Binary Classification Example With Python
Learn more about how to make a binary classification model with python using sklearn, lazypredict, and grid search method.
From the blog
What is Principal Component Analysis (PCA)
Principal Component Analysis applications in machine learning and how it simplifies data for better visualization and model performance.
The Perceptron: A Building Block of Neural Networks
Discover the role of a perceptron in neural networks, its mathematical foundation, limitations, and practical applications.
What is Confusion Matrix in Machine Learning
Learn about confusion matrix in machine learning for assessing classification models and enhancing prediction accuracy in your projects.
K-Means Clustering on Customer Data With Python
Simple python example of grouping customer data using k-means clustering algorithm with silhouette score for evaluation.
XGBoost Algorithm in Machine Learning
Discover XGBoost algorithm in machine learning, its benefits and how it compares to deep learning and decision trees.
Silhouette Score: Guide to Clustering Evaluation
Explore the role of silhouette score in clustering evaluation, its advantages, limitations, and practical applications.
Unraveling the FP Growth Algorithm
Discover the FP-growth algorithm for pattern mining, its benefits, and its role in unsupervised learning to reveal hidden insights.
LDA in Machine Learning
Discover LDA in machine learning for dimensionality reduction, classification, and how it compares to PCA, SVM, and logistic regression.
Heart Disease Prediction Using Machine Learning
Explore machine learning for heart disease prediction with algorithms like logistic regression, CNN, and SVM for early detection.
Exploring Ensemble Learning in Machine Learning
Explore ensemble learning in machine learning, dive into its techniques and benefits to boost your ML model's performance.
Unraveling KNN in Machine Learning
Explore KNN in machine learning, its applications, strengths, limitations, and optimization tips for better performance in your projects.