Deploying our Model using Streamlit

Streamlit Web Application which predicts the salary of a software engineers based on the data from stackoverflow

Problem:

Streamlit web application for salary prediction, backed by Linear Regression, Decision Trees, and Random Forest models, is a valuable tool for software engineers navigating the competitive tech job market. It harnesses the data from Stack Overflow to offer accurate salary predictions, enabling users to make informed career decisions. As the tech industry continues to evolve, data-driven insights are essential, and our application exemplifies how these insights can be made accessible to all, helping software engineers make informed choices about their professional journeys.

Dataset

Dataset available at : Salary Dataset

Requirements

You must have Scikit Learn, Matplotlib Pandas and Numpy.

• $ Pip install streamlit

App

Streamlit A faster way to build and share data apps

Running the project

1. Ensure that you are in the project home directory. Run the notebook "Salary-Prediction.ipynb" first

This would create a serialized version of our model and save it as "saved_steps.pkl"

2. Run app.py using below command to start Streamlit API

streamlit run app.py

By default, streamlit will run on port 8501.

3. Navigate to URL http://192.168.100.184:8501 (or) http://localhost:8501

You should be able to view the homepage.

Select the activity which you want, Explore or Predict

If everything goes well, you should Visiualize the data from Explore page or Predict the data from Predict page

If you like the project . Give it a star ⭐ and [FORK]