I am trying to recreate this plot from this website in Python instead of R: Background I have a dataframe called boston (the popular educational boston housing dataset). I created a multiple linear regression model with some variables with statsmodels api below. Everything works. I create a dataframe of actual values from the boston dataset and predicted values from above

# Tag: linear-regression

## ValueError:Reshape your data either using array.reshape(-1, 1)if your data has a single feature or array.reshape(1, -1) if it contains a single sample

**the code predict the house price with polynomial regression model and fastapi **` make class have an one parameter and have a 4 value #The train_plynomial_model is a function that takes the Features and returns polynomial model The predict is a function that predict the house price “ I tried to put this sentencenewfeature=newfeature.reshape(-1, 1) Answer You should change features

## Clustering different sets of points with different linear relationships to each other in Python

I need to cluster groups of points with the same linear relationship, as per the code and figure below. Obviously, I wouldn’t have the points that way; I would just have the following x and y. Note the following: the points respect linear relationships with high slope, they present a slight separation from each other, and they all have the

## How to take the item from string and use it as a value

I have a string and I need to use this string to fit a model. However when I try try to do this, of course it raises an error which can be seen below. How can I use this string to fit the model? Answer You need to evaluate the string into a Python object, ie do See documentation of

## Why isn’t this Linear Regression line a straight line?

I have points with x and y coordinates I want to fit a straight line to with Linear Regression but I get a jagged looking line. I am attemting to use LinearRegression from sklearn. To create the points run a for loop that randomly crates one hundred points into an array that is 100 x 2 in shape. I slice

## How to deal with “ValueError: array must not contain infs or NaNs” while running regressions in python

I have a df with growth variables and often some initial values are 0, in which case it produces infinite values when the value moves from zero to non-zeros. i.e. when i run PanelOLS, i get an error message Is there a way to ignore these entries to continue with the regression without having to drop them and create a

## Constrained Multi-Linear Regression using Gekko

I have a multilinear regression problem where I have the prior information about the range of the output (dependent variable y) – The prediction must always lie in that range. I want to find the coefficients (upper and lower bound) of each feature (independent variables) in order to make the linear regression model restricted to the desired range of output.

## Simple Linear Regression not converging

In my attempt to dig deeper in the math behind machine learning models, I’m implementing a Ordinary Least Square algorithm in Python, using vectorization. My references are: https://github.com/paulaceccon/courses/blob/main/machine_learning_specialization/supervisioned_regression/2_multiple_regression.pdf https://www.geeksforgeeks.org/linear-regression-implementation-from-scratch-using-python/ This is what I have now: The problem I’m facing is that my weights keep increasing until I end up getting a bunch of nans. I’ve been trying to find out

## scikit-learn LinearRegression IndexError

I am working on a LinearRegression model to fill the null values for the feature Rupeepersqft. When I run the code, I am receiving this error: This is the code which gives me the error: This is how the data looks like: Can anyone help me out with this? Answer To assign values to a column in Pandas.DataFrame you should

## How do I create a linear regression model for a file that has about 500 columns as y variables? Working with Python

This code manually selects a column from the y table and then joins it to the X table. The program then performs linear regression. Any idea how to do this for every single column from the y table? Answer You can regress multiple y’s on the same X’s at the same time. Something like this should work produces The first