https://github.com/amiraflak/data-mining

Data Mining Course - Spring 2024
https://github.com/amiraflak/data-mining

classification clustering data-analysis data-mining decision-tree-classifier eda pca

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Data Mining Course - Spring 2024

• Host: GitHub
• URL: https://github.com/amiraflak/data-mining
• Owner: AmirAflak
• Created: 2024-04-17T11:09:57.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-09-06T19:52:18.000Z (2 months ago)
• Last Synced: 2024-09-07T00:17:22.846Z (2 months ago)
• Topics: classification, clustering, data-analysis, data-mining, decision-tree-classifier, eda, pca
• Language: Jupyter Notebook
• Homepage:
• Size: 751 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data Mining Project

This project develops a machine learning model to classify customers based on their features and predict whether they will make a deposit into their newly opened account, using a dataset from a Portuguese bank's marketing campaign.

- Data Mining Course - Spring 2024

## Dataset Description

The dataset represents a marketing campaign by a Portuguese bank, containing customer information.

The dataset contains 11162 samples, with 16 features. The features include:

- Demographic information: age, job, marital status, education

- Financial information: default, housing, loan

- Marketing campaign information: contact, month, day of week, duration, campaign, pdays, previous, poutcome

- Target variable: deposit (binary categorical)

## Exploratory Data Analysis (EDA)

The EDA notebook provides an exploratory data analysis of the dataset, including:

- Data cleaning and preprocessing

- Visualization of the data using various plots and charts

## Classification 

The classification notebook develops a machine learning model to classify customers based on their observed features. The model is trained using a variety of algorithms, including KNN and decision trees. The performance of each model is evaluated using metrics such as accuracy, precision, and recall.

## Clustering 

The clustering notebook applies clustering algorithms to the dataset to identify patterns and group similar customers together. The algorithms used include k-means and hierarchical clustering. The results of the clustering analysis are visualized using various plots and charts.