ML_divorce_Classification-with-5-models

The code performs data preprocessing, machine learning model training, evaluation, and model saving for a binary classification problem on the divorce dataset.

Here's the step-by-step explanation of the code:

1. IMPORT LIBRARIES: Imports the necessary libraries. In this code, it imports the pandas and numpy libraries.

2. Read Data: Reads the dataset. It reads the file ../dataset/divorce_data.csv and assigns it to a variable named unprocessed_data.

3. Info & head & describe: Displays the first few rows of the dataset and provides basic statistics.

4. EDA (Exploratory Data Analysis): Visualizes and explores the dataset. This includes creating count plots, distribution plots, and correlation analysis.

5. TRAIN-TEST-SPLIT & PREPROCESSING: Splits the dataset into training and testing sets. It separates the target variable (Divorce) from the features and performs any necessary preprocessing steps.

6. EVALUATE FUNCTION: Defines a function to evaluate the performance of machine learning models. It computes metrics such as accuracy and F1-score.

7. MODELING: Fits various machine learning models to the training data and evaluates their performance on the test data. The models used include Logistic Regression, K-Nearest Neighbors, Random Forests, Naive Bayes, and an Artificial Neural Network (ANN).

8. SAVE MODELS: Saves the trained models to disk using the joblib library for Logistic Regression, KNN, Random Forests, and Naive Bayes, and the h5py library for the ANN model.

9. VISUALIZATION: Visualizes the performance of the models using bar plots.

Overall, this code performs data exploration, model training, evaluation, and model saving for a binary classification problem on the divorce dataset.