MBA Placement Prediction

Description

The dataset consists of placement data of students in an MBA university. It includes secondary and higher secondary school percentages and specialization. It also includes degree information, work experience, and salary offered to the placed students.

The goal of this project is to create an ML model to predict which students will get placed and the salary of the placed students.

The files starting with ‘status*’ are for the classification problem where the model predicts whether a student will get placed or not. The files starting with ‘salary*’ are for the regression problem where the model predicts the salary offered to the placed students.

For the status prediction problem, I have trained three models: LogisticRegression, RandomForestClassifier and XGBClassifier.

For the salary prediction problem, I have trained four models: LinearRegression, ElasticNet, RandomForestRegressor and XGBRegressor.

I have created Pipeline for each of these models, such that the data transformation and model training/predictions steps are assembled together.

For both the problems, I have done separate EDA and feature selection.

Tech Stack and concepts used

Python
Scikit-learn
Machine Learning Pipeline
Docker
Streamlit

Setup

Clone the project repo and open it.

Virtual Environment

Create a virtual environment for the project using
```
pipenv shell
```
Install required packages using
```
pipenv install
```

Docker Container

Build the docker image using
```
sudo docker build -t mba_placement .
```

Run the docker container using

sudo docker run -p 5000:5000 mba_placement

Open the URL http://localhost:5000/ to run and test the app.

Deploying to Cloud

Open the Deploy an app page of Streamlit.
Enter the GitHub repository details in which the streamlit_app.py file and model binaries are stored.
Click on Deploy button.
Open the URL https://share.streamlit.io/aniketsharma00411/mba_placement_prediction/main to run and test the app.

Status Prediction Results

Model	Validation Set Accuracy	Training+Validation Set Accuracy
LogisticRegression	95.35 %	89.53 %
RandomForestClassifier	93.02 %	96.51 %
XGBClassifier	97.67 %	98.84 %

Selected Model (XGBClassifier) Test Set Accuracy = 83.72 %

Salary Prediction Results

Model	Validation Set RMSE	Training+Validation Set RMSE
LinearRegression	72827.10	84563.22
ElasticNet	58410.36	89727.80
RandomForestRegressor	58509.22	51349.49
XGBRegressor	60382.35	53142.14

Selected Model (RandomForestRegressor) Test Set RMSE = 92649.18

aniketsharma00411/mba_placement_prediction