CSE 519 - Data Science Fundamentals

Project consists of three parts:

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

pip install -r requirements.txt

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)

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

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

.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

A Multifaceted Approach to Job Title Analysis