Creating a Personality Prediction System via Resume Analysis involves building a model that can predict a person's personality traits or characteristics based on the content and structure of their resumes.
Dataset from kaggle:
Personality Prediction System was trained on a labeled dataset of resumes, where each resume is associated with personality trait labels. The dataset includes resumes from various industries and professions, and personality trait labels were obtained through self-assessments or external evaluations. The dataset collection process focused on diversity and quality.
1. Data Collection: Gather a labeled dataset of resumes along with personality trait labels. Collect diverse resumes from different industries and professions. Ensure each resume is labeled with personality traits based on self-assessments or external evaluations.
2. Data Preprocessing: Clean and preprocess the text data, including removing irrelevant information, formatting inconsistencies, and handling missing data. Tokenize and vectorize the text to convert it into a numerical format suitable for machine learning.
3. Feature Engineering: Extract relevant features from the resumes, such as keywords, skills, job experiences, and education history. Utilize natural language processing (NLP) techniques to extract meaningful information from the text data.
4. Data Labeling: Annotate each resume with the corresponding personality trait labels. These labels could be obtained through self-assessments, psychological assessments, or external evaluations.
5. Data Splitting: Divide the dataset into training, validation, and test sets (e.g., 70% for training, 15% for validation, 15% for testing) to evaluate model performance.
6. Model Selection: Choose an appropriate machine learning or deep learning approach for personality prediction based on text data. Common choices include:
- Text classification models (e.g., Naive Bayes, Support Vector Machines, LSTM, BERT-based models)
- Regression models (predicting personality trait scores)
7. Model Training: Train the selected model on the training dataset using the extracted resume features as input and personality trait labels as targets. Fine-tune hyperparameters and optimize the model's architecture for better performance.
.png)
.png?raw=true)
.png?raw=true)
.png?raw=true)
.png?raw=true)
.png?raw=true)
.png?raw=true)
.png?raw=true)
.png?raw=true)
.png?raw=true)
.png?raw=true)
.png?raw=true)
Languages: Python
Framework: Jupyter Notebook || Pycharm
.png){kind=link}
.png){kind=link}
.png){kind=link}
.png){kind=link}
.png){kind=link}
.png){kind=link}
.png){kind=link}
.png){kind=link}
.png){kind=link}
.png){kind=link}
.png){kind=link}