Currently, I am at a beginner level, so if you notice any mistakes, please let me know.


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


def read_input_data():
    x_train,y_train = [],[]
    with open('trainingdata.txt','r') as file:
        data = [list(map(float, line.strip().split(','))) for line in file]
    data = np.array(data)
    # Identify minimum charge time for full charge
    full_charge= 8.0
    filtered_data = data[data[:,1]!=full_charge]
    x_train = filtered_data[:, 0].reshape(-1, 1)  # Charging time
    y_train=filtered_data[:, 1]  # Battery life

    return x_train,y_train

def prediction_model(x,y):
    lr_model = LinearRegression()
    lr_model.fit(x,y)
    return lr_model

def prediction(model,t):
    return model.predict(np.array([[t]]))[0]
    
if __name__ == '__main__':
    x_train,y_train = read_input_data()
    lrr_model = prediction_model(x_train,y_train)
    timeCharged = float(input().strip())
    if timeCharged>4:
        result = 8.00
    else:
        result = prediction(lrr_model,timeCharged)
    print(round(result,2))