Graduate Admission Predictor

This project aims in helping students in shortlisting universities with their profiles. The predicted output gives them a fair idea (percentage out of 100) about their chances for a particular university.

Click here to view the deployed WebApp using Heroku.

Source

This dataset is taken from kaggle for Self Learning.
Link : https://www.kaggle.com/mohansacharya/graduate-admissions

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'target variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features').

The following Regression models were used :

1. Linear Regression.
2. Decision Tree Regression
3. Random Forest Regression.
4. K-Neighbors Regression
5. Support Vector Regression
6. XGB Regression

Learning Outcome

1. During creating this model, I learnt how to analyse data using correlation between them, how to detect outliers if present in the data.
2. Model Selection and Deep understanding of various Regression Models was developed.

Usage

1. Download the following files and keep them in a same folder.

   • model.pkl
   • app.py
   • style.css
   • index.html

2. Open Annaconda Prompt in your computer and set the working directory as the folder which contain your files.

3. Execute python app.py .

   

4. Copy the URL and open it in browser.

5. See the Predictions by entering the Data at real time.

   

Solved in Google Colab

Go through the Example notebook to see an example where this model is used on the dataset provided by Kaggle.

You can also test-run the example on Google Colaboratory by clicking the following button.

Open in Colab

Issues

If you encounter any issue or have a feedback, please don't hesitate to raise an issue.

Do ⭐ the repository, if it helped you in anyway.