Machine Learning Models were applied to predict the mass of the brain based on gender, age ranges, and head size.

Overview

Brain Weight in Humans

Variations of head sizes and brain weights in humans

Kaggle dataset obtained from this link by Anubhab Swain.


Image obtained from Matthew Butler.

Models implemented by Anne Livia.

Context

This dataset was compiled using a medical study conducted on a group of people.

Content:

This dataset shows a few variations of head sizes and masses of brains, it also consists additional gender and age group columns.

  • Gender: 1 for Male, 2 for Female
  • Age Range: 1 represents > 18 years of age, 2 represents < 18 years of age
  • Head Size(cm^3): Head volume in cubic centimetres
  • Brain Weight(grams): - Mass of brains in grams

Question to be answered:

Predict the masses of the brains using the data, and later compare it with the actual masses of brains mentioned in the dataset.

Software Information

  • Python
  • Pandas
  • Scikit-learn
  • Matplotlib
  • Seaborn

Trained Models

  • Linear Regression:
    • R2 (only Head Size(cm^3) considered) = 0.73
    • R2 (All three attributes) = 0.74
  • Multi-layer Perceptron:
    • R2 (All three attributes) = 0.74
  • Decision Tree:
    • R2 (All three attributes) = 0.69
Owner
Anne Livia
Undergraduate student in Information Systems at UFPA
Anne Livia
Rafael Project- Classifying rockets to different types using data science algorithms.

Rocket-Classify Rafael Project- Classifying rockets to different types using data science algorithms. In this project we received data base with data

Hadassah Engel 5 Sep 18, 2021
DeepOBS: A Deep Learning Optimizer Benchmark Suite

DeepOBS - A Deep Learning Optimizer Benchmark Suite DeepOBS is a benchmarking suite that drastically simplifies, automates and improves the evaluation

Aaron Bahde 7 May 12, 2020
Single Image Random Dot Stereogram for Tensorflow

TensorFlow-SIRDS Single Image Random Dot Stereogram for Tensorflow SIRDS is a means to present 3D data in a 2D image. It allows for scientific data di

Greg Peatfield 5 Aug 10, 2022
Official implementation for TTT++: When Does Self-supervised Test-time Training Fail or Thrive

TTT++ This is an official implementation for TTT++: When Does Self-supervised Test-time Training Fail or Thrive? TL;DR: Online Feature Alignment + Str

VITA lab at EPFL 39 Dec 25, 2022
Best Practices on Recommendation Systems

Recommenders What's New (February 4, 2021) We have a new relase Recommenders 2021.2! It comes with lots of bug fixes, optimizations and 3 new algorith

Microsoft 14.8k Jan 03, 2023
Trainable Bilateral Filter Layer (PyTorch)

Trainable Bilateral Filter Layer (PyTorch) This repository contains our GPU-accelerated trainable bilateral filter layer (three spatial and one range

FabianWagner 26 Dec 25, 2022
QKeras: a quantization deep learning library for Tensorflow Keras

QKeras github.com/google/qkeras QKeras 0.8 highlights: Automatic quantization using QKeras; Stochastic behavior (including stochastic rouding) is disa

Google 437 Jan 03, 2023
CCPD: a diverse and well-annotated dataset for license plate detection and recognition

CCPD (Chinese City Parking Dataset, ECCV) UPdate on 10/03/2019. CCPD Dataset is now updated. We are confident that images in subsets of CCPD is much m

detectRecog 1.8k Dec 30, 2022
Code for intrusion detection system (IDS) development using CNN models and transfer learning

Intrusion-Detection-System-Using-CNN-and-Transfer-Learning This is the code for the paper entitled "A Transfer Learning and Optimized CNN Based Intrus

Western OC2 Lab 38 Dec 12, 2022
A collection of Google research projects related to Federated Learning and Federated Analytics.

Federated Research Federated Research is a collection of research projects related to Federated Learning and Federated Analytics. Federated learning i

Google Research 483 Jan 05, 2023
Official re-implementation of the Calibrated Adversarial Refinement model described in the paper Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

Official re-implementation of the Calibrated Adversarial Refinement model described in the paper Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

Elias Kassapis 31 Nov 22, 2022
Implementation of DropLoss for Long-Tail Instance Segmentation in Pytorch

[AAAI 2021]DropLoss for Long-Tail Instance Segmentation [AAAI 2021] DropLoss for Long-Tail Instance Segmentation Ting-I Hsieh*, Esther Robb*, Hwann-Tz

Tim 37 Dec 02, 2022
Official code of ICCV2021 paper "Residual Attention: A Simple but Effective Method for Multi-Label Recognition"

CSRA This is the official code of ICCV 2021 paper: Residual Attention: A Simple But Effective Method for Multi-Label Recoginition Demo, Train and Vali

163 Dec 22, 2022
This repo is duplication of jwyang/faster-rcnn.pytorch

Faster RCNN Pytorch This repo is duplication of jwyang/faster-rcnn.pytorch C/C++ code are removed and easier to study. Python 3.8.5 Ubuntu 20.04.1 LTS

Kim Jihwan 1 Jan 14, 2022
An implementation for the loss function proposed in Decoupled Contrastive Loss paper.

Decoupled-Contrastive-Learning This repository is an implementation for the loss function proposed in Decoupled Contrastive Loss paper. Requirements P

Ramin Nakhli 71 Dec 04, 2022
SGoLAM - Simultaneous Goal Localization and Mapping

SGoLAM - Simultaneous Goal Localization and Mapping PyTorch implementation of the MultiON runner-up entry, SGoLAM: Simultaneous Goal Localization and

10 Jan 05, 2023
TensorFlow implementation of ENet

TensorFlow-ENet TensorFlow implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. This model was tested on th

Kwotsin 255 Oct 17, 2022
Geneva is an artificial intelligence tool that defeats censorship by exploiting bugs in censors

Geneva is an artificial intelligence tool that defeats censorship by exploiting bugs in censors

Kevin Bock 1.5k Jan 06, 2023
Real time sign language recognition

The proposed work aims at converting american sign language gestures into English that can be understood by everyone in real time.

Mohit Kaushik 6 Jun 13, 2022
[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

EMI-Group 175 Dec 30, 2022