Here 5 Machine Learning Algorithms for Beginners:
1. Linear Regression
It models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data.
Tip: Use Linear Regression for predicting continuous outcomes like house prices, sales forecasts, or salaries.
Example: from sklearn.linear_model import LinearRegression;
model = LinearRegression().fit(X_train, y_train)
2. Logistic Regression
Logistic Regression is used for binary classification problems, not regression. It predicts the probability that an input belongs to a particular class.
Tip: Ideal for binary outcomes like spam detection, customer churn prediction, or disease diagnosis.
Example: from sklearn.linear_model import LogisticRegression;
model = LogisticRegression().fit(X_train, y_train)
3. Decision Trees
Models that split the data into branches based on feature values, leading to a decision or prediction.
Tip: Great for classification problems with clear decision rules. They can also be used for regression.
Example:
from sklearn.tree import DecisionTreeClassifier;
model = DecisionTreeClassifier().fit(X_train, y_train)
4. K-Nearest Neighbors (KNN)
KNN is a non-parametric algorithm that classifies a data point based on the majority class among its k-nearest neighbors in the feature space.
Tip: Use KNN for simple classification problems like image recognition or recommendation systems.
Example:
from sklearn.neighbors import KNeighborsClassifier;
model = KNeighborsClassifier(n_neighbors=3).fit(X_train, y_train)
5. K-Means Clustering
K-Means is an unsupervised learning algorithm that groups data into k clusters based on feature similarity. It’s useful for finding patterns or segments in the data.
Tip: Ideal for market segmentation, customer grouping, or image compression tasks.
Example:
from sklearn.cluster import KMeans;
model = KMeans(n_clusters=3).fit(X_train)
