hobbyist

for the exact code: https://github.com/animeesh/game-rock-paper-scissor code  random lib is used to get the random value  import random #input_list = ["Rock", "Paper", "Scissor"] while True:     input_user = input("Select the input: ")     print (input_user)      possible_action=["rock","paper","scissor"]     computer_action=random.choice(possible_action)          print(f"\nyou chose :{input_user},computer chose :{computer_action}.\n")     #hear starts the real technique     if input_user ==computer_action:         print(f"both player selected {input_user}.hence its a tie!")     elif input_user== "rock":         if computer_action=="scissor":             print("rock smashes scissor! you win")         else:             print("paper covers the rock! you lose") ...

dataset: https://www.kaggle.com/fmendes/fmendesdat263xdemos/download ......................................................................................................................................................... import pandas as  pd import matplotlib.pyplot as plt import numpy as np import pickle  from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression df_calories=pd.read_csv("ineuron/calories.csv") df_exercise=pd.read_csv("ineuron/exercise.csv") data=df_exercise.merge(df_calories, on='User_ID') #print(data.head()) #[:,0:-1] all rows and all expect last column (independent  variable) also feature column #data_x = data.iloc[:,:-1].values data_x= df.loc[:,[ 'Weight','Age','Duration']] data_y=df.iloc[:,-1] #[:,-1]all rows and only last column (dependent variable) also target column #data_y = data.iloc[:,-1].values x_train,x_test,y_train,y_test = train_test_split(data_x,data_y,test...