https://github.com/hiteshydv001/personality-prediction-system-via-cv-analysis-codeclause
Personality Prediction System via CV Analysis using machine learning
https://github.com/hiteshydv001/personality-prediction-system-via-cv-analysis-codeclause
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Personality Prediction System via CV Analysis using machine learning
- Host: GitHub
- URL: https://github.com/hiteshydv001/personality-prediction-system-via-cv-analysis-codeclause
- Owner: Hiteshydv001
- Created: 2023-09-28T06:10:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-02T05:04:15.000Z (over 1 year ago)
- Last Synced: 2025-02-13T03:44:21.454Z (3 months ago)
- Language: Python
- Size: 3.93 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Personality Prediction System via CV Analysis
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 Used
**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.
## Roadmap
**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.
## Screenshots
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## Tech Stack
**Languages:** Python
**Framework:** Jupyter Notebook || Pycharm
## 🔗 Links
## let's connect
[](https://www.linkedin.com/)