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love_calculator

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  # Python Tkinter GUI based "LOVE CALCULATOR" # import tkinter from tkinter import * # import random module import random # Creating GUI window root = Tk() # Defining the container size, width=400, height=240 root.geometry('400x240') # Title of the container root.title('Love Calculator????') # Function to calculate love percentage # between the user ans partner def calculate_love():     # value will contain digits between 0-9     st = '0123456789'     # result will be in double digits     digit = 2     temp = "".join(random.sample(st, digit))     result.config(text=temp) # Heading on Top heading = Label(root, text='Love Calculator - How much is he/she into you') heading.pack() # Slot/input for the first name slot1 = Label(root, text="Enter Your Name:") slot1.pack() name1 = Entry(root, border=5) name1.pack() # Slot/input for the partner name slot2 = Label(root, text="Enter Your Partner Name:") slot2.pack() name2 = ...
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3rd_fibonacci_series

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  n = int ( input ( " Enter a number up to generate fibonacci series: " )) a = 0 b = 1 while a < n :     print ( a , end = " " )     c = a + b     a = b #the value of a is updated to be equal to the value of b     b = c #the value of b is updated to be equal to the value of c ''' teration 1:     a = 0     b = 1     Since 0 < 20, the condition is true.     Output: 0 Iteration 2:     a = 1     b = 1     Since 1 < 20, the condition is true.     Output: 1 Iteration 3:     a = 1     b = 2     Since 1 < 20, the condition is true.     Output: 1 Iteration 4:     a = 2     b = 3     Since 2 < 20, the condition is true.     Output: 2 Iteration 5:     a = 3     b = 5     Since 3 < 20, the condition is true.     Output: 3 Iteration 6: ...
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ML Lab 6. Exp (sklearn decision tree)

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 import pandas as pd  from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn import metrics col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label'] pima = pd.read_csv("/content/diabetes.csv", header=None, names=col_names) pima.head() #Feature Selection #split dataset in features and target variable  feature_cols = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp','pedigree'] X = pima[feature_cols] y = pima.label X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) #Building Decision Tree Model clf = DecisionTreeClassifier() clf = clf.fit(X_train,y_train) y_pred = clf.predict(X_test) #Evaluating the Model print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) #Accuracy: 0.658008658008658 #Optimizing Deci...
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