Predicting personality using machine learning (ML) is a challenging but amazing task which includes us in deriving understandings from data like text, speech and pictures to classify the individuals into various character types and detect particular personality characteristics. In recent days we are undertaking various types of projects on personality prediction with the access of machine learning. By using our massive resources, we make the best research work for scholars. If you want to proceed machine learning projects but struck up in any area, get our support. The following is a guideline for designing our personality forecasting project using textual data.
- Objective Definition
Here, we develop a model to detect the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) based on user’s textual data.
- Data Collection
- Surveys: Gathering text samples along with character queries reactions.
- Public Datasets: We utilize datasets like the “Essays” dataset from the MyPersonality project that involves essays and character scores.
- Preprocessing of Data
- Text Cleaning: Eliminating the stop words, punctuation and perform stemming or lemmatization we make clear text.
- Feature Extraction: To transform data into numerical form we utilize methods such as TF-IDF, Word2Vec, and BERT embedding.
- Exploratory Data Analysis (EDA)
- We visualize the dispersion of various scores and types of personality.
- Understanding some patterns and correlations between our text features and native scores.
- Choosing Model
- Regression Models: When detecting consistent scores (g., 0-100 for Openness) we select this kind of framework.
- Classification Models: If we categorize into types (e.g., High vs. Low Openness) selecting this model is helpful.
- Framework Training
- For training, validation and test sets we divide the dataset.
- Instructing our models with Linear Regression, SVM, Gradient Boosting, Random Forest, and Neural Networks on the training data.
- Model Evaluation
- Regression Metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE) are the metrics we make use of it.
- Classification Metrics: We prefer some metrics such as Accuracy, Precision, Recall, AUC, F1-score, and ROC Curve for validation.
- Overfitting: To make sure that our model generalizes well we utilize cross-validation method.
- Hyperparameter Tuning
- Fine-tuning our model we use methods such as grid search and random search.
- Deployment
- We design a web and mobile application where users input text and the framework detects their character property.
- For deployment we use environment such as Flask, Django and TensorFlow.
- Review & Continuous Learning
- To offer the feedback based on detections we allow users and experts.
- Regularly we update and retrain the model with latest data.
- Conclusion & Future Work
- We file the limitations, achievements and field of developments.
- Improvements including:
- Multimodal Data: Implementing other data types like audio and images assist us.
- Transfer Learning: Utilization of frameworks such as BERT and GPT for better text demonstration supports our work.
Notes:
- Moral Considerations: When gathering data we ensure candidate to give detailed consent for making sure the data security and de-identification.
- Understandability: Detecting character is susceptible, methods such as SHAP and LIME will assist and we able to interpret and describe model detections become beneficial.
- Bias & Fairness: By ensuring the dataset is representative and the framework won’t inherit biases. We utilize the techniques such as Fairness indicators which helps us in validating and enhancing model fairness.
We recognize that when ML provides understanding into personality based on the data, human character is difficult, because the models serve as supportive techniques instead of definitive detectors. We often ensure users to analyze the prediction chance of state based on the data offered. Our experienced research team will provide a clear and original topics and research ideas. Scholars are not up to trend in technologies but we are experts and filled with the needed resources.
Personality Prediction Project Using Machine Learning Thesis Ideas
Get our thesis writing services as we have a numerous sophisticated way so as to provide best service to our scholars. Our assistance team are filled with professionals from machine learning so as to understand scholars queries easily. The thesis that we have framed as listed below get fascinated by our work.
- Smart-Hire Personality Prediction Using ML
Keywords:
Big Five Personality Model, Feature Analysis, Personality Prediction, Personality Traits
ML methods can be utilized to classify people based on their personality. We can predict the personality by utilizing big five personality traits. Our paper tried to merge phrase frequency methods to regulate the person’s personality prediction by using the ML methods namely KNN, CNN and Logistic Regression methods to predict the personality prediction.
- Ensemble Machine Learning Models in Predicting Personality Traits and Insights using Myers-Briggs Dataset
Keywords:
Myers-Briggs personality types, NLTK, logistic regression, SVM, Nave Bayes, Random Forest
Our paper uses different ML methods to predict person personality trait based on their social media posts. With this model we can classify them based on Myers-Briggs personality types. We have to use NLTK library to access and pre-process the data. Our model uses four ML methods like Logistic Regression, SVM, Naive Bayes and Random Forest. At last we have to compare our methods with the metrics.
- Machine Learning Approach for Personality Prediction from Resume using XGBoost Classifier and Comparing with Novel Random Forest Algorithm to Improve Accuracy
Keywords:
Novel XGBoost Algorithm, Ensemble Learning, Decision Tree, Gradient Boosting, Quality Jobs
Our paper uses ML based XGBoost methods to analyze the personality prediction and we have to compare this with our proposed random forest method to improve the accuracy rate in predicting personality traits. The dataset has been imported by utilizing the kaggle tool and the Jupiter notebook has been utilized to train the dataset by utilizing the proposed Random Forest method. Our Random forest method gives the high accuracy.
- Personality Prediction Based on Contextual Feature Embedding SBERT
Keywords:
Sentence-BERT (SBERT), K-Nearest Neighbors, Myers-Briggs Type Indicator (MBTI), Oversampling
Our paper utilizes an implicit method to optimize the process by utilizing ML method. Our work proposes different pre-processing methods namely data cleaning, Stopword removal and data balancing methods such as random oversampling are used. The sentence BERT (SBERT) can be used for context of the text. The MBTI and a different ML methods can be used namely SVM, LR, KNN and RF classifier to predict the person’s personality. SBERT with RF classifier gives the best performance to predict MBTI personality.
- Words Similarities on Personalities: A Language-Based Generalization Approach for Personality Factors Recognition
Keywords:
Machine learning, natural language processing, online social networking, technology social factors, knowledge transfer
The Five Factor Model (FFM) can allow the calculation of personality traits of everyone using text data. Our paper has some queries about personality traits and gives solutions for them. We aim to offer a method that has been utilized to learn the highest level of abstraction. Our results detected that the change in performance between the trained model that can be used to predict FFM personality traits. Our stochastic Gradient Descent method gives the best performance.
- MBTI Personality Prediction Using Machine Learning and SMOTE for Balancing Data Based on Statement Sentences
Keywords:
Personality, Word2Vec, SMOTE
Our goal is to examine the efficiency of the method by using the Word2Vec model to get a vector representation of the words in corpus data. We have used many ML methods like LR, linear support vector classification, stochastic gradient descent, RF, the extreme gradient boosting classifier and the cat boosting classifier. We also used the SMOTE method to solve the problem of imbalanced data. SMOTE method can be used to improve the performance of our model.
- Integrating graphology and machine learning for accurate prediction of personality: a novel approach
Keywords:
Graphology, Handwriting analysis
Our proposed method gives the optimal value for input parameters to predict the personality prediction with high accuracy. Our proposed solution can be executed using various steps like preprocessing and segmentation, extraction of image features, trait acquisition, training and testing of the model and the personality prediction. We also uses SVM with a classifier that gives an best accuracy.
- Prediction of Personality Trait using Machine Learning on Online Texts
Keywords:
Our proposed work can be use foretell four personality traits that can be linked with MBTI model. For more investigation of personality from text, preprocessing methods like tokenization, word stemming, stop word removal and feature selection by utilizing TF IDF. All the preprocessing methods can be used to increase the personality prediction. XGBoost classifier gives the best performance for different traits.
- A Comparative Analysis of Machine Learning Approaches in Personality Prediction Using MBTI
Keywords:
Our proposed work can trusted on twitter sentiment analysis to estimate the person personality classification and prediction. We not only uses Naïve Bayes, SVM, XGBoost classifier were utilized to predict the personality of twitter users their performance can also be compared. XGBoost will gives the better accuracy rate.
- Personality Prediction using Machine Learning
Keywords:
Clusters, k-means CLUSTERING ALGORITHM, Unsupervised Learning, Clustering
Our paper uses k-means clustering to classify the person personality. The important method we used to differentiate everyone based on personality type is ML. Each of them can select their career or interest based on prediction. Personality predictions will shortlist the candidate as to improve the efficiency of the work. Our K-means clustering classify the personality.
Milestones
MILESTONE 1: Research Proposal
Finalize Journal (Indexing)
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Fix Implementation Plan
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Tools/Plan Approval
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