Create a linear model that can identify all the numbers in this picture.

The variables represent the cells in the matrix starting from $(0,0)$ all the way to $(4,2).$

Reduce the variables to the minimal set by looking at the p$-$values. The R$^2$ of the best model is $1.$ The code isn't working right.

import warnings

warnings.filterwarnings$("$ignore$")$

import pandas as pd

import numpy as np

import statsmodels.api as sm

# Name your matrix variables mij with i from $0$ to $4$ and j from $0$ to $2$

m$00=7$

m$01=2$

m$02=5$

m$10=3$

m$11=9$

m$12=1$

m$20=4$

m$21=6$

m$22=8$

m$30=0$

m$31=4$

m$32=3$

m$40=2$

m$41=1$

m$42=5$

# Your code starts after this line

data $=\{$

$\text{'}$x$0\text{'}$: $[0,0,0,1,1,1,2,2,2,3,3,3,4,4,4],$

$\text{'}$x$1\text{'}$: $[0,1,2,0,1,2,0,1,2,0,1,2,0,1,2],$

$\text{'}$y$\text{'}$: $[$m$00,$ m$01,$ m$02,$ m$10,$ m$11,$ m$12,$ m$20,$ m$21,$ m$22,$ m$30,$ m$31,$ m$32,$ m$40,$ m$41,$ m$42]$

$\}$

df $=$ pd$.$DataFrame$($data$)$

X $=$ df$[[\text{'}$x$0\text{'},\text{'}$x$1\text{'}]]$

X $=$ sm$.$add$\_$constant$($X$)$

y $=$ df$[\text{'}$y$\text{'}]$

result $=$ sm$.$OLS$($y$,$ X$).$fit$()$

# Your code ends before this line

print$($result$.$summary$())$