Analysis of ‘Customer Personality Analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/imakash3011/customer-personality-analysis on 21 November 2021.

--- Dataset description provided by original source is as follows ---

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

Target

Need to perform clustering to summarize customer segments.

Solution

You can take help from following link to know more about the approach to solve this problem. Visit this URL

Inspiration

happy learning....

Hope you like this dataset please don't forget to like this dataset

--- Original source retains full ownership of the source dataset ---

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.

Acknowledgement

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

Solution

You can take help from following link to know more about the approach to solve this problem. Visit this URL

Inspiration

happy learning....

Hope you like this dataset please don't forget to like this dataset

Customer Personality Analysis Dataset from https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis used for Business Intelligence and Data Stewardship Preprocessed with preprocessing.ipynb

20% validation split of doi://10.82556/x0q6-dm10

We use the https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis dataset to predict whether customers buy in web, store or by catalog.

Dataset from https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis used for Business Intelligence and Data Stewardship exercises

Please give me an upvote if you find it useful!! Dataset Description: Personality Traits and Social Behavior This dataset contains behavioral and psychological data aimed at classifying individuals as Introverts or Extroverts. It captures how social preferences and habits correlate with personality types, making it ideal for machine learning models, psychological research, and social behavior studies.

🔍 Key Features: Time_spent_Alone (Numeric): Average time an individual spends alone (in hours).

Stage_fear (Categorical: Yes/No): Indicates if the individual experiences stage fright.

Social_event_attendance (Numeric): Number of social events attended recently.

Going_outside (Numeric): Frequency of going outside for non-essential reasons.

Drained_after_socializing (Categorical: Yes/No): Shows whether the person feels mentally exhausted after social interaction.

Friends_circle_size (Numeric): Count of close friends in the individual’s social circle.

Post_frequency (Numeric): Frequency of social media posting.

Personality (Target Label: Introvert/Extrovert): Personality classification based on observed traits.

🎯 Potential Use Cases: Predictive modeling for personality classification.

Feature analysis to understand behavioral differences between introverts and extroverts.

Building recommendation systems or personalized experiences based on social behavior.

Educational tools for self-assessment or career guidance.

60% training split of doi://10.82556/x0q6-dm10

1) Data Introduction • The (MBTI) Myers-Briggs Personality Type Dataset is a text-based personality analysis dataset that contains posts written by approximately 8,600 users with a simplified MBTI label based on the N (iNtuitive) or S (Sensing) trait of each user.

2) Data Utilization

(1) The (MBTI) Myers-Briggs Personality Type Dataset has characteristics that: • Each row contains a user's N/S label (derived from their MBTI type) and post texts written by that user. • The data consists of natural language written by various individuals with either N or S traits, making it suitable for linguistic style analysis and disposition-based classification.

(2) The (MBTI) Myers-Briggs Personality Type Dataset can be used to: • Develop N/S Trait Prediction Models: Based on users' textual data, machine learning models can be developed to predict whether a user leans toward iNtuitive or Sensing personality traits. • Analyze Language Style and Behavior Patterns: This dataset enables psychological and social media research by examining differences in linguistic characteristics, expression styles, and online behavior patterns between N-type and S-type individuals.

🧠 About the Dataset This dataset was created to explore and analyze behavioral patterns that may help predict an individual's personality type Introvert or Extrovert based on lifestyle and social interaction metrics. It simulates responses from individuals with varying social habits, preferences, and psychological tendencies.

Each row represents an individual's self-reported behavior, with features focusing on their social energy, online activity, and personal tendencies.

📊 Features Time_spent_Alone (int) – Average number of hours spent alone per day.

Stage_fear (Yes/No) – Whether the individual experiences fear or anxiety when speaking or performing in front of an audience.

Social_event_attendance (int) – Number of social events attended per month.

Going_outside (int) – Number of days per week the individual goes outside for leisure or social activities.

Drained_after_socializing (Yes/No) – Whether the individual feels mentally or emotionally drained after socializing.

Friends_circle_size (int) – Estimated number of close friends or regular companions.

Post_frequency (int) – Number of personal social media posts per month.

Personality (Introvert/Extrovert) – The target variable indicating the individual's personality classification.

🔍 Use Case This dataset is ideal for building supervised classification models (e.g., logistic regression, decision trees, random forest, etc.) to predict personality types. It also supports feature importance analysis, exploratory data visualization, and psychological behavioral profiling.

Virtual reality (VR) is a useful tool to study consumer behavior while they are immersed in a realistic scenario. Among several other factors, personality traits have been shown to have a substantial influence on purchasing behavior. The primary objective of this study was to classify consumers based on the Big Five personality domains using their behavior while performing different tasks in a virtual shop. The personality recognition was ascertained using behavioral measures received from VR hardware, including eye-tracking, navigation, posture and interaction. Responses from 60 participants were collected while performing free and directed search tasks in a virtual hypermarket. A set of behavioral features was processed, and the personality domains were recognized using a statistical supervised machine learning classifier algorithm via a support vector machine. The results suggest that the open-mindedness personality type can be classified using eye gaze patterns, while extraversion is related to posture and interactions. However, a combination of signals must be exhibited to detect conscientiousness and negative emotionality. The combination of all measures and tasks provides better classification accuracy for all personality domains. The study indicates that a consumer’s personality can be recognized using the behavioral sensors included in commercial VR devices during a purchase in a virtual retail store.

20% test split of https://doi.org/10.82556/re0n-rc68

Source: The dataset is titled PANDORA and is retrieved from the https://psy.takelab.fer.hr/datasets/all/pandora/. the PANDORA dataset is the only dataset that contains personality-relevant information for multiple personality models. It consists of Reddit comments with their corresponding scores for the Big Five Traits, MBTI values and the Enneagrams for more than 10k users. This Dataset: This dataset is a subset of Reddit comments from PANDORA focused only on the Big Five Traits. The… See the full description on the dataset page: https://huggingface.co/datasets/Fatima0923/Automated-Personality-Prediction.

i.e.

Characteristics of recent work in Image-based personality analysis on social media.

60% training split of https://doi.org/10.82556/re0n-rc68

Dataset Description This dataset captures personality trait scores based on textual descriptions of behaviors and preferences. The traits are based on the Big Five Personality Traits model, which includes - Openness: Creativity, curiosity, and willingness to try new experiences. - Conscientiousness: Organization, responsibility, and dependability. - Extraversion: Sociability, energy, and outgoingness. - Agreeableness: Cooperation, kindness, and trust. - Neuroticism: Emotional stability, anxiety, and moodiness.

The dataset consists of text-based statements about personal behaviors, and each statement is mapped to corresponding values for each of the Big Five traits. A value of "1" indicates the presence of the trait, while "0" indicates its absence.

Context This dataset is inspired by personality psychology, particularly the Five-Factor Model, which is widely used to understand and quantify human personality. The dataset could be used in various applications, including personality prediction models, behavioral analysis, and sentiment analysis.

Sources & Inspiration The data likely stems from self-reports or surveys in psychological studies, with the goal of assessing personality traits through text analysis. It can be utilized for machine learning or AI-based personality prediction and could also be a valuable resource for academic research on personality psychology, social behavior, and personal development.

This dataset was developed with the assistance of AI-driven insights to structure and align the data with the Big Five Personality Traits framework, ensuring clarity and usability for research and analysis.

Results of the mixed-effects analysis for the ratings of extraversion.

wearable device-based measurements can collect rich data about individual physiological activities in real-life situations without interfering with normal life

The data logged files of each participant in the "personality log files.rar". In addition personality data of each participant and the active window analysis data. The participants' data for correlations for the first study.