Multiple-Linear-Regression | simple python program | Machine Learning library

by mahesh147 Python Version: Current License: No License

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kandi X-RAY | Multiple-Linear-Regression Summary

Multiple-Linear-Regression is a Python library typically used in Artificial Intelligence, Machine Learning applications. Multiple-Linear-Regression has no bugs, it has no vulnerabilities and it has low support. However Multiple-Linear-Regression build file is not available. You can download it from GitHub.

A simple python program that implements a very basic Multiple Linear Regression model

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Multiple-Linear-Regression has a low active ecosystem.

It has 18 star(s) with 21 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

Multiple-Linear-Regression has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Multiple-Linear-Regression is current.

Quality

Multiple-Linear-Regression has no bugs reported.

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Multiple-Linear-Regression has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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Multiple-Linear-Regression does not have a standard license declared.

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Without a license, all rights are reserved, and you cannot use the library in your applications.

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Multiple-Linear-Regression releases are not available. You will need to build from source code and install.

Multiple-Linear-Regression has no build file. You will be need to create the build yourself to build the component from source.

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Multiple-Linear-Regression Key Features

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Multiple-Linear-Regression Examples and Code Snippets

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Community Discussions

Trending Discussions on Multiple-Linear-Regression

Multiple Linear Regression with TensorFlow

What is the best method for doing multiple regression on a reactive dataframe in r shiny?

R shiny how to "box" a simple text on a shiny page

How do I make a linear fit line in R with ggplot2 for multiple y-series?

Multiple Linear Regression - TypeError: fit() missing 1 required positional argument: 'y'

how to properly read a json file using pandas

How to calculate adjusted R2 score for non-linear models

ValueError: matmul when trying to fit sklearn's linear regressor to pandas dataframe instanses

NameError: name 'X' is not defined sklearn

sklearn OneHotEncoder outputs non-array object error

QUESTION

Multiple Linear Regression with TensorFlow

Asked 2021-Apr-14 at 21:44

I'm trying to perform a Multiple Linear Regression with TensorFlow and confront the results with statsmodels library.

I generated two random variables X1 and X2 (so that anyone can reproduce it) that will explain the Y variable. The X2 variable is completely useless for this regression, it's just noise with a big scale so that the coefficient will result not significant (p-val close to 1). At the end I should obtain a model that is basically. y_data = alpha + (0.25)x1 + (0.00)x2 + error.

I tried to adapt this code to my randomly generated data, but unfortunately, this is not working at all. This below is my try:

...

ANSWER

Answered 2021-Apr-14 at 21:44

The key issues with your code are the following:

While it is necessary to add a column of ones to the features matrix x_data before running the regression with statsmodels, this is not necessary when running the regression with tensorflow. This means that you are passing 3 features to tensorflow instead of 2, where the additional feature (the first column of x_data) is constant.
You are normalizing x_data after the first column of ones has already been added with x_data = sm.add_constant(x_data). As a column of ones has zero variance, after normalization you get a column of nan (as you are dividing by zero). This means that the first of the 3 features that you are passing to tensorflow is completely missing (i.e. it's always nan).
While statsmodels takes as inputs at first y and then X, tensorflow takes as inputs at first X and then y. This means that you have switched the features and target when running the regression in tensorflow.

I included a complete example below.

Source https://stackoverflow.com/questions/67095821

QUESTION

What is the best method for doing multiple regression on a reactive dataframe in r shiny?

Asked 2021-Jan-19 at 20:06

I have a reactive dataframe and I want the user to select the dependent and multiple independent variables from that reactive dataframe and return the regression outputs. Does anyone have recommendations on the best way to do multiple regression on a reactive dataframe in Shiny?

I see this thread: Using R Shiny for Multiple Linear Regression (SelectInput --> multiple=TRUE)

but I have commented showing the code doesn't work.

I also see this question: Perform multiple linear regression with variables based on shiny widget selection

But that is just a simple 1 to 1 regression.

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ANSWER

Answered 2021-Jan-19 at 19:08

Ok so I took a look at the first answer you said didn't work and I've slightly modified it to allow you to also select the dependent variable. You get an error when you include the dependent variable in the independent variables but I'm sure you could figure out a way to make sure that the dependent variable isn't included in the independent variables as a selection.

Source https://stackoverflow.com/questions/65796451

QUESTION

R shiny how to "box" a simple text on a shiny page

Asked 2021-Jan-05 at 23:31

I'm using documentation https://shiny.rstudio.com/tutorial/written-tutorial/lesson2/ and more precisely the following code to add a simple paragraph to my shiny page:

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ANSWER

Answered 2021-Jan-05 at 23:31

It's not about HTML it's CSS what you should look for. (;

For example you could copy & paste the CSS styling rules from the webpage you linked into you shiny app (not the recommend way but quick & dirty) to change the appearance of the code tag like so:

Source https://stackoverflow.com/questions/65587869

QUESTION

How do I make a linear fit line in R with ggplot2 for multiple y-series?

Asked 2020-Oct-05 at 18:10

This is my first foray into ggplot2 and I am experiencing difficulties. I'm trying to plot two series of random numbers against an incremented x-axis while showing linear regression for both. So far, I've succeeded in plotting the scatterplots, but the regression line keeps throwing errors. I know it's possible, but I'm missing something for executing the idea. I'm running RStudio Desktop version 1.3.1056, Water Lily with tidyverse loaded.

I know this works to display the scatterplot (I'm open to more elegant variants if suggested):

...

ANSWER

Answered 2020-Oct-05 at 18:10

Try this example. I believe is close to what you want. Please next time include data to reproduce your issue in a proper format using dput(). It looks like some variable is missing in your data or you are placing the wrong name. This example can be a good point to start (Also included some solutions using your real data from github):

Source https://stackoverflow.com/questions/64213270

QUESTION

Multiple Linear Regression - TypeError: fit() missing 1 required positional argument: 'y'

Asked 2020-Aug-31 at 13:45

import pandas as pd
from sklearn.linear_model import LinearRegression as lm

x = data_all[combi_list[0][1:]]
y = data_all[combi_list[0][0]]

lm.fit(x, y)

...

ANSWER

Answered 2020-Aug-31 at 13:45

In your code

Source https://stackoverflow.com/questions/63671815

QUESTION

how to properly read a json file using pandas

Asked 2020-Apr-08 at 20:24

Currently I',m developing a application to analyze stack overflow questions. So I got the data as a json file from the stack API and used following codes to read the data in json file as follows.

...

ANSWER

Answered 2020-Apr-08 at 20:24

Is this the output you are looking for?

Source https://stackoverflow.com/questions/61108658

QUESTION

How to calculate adjusted R2 score for non-linear models

Asked 2020-Mar-21 at 16:33

As mentioned in this post, the adjusted R2 score can be calculated via the following equation, where n is the number of samples, p is the number of parameters of the model.

...

ANSWER

Answered 2020-Mar-21 at 16:33

Short answer: don't do it (notice that all the posts you link to are about linear regression).

Long answer:

To start with, your definition that

p is the number of parameters of the model

is not correct. p is the number of explanatory variables used by the model (source).

In agreement to this definition, the post you have linked to actually uses X.shape[1] instead of model.coef_; the latter is suggested in a comment, and it is not correct either (see own comment there).

So, if you insist on computing the r-squared for your GBM model, you can always adjust the code from the linked post (after getting your predictions y_pred), taking also advantage of scikit-learn r2_score:

Source https://stackoverflow.com/questions/60785504

QUESTION

ValueError: matmul when trying to fit sklearn's linear regressor to pandas dataframe instanses

Asked 2020-Feb-06 at 19:50

I've been trying to perform a simple multivariate linear regression on some dummy data using sklearn. I initially passed sklearn.linear_model.LinearRegression.fit numpy arrays and kept getting this error:

ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 1)

which I thought was due to some mistake with the transposition of my arrays or something, so I pulled up a tutorial that used pandas dataframes and set out my code in the same way:

...

ANSWER

Answered 2019-Dec-06 at 02:58

You train your model using shape of (6,2) dataset.if you check shape of df

df.shape = (6,2).

And when you try to predict you are trying with different shape of dataset.

x.shape=(30,1)

what you need is to use the correct shape of dataset.Try this

Source https://stackoverflow.com/questions/59205523

QUESTION

NameError: name 'X' is not defined sklearn

Asked 2019-Jun-24 at 15:32

I am working through this multiple regression problem with this walk through however the code that starts at

section : #Treating categorical variables with One-hot-encoding at website: https://towardsdatascience.com/what-makes-a-movie-hit-a-jackpot-learning-from-data-with-multiple-linear-regression-339f6c1a7022

I ran code up to this point but it doesn't work for (X)

Actual code:

...

ANSWER

Answered 2019-Jun-24 at 15:30

Your code shouldn't be able to work because you left out 40 lines of codes that she wrote before that snippet of codes. She has defined X earlier. The codes can be obtained from Github.

Source https://stackoverflow.com/questions/56738929

QUESTION

sklearn OneHotEncoder outputs non-array object error

Asked 2019-Jun-04 at 15:36

I am working through a Python Machine Learning Course on Udemy on the following dataset (showing the first few rows only)

...

ANSWER

Answered 2019-Jun-04 at 12:51

In this case I think this is just a result of the mixed data types in your input and outputs. For example if you examine x:

Source https://stackoverflow.com/questions/56443049

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Multiple-Linear-Regression

You can download it from GitHub.
You can use Multiple-Linear-Regression like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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