: "What percentage of the variance in y does my model explain?"
• Range: 0 to 1 (can be negative for terrible models)
• R² = 0.85: The model explains 85% of the variation in y. The remaining 15% is unexplained (noise, missing features).
• R² = 0: The model is no better than just predicting the mean of y for everything.

Step 1: Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from statsmodels.stats.outliers_influence import variance_inflation_factor
import warnings
warnings.filterwarnings('ignore')

Step 2: Load & Explore Data

# Load data
df = pd.read_csv('housing.csv')

# Quick look
print(df.shape)          # (rows, columns)
print(df.info())         # dtypes, null counts
print(df.describe())     # summary statistics
print(df.isnull().sum()) # missing values per column

# Visualize target distribution
plt.figure(figsize=(8, 4))
sns.histplot(df['Price'], bins=30, kde=True)
plt.title('Distribution of House Prices')
plt.show()

# Correlation heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm', center=0, fmt='.2f')
plt.title('Feature Correlation Matrix')
plt.show()

# Scatter plots of top features vs target
sns.pairplot(df[['SquareFeet', 'Bedrooms', 'Age', 'Price']])
plt.show()

What is a Decision Tree?

A decision tree is a flowchart-like model that makes predictions by asking a series of yes/no questions about features, splitting the data at each step until it reaches a prediction.

Building a Classification Tree

from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import matplotlib.pyplot as plt

# Load and split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42, stratify=y
)

# Build a basic tree
tree = DecisionTreeClassifier(random_state=42)
tree.fit(X_train, y_train)

# Evaluate
y_pred = tree.predict(X_test)
print(f"Train Accuracy: {tree.score(X_train, y_train):.4f}")
print(f"Test Accuracy:  {accuracy_score(y_test, y_pred):.4f}")
print("\nClassification Report:")
print(classification_report(y_test, y_pred))

What is K-Means?

K-Means is an unsupervised algorithm that groups data into K clusters based on similarity. Each data point belongs to the cluster with the nearest centroid (center point).

You must specify K (the number of clusters) before running the algorithm.

Agglomerative clustering is a bottom-up approach: start with every point as its own cluster, then merge the two closest clusters repeatedly until everything is one big cluster.

Step 1: Preprocessing (Essential for All Clustering)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler

# Load data
df = pd.read_csv('customers.csv')

# Select numeric features for clustering
features = ['Age', 'Income', 'SpendingScore']
X = df[features]

# ALWAYS scale before clustering!
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print(f"Scaled data shape: {X_scaled.shape}")
print(f"Mean (should be ~0): {X_scaled.mean(axis=0).round(2)}")
print(f"Std  (should be ~1): {X_scaled.std(axis=0).round(2)}")