├── dfs.py
├── bfs.py
├── N_Queen.py
├── hangman_art.py
├── hangman.py
├── waterjug.py
├── waterjugbfs.py
├── MLPClassifier.py
├── hangman_words.py
└── MLPClassifier.ipynb


/dfs.py:
--------------------------------------------------------------------------------
 1 | graph= {
 2 | 'A' : ['B', 'C'],
 3 | 'B' : ['D', 'E'],
 4 | 'C' : ['F'],
 5 | 'D' : [],
 6 | 'E' : ['F'],
 7 | 'F' : [],
 8 | }
 9 | visited = set()
10 | def dfs(visited, graph, node):
11 |     if node not in visited:
12 |         print(node)
13 |         visited.add(node)
14 |         for neighbour in graph[node]:
15 |             dfs(visited, graph, neighbour)
16 | print("Following is the Depth-First Search")
17 | dfs(visited, graph, 'A')
18 |     
19 | 


--------------------------------------------------------------------------------
/bfs.py:
--------------------------------------------------------------------------------
 1 | graph={
 2 |     'P':['Q','R','S'],
 3 |     'Q':['P','R'],
 4 |     'R':['P','Q','T'],
 5 |     'T':['R'],
 6 |     'S':['P']
 7 | }
 8 | visited=[]
 9 | queue=[]
10 | def bfs(visited,graph,node):
11 |     visited.append(node)
12 |     queue.append(node)
13 |     while queue:
14 |         m=queue.pop(0)
15 |         print(m,end=" ")
16 |         for neighbour in graph[m]:
17 |             if neighbour not in visited:
18 |                 visited.append(neighbour)
19 |                 queue.append(neighbour)
20 | print("following is the breadth first search")
21 | bfs(visited,graph,'P')
22 | 


--------------------------------------------------------------------------------
/N_Queen.py:
--------------------------------------------------------------------------------
 1 | class solution:
 2 |     def __init__(self):
 3 |         self.MAX = 20
 4 |         self.A = [0]*self.MAX
 5 |     def placement(self,i,j):
 6 |         for k in range(1,i):
 7 |             if(self.A[k] == j) or abs(self.A[k] -j) == abs(k-i):
 8 |                 return False
 9 |         print(self.A)
10 |         return True
11 |     def printplacedqueen(self,N):
12 |         print('Arrangement-->')
13 |         print()
14 | 
15 |         for i in range(1,N+1):
16 |             for j in range(1,N+1):
17 |                 if self.A[i] !=j:
18 |                     print('\t_',end =' ')
19 |                 else:
20 |                     print('\tQ',end = ' ')
21 |             print()
22 |             print()
23 | 
24 |     def N_Queens(self,i,j):
25 |         for k in range(1,N+1):
26 |             if self.placement(i,k):
27 |                 self.A[i] = k
28 |                 if i == N:
29 |                     self.printplacedqueen(N)
30 |                 else:
31 |                     self.N_Queens(i+1,N)
32 | N= int(input("enter the queens value"))
33 | obj = solution()
34 | obj.N_Queens(1,N)
35 | 


--------------------------------------------------------------------------------
/hangman_art.py:
--------------------------------------------------------------------------------
 1 | stages = ['''
 2 |   +---+
 3 |   |   |
 4 |   O   |
 5 |  /|\  |
 6 |  / \  |
 7 |       |
 8 | =========
 9 | ''', '''
10 |   +---+
11 |   |   |
12 |   O   |
13 |  /|\  |
14 |  /    |
15 |       |
16 | =========
17 | ''', '''
18 |   +---+
19 |   |   |
20 |   O   |
21 |  /|\  |
22 |       |
23 |       |
24 | =========
25 | ''', '''
26 |   +---+
27 |   |   |
28 |   O   |
29 |  /|   |
30 |       |
31 |       |
32 | =========''', '''
33 |   +---+
34 |   |   |
35 |   O   |
36 |   |   |
37 |       |
38 |       |
39 | =========
40 | ''', '''
41 |   +---+
42 |   |   |
43 |   O   |
44 |       |
45 |       |
46 |       |
47 | =========
48 | ''', '''
49 |   +---+
50 |   |   |
51 |       |
52 |       |
53 |       |
54 |       |
55 | =========
56 | ''']
57 | 
58 | logo = ''' 
59 |  _                                             
60 | | |                                            
61 | | |__   __ _ _ __   __ _ _ __ ___   __ _ _ __  
62 | | '_ \ / _` | '_ \ / _` | '_ ` _ \ / _` | '_ \ 
63 | | | | | (_| | | | | (_| | | | | | | (_| | | | |
64 | |_| |_|\__,_|_| |_|\__, |_| |_| |_|\__,_|_| |_|
65 |                     __/ |                      
66 |                    |___/    '''
67 | 
68 |                                                                     
69 |                                                                     


--------------------------------------------------------------------------------
/hangman.py:
--------------------------------------------------------------------------------
 1 | import random
 2 | 
 3 | from hangman_words import word_list
 4 | from hangman_art import stages, logo
 5 | chosen_word=random.choice(word_list)
 6 | lives=6
 7 | print(f'Psst, the solution is {chosen_word}.')
 8 | print(logo)
 9 | print(len(stages))
10 | display=[]
11 | guesses=[]
12 | for i in range(len(chosen_word)):
13 |     display.append("_")
14 | while "_" in display and lives >0:
15 |     guess=input("Guess a letter:").lower()
16 |     if len(guess)==1 and guess.isalpha():
17 |         if guess in guessed:
18 |             print("letter already printed")
19 |             continue
20 |         guessed.append(guess)
21 |         for i in range(len(chosen_word)):
22 |             letter = chosen_word[i]
23 |             if  letter==guess:
24 |                 display[i]=letter
25 |         if guess not in chosen_word:
26 |             if lives>0:
27 |                 lives = lives-1
28 |                 if lives==0:
29 |                     print(stages[lives])
30 |                     print(f'the solution is {chosen_word}.')
31 |                     print("You loose")
32 |                     exit(1)
33 |         print(f"{' '.join(display)}")
34 |         print(lives)
35 |         print(stages[lives])
36 |     else:
37 |         print("guess should be a character rather than a word")
38 | else:
39 |     print("you have won")
40 | 


--------------------------------------------------------------------------------
/waterjug.py:
--------------------------------------------------------------------------------
 1 | import math
 2 | a = int(input("Enter Jug A capacity: "))
 3 | b = int(input("Enter jug B capacity: "))
 4 | ai = int(input("Initially water in jug A: "))
 5 | bi = int(input("Initially water in Jug B: "))
 6 | af = int(input("Final state of Jug A: "))
 7 | bf = int(input("Final state of Jug B: "))
 8 | if a<=0 or b<=0:
 9 |     print("Jug capacities must be positive")
10 |     exit(1)
11 | if ai < 0 or bi < 0 or af < 0 or bf < 0:
12 |     print("Negative values are not allowed")
13 |     exit(1)
14 | def wjug(a,b,ai,bi,af,bf):
15 |     print("List of operations you can do:\n")
16 |     print("1.Fill Jug A completely")
17 |     print("2.Fill Jug B completely")
18 |     print("3.Empty Jug A completely")
19 |     print("4.Empty Jug B completely")
20 |     print("5.Pour from Jug A till Jug B is full or A becomes empty")
21 |     print("6.Pour from Jug B till Jug A is full or B becomes empty")
22 |     print("7.Pour all from Jug B to Jug A")
23 |     print("8.Pour all from Jug A to Jug B")
24 | 
25 |     while ai!=af or bi!=bf:
26 |         op=int(input("Enter type operatiuon(1-8):"))
27 |         if op==1:
28 |             ai=a
29 |         elif op==2:
30 |             bi=b
31 |         elif op==3:
32 |             ai=0
33 |         elif op==4:
34 |             bi=o
35 |         elif op==5:
36 |             pour_amount= min(ai,b-bi)
37 |             ai-=pour_amount
38 |             bi+=pour_amount
39 |         elif op==6:
40 |             pour_amount= min(bi,a-ai)
41 |             bi-=pour_amount
42 |             ai+=pour_amount
43 |         elif op==7:
44 |             pour_amount=min(bi,a-ai)
45 |             ai+=pour_amount
46 |             bi-=pour_amount
47 |         elif op==8:
48 |             pour_amount= min(ai,b-bi)
49 |             bi+=pour_amount
50 |             ai-=pour_amount
51 |         else:
52 |             print("Invalid opertaion please choose a number between 1 and 8")
53 |             continue
54 |         print(f"Jug A:{ai}, Jug B{bi}")
55 |         if ai==af and bi==bf:
56 |             print("Final state reached: Jug A =", ai, ", Jug B =",bi)
57 |             return
58 |         print("Final state reached: Jug A =", ai, ",Jug B =", bi)
59 |         
60 | gcd= math.gcd(a,b)
61 | if(af<=a and bf<=b) and (af%gcd==bf%gcd==0):
62 |     wjug(a,b,ai,bi,af,bf)
63 | else:
64 |     print("The final state  is not achievable with given capabilities ")
65 |     exit(1)
66 |     
67 | 


--------------------------------------------------------------------------------
/waterjugbfs.py:
--------------------------------------------------------------------------------
 1 | import math
 2 | from collections import deque
 3 | 
 4 | ''' Input capacities and initial/final states for jugs'''
 5 | a = int(input("Enter Jug A Capacity: "))
 6 | b = int(input("Enter Jug B Capacity: "))
 7 | ai = int(input("Initially Water in Jug A: "))
 8 | bi = int(input("Initially Water in Jug B: "))
 9 | af = int(input("Final State of Jug A: "))
10 | bf = int(input("Final State of Jug B: "))
11 | 
12 | # Check for negative values and whether initial state is equal to final state
13 | if a <= 0 or b <= 0:
14 |     print("Jug capacities must be positive.")
15 |     exit(1)
16 | if ai < 0 or bi < 0 or af < 0 or bf < 0:
17 |     print("Negative values are not allowed.")
18 |     exit(1)
19 | if ai==af and bi==bf:
20 |     print(f"initial state is already the final state: juga{ai} and jugb={bi}")
21 |     exit()
22 | # Define the water jug solver function using BFS
23 | def bfs_wjug(a, b, ai, bi, af, bf):
24 |     visited = set()
25 |     queue = deque([(ai, bi, [])])  # (Jug A state, Jug B state, List of operations)
26 | 
27 |     while queue:
28 |         curr_ai, curr_bi, operations = queue.popleft()
29 | 
30 |         if (curr_ai, curr_bi) in visited:
31 |             continue
32 |         visited.add((curr_ai, curr_bi))
33 | 
34 |         # Check if the final state is reached
35 |         if curr_ai == af and curr_bi == bf:
36 |             for i, op in enumerate(operations):
37 |                 print(f"Step {i + 1}: {op}")
38 |             print(f"Final State Reached: Jug A = {curr_ai}, Jug B = {curr_bi}")
39 |             return
40 | 
41 |         # List of possible operations
42 |         possible_operations = [
43 |             (a, curr_bi, "Fill Jug A"),  # Fill Jug A
44 |             (curr_ai, b, "Fill Jug B"),  # Fill Jug B
45 |             (0, curr_bi, "Empty Jug A"),  # Empty Jug A
46 |             (curr_ai, 0, "Empty Jug B"),  # Empty Jug B
47 |             (curr_ai - min(curr_ai, b - curr_bi), curr_bi + min(curr_ai, b - curr_bi), "Pour from A to B"),  # Pour A to B
48 |             (curr_ai + min(curr_bi, a - curr_ai), curr_bi - min(curr_bi, a - curr_ai), "Pour from B to A"),  # Pour B to A
49 |         ]
50 | 
51 |         # Add each possible operation to the queue
52 |         for next_ai, next_bi, op in possible_operations:
53 |             if (next_ai, next_bi) not in visited:
54 |                 queue.append((next_ai, next_bi, operations + [op]))
55 | 
56 |     print("No solution found.")
57 |     return
58 | 
59 | # Check if the final state can be achievable using GCD
60 | gcd = math.gcd(a, b)
61 | 
62 | if (af <= a and bf <= b) and (af % gcd == bf % gcd == 0):
63 |     bfs_wjug(a, b, ai, bi, af, bf)
64 | else:
65 |     print("The final state is not achievable with the given capacities.")
66 |     exit()
67 | 


--------------------------------------------------------------------------------
/MLPClassifier.py:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env python
  2 | # coding: utf-8
  3 | 
  4 | # In[1]:
  5 | 
  6 | 
  7 | import numpy as np
  8 | import pandas as pd
  9 | data = pd.read_csv('HR_comma_sep.csv')
 10 | data.head()
 11 | 
 12 | 
 13 | # In[2]:
 14 | 
 15 | 
 16 | data.info()
 17 | 
 18 | 
 19 | # In[3]:
 20 | 
 21 | 
 22 | data['Departments'].value_counts()
 23 | 
 24 | 
 25 | # In[4]:
 26 | 
 27 | 
 28 | data['Departments'].unique()
 29 | 
 30 | 
 31 | # In[5]:
 32 | 
 33 | 
 34 | data['salary'].unique()
 35 | 
 36 | 
 37 | # In[6]:
 38 | 
 39 | 
 40 | from sklearn import preprocessing
 41 | le = preprocessing.LabelEncoder()
 42 | print(le)
 43 | data['salary']=le.fit_transform(data['salary'])
 44 | data['Departments']=le.fit_transform(data['Departments'])
 45 | 
 46 | 
 47 | # In[7]:
 48 | 
 49 | 
 50 | data['salary'].unique()
 51 | 
 52 | 
 53 | # In[8]:
 54 | 
 55 | 
 56 | X=data.iloc[:,:-1]
 57 | X
 58 | 
 59 | 
 60 | # In[9]:
 61 | 
 62 | 
 63 | X=data[['satisfaction_level','last_evaluation','number_project','average_montly_hours','time_spend_company','Work_accident','promotion_last_5years','Departments','salary']]
 64 | y=data['left']
 65 | 
 66 | 
 67 | from sklearn.model_selection import train_test_split
 68 | X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.3, random_state=42)
 69 | 
 70 | 
 71 | # In[10]:
 72 | 
 73 | 
 74 | X_train
 75 | 
 76 | 
 77 | # In[11]:
 78 | 
 79 | 
 80 | y_train
 81 | 
 82 | 
 83 | # In[16]:
 84 | 
 85 | 
 86 | from sklearn.neural_network import MLPClassifier
 87 | clf = MLPClassifier(
 88 |                     hidden_layer_sizes=(6,5),
 89 |                     random_state=5,
 90 |                     verbose=True,
 91 |                     learning_rate_init=0.01,
 92 |                    )
 93 | clf.fit(X_train,y_train)
 94 | 
 95 | 
 96 | # In[17]:
 97 | 
 98 | 
 99 | ypred=clf.predict(X_test)
100 | from sklearn.metrics import accuracy_score
101 | accuracy_score(y_test,ypred)
102 | 
103 | 
104 | # In[18]:
105 | 
106 | 
107 | X_test.shape
108 | 
109 | 
110 | # In[19]:
111 | 
112 | 
113 | n=pd.DataFrame({
114 |     'satisfaction_level':[0.78],
115 |     'last_evaluation':[0.53],
116 |     'number_project':[2],
117 |     'average_montly_hours':[157],
118 |     'time_spend_company':[3],
119 |     'Work_accident':[0],
120 |     'promotion_last_5years':[0],
121 |     'Departments':[1],
122 |     'salary':[1]
123 |     
124 |     
125 | })
126 | 
127 | 
128 | # In[20]:
129 | 
130 | 
131 | new_data=clf.predict(n)
132 | print(new_data)
133 | 
134 | 
135 | # In[22]:
136 | 
137 | 
138 | from sklearn.metrics import classification_report
139 | print(classification_report(y_test, ypred))
140 | 
141 | 
142 | # In[23]:
143 | 
144 | 
145 | from sklearn.metrics import confusion_matrix
146 | ypred = clf.predict(X_test)
147 | print(confusion_matrix(y_test, ypred))
148 | 
149 | 
150 | # In[24]:
151 | 
152 | 
153 | print(y_train.value_counts())
154 | print(y_test.value_counts())
155 | 
156 | 
157 | # In[ ]:
158 | 
159 | 
160 | 
161 | 
162 | 


--------------------------------------------------------------------------------
/hangman_words.py:
--------------------------------------------------------------------------------
  1 | word_list = [
  2 | 'abruptly', 
  3 | 'absurd', 
  4 | 'abyss', 
  5 | 'affix', 
  6 | 'askew', 
  7 | 'avenue', 
  8 | 'awkward', 
  9 | 'axiom', 
 10 | 'azure', 
 11 | 'bagpipes', 
 12 | 'bandwagon', 
 13 | 'banjo', 
 14 | 'bayou', 
 15 | 'beekeeper',  
 16 | 'blitz', 
 17 | 'blizzard', 
 18 | 'boggle', 
 19 | 'bookworm', 
 20 | 'boxcar', 
 21 | 'boxful', 
 22 | 'buckaroo', 
 23 | 'buffalo', 
 24 | 'buffoon', 
 25 | 'buxom', 
 26 | 'buzzard', 
 27 | 'buzzing', 
 28 | 'buzzwords', 
 29 | 'caliph', 
 30 | 'cobweb', 
 31 | 'cockiness', 
 32 | 'croquet', 
 33 | 'crypt', 
 34 | 'curacao', 
 35 | 'cycle', 
 36 | 'daiquiri', 
 37 | 'dirndl', 
 38 | 'disavow', 
 39 | 'dizzying', 
 40 | 'duplex', 
 41 | 'dwarves', 
 42 | 'embezzle', 
 43 | 'equip', 
 44 | 'espionage', 
 45 | 'euouae', 
 46 | 'exodus', 
 47 | 'faking', 
 48 | 'fishhook', 
 49 | 'fixable', 
 50 | 'fjord', 
 51 | 'flapjack', 
 52 | 'flopping', 
 53 | 'fluffiness', 
 54 | 'flyby', 
 55 | 'foxglove', 
 56 | 'frazzled', 
 57 | 'frizzled', 
 58 | 'fuchsia', 
 59 | 'funny', 
 60 | 'gabby', 
 61 | 'galaxy', 
 62 | 'galvanize', 
 63 | 'gazebo', 
 64 | 'giaour', 
 65 | 'gizmo', 
 66 | 'glowworm', 
 67 | 'glyph', 
 68 | 'gnarly', 
 69 | 'gnostic', 
 70 | 'gossip', 
 71 | 'grogginess', 
 72 | 'haiku', 
 73 | 'haphazard', 
 74 | 'hyphen', 
 75 | 'iatrogenic', 
 76 | 'icebox', 
 77 | 'injury', 
 78 | 'ivory', 
 79 | 'ivy', 
 80 | 'jackpot', 
 81 | 'jaundice', 
 82 | 'jawbreaker', 
 83 | 'jaywalk', 
 84 | 'jazziest', 
 85 | 'jazzy', 
 86 | 'jelly', 
 87 | 'jigsaw', 
 88 | 'jinx', 
 89 | 'jiujitsu', 
 90 | 'jockey', 
 91 | 'jogging', 
 92 | 'joking', 
 93 | 'jovial', 
 94 | 'joyful', 
 95 | 'juicy', 
 96 | 'jukebox', 
 97 | 'jumbo', 
 98 | 'kayak', 
 99 | 'kazoo', 
100 | 'keyhole', 
101 | 'khaki', 
102 | 'kilobyte', 
103 | 'kiosk', 
104 | 'kitsch', 
105 | 'kiwifruit', 
106 | 'klutz', 
107 | 'knapsack', 
108 | 'larynx', 
109 | 'lengths', 
110 | 'lucky', 
111 | 'luxury', 
112 | 'lymph', 
113 | 'marquis', 
114 | 'matrix', 
115 | 'megahertz', 
116 | 'microwave', 
117 | 'mnemonic', 
118 | 'mystify', 
119 | 'naphtha', 
120 | 'nightclub', 
121 | 'nowadays', 
122 | 'numbskull', 
123 | 'nymph', 
124 | 'onyx', 
125 | 'ovary', 
126 | 'oxidize', 
127 | 'oxygen', 
128 | 'pajama', 
129 | 'peekaboo', 
130 | 'phlegm', 
131 | 'pixel', 
132 | 'pizazz', 
133 | 'pneumonia', 
134 | 'polka', 
135 | 'pshaw', 
136 | 'psyche', 
137 | 'puppy', 
138 | 'puzzling', 
139 | 'quartz', 
140 | 'queue', 
141 | 'quips', 
142 | 'quixotic', 
143 | 'quiz', 
144 | 'quizzes', 
145 | 'quorum', 
146 | 'razzmatazz', 
147 | 'rhubarb', 
148 | 'rhythm', 
149 | 'rickshaw', 
150 | 'schnapps', 
151 | 'scratch', 
152 | 'shiv', 
153 | 'snazzy', 
154 | 'sphinx', 
155 | 'spritz', 
156 | 'squawk', 
157 | 'staff', 
158 | 'strength', 
159 | 'strengths', 
160 | 'stretch', 
161 | 'stronghold', 
162 | 'stymied', 
163 | 'subway', 
164 | 'swivel', 
165 | 'syndrome', 
166 | 'thriftless', 
167 | 'thumbscrew', 
168 | 'topaz', 
169 | 'transcript', 
170 | 'transgress', 
171 | 'transplant', 
172 | 'triphthong', 
173 | 'twelfth', 
174 | 'twelfths', 
175 | 'unknown', 
176 | 'unworthy', 
177 | 'unzip', 
178 | 'uptown', 
179 | 'vaporize', 
180 | 'vixen', 
181 | 'vodka', 
182 | 'voodoo', 
183 | 'vortex', 
184 | 'voyeurism', 
185 | 'walkway', 
186 | 'waltz', 
187 | 'wave', 
188 | 'wavy', 
189 | 'waxy', 
190 | 'wellspring', 
191 | 'wheezy', 
192 | 'whiskey', 
193 | 'whizzing', 
194 | 'whomever', 
195 | 'wimpy', 
196 | 'witchcraft', 
197 | 'wizard', 
198 | 'woozy', 
199 | 'wristwatch', 
200 | 'wyvern', 
201 | 'xylophone', 
202 | 'yachtsman', 
203 | 'yippee', 
204 | 'yoked', 
205 | 'youthful', 
206 | 'yummy', 
207 | 'zephyr', 
208 | 'zigzag', 
209 | 'zigzagging', 
210 | 'zilch', 
211 | 'zipper', 
212 | 'zodiac', 
213 | 'zombie', 
214 | ]
215 | 


--------------------------------------------------------------------------------
/MLPClassifier.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "id": "6bbb6e0d",
   7 |    "metadata": {},
   8 |    "outputs": [
   9 |     {
  10 |      "data": {
  11 |       "text/html": [
  12 |        "<div>\n",
  13 |        "<style scoped>\n",
  14 |        "    .dataframe tbody tr th:only-of-type {\n",
  15 |        "        vertical-align: middle;\n",
  16 |        "    }\n",
  17 |        "\n",
  18 |        "    .dataframe tbody tr th {\n",
  19 |        "        vertical-align: top;\n",
  20 |        "    }\n",
  21 |        "\n",
  22 |        "    .dataframe thead th {\n",
  23 |        "        text-align: right;\n",
  24 |        "    }\n",
  25 |        "</style>\n",
  26 |        "<table border=\"1\" class=\"dataframe\">\n",
  27 |        "  <thead>\n",
  28 |        "    <tr style=\"text-align: right;\">\n",
  29 |        "      <th></th>\n",
  30 |        "      <th>satisfaction_level</th>\n",
  31 |        "      <th>last_evaluation</th>\n",
  32 |        "      <th>number_project</th>\n",
  33 |        "      <th>average_montly_hours</th>\n",
  34 |        "      <th>time_spend_company</th>\n",
  35 |        "      <th>Work_accident</th>\n",
  36 |        "      <th>left</th>\n",
  37 |        "      <th>promotion_last_5years</th>\n",
  38 |        "      <th>Departments</th>\n",
  39 |        "      <th>salary</th>\n",
  40 |        "    </tr>\n",
  41 |        "  </thead>\n",
  42 |        "  <tbody>\n",
  43 |        "    <tr>\n",
  44 |        "      <th>0</th>\n",
  45 |        "      <td>0.38</td>\n",
  46 |        "      <td>0.53</td>\n",
  47 |        "      <td>2</td>\n",
  48 |        "      <td>157</td>\n",
  49 |        "      <td>3</td>\n",
  50 |        "      <td>0</td>\n",
  51 |        "      <td>1</td>\n",
  52 |        "      <td>0</td>\n",
  53 |        "      <td>sales</td>\n",
  54 |        "      <td>low</td>\n",
  55 |        "    </tr>\n",
  56 |        "    <tr>\n",
  57 |        "      <th>1</th>\n",
  58 |        "      <td>0.80</td>\n",
  59 |        "      <td>0.86</td>\n",
  60 |        "      <td>5</td>\n",
  61 |        "      <td>262</td>\n",
  62 |        "      <td>6</td>\n",
  63 |        "      <td>0</td>\n",
  64 |        "      <td>1</td>\n",
  65 |        "      <td>0</td>\n",
  66 |        "      <td>sales</td>\n",
  67 |        "      <td>medium</td>\n",
  68 |        "    </tr>\n",
  69 |        "    <tr>\n",
  70 |        "      <th>2</th>\n",
  71 |        "      <td>0.11</td>\n",
  72 |        "      <td>0.88</td>\n",
  73 |        "      <td>7</td>\n",
  74 |        "      <td>272</td>\n",
  75 |        "      <td>4</td>\n",
  76 |        "      <td>0</td>\n",
  77 |        "      <td>1</td>\n",
  78 |        "      <td>0</td>\n",
  79 |        "      <td>sales</td>\n",
  80 |        "      <td>medium</td>\n",
  81 |        "    </tr>\n",
  82 |        "    <tr>\n",
  83 |        "      <th>3</th>\n",
  84 |        "      <td>0.72</td>\n",
  85 |        "      <td>0.87</td>\n",
  86 |        "      <td>5</td>\n",
  87 |        "      <td>223</td>\n",
  88 |        "      <td>5</td>\n",
  89 |        "      <td>0</td>\n",
  90 |        "      <td>1</td>\n",
  91 |        "      <td>0</td>\n",
  92 |        "      <td>sales</td>\n",
  93 |        "      <td>low</td>\n",
  94 |        "    </tr>\n",
  95 |        "    <tr>\n",
  96 |        "      <th>4</th>\n",
  97 |        "      <td>0.37</td>\n",
  98 |        "      <td>0.52</td>\n",
  99 |        "      <td>2</td>\n",
 100 |        "      <td>159</td>\n",
 101 |        "      <td>3</td>\n",
 102 |        "      <td>0</td>\n",
 103 |        "      <td>1</td>\n",
 104 |        "      <td>0</td>\n",
 105 |        "      <td>sales</td>\n",
 106 |        "      <td>low</td>\n",
 107 |        "    </tr>\n",
 108 |        "  </tbody>\n",
 109 |        "</table>\n",
 110 |        "</div>"
 111 |       ],
 112 |       "text/plain": [
 113 |        "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
 114 |        "0                0.38             0.53               2                   157   \n",
 115 |        "1                0.80             0.86               5                   262   \n",
 116 |        "2                0.11             0.88               7                   272   \n",
 117 |        "3                0.72             0.87               5                   223   \n",
 118 |        "4                0.37             0.52               2                   159   \n",
 119 |        "\n",
 120 |        "   time_spend_company  Work_accident  left  promotion_last_5years Departments  \\\n",
 121 |        "0                   3              0     1                      0       sales   \n",
 122 |        "1                   6              0     1                      0       sales   \n",
 123 |        "2                   4              0     1                      0       sales   \n",
 124 |        "3                   5              0     1                      0       sales   \n",
 125 |        "4                   3              0     1                      0       sales   \n",
 126 |        "\n",
 127 |        "   salary  \n",
 128 |        "0     low  \n",
 129 |        "1  medium  \n",
 130 |        "2  medium  \n",
 131 |        "3     low  \n",
 132 |        "4     low  "
 133 |       ]
 134 |      },
 135 |      "execution_count": 1,
 136 |      "metadata": {},
 137 |      "output_type": "execute_result"
 138 |     }
 139 |    ],
 140 |    "source": [
 141 |     "import numpy as np\n",
 142 |     "import pandas as pd\n",
 143 |     "data = pd.read_csv('HR_comma_sep.csv')\n",
 144 |     "data.head()"
 145 |    ]
 146 |   },
 147 |   {
 148 |    "cell_type": "code",
 149 |    "execution_count": 2,
 150 |    "id": "6da0e596",
 151 |    "metadata": {},
 152 |    "outputs": [
 153 |     {
 154 |      "name": "stdout",
 155 |      "output_type": "stream",
 156 |      "text": [
 157 |       "<class 'pandas.core.frame.DataFrame'>\n",
 158 |       "RangeIndex: 14999 entries, 0 to 14998\n",
 159 |       "Data columns (total 10 columns):\n",
 160 |       " #   Column                 Non-Null Count  Dtype  \n",
 161 |       "---  ------                 --------------  -----  \n",
 162 |       " 0   satisfaction_level     14999 non-null  float64\n",
 163 |       " 1   last_evaluation        14999 non-null  float64\n",
 164 |       " 2   number_project         14999 non-null  int64  \n",
 165 |       " 3   average_montly_hours   14999 non-null  int64  \n",
 166 |       " 4   time_spend_company     14999 non-null  int64  \n",
 167 |       " 5   Work_accident          14999 non-null  int64  \n",
 168 |       " 6   left                   14999 non-null  int64  \n",
 169 |       " 7   promotion_last_5years  14999 non-null  int64  \n",
 170 |       " 8   Departments            14999 non-null  object \n",
 171 |       " 9   salary                 14999 non-null  object \n",
 172 |       "dtypes: float64(2), int64(6), object(2)\n",
 173 |       "memory usage: 1.1+ MB\n"
 174 |      ]
 175 |     }
 176 |    ],
 177 |    "source": [
 178 |     "data.info()"
 179 |    ]
 180 |   },
 181 |   {
 182 |    "cell_type": "code",
 183 |    "execution_count": 3,
 184 |    "id": "48033e0a",
 185 |    "metadata": {},
 186 |    "outputs": [
 187 |     {
 188 |      "data": {
 189 |       "text/plain": [
 190 |        "sales          4140\n",
 191 |        "technical      2720\n",
 192 |        "support        2229\n",
 193 |        "IT             1227\n",
 194 |        "product_mng     902\n",
 195 |        "marketing       858\n",
 196 |        "RandD           787\n",
 197 |        "accounting      767\n",
 198 |        "hr              739\n",
 199 |        "management      630\n",
 200 |        "Name: Departments, dtype: int64"
 201 |       ]
 202 |      },
 203 |      "execution_count": 3,
 204 |      "metadata": {},
 205 |      "output_type": "execute_result"
 206 |     }
 207 |    ],
 208 |    "source": [
 209 |     "data['Departments'].value_counts()"
 210 |    ]
 211 |   },
 212 |   {
 213 |    "cell_type": "code",
 214 |    "execution_count": 4,
 215 |    "id": "22e62021",
 216 |    "metadata": {},
 217 |    "outputs": [
 218 |     {
 219 |      "data": {
 220 |       "text/plain": [
 221 |        "array(['sales', 'accounting', 'hr', 'technical', 'support', 'management',\n",
 222 |        "       'IT', 'product_mng', 'marketing', 'RandD'], dtype=object)"
 223 |       ]
 224 |      },
 225 |      "execution_count": 4,
 226 |      "metadata": {},
 227 |      "output_type": "execute_result"
 228 |     }
 229 |    ],
 230 |    "source": [
 231 |     "data['Departments'].unique()"
 232 |    ]
 233 |   },
 234 |   {
 235 |    "cell_type": "code",
 236 |    "execution_count": 5,
 237 |    "id": "ab71dd46",
 238 |    "metadata": {},
 239 |    "outputs": [
 240 |     {
 241 |      "data": {
 242 |       "text/plain": [
 243 |        "array(['low', 'medium', 'high'], dtype=object)"
 244 |       ]
 245 |      },
 246 |      "execution_count": 5,
 247 |      "metadata": {},
 248 |      "output_type": "execute_result"
 249 |     }
 250 |    ],
 251 |    "source": [
 252 |     "data['salary'].unique()"
 253 |    ]
 254 |   },
 255 |   {
 256 |    "cell_type": "code",
 257 |    "execution_count": 6,
 258 |    "id": "9e6c666e",
 259 |    "metadata": {},
 260 |    "outputs": [
 261 |     {
 262 |      "name": "stdout",
 263 |      "output_type": "stream",
 264 |      "text": [
 265 |       "LabelEncoder()\n"
 266 |      ]
 267 |     }
 268 |    ],
 269 |    "source": [
 270 |     "from sklearn import preprocessing\n",
 271 |     "le = preprocessing.LabelEncoder()\n",
 272 |     "print(le)\n",
 273 |     "data['salary']=le.fit_transform(data['salary'])\n",
 274 |     "data['Departments']=le.fit_transform(data['Departments'])\n"
 275 |    ]
 276 |   },
 277 |   {
 278 |    "cell_type": "code",
 279 |    "execution_count": 7,
 280 |    "id": "41104ac7",
 281 |    "metadata": {},
 282 |    "outputs": [
 283 |     {
 284 |      "data": {
 285 |       "text/plain": [
 286 |        "array([1, 2, 0])"
 287 |       ]
 288 |      },
 289 |      "execution_count": 7,
 290 |      "metadata": {},
 291 |      "output_type": "execute_result"
 292 |     }
 293 |    ],
 294 |    "source": [
 295 |     "data['salary'].unique()"
 296 |    ]
 297 |   },
 298 |   {
 299 |    "cell_type": "code",
 300 |    "execution_count": 8,
 301 |    "id": "1e489dbb",
 302 |    "metadata": {},
 303 |    "outputs": [
 304 |     {
 305 |      "data": {
 306 |       "text/html": [
 307 |        "<div>\n",
 308 |        "<style scoped>\n",
 309 |        "    .dataframe tbody tr th:only-of-type {\n",
 310 |        "        vertical-align: middle;\n",
 311 |        "    }\n",
 312 |        "\n",
 313 |        "    .dataframe tbody tr th {\n",
 314 |        "        vertical-align: top;\n",
 315 |        "    }\n",
 316 |        "\n",
 317 |        "    .dataframe thead th {\n",
 318 |        "        text-align: right;\n",
 319 |        "    }\n",
 320 |        "</style>\n",
 321 |        "<table border=\"1\" class=\"dataframe\">\n",
 322 |        "  <thead>\n",
 323 |        "    <tr style=\"text-align: right;\">\n",
 324 |        "      <th></th>\n",
 325 |        "      <th>satisfaction_level</th>\n",
 326 |        "      <th>last_evaluation</th>\n",
 327 |        "      <th>number_project</th>\n",
 328 |        "      <th>average_montly_hours</th>\n",
 329 |        "      <th>time_spend_company</th>\n",
 330 |        "      <th>Work_accident</th>\n",
 331 |        "      <th>left</th>\n",
 332 |        "      <th>promotion_last_5years</th>\n",
 333 |        "      <th>Departments</th>\n",
 334 |        "    </tr>\n",
 335 |        "  </thead>\n",
 336 |        "  <tbody>\n",
 337 |        "    <tr>\n",
 338 |        "      <th>0</th>\n",
 339 |        "      <td>0.38</td>\n",
 340 |        "      <td>0.53</td>\n",
 341 |        "      <td>2</td>\n",
 342 |        "      <td>157</td>\n",
 343 |        "      <td>3</td>\n",
 344 |        "      <td>0</td>\n",
 345 |        "      <td>1</td>\n",
 346 |        "      <td>0</td>\n",
 347 |        "      <td>7</td>\n",
 348 |        "    </tr>\n",
 349 |        "    <tr>\n",
 350 |        "      <th>1</th>\n",
 351 |        "      <td>0.80</td>\n",
 352 |        "      <td>0.86</td>\n",
 353 |        "      <td>5</td>\n",
 354 |        "      <td>262</td>\n",
 355 |        "      <td>6</td>\n",
 356 |        "      <td>0</td>\n",
 357 |        "      <td>1</td>\n",
 358 |        "      <td>0</td>\n",
 359 |        "      <td>7</td>\n",
 360 |        "    </tr>\n",
 361 |        "    <tr>\n",
 362 |        "      <th>2</th>\n",
 363 |        "      <td>0.11</td>\n",
 364 |        "      <td>0.88</td>\n",
 365 |        "      <td>7</td>\n",
 366 |        "      <td>272</td>\n",
 367 |        "      <td>4</td>\n",
 368 |        "      <td>0</td>\n",
 369 |        "      <td>1</td>\n",
 370 |        "      <td>0</td>\n",
 371 |        "      <td>7</td>\n",
 372 |        "    </tr>\n",
 373 |        "    <tr>\n",
 374 |        "      <th>3</th>\n",
 375 |        "      <td>0.72</td>\n",
 376 |        "      <td>0.87</td>\n",
 377 |        "      <td>5</td>\n",
 378 |        "      <td>223</td>\n",
 379 |        "      <td>5</td>\n",
 380 |        "      <td>0</td>\n",
 381 |        "      <td>1</td>\n",
 382 |        "      <td>0</td>\n",
 383 |        "      <td>7</td>\n",
 384 |        "    </tr>\n",
 385 |        "    <tr>\n",
 386 |        "      <th>4</th>\n",
 387 |        "      <td>0.37</td>\n",
 388 |        "      <td>0.52</td>\n",
 389 |        "      <td>2</td>\n",
 390 |        "      <td>159</td>\n",
 391 |        "      <td>3</td>\n",
 392 |        "      <td>0</td>\n",
 393 |        "      <td>1</td>\n",
 394 |        "      <td>0</td>\n",
 395 |        "      <td>7</td>\n",
 396 |        "    </tr>\n",
 397 |        "    <tr>\n",
 398 |        "      <th>...</th>\n",
 399 |        "      <td>...</td>\n",
 400 |        "      <td>...</td>\n",
 401 |        "      <td>...</td>\n",
 402 |        "      <td>...</td>\n",
 403 |        "      <td>...</td>\n",
 404 |        "      <td>...</td>\n",
 405 |        "      <td>...</td>\n",
 406 |        "      <td>...</td>\n",
 407 |        "      <td>...</td>\n",
 408 |        "    </tr>\n",
 409 |        "    <tr>\n",
 410 |        "      <th>14994</th>\n",
 411 |        "      <td>0.40</td>\n",
 412 |        "      <td>0.57</td>\n",
 413 |        "      <td>2</td>\n",
 414 |        "      <td>151</td>\n",
 415 |        "      <td>3</td>\n",
 416 |        "      <td>0</td>\n",
 417 |        "      <td>1</td>\n",
 418 |        "      <td>0</td>\n",
 419 |        "      <td>8</td>\n",
 420 |        "    </tr>\n",
 421 |        "    <tr>\n",
 422 |        "      <th>14995</th>\n",
 423 |        "      <td>0.37</td>\n",
 424 |        "      <td>0.48</td>\n",
 425 |        "      <td>2</td>\n",
 426 |        "      <td>160</td>\n",
 427 |        "      <td>3</td>\n",
 428 |        "      <td>0</td>\n",
 429 |        "      <td>1</td>\n",
 430 |        "      <td>0</td>\n",
 431 |        "      <td>8</td>\n",
 432 |        "    </tr>\n",
 433 |        "    <tr>\n",
 434 |        "      <th>14996</th>\n",
 435 |        "      <td>0.37</td>\n",
 436 |        "      <td>0.53</td>\n",
 437 |        "      <td>2</td>\n",
 438 |        "      <td>143</td>\n",
 439 |        "      <td>3</td>\n",
 440 |        "      <td>0</td>\n",
 441 |        "      <td>1</td>\n",
 442 |        "      <td>0</td>\n",
 443 |        "      <td>8</td>\n",
 444 |        "    </tr>\n",
 445 |        "    <tr>\n",
 446 |        "      <th>14997</th>\n",
 447 |        "      <td>0.11</td>\n",
 448 |        "      <td>0.96</td>\n",
 449 |        "      <td>6</td>\n",
 450 |        "      <td>280</td>\n",
 451 |        "      <td>4</td>\n",
 452 |        "      <td>0</td>\n",
 453 |        "      <td>1</td>\n",
 454 |        "      <td>0</td>\n",
 455 |        "      <td>8</td>\n",
 456 |        "    </tr>\n",
 457 |        "    <tr>\n",
 458 |        "      <th>14998</th>\n",
 459 |        "      <td>0.37</td>\n",
 460 |        "      <td>0.52</td>\n",
 461 |        "      <td>2</td>\n",
 462 |        "      <td>158</td>\n",
 463 |        "      <td>3</td>\n",
 464 |        "      <td>0</td>\n",
 465 |        "      <td>1</td>\n",
 466 |        "      <td>0</td>\n",
 467 |        "      <td>8</td>\n",
 468 |        "    </tr>\n",
 469 |        "  </tbody>\n",
 470 |        "</table>\n",
 471 |        "<p>14999 rows × 9 columns</p>\n",
 472 |        "</div>"
 473 |       ],
 474 |       "text/plain": [
 475 |        "       satisfaction_level  last_evaluation  number_project  \\\n",
 476 |        "0                    0.38             0.53               2   \n",
 477 |        "1                    0.80             0.86               5   \n",
 478 |        "2                    0.11             0.88               7   \n",
 479 |        "3                    0.72             0.87               5   \n",
 480 |        "4                    0.37             0.52               2   \n",
 481 |        "...                   ...              ...             ...   \n",
 482 |        "14994                0.40             0.57               2   \n",
 483 |        "14995                0.37             0.48               2   \n",
 484 |        "14996                0.37             0.53               2   \n",
 485 |        "14997                0.11             0.96               6   \n",
 486 |        "14998                0.37             0.52               2   \n",
 487 |        "\n",
 488 |        "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
 489 |        "0                       157                   3              0     1   \n",
 490 |        "1                       262                   6              0     1   \n",
 491 |        "2                       272                   4              0     1   \n",
 492 |        "3                       223                   5              0     1   \n",
 493 |        "4                       159                   3              0     1   \n",
 494 |        "...                     ...                 ...            ...   ...   \n",
 495 |        "14994                   151                   3              0     1   \n",
 496 |        "14995                   160                   3              0     1   \n",
 497 |        "14996                   143                   3              0     1   \n",
 498 |        "14997                   280                   4              0     1   \n",
 499 |        "14998                   158                   3              0     1   \n",
 500 |        "\n",
 501 |        "       promotion_last_5years  Departments  \n",
 502 |        "0                          0            7  \n",
 503 |        "1                          0            7  \n",
 504 |        "2                          0            7  \n",
 505 |        "3                          0            7  \n",
 506 |        "4                          0            7  \n",
 507 |        "...                      ...          ...  \n",
 508 |        "14994                      0            8  \n",
 509 |        "14995                      0            8  \n",
 510 |        "14996                      0            8  \n",
 511 |        "14997                      0            8  \n",
 512 |        "14998                      0            8  \n",
 513 |        "\n",
 514 |        "[14999 rows x 9 columns]"
 515 |       ]
 516 |      },
 517 |      "execution_count": 8,
 518 |      "metadata": {},
 519 |      "output_type": "execute_result"
 520 |     }
 521 |    ],
 522 |    "source": [
 523 |     "X=data.iloc[:,:-1]\n",
 524 |     "X"
 525 |    ]
 526 |   },
 527 |   {
 528 |    "cell_type": "code",
 529 |    "execution_count": 9,
 530 |    "id": "2787d7a6",
 531 |    "metadata": {},
 532 |    "outputs": [],
 533 |    "source": [
 534 |     "X=data[['satisfaction_level','last_evaluation','number_project','average_montly_hours','time_spend_company','Work_accident','promotion_last_5years','Departments','salary']]\n",
 535 |     "y=data['left']\n",
 536 |     "\n",
 537 |     "\n",
 538 |     "from sklearn.model_selection import train_test_split\n",
 539 |     "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.3, random_state=42)"
 540 |    ]
 541 |   },
 542 |   {
 543 |    "cell_type": "code",
 544 |    "execution_count": 10,
 545 |    "id": "4484d1e7",
 546 |    "metadata": {},
 547 |    "outputs": [
 548 |     {
 549 |      "data": {
 550 |       "text/html": [
 551 |        "<div>\n",
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 553 |        "    .dataframe tbody tr th:only-of-type {\n",
 554 |        "        vertical-align: middle;\n",
 555 |        "    }\n",
 556 |        "\n",
 557 |        "    .dataframe tbody tr th {\n",
 558 |        "        vertical-align: top;\n",
 559 |        "    }\n",
 560 |        "\n",
 561 |        "    .dataframe thead th {\n",
 562 |        "        text-align: right;\n",
 563 |        "    }\n",
 564 |        "</style>\n",
 565 |        "<table border=\"1\" class=\"dataframe\">\n",
 566 |        "  <thead>\n",
 567 |        "    <tr style=\"text-align: right;\">\n",
 568 |        "      <th></th>\n",
 569 |        "      <th>satisfaction_level</th>\n",
 570 |        "      <th>last_evaluation</th>\n",
 571 |        "      <th>number_project</th>\n",
 572 |        "      <th>average_montly_hours</th>\n",
 573 |        "      <th>time_spend_company</th>\n",
 574 |        "      <th>Work_accident</th>\n",
 575 |        "      <th>promotion_last_5years</th>\n",
 576 |        "      <th>Departments</th>\n",
 577 |        "      <th>salary</th>\n",
 578 |        "    </tr>\n",
 579 |        "  </thead>\n",
 580 |        "  <tbody>\n",
 581 |        "    <tr>\n",
 582 |        "      <th>12602</th>\n",
 583 |        "      <td>0.10</td>\n",
 584 |        "      <td>0.84</td>\n",
 585 |        "      <td>7</td>\n",
 586 |        "      <td>250</td>\n",
 587 |        "      <td>4</td>\n",
 588 |        "      <td>0</td>\n",
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 593 |        "    <tr>\n",
 594 |        "      <th>4889</th>\n",
 595 |        "      <td>0.57</td>\n",
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 599 |        "      <td>3</td>\n",
 600 |        "      <td>1</td>\n",
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 602 |        "      <td>4</td>\n",
 603 |        "      <td>2</td>\n",
 604 |        "    </tr>\n",
 605 |        "    <tr>\n",
 606 |        "      <th>1572</th>\n",
 607 |        "      <td>0.39</td>\n",
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 616 |        "    </tr>\n",
 617 |        "    <tr>\n",
 618 |        "      <th>13375</th>\n",
 619 |        "      <td>0.91</td>\n",
 620 |        "      <td>0.68</td>\n",
 621 |        "      <td>4</td>\n",
 622 |        "      <td>132</td>\n",
 623 |        "      <td>4</td>\n",
 624 |        "      <td>0</td>\n",
 625 |        "      <td>0</td>\n",
 626 |        "      <td>0</td>\n",
 627 |        "      <td>2</td>\n",
 628 |        "    </tr>\n",
 629 |        "    <tr>\n",
 630 |        "      <th>879</th>\n",
 631 |        "      <td>0.82</td>\n",
 632 |        "      <td>0.97</td>\n",
 633 |        "      <td>5</td>\n",
 634 |        "      <td>263</td>\n",
 635 |        "      <td>5</td>\n",
 636 |        "      <td>0</td>\n",
 637 |        "      <td>0</td>\n",
 638 |        "      <td>9</td>\n",
 639 |        "      <td>2</td>\n",
 640 |        "    </tr>\n",
 641 |        "    <tr>\n",
 642 |        "      <th>...</th>\n",
 643 |        "      <td>...</td>\n",
 644 |        "      <td>...</td>\n",
 645 |        "      <td>...</td>\n",
 646 |        "      <td>...</td>\n",
 647 |        "      <td>...</td>\n",
 648 |        "      <td>...</td>\n",
 649 |        "      <td>...</td>\n",
 650 |        "      <td>...</td>\n",
 651 |        "      <td>...</td>\n",
 652 |        "    </tr>\n",
 653 |        "    <tr>\n",
 654 |        "      <th>5191</th>\n",
 655 |        "      <td>0.52</td>\n",
 656 |        "      <td>0.96</td>\n",
 657 |        "      <td>4</td>\n",
 658 |        "      <td>246</td>\n",
 659 |        "      <td>3</td>\n",
 660 |        "      <td>0</td>\n",
 661 |        "      <td>0</td>\n",
 662 |        "      <td>8</td>\n",
 663 |        "      <td>1</td>\n",
 664 |        "    </tr>\n",
 665 |        "    <tr>\n",
 666 |        "      <th>13418</th>\n",
 667 |        "      <td>0.49</td>\n",
 668 |        "      <td>0.65</td>\n",
 669 |        "      <td>4</td>\n",
 670 |        "      <td>233</td>\n",
 671 |        "      <td>7</td>\n",
 672 |        "      <td>0</td>\n",
 673 |        "      <td>0</td>\n",
 674 |        "      <td>7</td>\n",
 675 |        "      <td>2</td>\n",
 676 |        "    </tr>\n",
 677 |        "    <tr>\n",
 678 |        "      <th>5390</th>\n",
 679 |        "      <td>0.66</td>\n",
 680 |        "      <td>0.73</td>\n",
 681 |        "      <td>5</td>\n",
 682 |        "      <td>249</td>\n",
 683 |        "      <td>2</td>\n",
 684 |        "      <td>0</td>\n",
 685 |        "      <td>0</td>\n",
 686 |        "      <td>8</td>\n",
 687 |        "      <td>2</td>\n",
 688 |        "    </tr>\n",
 689 |        "    <tr>\n",
 690 |        "      <th>860</th>\n",
 691 |        "      <td>0.79</td>\n",
 692 |        "      <td>1.00</td>\n",
 693 |        "      <td>4</td>\n",
 694 |        "      <td>218</td>\n",
 695 |        "      <td>5</td>\n",
 696 |        "      <td>0</td>\n",
 697 |        "      <td>0</td>\n",
 698 |        "      <td>7</td>\n",
 699 |        "      <td>1</td>\n",
 700 |        "    </tr>\n",
 701 |        "    <tr>\n",
 702 |        "      <th>7270</th>\n",
 703 |        "      <td>0.98</td>\n",
 704 |        "      <td>0.86</td>\n",
 705 |        "      <td>2</td>\n",
 706 |        "      <td>219</td>\n",
 707 |        "      <td>4</td>\n",
 708 |        "      <td>0</td>\n",
 709 |        "      <td>0</td>\n",
 710 |        "      <td>7</td>\n",
 711 |        "      <td>1</td>\n",
 712 |        "    </tr>\n",
 713 |        "  </tbody>\n",
 714 |        "</table>\n",
 715 |        "<p>10499 rows × 9 columns</p>\n",
 716 |        "</div>"
 717 |       ],
 718 |       "text/plain": [
 719 |        "       satisfaction_level  last_evaluation  number_project  \\\n",
 720 |        "12602                0.10             0.84               7   \n",
 721 |        "4889                 0.57             0.68               4   \n",
 722 |        "1572                 0.39             0.48               2   \n",
 723 |        "13375                0.91             0.68               4   \n",
 724 |        "879                  0.82             0.97               5   \n",
 725 |        "...                   ...              ...             ...   \n",
 726 |        "5191                 0.52             0.96               4   \n",
 727 |        "13418                0.49             0.65               4   \n",
 728 |        "5390                 0.66             0.73               5   \n",
 729 |        "860                  0.79             1.00               4   \n",
 730 |        "7270                 0.98             0.86               2   \n",
 731 |        "\n",
 732 |        "       average_montly_hours  time_spend_company  Work_accident  \\\n",
 733 |        "12602                   250                   4              0   \n",
 734 |        "4889                    154                   3              1   \n",
 735 |        "1572                    154                   3              0   \n",
 736 |        "13375                   132                   4              0   \n",
 737 |        "879                     263                   5              0   \n",
 738 |        "...                     ...                 ...            ...   \n",
 739 |        "5191                    246                   3              0   \n",
 740 |        "13418                   233                   7              0   \n",
 741 |        "5390                    249                   2              0   \n",
 742 |        "860                     218                   5              0   \n",
 743 |        "7270                    219                   4              0   \n",
 744 |        "\n",
 745 |        "       promotion_last_5years  Departments  salary  \n",
 746 |        "12602                      0            6       1  \n",
 747 |        "4889                       0            4       2  \n",
 748 |        "1572                       0            9       1  \n",
 749 |        "13375                      0            0       2  \n",
 750 |        "879                        0            9       2  \n",
 751 |        "...                      ...          ...     ...  \n",
 752 |        "5191                       0            8       1  \n",
 753 |        "13418                      0            7       2  \n",
 754 |        "5390                       0            8       2  \n",
 755 |        "860                        0            7       1  \n",
 756 |        "7270                       0            7       1  \n",
 757 |        "\n",
 758 |        "[10499 rows x 9 columns]"
 759 |       ]
 760 |      },
 761 |      "execution_count": 10,
 762 |      "metadata": {},
 763 |      "output_type": "execute_result"
 764 |     }
 765 |    ],
 766 |    "source": [
 767 |     "X_train"
 768 |    ]
 769 |   },
 770 |   {
 771 |    "cell_type": "code",
 772 |    "execution_count": 11,
 773 |    "id": "2620323e",
 774 |    "metadata": {},
 775 |    "outputs": [
 776 |     {
 777 |      "data": {
 778 |       "text/plain": [
 779 |        "12602    1\n",
 780 |        "4889     0\n",
 781 |        "1572     1\n",
 782 |        "13375    0\n",
 783 |        "879      1\n",
 784 |        "        ..\n",
 785 |        "5191     0\n",
 786 |        "13418    0\n",
 787 |        "5390     0\n",
 788 |        "860      1\n",
 789 |        "7270     0\n",
 790 |        "Name: left, Length: 10499, dtype: int64"
 791 |       ]
 792 |      },
 793 |      "execution_count": 11,
 794 |      "metadata": {},
 795 |      "output_type": "execute_result"
 796 |     }
 797 |    ],
 798 |    "source": [
 799 |     "y_train"
 800 |    ]
 801 |   },
 802 |   {
 803 |    "cell_type": "code",
 804 |    "execution_count": 16,
 805 |    "id": "f0c0dbf9",
 806 |    "metadata": {},
 807 |    "outputs": [
 808 |     {
 809 |      "name": "stdout",
 810 |      "output_type": "stream",
 811 |      "text": [
 812 |       "Iteration 1, loss = 0.61512605\n",
 813 |       "Iteration 2, loss = 0.57545658\n",
 814 |       "Iteration 3, loss = 0.55823146\n",
 815 |       "Iteration 4, loss = 0.53011644\n",
 816 |       "Iteration 5, loss = 0.50549749\n",
 817 |       "Iteration 6, loss = 0.48004244\n",
 818 |       "Iteration 7, loss = 0.47915513\n",
 819 |       "Iteration 8, loss = 0.46239153\n",
 820 |       "Iteration 9, loss = 0.47441120\n",
 821 |       "Iteration 10, loss = 0.46241650\n",
 822 |       "Iteration 11, loss = 0.45068143\n",
 823 |       "Iteration 12, loss = 0.45071101\n",
 824 |       "Iteration 13, loss = 0.45213613\n",
 825 |       "Iteration 14, loss = 0.46049483\n",
 826 |       "Iteration 15, loss = 0.45897398\n",
 827 |       "Iteration 16, loss = 0.46170601\n",
 828 |       "Iteration 17, loss = 0.45527116\n",
 829 |       "Iteration 18, loss = 0.44996595\n",
 830 |       "Iteration 19, loss = 0.44982305\n",
 831 |       "Iteration 20, loss = 0.45384764\n",
 832 |       "Iteration 21, loss = 0.46981282\n",
 833 |       "Iteration 22, loss = 0.45010489\n",
 834 |       "Iteration 23, loss = 0.46852413\n",
 835 |       "Iteration 24, loss = 0.45242336\n",
 836 |       "Iteration 25, loss = 0.45769894\n",
 837 |       "Iteration 26, loss = 0.45074974\n",
 838 |       "Iteration 27, loss = 0.44067556\n",
 839 |       "Iteration 28, loss = 0.43205930\n",
 840 |       "Iteration 29, loss = 0.41680331\n",
 841 |       "Iteration 30, loss = 0.40752887\n",
 842 |       "Iteration 31, loss = 0.39186392\n",
 843 |       "Iteration 32, loss = 0.37054743\n",
 844 |       "Iteration 33, loss = 0.35797517\n",
 845 |       "Iteration 34, loss = 0.34539661\n",
 846 |       "Iteration 35, loss = 0.33669847\n",
 847 |       "Iteration 36, loss = 0.33052333\n",
 848 |       "Iteration 37, loss = 0.31754074\n",
 849 |       "Iteration 38, loss = 0.31470388\n",
 850 |       "Iteration 39, loss = 0.31036267\n",
 851 |       "Iteration 40, loss = 0.30717479\n",
 852 |       "Iteration 41, loss = 0.31789566\n",
 853 |       "Iteration 42, loss = 0.31782866\n",
 854 |       "Iteration 43, loss = 0.30641517\n",
 855 |       "Iteration 44, loss = 0.30123487\n",
 856 |       "Iteration 45, loss = 0.30474254\n",
 857 |       "Iteration 46, loss = 0.31051473\n",
 858 |       "Iteration 47, loss = 0.28624198\n",
 859 |       "Iteration 48, loss = 0.29316190\n",
 860 |       "Iteration 49, loss = 0.28463864\n",
 861 |       "Iteration 50, loss = 0.28582673\n",
 862 |       "Iteration 51, loss = 0.28378548\n",
 863 |       "Iteration 52, loss = 0.28098729\n",
 864 |       "Iteration 53, loss = 0.28513299\n",
 865 |       "Iteration 54, loss = 0.27992380\n",
 866 |       "Iteration 55, loss = 0.28034603\n",
 867 |       "Iteration 56, loss = 0.28447776\n",
 868 |       "Iteration 57, loss = 0.27920526\n",
 869 |       "Iteration 58, loss = 0.27545140\n",
 870 |       "Iteration 59, loss = 0.27418263\n",
 871 |       "Iteration 60, loss = 0.26921345\n",
 872 |       "Iteration 61, loss = 0.27200225\n",
 873 |       "Iteration 62, loss = 0.26757142\n",
 874 |       "Iteration 63, loss = 0.26824884\n",
 875 |       "Iteration 64, loss = 0.26115995\n",
 876 |       "Iteration 65, loss = 0.25875549\n",
 877 |       "Iteration 66, loss = 0.25919820\n",
 878 |       "Iteration 67, loss = 0.26178076\n",
 879 |       "Iteration 68, loss = 0.26228984\n",
 880 |       "Iteration 69, loss = 0.26001505\n",
 881 |       "Iteration 70, loss = 0.25199503\n",
 882 |       "Iteration 71, loss = 0.27545142\n",
 883 |       "Iteration 72, loss = 0.25545200\n",
 884 |       "Iteration 73, loss = 0.25464872\n",
 885 |       "Iteration 74, loss = 0.24550959\n",
 886 |       "Iteration 75, loss = 0.24336325\n",
 887 |       "Iteration 76, loss = 0.24215966\n",
 888 |       "Iteration 77, loss = 0.23850104\n",
 889 |       "Iteration 78, loss = 0.23889253\n",
 890 |       "Iteration 79, loss = 0.23109586\n",
 891 |       "Iteration 80, loss = 0.23399638\n",
 892 |       "Iteration 81, loss = 0.23802679\n",
 893 |       "Iteration 82, loss = 0.23385322\n",
 894 |       "Iteration 83, loss = 0.24284518\n",
 895 |       "Iteration 84, loss = 0.23134733\n",
 896 |       "Iteration 85, loss = 0.24330075\n",
 897 |       "Iteration 86, loss = 0.22532877\n",
 898 |       "Iteration 87, loss = 0.22568393\n",
 899 |       "Iteration 88, loss = 0.22081075\n",
 900 |       "Iteration 89, loss = 0.22846698\n",
 901 |       "Iteration 90, loss = 0.22502850\n",
 902 |       "Iteration 91, loss = 0.21784989\n",
 903 |       "Iteration 92, loss = 0.21292884\n",
 904 |       "Iteration 93, loss = 0.18813462\n",
 905 |       "Iteration 94, loss = 0.17769639\n",
 906 |       "Iteration 95, loss = 0.18582654\n",
 907 |       "Iteration 96, loss = 0.18127325\n",
 908 |       "Iteration 97, loss = 0.16734828\n",
 909 |       "Iteration 98, loss = 0.18286465\n",
 910 |       "Iteration 99, loss = 0.16981846\n",
 911 |       "Iteration 100, loss = 0.17330641\n",
 912 |       "Iteration 101, loss = 0.15969499\n",
 913 |       "Iteration 102, loss = 0.15829448\n",
 914 |       "Iteration 103, loss = 0.15677615\n",
 915 |       "Iteration 104, loss = 0.16861506\n",
 916 |       "Iteration 105, loss = 0.15665635\n",
 917 |       "Iteration 106, loss = 0.17290740\n",
 918 |       "Iteration 107, loss = 0.16313185\n",
 919 |       "Iteration 108, loss = 0.15127830\n",
 920 |       "Iteration 109, loss = 0.15304460\n",
 921 |       "Iteration 110, loss = 0.15963898\n",
 922 |       "Iteration 111, loss = 0.16963601\n",
 923 |       "Iteration 112, loss = 0.15063887\n",
 924 |       "Iteration 113, loss = 0.15393529\n",
 925 |       "Iteration 114, loss = 0.17528787\n",
 926 |       "Iteration 115, loss = 0.17271381\n",
 927 |       "Iteration 116, loss = 0.15426095\n",
 928 |       "Iteration 117, loss = 0.14971746\n",
 929 |       "Iteration 118, loss = 0.15078193\n",
 930 |       "Iteration 119, loss = 0.16200046\n",
 931 |       "Iteration 120, loss = 0.15242240\n",
 932 |       "Iteration 121, loss = 0.15700943\n",
 933 |       "Iteration 122, loss = 0.15836204\n",
 934 |       "Iteration 123, loss = 0.15847074\n",
 935 |       "Iteration 124, loss = 0.15069127\n",
 936 |       "Iteration 125, loss = 0.14876175\n",
 937 |       "Iteration 126, loss = 0.14993943\n",
 938 |       "Iteration 127, loss = 0.15168619\n",
 939 |       "Iteration 128, loss = 0.16245349\n",
 940 |       "Iteration 129, loss = 0.15119375\n",
 941 |       "Iteration 130, loss = 0.15576164\n",
 942 |       "Iteration 131, loss = 0.15837115\n",
 943 |       "Iteration 132, loss = 0.15242943\n",
 944 |       "Iteration 133, loss = 0.15235532\n",
 945 |       "Iteration 134, loss = 0.15423949\n",
 946 |       "Iteration 135, loss = 0.15300715\n",
 947 |       "Iteration 136, loss = 0.15038034\n",
 948 |       "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
 949 |      ]
 950 |     },
 951 |     {
 952 |      "data": {
 953 |       "text/html": [
 954 |        "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(6, 5), learning_rate_init=0.01,\n",
 955 |        "              random_state=5, verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(hidden_layer_sizes=(6, 5), learning_rate_init=0.01,\n",
 956 |        "              random_state=5, verbose=True)</pre></div></div></div></div></div>"
 957 |       ],
 958 |       "text/plain": [
 959 |        "MLPClassifier(hidden_layer_sizes=(6, 5), learning_rate_init=0.01,\n",
 960 |        "              random_state=5, verbose=True)"
 961 |       ]
 962 |      },
 963 |      "execution_count": 16,
 964 |      "metadata": {},
 965 |      "output_type": "execute_result"
 966 |     }
 967 |    ],
 968 |    "source": [
 969 |     "from sklearn.neural_network import MLPClassifier\n",
 970 |     "clf = MLPClassifier(\n",
 971 |     "                    hidden_layer_sizes=(6,5),\n",
 972 |     "                    random_state=5,\n",
 973 |     "                    verbose=True,\n",
 974 |     "                    learning_rate_init=0.01,\n",
 975 |     "                   )\n",
 976 |     "clf.fit(X_train,y_train)"
 977 |    ]
 978 |   },
 979 |   {
 980 |    "cell_type": "code",
 981 |    "execution_count": 17,
 982 |    "id": "a8d6f0bd",
 983 |    "metadata": {},
 984 |    "outputs": [
 985 |     {
 986 |      "data": {
 987 |       "text/plain": [
 988 |        "0.9386666666666666"
 989 |       ]
 990 |      },
 991 |      "execution_count": 17,
 992 |      "metadata": {},
 993 |      "output_type": "execute_result"
 994 |     }
 995 |    ],
 996 |    "source": [
 997 |     "ypred=clf.predict(X_test)\n",
 998 |     "from sklearn.metrics import accuracy_score\n",
 999 |     "accuracy_score(y_test,ypred)"
1000 |    ]
1001 |   },
1002 |   {
1003 |    "cell_type": "code",
1004 |    "execution_count": 18,
1005 |    "id": "08e0c1f2",
1006 |    "metadata": {},
1007 |    "outputs": [
1008 |     {
1009 |      "data": {
1010 |       "text/plain": [
1011 |        "(4500, 9)"
1012 |       ]
1013 |      },
1014 |      "execution_count": 18,
1015 |      "metadata": {},
1016 |      "output_type": "execute_result"
1017 |     }
1018 |    ],
1019 |    "source": [
1020 |     "X_test.shape"
1021 |    ]
1022 |   },
1023 |   {
1024 |    "cell_type": "code",
1025 |    "execution_count": 19,
1026 |    "id": "2d6b1dfe",
1027 |    "metadata": {},
1028 |    "outputs": [],
1029 |    "source": [
1030 |     "n=pd.DataFrame({\n",
1031 |     "    'satisfaction_level':[0.78],\n",
1032 |     "    'last_evaluation':[0.53],\n",
1033 |     "    'number_project':[2],\n",
1034 |     "    'average_montly_hours':[157],\n",
1035 |     "    'time_spend_company':[3],\n",
1036 |     "    'Work_accident':[0],\n",
1037 |     "    'promotion_last_5years':[0],\n",
1038 |     "    'Departments':[1],\n",
1039 |     "    'salary':[1]\n",
1040 |     "    \n",
1041 |     "    \n",
1042 |     "})"
1043 |    ]
1044 |   },
1045 |   {
1046 |    "cell_type": "code",
1047 |    "execution_count": 20,
1048 |    "id": "e9918395",
1049 |    "metadata": {},
1050 |    "outputs": [
1051 |     {
1052 |      "name": "stdout",
1053 |      "output_type": "stream",
1054 |      "text": [
1055 |       "[0]\n"
1056 |      ]
1057 |     }
1058 |    ],
1059 |    "source": [
1060 |     "new_data=clf.predict(n)\n",
1061 |     "print(new_data)"
1062 |    ]
1063 |   },
1064 |   {
1065 |    "cell_type": "code",
1066 |    "execution_count": 22,
1067 |    "id": "061b0348",
1068 |    "metadata": {},
1069 |    "outputs": [
1070 |     {
1071 |      "name": "stdout",
1072 |      "output_type": "stream",
1073 |      "text": [
1074 |       "              precision    recall  f1-score   support\n",
1075 |       "\n",
1076 |       "           0       0.97      0.95      0.96      3428\n",
1077 |       "           1       0.84      0.91      0.88      1072\n",
1078 |       "\n",
1079 |       "    accuracy                           0.94      4500\n",
1080 |       "   macro avg       0.91      0.93      0.92      4500\n",
1081 |       "weighted avg       0.94      0.94      0.94      4500\n",
1082 |       "\n"
1083 |      ]
1084 |     }
1085 |    ],
1086 |    "source": [
1087 |     "from sklearn.metrics import classification_report\n",
1088 |     "print(classification_report(y_test, ypred))"
1089 |    ]
1090 |   },
1091 |   {
1092 |    "cell_type": "code",
1093 |    "execution_count": 23,
1094 |    "id": "ebad03ae",
1095 |    "metadata": {},
1096 |    "outputs": [
1097 |     {
1098 |      "name": "stdout",
1099 |      "output_type": "stream",
1100 |      "text": [
1101 |       "[[3248  180]\n",
1102 |       " [  96  976]]\n"
1103 |      ]
1104 |     }
1105 |    ],
1106 |    "source": [
1107 |     "from sklearn.metrics import confusion_matrix\n",
1108 |     "ypred = clf.predict(X_test)\n",
1109 |     "print(confusion_matrix(y_test, ypred))"
1110 |    ]
1111 |   },
1112 |   {
1113 |    "cell_type": "code",
1114 |    "execution_count": 24,
1115 |    "id": "7c034037",
1116 |    "metadata": {},
1117 |    "outputs": [
1118 |     {
1119 |      "name": "stdout",
1120 |      "output_type": "stream",
1121 |      "text": [
1122 |       "0    8000\n",
1123 |       "1    2499\n",
1124 |       "Name: left, dtype: int64\n",
1125 |       "0    3428\n",
1126 |       "1    1072\n",
1127 |       "Name: left, dtype: int64\n"
1128 |      ]
1129 |     }
1130 |    ],
1131 |    "source": [
1132 |     "print(y_train.value_counts())\n",
1133 |     "print(y_test.value_counts())"
1134 |    ]
1135 |   },
1136 |   {
1137 |    "cell_type": "code",
1138 |    "execution_count": null,
1139 |    "id": "30cb511b",
1140 |    "metadata": {},
1141 |    "outputs": [],
1142 |    "source": []
1143 |   }
1144 |  ],
1145 |  "metadata": {
1146 |   "kernelspec": {
1147 |    "display_name": "Python 3 (ipykernel)",
1148 |    "language": "python",
1149 |    "name": "python3"
1150 |   },
1151 |   "language_info": {
1152 |    "codemirror_mode": {
1153 |     "name": "ipython",
1154 |     "version": 3
1155 |    },
1156 |    "file_extension": ".py",
1157 |    "mimetype": "text/x-python",
1158 |    "name": "python",
1159 |    "nbconvert_exporter": "python",
1160 |    "pygments_lexer": "ipython3",
1161 |    "version": "3.11.4"
1162 |   }
1163 |  },
1164 |  "nbformat": 4,
1165 |  "nbformat_minor": 5
1166 | }
1167 | 


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