A Multifaceted Approach to Job Title Analysis

CSE 519 - Data Science Fundamentals

Project Description

Project consists of three parts:

1. Salary Prediction
2. Job Clustering
3. Job Satisfaction Analysis

Installing libraries

pip install -r requirements.txt

File Descriptions

Web Scraping Job titles.ipynb - Code for Web Scraping Job titles from CareerBuilder.com
Salary Prediction.ipynb - Code for Salary Prediction using Machine Learning
Job_Satisfaction.ipynb - Code for Job Satisfaction Analysis and Graphs
run_app.py - Code for running Streamlit app (Salary Prediction and Job Clustering)

Datasets

Job Information.csv - Dataset built by scraping web data from CareerBuilder.com
WA_Fn-UseC_-HR-Employee-Attrition.csv - Dataset download from Kaggle

ML Model

salary_model_30_11.pkl - Weighted Model developed using a combination of Regressors (refer to Salary Prediction.ipynb)

How to run the code

.ipynb files (Jupyter Notebook Files) can be run either using the command jupyter notebook or jupyter lab, or can be run directly on Google Colab (after mounting the Google Drive).

To run the file run_app.py, run the following command in the terminal:

streamlit run run_app.py

Project Report

A Multifaceted Approach to Job Title Analysis