call out for reading dataset in various other format

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k nearest neighbors with well defined k value

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list of keyword in python

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one should not use this word as avariable in python it will throw an erroe ...............................................................................................  import keyword print (keyword.kwlist) output: ['False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']
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using your age and duration of workout finding how much calories you burned!

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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...
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