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 1

Understanding Dataset

Data change and Data Statistics

Data Preprocessing 2

Missing values

Replacing missing values

Correlation Matrix

Data Preprocessing 3

Outliers

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  4

Handling outliers

Feature Engineering

Models  Selected

·K-Nearest Neighbor (KNN)

·Logistic regression

·AdaBoost

·GradientBoosting

·RandomForest

·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 CV

Hyperparameter 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