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Overview:

The goal of this project is to classify whether a client will subscribe to a term deposit based on a given dataset. The project involves the comparison of six machine learning models and one deep learning model (Neural Network). The workflow includes data exploration, preprocessing, and model evaluation using accuracy, precision, recall, and f1-score.

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Key Features:

1. Machine Learning Models:

Six models are compared: Decision Tree, Random Forest, XGBoost, Logistic Regression, Support Vector Machine (SVM), and Naive Bayes. Each model provides different approaches to classification.

2. Neural Network:

A deep learning model is evaluated alongside traditional machine learning models to assess its performance in classifying client subscriptions.

3. Evaluation Metrics:

Each model is evaluated using accuracy, precision, recall, and f1-score to ensure a comprehensive comparison of their predictive capabilities.

4. Data Preprocessing:

Includes normalization and splitting the dataset into training and testing sets to ensure consistency across models.

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Links:

• GitHub