Comprehensive-Data-Mining-Analysis-Up

This repository contains a comprehensive data mining project that includes data preprocessing, exploratory data analysis, model training and evaluation, and clustering analysis. Various machine learning techniques are applied, such as Artificial Neural Networks (ANN), Gradient Boosting, Ensemble Learning, K-Means Clustering, and Principal Component Analysis (PCA).

Table of Contents

• Introduction
• Project Structure
• Dataset
• Installation
• Usage
• Results
• License

Introduction

The project aims to demonstrate the application of multiple machine learning techniques to a dataset to uncover patterns, build predictive models, and perform clustering analysis. The dataset used in this project is a publicly available bank marketing dataset, which includes various features related to client demographics and contact information.

Project Structure

The project is organized as follows:

• notebooks/: Jupyter notebooks containing the analysis and model training.
• data/: Directory to store the dataset.
• models/: Directory to save trained models.
• plots/: Directory to save generated plots and visualizations.
• README.md: Project documentation.

Dataset

The dataset used in this project is the Bank Marketing Dataset from the UCI Machine Learning Repository. It includes features such as age, job, marital status, education, and more.

Data Fields

• age: Age of the client.
• job: Type of job.
• marital: Marital status.
• education: Level of education.
• default: Has credit in default?
• housing: Has housing loan?
• loan: Has personal loan?
• contact: Type of contact communication.
• month: Last contact month of year.
• day_of_week: Last contact day of the week.
• duration: Last contact duration, in seconds.
• campaign: Number of contacts performed during this campaign.
• pdays: Number of days since the client was last contacted from a previous campaign.
• previous: Number of contacts performed before this campaign.
• poutcome: Outcome of the previous marketing campaign.
• emp.var.rate: Employment variation rate.
• cons.price.idx: Consumer price index.
• cons.conf.idx: Consumer confidence index.
• euribor3m: Euribor 3 month rate.
• nr.employed: Number of employees.
• y: Has the client subscribed to a term deposit? (target variable)

Installation

To run this project, you need to have Python installed along with the following libraries:

pip install numpy pandas scikit-learn keras xgboost matplotlib seaborn missingno