Machine Learning Top 5 Models Implementation A-Z

 Buy Now
  

Price: 19.99$

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 study Problem Statement Data  Data Preprocessing 1Understanding Dataset Data change and Data Statistics Data Preprocessing 2Missing values Replacing missing values Correlation Matrix Data Preprocessing 3Outliers Outliers Detection Techniques Percentile-based outlier detection Mean Absolute Deviation (MAD)-based outlier detection Standard Deviation (STD)-based outlier detection Majority-vote based outlier detection Visualizing outlier Data Preprocessing  4Handling outliers Feature Engineering Models  Selected·K-Nearest Neighbor (KNN) ·Logistic regression ·Ada Boost ·Gradient Boosting ·Random Forest ·Performing the Baseline Training Understanding the testing matrix ·The Mean accuracy of the trained models ·The ROC-AUC score ROC AUC  Performing the Baseline Testing Problems with this Approach Optimization Techniques·Understanding key concepts to optimize the approach Cross-validation The approach of using CVHyperparameter tuning Grid search parameter tuning Random search parameter tuning Optimized  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 approach Log transformation of features Voting-based ensemble ML model ·Running ML models on real test data Best approach & Summary Examples with No Code Downloads – Full Code