linearRegression.py

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

#X = np.array(([1, 1], [1, 2], [2, 2], [2,3]))
#y = np.array(([ 6,  8,  9, 11]))

rows = int(input("Input number of rows: "))
cols = int(input("Input number of features (not including label): "))

print("input table (with y)")
X = list()
y = list()
for i in range(rows):
    inp = list(map(int, input().split()))
    X.append(inp[:-1])
    y.append(inp[-1])

X = np.array(tuple(X))
y = np.array((y))
#print(tuple([y]))

reg = LinearRegression().fit(X, y)

print("score", reg.score(X, y))
print("intercept", reg.intercept_)
print("coeff", reg.coef_)