Data Exploration and Mining

Author: Eliza Fury

Overview

This project focuses on applying data mining techniques to analyze a dataset and extract meaningful insights. It includes both descriptive and predictive mining tasks to explore patterns, relationships, and trends in the data.

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Setup

1. Prerequisites

Ensure you have the following installed:

• Python (version 3.7 or higher)
• pip (Python package manager)

2. Environment Setup

It is recommended to set up a virtual environment for this project. Use the following commands:

# Create a virtual environment
python -m venv env

# Activate the virtual environment
# On Windows:
env\Scripts\activate
# On macOS/Linux:
source env/bin/activate

3. Install Required Libraries

Run the following commands to install the necessary Python packages:

pip install pandas matplotlib prefixspan mlxtend

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Project Structure

Objectives

The main goals of the project are:

• To clean and preprocess the dataset to ensure data quality.
• To perform descriptive mining by identifying associations and clustering patterns within the dataset.
• To conduct predictive mining using methods such as decision trees, regression models, and neural networks.
• To evaluate the results and provide actionable recommendations based on the analysis.

Key Components

The project will cover the following:

1. Data Cleaning and Preparation

   • Handling missing values and duplicates
   • Converting data types
   • Ensuring dataset readiness for analysis

2. Descriptive Mining

   • Association analysis to uncover relationships between items
   • Clustering to identify natural groupings in the data

3. Predictive Mining

   • Building and evaluating decision tree models
   • Creating regression models to predict outcomes
   • Implementing neural networks for advanced predictions

4. Evaluation and Recommendations

   • Comparing models based on metrics such as accuracy and ROC curves
   • Summarizing key findings
   • Providing recommendations supported by analysis

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Tools and Libraries

The project utilizes Python and key data science libraries, including:

• pandas: Data manipulation and analysis
• matplotlib: Data visualization
• mlxtend: Tools for association analysis
• prefixspan: Sequence pattern mining

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Deliverables

The final deliverables for this project include:

1. A comprehensive report summarizing the methods, results, and recommendations.
2. Visualizations and insights derived from the analysis.
3. Supporting files such as datasets and model outputs.