Li ne ar M od el s Linear Regression A simple algorithm that models a linear relationship between inputs and a continuous numerical output variable u s e c as e s Stock price predictio Predicting hous.
a lgorithm d esc ription A simple algorithm that models a linear numerical output variable Linear Models A simple algorithm that models a linear Logistic Regression relationship between inputs and a categorical output (1 or 0) use cases Stock price predictio Predicting housing price Predicting customer lifetime value use cases Credit risk score predictio Customer churn prediction Part of the regression family — it penalizes Ridge Regression features that have low predictive outcomes by shrinking their coefficients closer to zero Can Part of the regression family — it penalizes Lasso Regression features that have low predictive outcomes by shrinking their coefficients to zero Can be used for classification or regression Decision Tree models make decision rules on De cision Tree the features to produce predictions It can be used for classification or regression An ensemble learning method that combines Tree-Based Models Supervised Learning Random Forests Gradient Boosting Regression the output of multiple decision trees Gradient Boosting Regression employs boosting to make predictive models from an ensemble of weak predictive learners Gradient Boosting algorithm that is efficient & GBoost X flexible Can be used for both classification and regression tasks A gradient boosting framework that is designed LightGBM Regressor > a dva nta g es di sa dva nta g es Explainable metho Assumes linearity between inputs and outpu Interpretable results by its output coefficient Sensitive to outlier Faster to train than other machine learning Can underfit with small, high-dimensional data models Interpretable and explainabl Less prone to overfitting when using Assumes linearity between inputs and output Can overfit with small, high-dimensional data regularizatio Applicable for multi-class predictions use cases Predictive maintenance for automobile Sales revenue prediction be used for classification or regression to be more efficient than other implementations K-Means approach—it determines K clusters based on euclidean distances Less prone to overfitting Best suited where data suffer from use cases Predicting housing price Predicting clinical outcomes based on health data use cases Customer churn predictio Credit score modelin All the predictors are kept in the final mode Doesn't perform feature selection multicollinearit Explainable & interpretable Less prone to overfittin Can lead to poor interpretability as it can Can handle high-dimensional dat keep highly correlated variables Explainable and interpretabl Prone to overfittin Can handle missing values Sensitive to outliers Reduces overfittin Higher accuracy compared to other models Training complexity can be high Not very interpretable Better accuracy compared to other Sensitive to outliers and can therefore cause No need for feature selection Disease prediction use cases Credit score modelin Predicting housing prices use cases Predicting car emission Predicting ride hailing fare amount use cases Churn predictio Claims processing in insurance use cases Predicting flight time for airline Predicting cholesterol levels based on health data K-Means is the most widely used clustering use cases Customer segmentatio Recommendation systems regression model overfittin It can handle multicollinearit Computationally expensive and has high It can handle non-linear relationships complexity Provides accurate result Hyperparameter tuning can be comple Captures non linear relationships Does not perform well on sparse datasets Can handle large amounts of dat Can overfit due to leaf-wise splitting and high Computational efficient Low memory usage & fast training spee sensitivit Hyperparameter tuning can be complex Scales to large datasets Requires the expected number of clusters Simple to implement and interpret from the beginning Results in tight clusters Has troubles with varying cluster sizes and Clustering Hierarchical Clustering A "bottom-up" approach where each data point is treated as its own cluster—and then the closest two clusters are merged together Gaussian Mixture distributed clusters within a dataset Models Fraud detectio Document clustering based on similarity There is no need to specify the number of clusters The resulting dendrogram is informative Doesn’t always result in the best clustering Not suitable for large datasets due to high complexity use cases Customer segmentatio Recommendation systems Computes a probability for an observation Requires complex tuning Can identify overlapping clusters components or clusters belonging to a cluster Requires setting the number of expected mixture More accurate results compared to K-means Rule based approach that identifies the most Apriori algorithm use cases iteratively A probabilistic model for modeling normally Association Unsupervised Learning densities > Top Machine Learning Algorithms Linear Regression relationship between inputs and a continuous a ppli cation s frequent itemset in a given dataset where prior knowledge of frequent itemset properties is used use cases Product placements Recommendation engines Promotion optimization Results are intuitive and Interpretable Generates many uninteresting itemsets Exhaustive approach as it finds all rules Computationally and memory intensive based on the confidence and support Results in many overlapping item sets