Customer Personality Analysis

with R Program Language
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Roadmap
4. Contributing
5. License
6. Contact
7. Acknowledgments

About The Project

Context

Problem Statement

Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers.

Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment.

Content

Attributes

People

• ID: Customer's unique identifier
• Year_Birth: Customer's birth year
• Education: Customer's education level
• Marital_Status: Customer's marital status
• Income: Customer's yearly household income
• Kidhome: Number of children in customer's household
• Teenhome: Number of teenagers in customer's household
• Dt_Customer: Date of customer's enrollment with the company
• Recency: Number of days since customer's last purchase
• Complain: 1 if the customer complained in the last 2 years, 0 otherwise

Products

• MntWines: Amount spent on wine in last 2 years
• MntFruits: Amount spent on fruits in last 2 years
• MntMeatProducts: Amount spent on meat in last 2 years
• MntFishProducts: Amount spent on fish in last 2 years
• MntSweetProducts: Amount spent on sweets in last 2 years
• MntGoldProds: Amount spent on gold in last 2 years

Promotion

• NumDealsPurchases: Number of purchases made with a discount
• AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise
• AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise
• AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise
• AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise
• AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise
• Response: 1 if customer accepted the offer in the last campaign, 0 otherwise

Place

• NumWebPurchases: Number of purchases made through the company’s website
• NumCatalogPurchases: Number of purchases made using a catalogue
• NumStorePurchases: Number of purchases made directly in stores
• NumWebVisitsMonth: Number of visits to company’s website in the last month

Target

Need to perform clustering to summarize customer segments.

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Built With

• R

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

Tools

• R
• R Studio

Library

• caTools

install.packages('caTools')

• naniar

install.packages("naniar")

• lubridate

install.packages("lubridate")

• ggplot2

install.packages("ggplot2")

• ggcorrplot

install.packages("ggcorrplot")

• seriation

install.packages("seriation")

• rpart

install.packages("rpart")

• rpart.plot

install.packages("rpart.plot")

• tidyverse

install.packages("tidyverse")

• RColorBrewer

install.packages("RColorBrewer")

• ROCR

install.packages("ROCR")

• tidyr

install.packages("tidyr")

• dplyr

install.packages("dplyr")

• caret

install.packages("caret")

• plotly

install.packages("plotly")

• FactoMinerR

install.packages("FactoMinerR")

• factoextra

install.packages("factoextra")

• cluster

install.packages("cluster")

Installation

git clone https://github.com/MarioAvolio/Customer-Personality-Analysis-with-R.git

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Roadmap

• Description Of Data
• Data Preprocessing
• EDA
• PCA
• Analysis Algorithm
  • K-Means
  • D-Tree

See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

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Contact

Project Link: https://github.com/MarioAvolio/Customer-Personality-Analysis-with-R

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Acknowledgments

The dataset for this project is provided by Dr. Omar Romero-Hernandez.

• Dataset
• Structure Project

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