College Predictor System

This the project of the Data Science where we predict the collegese for the student after their marks by the model. The model is based on the report of the various NIT and IIT colleges and their admission procedure.

First, we analyze the data of the different colleges and their stat for admission in various programs, then the model suggest the student top colleges as per their features to get admission in it.

Process to make the model :-

1. Exploratory Data Analysis
2. Pre - Processing
3. Data Visualization
4. Training and Testing Dataset
5. Creating Model
6. Optimizing the model
7. check accuracy

Software and Tools Required

1. Github Account
2. Render Account
3. VS Code IDE
4. Git CLI

Steps to generate whole model and deploment

1. Generate model and do the analysis of the model at any platform

2. Atlast do the pickling

   import pickle
   
   pickle.dump(model, open('FileName', 'wb')) # To generate the pickle file
   
   pickle_model = pickle.load(open('FileName, 'rb')) # to load the pickle file
   

3. create a repository of that model with readme file and gitignore

4. Now create a folder in your system, open cmd here and clone that repository there

   git clone LINK
   

5. Now open VS Code from that same location after adding your .pynb and the model.pkl.

6. Create a new Environment

   conda create -p venv python==3.7 -y
   

7. Get into the environemnt by typing

   git activate venv/
   

8. Very Important - Create Requirements.txt where we just write all the libraries

   1. Flask
   2. numpy
   3. pandas
   4. scikit-learn
   5. matplotlib
   6. gunicorn

   pip install -r requirements.txt
   

9. Configure your UserName and Email ID

   git config --global user.name "name"
   
   git config --global user.email "email"
   

10. For adding files into git

    git add FileName
    
        or
    
    git add .   (For whole files)
    

11. Checking the git status

    git status
    

12. After this we have to commit that all

    git commit -m "Message"
    

13. After all that we have to push the files into the github

    git push origin main
    

14. After all that we have to create the app.py app which act as a main app behind all that with the help of Flask.

15. Then we have to create a html page for frontend and visualization.

16. Now after all that we have to go on to render.com and deploy our app with the instructions.