Title: Graduate-Admissions-Predictor

Overview

Title: Graduate-Admissions-Predictor

-- Project Status: [Completed ]

Project Intro/Objective

The purpose of this project is create a predictive model capable of identifying the probability of a person securing an admit based on their personal profile parameters. This dataset was built with the purpose of helping students in shortlisting universities with their profiles. The predicted output gives them a fair idea about their chances for a particular university.

Methods Used

  • Inferential Statistics
  • Machine Learning
  • Feature Engineering
  • Predictive Modeling
  • Deep Learning
  • Data Visualization

Technologies

  • Python
  • Pandas, TensorFlow, SkLearn
  • Collab

Project Description

  • This Notebook is based off an open source dataset available on www.kaggle.com where I have created models to predict the chances of a student securing a graduate admission based on thier profile! various parameters have been taken into accomodation. Performed scaling and dimentionality reduction based on VIF for getting better results. The Best Mean Squared Error (MSE) Numeric Obtained By a Few Models was: 0.0641 and an Accuray of About 80%
  • All models are subject to betterment with more stringent hyper-parameter tuning. This can be achieved by random selection, brute force methods, etc. Various other classifiers can also be used, but the most standard classifiers have been considered in this notebook.
  • Recommend standard practices for data transformation, outlier detection, and null value substitution have been incorporated in this notebook.
  • Visualizations include scatter plots and hist plots!

Acknowledgements

This dataset is inspired by the UCLA Graduate Dataset. The test scores and GPA are in the older format. The dataset is owned by Mohan S Acharya.

Inspiration

This dataset was built with the purpose of helping students in shortlisting universities with their profiles. The predicted output gives them a fair idea about their chances for a particular university.

Citation

Mohan S Acharya, Asfia Armaan, Aneeta S Antony : A Comparison of Regression Models for Prediction of Graduate Admissions, IEEE International Conference on Computational Intelligence in Data Science 2019

Contact

Owner
Akarsh Singh
Data Scientist, Grad Student, Avid Researcher in the domains of ML, Deep Learning, and Stats. In a nutshell, I enjoy transforming data into valuable knowledge!
Akarsh Singh
Space Ship Simulator using python

FlyOver Basic space-ship simulator using python How to run? Just double click run.py What modules do i need? All modules that i currently using is bui

0 Oct 09, 2022
Large scale embeddings on a single machine.

Marius Marius is a system under active development for training embeddings for large-scale graphs on a single machine. Training on large scale graphs

Marius 107 Jan 03, 2023
BisQue is a web-based platform designed to provide researchers with organizational and quantitative analysis tools for 5D image data. Users can extend BisQue by implementing containerized ML workflows.

Overview BisQue is a web-based platform specifically designed to provide researchers with organizational and quantitative analysis tools for up to 5D

Vision Research Lab @ UCSB 26 Nov 29, 2022
Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)

Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021) authors: Boris Knyazev, Michal Drozdzal, Graham Taylor, Adriana Romero-Soriano Overv

Facebook Research 462 Jan 03, 2023
Jittor implementation of PCT:Point Cloud Transformer

PCT: Point Cloud Transformer This is a Jittor implementation of PCT: Point Cloud Transformer.

MenghaoGuo 547 Jan 03, 2023
Use Python, OpenCV, and MediaPipe to control a keyboard with facial gestures

CheekyKeys A Face-Computer Interface CheekyKeys lets you control your keyboard using your face. View a fuller demo and more background on the project

69 Nov 09, 2022
A dual benchmarking study of visual forgery and visual forensics techniques

A dual benchmarking study of facial forgery and facial forensics In recent years, visual forgery has reached a level of sophistication that humans can

8 Jul 06, 2022
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im

Valentin Wolf 86 Nov 16, 2022
Tutorial on active learning with the Nvidia Transfer Learning Toolkit (TLT).

Active Learning with the Nvidia TLT Tutorial on active learning with the Nvidia Transfer Learning Toolkit (TLT). In this tutorial, we will show you ho

Lightly 25 Dec 03, 2022
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

AI Wizards for Software Management (AWSM) Research Group 14 Nov 13, 2022
Unofficial TensorFlow implementation of the Keyword Spotting Transformer model

Keyword Spotting Transformer This is the unofficial TensorFlow implementation of the Keyword Spotting Transformer model. This model is used to train o

Intelligent Machines Limited 8 May 11, 2022
Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

The official implementation of Mask-aware IoU and maYOLACT detector. Our implementation is based on mmdetection. Mask-aware IoU for Anchor Assignment

Kemal Oksuz 46 Sep 29, 2022
SatelliteNeRF - PyTorch-based Neural Radiance Fields adapted to satellite domain

SatelliteNeRF PyTorch-based Neural Radiance Fields adapted to satellite domain.

Kai Zhang 46 Nov 20, 2022
Using Hotel Data to predict High Value And Potential VIP Guests

Description Using hotel data and AI to predict high value guests and potential VIP guests. Hotel can leverage on prediction resutls to run more effect

HCG 12 Feb 14, 2022
Pytorch Lightning code guideline for conferences

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

Pytorch Lightning 1k Jan 02, 2023
GE2340 project source code without credentials.

GE2340-Project-Public GE2340 project source code without credentials. Run the bot.py to start the bot Telegram: @jasperwong_ge2340_bot If the bot does

0 Feb 10, 2022
the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet]

BGNet This repository contains the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet] Environment Python 3.6.* C

3DCV developer 87 Nov 29, 2022
ICCV2021 - Mining Contextual Information Beyond Image for Semantic Segmentation

Introduction The official repository for "Mining Contextual Information Beyond Image for Semantic Segmentation". Our full code has been merged into ss

55 Nov 09, 2022
This application explain how we can easily integrate Deepface framework with Python Django application

deepface_suite This application explain how we can easily integrate Deepface framework with Python Django application install redis cache install requ

Mohamed Naji Aboo 3 Apr 18, 2022
Learning Representational Invariances for Data-Efficient Action Recognition

Learning Representational Invariances for Data-Efficient Action Recognition Official PyTorch implementation for Learning Representational Invariances

Virginia Tech Vision and Learning Lab 27 Nov 22, 2022