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 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