Welcome to the TechnoMagicLand Visitor Behavior Analysis repository! This case study focuses on data preprocessing and behavioral analysis of visitors to TechnoMagicLand. The goal is to identify repeat visitors and enhance engagement strategies through various data analysis techniques.
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- Project Overview
- Topics Covered
- Data Description
- Installation
- Usage
- Analysis Techniques
- Results
- Contributing
- License
This project delves into the visitor behavior at TechnoMagicLand, a fictional amusement park. By analyzing visitor data, we aim to uncover patterns that can help improve engagement strategies. The repository includes scripts and documentation for performing various analyses using R, focusing on:
- Identifying repeat visitors
- Understanding visitor demographics
- Enhancing overall visitor experience
This repository encompasses a range of topics in data analysis, including:
- Big Data
- Clustering
- CRISP-DM methodology
- Data Analysis
- Data Preprocessing
- Data Visualization
- Education Project
- Exploratory Data Analysis
- K-means Clustering
- User Segmentation
- Visitor Behavior
The dataset consists of visitor logs from TechnoMagicLand, including attributes such as:
- Visitor ID
- Visit Date
- Duration of Visit
- Attractions Visited
- Visitor Demographics (age, gender, etc.)
This data provides a rich foundation for exploring visitor behavior patterns.
To get started with this project, follow these steps:
-
Clone the repository:
git clone https://github.com/park-kwang-woon/tml-visitor-behavior-analysis.git
-
Navigate to the project directory:
cd tml-visitor-behavior-analysis -
Install the required R packages. You can use the following command in R:
install.packages(c("dplyr", "ggplot2", "cluster", "factoextra"))
After installation, you can run the analysis scripts in R. Each script is documented to guide you through the analysis process.
-
Load the dataset:
data <- read.csv("data/visitor_data.csv")
-
Run the analysis:
source("scripts/clustering_analysis.R") -
Visualize the results:
source("scripts/visualization.R")
For detailed instructions, refer to the individual script documentation.
Data preprocessing is a crucial step in any analysis. In this project, we perform the following tasks:
- Data Cleaning: Remove duplicates and handle missing values.
- Data Transformation: Normalize numerical values and encode categorical variables.
- Feature Engineering: Create new features that may enhance the analysis, such as visit frequency.
Clustering helps us group similar visitors based on their behavior. We use K-means clustering for this analysis. The steps include:
- Selecting relevant features.
- Standardizing the data.
- Determining the optimal number of clusters using the elbow method.
- Running the K-means algorithm.
Understanding relationships between different variables is key. We use correlation matrices to identify strong relationships. This helps us understand which factors influence visitor behavior.
Visualization aids in interpreting data. We create various plots, including:
- Histograms to show visitor demographics.
- Scatter plots to illustrate relationships between variables.
- Heatmaps for correlation analysis.
The analysis reveals interesting insights into visitor behavior:
- Repeat Visitors: A significant percentage of visitors return multiple times.
- Attraction Preferences: Certain attractions are more popular among specific age groups.
- Visit Duration: Longer visits correlate with higher engagement.
These insights can inform marketing strategies and enhance visitor experiences.
We welcome contributions to this project. If you have ideas for improvements or additional analyses, please fork the repository and submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
For more information and to download the latest releases, visit Releases.