import pandas as pd
from sklearn.linear_model import LinearRegression
import scikitplot as skplt
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
df = pd.read_csv('alterationa1.csv')
model = LinearRegression()
model.max_iter = 12000
XT = []
yt = []
def fitthemodel():
XT = df[[
'BlueAPCP_a', 'BlueAPC', 'BlueTPCP_a', 'BlueTPC', 'BlueCPC_c',
'BlueEIEP_a', 'RedAPCP_a', 'RedAPC', 'RedTPCP_a', 'RedTPC', 'RedCPC_c',
'RedEIEP_a'
]].values
yt = df['BlueFinalDiff'].values
model.fit(XT, yt)
#Training Data
XP = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
while True:
fitthemodel()
XP[0][0] = float(input("BlueAPCP_a: "))
XP[0][1] = float(input("BlueAPC: "))
