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Study Habit Derivatives

Study Habit Derivatives

This project analyzes study habits and derives potential professions for students based on their responses. The analysis leverages Python, natural language processing (NLP), and data manipulation libraries to process and interpret survey data.

Introduction

The Study Habit Derivatives project aims to analyze student survey data to understand their study habits and predict potential professions based on their responses. By using NLP techniques, the project identifies patterns in study habits and suggests suitable careers.

Usage

Upload your survey data CSV file and run the provided Jupyter notebook. The notebook processes the data and predicts potential professions based on the study habits of the students.

Libraries Used

  • pandas: For data manipulation and analysis.
  • transformers: For natural language processing tasks using pre-trained models.
  • google.colab: For file upload functionality in Google Colab.
  • StringIO: For handling in-memory file operations.

Code Explanation

The code performs the following key functions:

  1. Import Libraries: Imports necessary libraries for data manipulation, NLP, file uploads, and handling in-memory file operations.
  2. Upload and Load Data: Handles file upload, decoding, and loading the CSV data into a pandas DataFrame.
  3. Initialize the NLP Model: Initializes a masked language model using the transformers library and demonstrates its functionality.
  4. Data Preparation: Renames the columns for easier handling and selects the relevant columns for analysis.
  5. Generate Sentences for Prediction: Creates sentences incorporating study habits, ready for masked language model prediction.
  6. Predict Professions: Defines a function to apply the masked language model and predict potential professions, storing the results in a new column.

Possibilities

This analysis opens up several possibilities for applications:

  • Career Guidance: Predicting suitable professions based on study habits can help in personalized career guidance.
  • Educational Insights: Understanding study habits can lead to improved educational strategies and resource allocation.
  • Personal Development: Students can gain insights into their strengths and potential career paths, aiding in personal development.