Admission Prediction

This is an admission prediction feature based on a linear model trained on a dataset containing 400 records. This model aims to predict the likelihood of admission to a university based on various factors.

Dataset

The training dataset, named "admission_predict.csv," is a collection of records that includes the following features:

• GRE Score: The score obtained in the Graduate Record Examination.

• TOEFL Score: The score obtained in the Test of English as a Foreign Language.

• University Rating: The rating of the university attended by the applicant (on a scale of 1 to 5).

• Statement of Purpose (SOP): The quality of the applicant's statement of purpose (on a scale of 1 to 5).

• Letter of Recommendation (LOR): The strength of the applicant's letter of recommendation (on a scale of 1 to 5).

• CGPA: The Cumulative Grade Point Average achieved by the applicant.

• Research: Whether or not the applicant has research experience (0 for no, 1 for yes).

Screenshots

1. This is the homePage of the website

2. This is the Prediction Page Where, we have to enter Details, then it will send, Details to model, then model will predict the Prediction

3. This is the output page where it will show the result of the prediction

Virtual Environment

Create a virtual environment to ensure that the project runs smoothly without any impact on your system's environment.

python -m venv <env_name>

And activate virtual environment by running command

<env_name>\Scripts\activate

Installation

Install my-project in your envrionment

step1 : clone the repo

• git clone https://github.com/Abhi-vish/mldeploy.git

step2 : install requirements.txt package by running commnad

• pip install -r requirements.txt

step3 : open terminal and run commnad

• python app.py

🚀 About Me

I'm a student...

Lessons Learned

While building this project, I learned several key concepts and faced various challenges. Here are the details:

• I learned about Linear Regression and its application in predicting outcomes based on continuous variables.
• I gained knowledge about feature selection techniques, including methods to identify and handle collinearity and multicollinearity in the dataset.
• I became familiar with preprocessing techniques such as standard scaling, which helps in normalizing the features to ensure fair comparisons.
• I also learned about the variance influence factor and its significance in understanding the impact of individual data points on the overall model.
• Throughout the project, I worked with different libraries and tools to handle and preprocess the data effectively, ensuring its suitability for the machine learning algorithms.