Case Studies in Supervised Learning

Duration: 5 min

This module delves into real-world applications of supervised learning algorithms, including Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and Gradient Boosting. Understanding these case studies is crucial for applying machine learning techniques effectively in various domains.

Linear Regression Case Study

Linear Regression is a fundamental supervised learning algorithm used for predicting continuous outcomes. In this case study, we will explore how Linear Regression can be applied to predict housing prices based on various features such as square footage, number of bedrooms, and location.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Load dataset
data = pd.read_csv('housing_data.csv')

# Features and target
X = data.drop('price', axis=1)
y = data['price']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model
model = LinearRegression()
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluate
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

Try it in Google Colab:

Mean Squared Error: 23456.789

Logistic Regression Case Study

Logistic Regression is widely used for binary classification problems. In this case study, we will examine how Logistic Regression can be employed to predict whether a patient has a particular disease based on symptoms and medical history.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load dataset
data = pd.read_csv('patient_data.csv')

# Features and target
X = data.drop('disease', axis=1)
y = data['disease']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model
model = LogisticRegression()
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluate
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')

💡 Tip: Ensure your data is properly preprocessed and scaled before training Logistic Regression to achieve optimal performance.

Key Concepts

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