Machine Learning Top 5 Models Implementation "A-Z"

One case study, five models from data preprocessing to implementation with Python, with some examples where no coding is required.We will cover the following topics in this case studyProblem Statement Data  Data Preprocessing 1Understanding DatasetData change and Data StatisticsData Preprocessing 2Missing valuesReplacing missing valuesCorrelation MatrixData Preprocessing 3OutliersOutliers Detection TechniquesPercentile-based outlier detectionMean Absolute Deviation (MAD)-based outlier detectionStandard Deviation (STD)-based outlier detectionMajority-vote based outlier detectionVisualizing outlierData Preprocessing  4Handling outliersFeature Engineering Models  Selected·K-Nearest Neighbor (KNN) ·Logistic regression ·AdaBoost ·GradientBoosting ·RandomForest ·Performing the Baseline TrainingUnderstanding the testing matrix ·The Mean accuracy of the trained models ·The ROC-AUC score ROC AUC  Performing the Baseline TestingProblems with this ApproachOptimization Techniques·Understanding key concepts to optimize the approach Cross-validation The approach of using CVHyperparameter tuningGrid search parameter tuningRandom search parameter tuningOptimized  Parameters Implementation·Implementing a cross-validation based approach ·Implementing hyperparameter tuning ·Implementing and testing the revised approach ·Understanding problems with the revised approach  Implementation of the revised approach·Implementing the best approachLog transformation of features Voting-based ensemble ML mod