linear-model

LAMA - automatic model creation framework

Basic exercises of chinese information processing

Detect whether a social media comment is insulting or derogatory

A demo showcasing linear regression, reduced-rank regression and a linear system identification algorithm for modelling time series -- and when to apply them.

Kernel Localized Linear Regression (KLLR)

MLJ.jl interface for GLM.jl models

Practical Extension of Introductory Statistics in Psychology using R

This is a mini project based on two pre-trained models Linear and Convolutional neural networks, and perform predictions in real time according to a draw area where you can draw the digit using the mouse.

TensorFlow2 digits classification - Linear Classifier and MLP

Tool demonstrating building credit risk models

Using the logistic regression model to solve some real-world problems, we can explorer different possibilities and outcomes from it.

[deprecated] Linear model module mingw

This repo explores tweets and government data related to flu vaccination

Splitting data into Linear Model, Exponential, Qaudratic, Additive seasonality , Additive Seasonality Quadratic , Multiplicative Seasonality, Multiplicative Additive Seasonality. Prediction for new time period

Assignment-06-Logistic-Regression. Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no") Attribute information For bank dataset Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no") 8. Missing Attribute Values: None

This repository contains the notes, codes, assignments, quizzes and other additional materials about the course "AI for Medical Prognosis" from DeepLearning.AI Coursera.

Housing Prediction Kaggle competition https://www.kaggle.com/c/house-prices-advanced-regression-techniques

Simple Linear Regression in Python using Scatter Plot. Update it with your dataset. This code will work for any dependency of form H:X->Y . I have attached a pdf document of my own notes for this model. Feel free to download. Note : The pdf is for help purpose. Any type of reuse or restructuring is subject to copyright.

find the chance of admission of a candidate based on his/her GRE Score (out of 340), TOEFL Score (out of 120), rating of the University (out of 5) in which he/she is trying to get admission, Strength of the SOP (out of 5), strength of the Letter Of Recommendation (out of 5), CGPA (out of 10) and the research experience (0 or 1) deployed on GCP