Overview: Linear Regression is a foundational algorithm in machine learning, used for predicting a quantitative response. It's based on the relationship between dependent and independent variables by fitting a linear equation to observed data.
Use Cases: Real estate pricing, stock market forecasting, and risk assessment in insurance.
Advantages: Simple, easy to implement and interpret.
Disadvantages: Assumes a linear relationship, sensitive to outliers.
// Pseudocode for Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)Overview: Despite its name, Logistic Regression is used for binary classification tasks. It predicts the probability of the target variable being true by using a logistic function.
Use Cases: Email spam detection, credit scoring, and medical diagnosis.
Advantages: Provides probabilities for outcomes, interpretable.
Disadvantages: Assumes linear decision boundary, not suitable for complex relationships.
// Pseudocode for Logistic Regression
model = LogisticRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)Overview: Decision Trees are flowchart-like tree structures where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome.
Use Cases: Customer segmentation, business decision making, and drug response prediction.
Advantages: Easy to understand and interpret, can handle both numerical and categorical data.
Disadvantages: Prone to overfitting, can become complex.
// Pseudocode for Decision Tree
tree = DecisionTreeClassifier()
tree.fit(X_train, y_train)
predictions = tree.predict(X_test)Overview: Random Forests are an ensemble learning method, building multiple decision trees and merging them together to get a more accurate and stable prediction.
Use Cases: Banking, stock market, and e-commerce for predicting customer behavior.
Advantages: Reduces overfitting, improves accuracy.
Disadvantages: More complex and computationally intensive than decision trees.
// Pseudocode for Random Forest
forest = RandomForestClassifier()
forest.fit(X_train, y_train)
predictions = forest.predict(X_test)Overview: SVM is a powerful and versatile supervised learning algorithm used for classification and regression. It works by finding the hyperplane that best divides a dataset into classes.
Use Cases: Face detection, handwriting recognition, and classification of images.
Advantages: Effective in high-dimensional spaces, memory efficient.
Disadvantages: Not suitable for larger datasets, sensitive to the choice of kernel parameters.
// Pseudocode for SVM
svm = SVC(kernel='linear')
svm.fit(X_train, y_train)
predictions = svm.predict(X_test)Overview: KNN is a simple, instance-based learning algorithm where the class of a sample is determined by the majority class among its k-nearest neighbors.
Use Cases: Recommendation systems, classification tasks, and pattern recognition.
Advantages: Simple, easy to implement, and intuitive.
Disadvantages: Computationally expensive, especially with large datasets.
// Pseudocode for KNN
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
predictions = knn.predict(X_test)Overview: K-Means is a popular unsupervised learning algorithm used for clustering. It partitions n observations into k clusters where each observation belongs to the cluster with the nearest mean.
Use Cases: Market segmentation, data compression, and pattern recognition.
Advantages: Easy to implement, scales well to large datasets.
Disadvantages: Needs the number of clusters to be specified, sensitive to initial seeds and outliers.
// Pseudocode for K-Means Clustering
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
centroids = kmeans.cluster_centers_Overview: Neural Networks are a set of algorithms, modeled loosely after the human brain, designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering raw input.
Use Cases: Image and speech recognition, medical diagnosis, and financial fraud detection.
Advantages: Can model complex non-linear relationships, highly flexible.
Disadvantages: Requires a lot of data, computationally intensive, prone to overfitting.
// Pseudocode for a simple Neural Network
network = NeuralNetwork()
network.add(Layer("input", shape=(784,)))
network.add(Layer("hidden", units=128, activation='relu'))
network.add(Layer("output", units=10, activation='softmax'))
network.compile(loss='categorical_crossentropy', optimizer='adam')
network.fit(X_train, y_train)Overview: GBM is an ensemble technique that builds models sequentially, each new model correcting errors made by previous ones.
Use Cases: Web search ranking, ecology, and anomaly detection.
Advantages: Often provides predictive accuracy that cannot be beaten, lots of flexibility.
Disadvantages: Can be prone to overfitting, requires careful tuning of parameters.
// Pseudocode for GBM
gbm = GradientBoostingClassifier()
gbm.fit(X_train, y_train)
predictions = gbm.predict(X_test)Overview: PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
Use Cases: Dimensionality reduction, exploratory data analysis, and noise reduction.
Advantages: Reduces complexity, improves algorithm performance.
Disadvantages: Can lead to information loss, sensitive to scaling.
// Pseudocode for PCA
pca = PCA(n_components=2)
X_reduced = pca.fit_transform(X)