import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, PolynomialFeatures
from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score, classification_report, mean_squared_error, r2_score
from imblearn.over_sampling import SMOTE
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from dmba import regressionSummary
from dmba import backward_elimination, stepwise_selection
from dmba import adjusted_r2_score, AIC_score
import statsmodels.formula.api as sm
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from sklearn.tree import DecisionTreeClassifier, plot_tree
%matplotlib inline
from utils import *

import warnings

Campus Placement Dataset Analysis

Dataset

col_names = [
    "SerialNumber", 
    "Gender", 
    "LowerSecondarySchollGrade%", 
    "LowerSecondarySchollBoard", 
    "HigherSecondarySchollGrade%", 
    "HigherSecondarySchollBoard",
    "HigherSecondarySchollSpecialization",
    "DegreeGrade%",
    "DegreeSpecialization",
    "WorkExperience",
    "EmployabilityTestGrade%",
    "MBASpecialization",
    "MBAGrade%",
    "PlacementStatus",
    "Salary"
]
data = pd.read_csv("Placement_Data_Full_Class.csv", names=col_names, header=0)
data.head(10)

	SerialNumber	Gender	LowerSecondarySchollGrade%	LowerSecondarySchollBoard	HigherSecondarySchollGrade%	HigherSecondarySchollBoard	HigherSecondarySchollSpecialization	DegreeGrade%	DegreeSpecialization	WorkExperience	EmployabilityTestGrade%	MBASpecialization	MBAGrade%	PlacementStatus	Salary
0	1	M	67.00	Others	91.00	Others	Commerce	58.00	Sci&Tech	No	55.00	Mkt&HR	58.80	Placed	270000.0
1	2	M	79.33	Central	78.33	Others	Science	77.48	Sci&Tech	Yes	86.50	Mkt&Fin	66.28	Placed	200000.0
2	3	M	65.00	Central	68.00	Central	Arts	64.00	Comm&Mgmt	No	75.00	Mkt&Fin	57.80	Placed	250000.0
3	4	M	56.00	Central	52.00	Central	Science	52.00	Sci&Tech	No	66.00	Mkt&HR	59.43	Not Placed	NaN
4	5	M	85.80	Central	73.60	Central	Commerce	73.30	Comm&Mgmt	No	96.80	Mkt&Fin	55.50	Placed	425000.0
5	6	M	55.00	Others	49.80	Others	Science	67.25	Sci&Tech	Yes	55.00	Mkt&Fin	51.58	Not Placed	NaN
6	7	F	46.00	Others	49.20	Others	Commerce	79.00	Comm&Mgmt	No	74.28	Mkt&Fin	53.29	Not Placed	NaN
7	8	M	82.00	Central	64.00	Central	Science	66.00	Sci&Tech	Yes	67.00	Mkt&Fin	62.14	Placed	252000.0
8	9	M	73.00	Central	79.00	Central	Commerce	72.00	Comm&Mgmt	No	91.34	Mkt&Fin	61.29	Placed	231000.0
9	10	M	58.00	Central	70.00	Central	Commerce	61.00	Comm&Mgmt	No	54.00	Mkt&Fin	52.21	Not Placed	NaN

First thing to notice here is that if the status is "Not Placed", it seems that the salary will be NaN. Just a remainder to not treat it as missing data. It will be necessary to check if there is someone "Placed" with a NaN salary. Some encoding will be necessary for the categorical data.

The first column seems to be just an ID. It will be droped.

Let's take a better look at the features information

Categorical features

Gender

data["Gender"].value_counts()

M    139
F     76
Name: Gender, dtype: int64

Lower Secondary Education

data["LowerSecondarySchollBoard"].value_counts()

Central    116
Others      99
Name: LowerSecondarySchollBoard, dtype: int64

Higher Secondary Education

data["HigherSecondarySchollBoard"].value_counts()

Others     131
Central     84
Name: HigherSecondarySchollBoard, dtype: int64

Specialization

data["HigherSecondarySchollSpecialization"].value_counts()

Commerce    113
Science      91
Arts         11
Name: HigherSecondarySchollSpecialization, dtype: int64

Degree Specialization

data["DegreeSpecialization"].value_counts()

Comm&Mgmt    145
Sci&Tech      59
Others        11
Name: DegreeSpecialization, dtype: int64

Work Experience

data["WorkExperience"].value_counts()

No     141
Yes     74
Name: WorkExperience, dtype: int64

Placement Status

data["PlacementStatus"].value_counts()

Placed        148
Not Placed     67
Name: PlacementStatus, dtype: int64

Exploratory Analysis

Descrption

data.describe()

	SerialNumber	LowerSecondarySchollGrade%	HigherSecondarySchollGrade%	DegreeGrade%	EmployabilityTestGrade%	MBAGrade%	Salary
count	215.000000	215.000000	215.000000	215.000000	215.000000	215.000000	148.000000
mean	108.000000	67.303395	66.333163	66.370186	72.100558	62.278186	288655.405405
std	62.209324	10.827205	10.897509	7.358743	13.275956	5.833385	93457.452420
min	1.000000	40.890000	37.000000	50.000000	50.000000	51.210000	200000.000000
25%	54.500000	60.600000	60.900000	61.000000	60.000000	57.945000	240000.000000
50%	108.000000	67.000000	65.000000	66.000000	71.000000	62.000000	265000.000000
75%	161.500000	75.700000	73.000000	72.000000	83.500000	66.255000	300000.000000
max	215.000000	89.400000	97.700000	91.000000	98.000000	77.890000	940000.000000

data.isnull().sum()

SerialNumber                            0
Gender                                  0
LowerSecondarySchollGrade%              0
LowerSecondarySchollBoard               0
HigherSecondarySchollGrade%             0
HigherSecondarySchollBoard              0
HigherSecondarySchollSpecialization     0
DegreeGrade%                            0
DegreeSpecialization                    0
WorkExperience                          0
EmployabilityTestGrade%                 0
MBASpecialization                       0
MBAGrade%                               0
PlacementStatus                         0
Salary                                 67
dtype: int64

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
import seaborn as sns
from matplotlib import pyplot as plt

dataset = pd.read_csv('Placement_Data_Full_Class.csv')

sns.set(style="whitegrid")
sns.distplot(dataset.salary)

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='salary', ylabel='Density'>

We will fill missing salaries with 0s as they represent students who have not got placement offer.

dataset['salary']=dataset['salary'].fillna(0)
data.drop("SerialNumber", axis=1, inplace=True)

Let's analyse the distribution of the PlacementStatus column

plot = sns.countplot(x="PlacementStatus", data=data)
plt.title("Placement Distribution")
plt.xlabel("Placement Status")
plot.get_figure().savefig("images/PlacementStatus.png")

print('Placed', round(data["PlacementStatus"].value_counts()[0]/len(data) * 100,2), '% of the dataset')
print('Not placed', round(data["PlacementStatus"].value_counts()[1]/len(data) * 100,2), '% of the dataset')

Placed 68.84 % of the dataset
Not placed 31.16 % of the dataset

data[data["PlacementStatus"] == "Not Placed"]["PlacementStatus"].value_counts()

Not Placed    67
Name: PlacementStatus, dtype: int64

Converting rupees to US

$ (1 rupee = $ 0.013)

dataset.index = dataset['sl_no']
dataset.drop('sl_no', axis=1, inplace=True)

dataset['salary'] = dataset['salary'] * 0.013
dataset.head(10)

	gender	ssc_p	ssc_b	hsc_p	hsc_b	hsc_s	degree_p	degree_t	workex	etest_p	specialisation	mba_p	status	salary
sl_no
1	M	67.00	Others	91.00	Others	Commerce	58.00	Sci&Tech	No	55.00	Mkt&HR	58.80	Placed	3510.0
2	M	79.33	Central	78.33	Others	Science	77.48	Sci&Tech	Yes	86.50	Mkt&Fin	66.28	Placed	2600.0
3	M	65.00	Central	68.00	Central	Arts	64.00	Comm&Mgmt	No	75.00	Mkt&Fin	57.80	Placed	3250.0
4	M	56.00	Central	52.00	Central	Science	52.00	Sci&Tech	No	66.00	Mkt&HR	59.43	Not Placed	0.0
5	M	85.80	Central	73.60	Central	Commerce	73.30	Comm&Mgmt	No	96.80	Mkt&Fin	55.50	Placed	5525.0
6	M	55.00	Others	49.80	Others	Science	67.25	Sci&Tech	Yes	55.00	Mkt&Fin	51.58	Not Placed	0.0
7	F	46.00	Others	49.20	Others	Commerce	79.00	Comm&Mgmt	No	74.28	Mkt&Fin	53.29	Not Placed	0.0
8	M	82.00	Central	64.00	Central	Science	66.00	Sci&Tech	Yes	67.00	Mkt&Fin	62.14	Placed	3276.0
9	M	73.00	Central	79.00	Central	Commerce	72.00	Comm&Mgmt	No	91.34	Mkt&Fin	61.29	Placed	3003.0
10	M	58.00	Central	70.00	Central	Commerce	61.00	Comm&Mgmt	No	54.00	Mkt&Fin	52.21	Not Placed	0.0

Gender based analysis

sns.barplot(x = dataset['gender'],y = dataset['salary'])

<AxesSubplot:xlabel='gender', ylabel='salary'>

Swarmplot of Gender VS Salary

_ = sns.swarmplot(x='gender', y='salary', data=dataset)
_ = plt.xlabel('Gender')
_ = plt.ylabel('Salary in $')
_ = plt.title('Swarmplot of Gender and Salary')
plt.show()

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 12.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

print('Men', round(data["Gender"].value_counts()[0]/len(data) * 100,2), '% of the dataset')
print('Women', round(data["Gender"].value_counts()[1]/len(data) * 100,2), '% of the dataset')

Men 64.65 % of the dataset
Women 35.35 % of the dataset

Lower Secondary Grade Based

sns.barplot(x = dataset['gender'],y = dataset['ssc_p'])

<AxesSubplot:xlabel='gender', ylabel='ssc_p'>

Higher Secondary Grade Based

sns.barplot(x = dataset['gender'],y = dataset['hsc_p'])

<AxesSubplot:xlabel='gender', ylabel='hsc_p'>

Undergraduation Degree Based

sns.barplot(x = dataset['gender'],y = dataset['degree_p'])

<AxesSubplot:xlabel='gender', ylabel='degree_p'>

Specialization

sns.countplot(x="DegreeSpecialization", hue="Gender", data=data)

<AxesSubplot:xlabel='DegreeSpecialization', ylabel='count'>

sns.barplot(x="DegreeSpecialization", y="Salary", hue="Gender", data=data)

<AxesSubplot:xlabel='DegreeSpecialization', ylabel='Salary'>

Post Graduation Based

sns.barplot(x = dataset['gender'],y = dataset['mba_p'])

<AxesSubplot:xlabel='gender', ylabel='mba_p'>

Specialisation

MBA Specialisation

# MBASpecialization
sns.countplot(x="MBASpecialization", hue="Gender", data=data)

<AxesSubplot:xlabel='MBASpecialization', ylabel='count'>

sns.barplot(x="MBASpecialization", y="Salary", hue="Gender", data=data)

<AxesSubplot:xlabel='MBASpecialization', ylabel='Salary'>

Mean

sns.boxplot(x = dataset['salary'],y= dataset['gender'],saturation=1)
mean_of_both=round(dataset['salary'].groupby(dataset['gender']).mean())
median_both=round(dataset['salary'].groupby(dataset['gender']).median())
print('The mean salary of Male is {} while Female is {}'.format(mean_of_both[1],mean_of_both[0]))
print('The median salary of Male is {} while Female is {}'.format(median_both[1],median_both[0]))
plt.show()

The mean salary of Male is 2796.0 while Female is 2195.0
The median salary of Male is 3250.0 while Female is 2808.0

Observation

Men are getting higher salaries than women even when women scored higher percentages during their school and college degrees. 1. The Range of salary is high for boys with the median of 2.5 Lakhs per annum 2. The Median salary for girls is 2.1 Lakhs per annum 3. The highest package is offered to a boy which is nearly to 10 Lakhs per annum 4. The highest package offered for girls is near to 7 Lakhs per annum Women that came from men-dominated fields receive lower salaries, but receive higher salaries if they came from "Others" fields. However, the amount of data from the "Others" field is not large enough to give a conclusive answer, more data would be required.

Placement status based analysis

sns.set()
_ = plt.hist(dataset['salary'], bins=25)
_ = plt.xlabel('Salary in $')
_ = plt.ylabel('Count')
_ = plt.title('Salary Histogram')
plt.show() 

Lower Secondary Education Based

Swarmplot of Secondary Education and Salary

_ = sns.swarmplot(x='ssc_b', y='salary', data=dataset)
_ = plt.xlabel('Board of Secondary Education')
_ = plt.ylabel('Salary in $')
_ = plt.title('Swarmplot of Board of Secondary Education and Salary')
plt.show()

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 11.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

_ = plt.hist(dataset['ssc_p'], bins=25)
_ = plt.xlabel('Secondary Education Percentage')
_ = plt.ylabel('Count')
_ = plt.title('Secondary Education Percentage Histogram')
plt.show() 

sns.boxplot(x = dataset['status'], y = dataset['ssc_p'])

<AxesSubplot:xlabel='status', ylabel='ssc_p'>

Higher Secondary Education Based

Swarmplot of Higher Education and Salary

_ = sns.swarmplot(x='hsc_b', y='salary', data=dataset)
_ = plt.xlabel('Board of Higher Secondary Education')
_ = plt.ylabel('Salary in $')
_ = plt.title('Swarmplot of Board of Higher Secondary Education and Salary')
plt.show()

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 11.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

_ = sns.swarmplot(x='hsc_s', y='salary', data=dataset)
_ = plt.xlabel('Specialization in Higher Secondary Education')
_ = plt.ylabel('Salary in $')
_ = plt.title('Swarmplot of Board of Specialization in Hihger Secondary Education and Salary')
plt.show()

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 19.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 16.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

_ = plt.hist(dataset['hsc_p'], bins=25)
_ = plt.xlabel('Higher Secondary Education Percentage')
_ = plt.ylabel('Count')
_ = plt.title('Higher Secondary Education Percentage Histogram')
plt.show() 

sns.boxplot(x = dataset['status'], y = dataset['hsc_p'])

<AxesSubplot:xlabel='status', ylabel='hsc_p'>

Undergraduate Degree Based

Swarmplot Of Undergraduate Education field and salary

_ = sns.swarmplot(x='degree_t', y='salary', data=dataset)
_ = plt.xlabel('Undergraduate Degree Field')
_ = plt.ylabel('salary in $')
_ = plt.title('Swarmplot of Undergraduate Degree Field and Salary')
plt.show()

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 31.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

_ = plt.hist(dataset['degree_p'], bins=25)
_ = plt.xlabel('Undergraduate Degree Percentage')
_ = plt.ylabel('Count')
_ = plt.title('Undergraduate Degree Percentage Histogram')
plt.show() 

sns.boxplot(x = dataset['status'], y = dataset['degree_p'])

<AxesSubplot:xlabel='status', ylabel='degree_p'>

Swarmplot of Work Experience and Salary

_ = sns.swarmplot(x='workex', y='salary', data=dataset)
_ = plt.xlabel('Work Experience')
_ = plt.ylabel('Salary in $')
_ = plt.title('Swarmplot of Work Experience and Salary')
plt.show()

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 22.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

Post Graduation Based

Swarmplot Of Post graduation and salary

_ = sns.swarmplot(x='specialisation', y='salary', data=dataset)
_ = plt.xlabel('MBA Specialization')
_ = plt.ylabel('Salary in $')
_ = plt.title('Swarmplot of MBA Specialization and Salary')
plt.show()

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 17.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

_ = plt.hist(dataset['mba_p'], bins=25)
_ = plt.xlabel('MBA Percentage')
_ = plt.ylabel('Count')
_ = plt.title('MBA Percentage Histogram')
plt.show() 

sns.boxplot(x = dataset['status'], y = dataset['mba_p'])

<AxesSubplot:xlabel='status', ylabel='mba_p'>

Employment Test Percentage

_ = plt.hist(dataset['etest_p'], bins=25)
_ = plt.xlabel('Employment Test Percentage')
_ = plt.ylabel('Count')
_ = plt.title('Employment Test Percentage Histogram')
plt.show() 

Observation

Students with higher percentages/better academic results were able to perform well during placements compared to those who had relatively lower academic results

Salary vs Academic results

Does the grade in school affect your future life?

Lower Secondary Eduacation vs Salary

sns.jointplot(x = dataset['ssc_p'], y = dataset['salary'], kind='hex')

<seaborn.axisgrid.JointGrid at 0x1aa9b18afa0>

Higher Secondary Education vs Salary

sns.jointplot(x = dataset['hsc_p'], y = dataset['salary'], kind='hex')

<seaborn.axisgrid.JointGrid at 0x1aa9af1ac70>

Undergraduate vs Salary

sns.jointplot(x = dataset['degree_p'], y = dataset['salary'], kind='hex')

<seaborn.axisgrid.JointGrid at 0x1aa9b319b50>

Post Graduation vs Salary

sns.jointplot(x = dataset['mba_p'], y = dataset['salary'], kind='hex')

<seaborn.axisgrid.JointGrid at 0x1aa99b93c40>

Which stream students are getting more placed and which stream students are mostly not placed?

sns.violinplot(x=dataset["degree_t"], y=dataset["salary"], data=dataset)
sns.stripplot(x=dataset["degree_t"], y=dataset["salary"], data=dataset,hue=dataset['status'])
plt.show()

Observation

There seems to be no relation between grades in previous education and salaries. Students with average percentage of 60-70 are able to get around 250000 INR anually. Higher percentage does not neccesarily corresponds to higher salary package. The Secondary board that the student came from seems to have no impact here, but the outliers came from the central board, which is interesting. In Higher Secondary education Science specialization leads to a better salary than the other options (specially Arts) The stream in which the students mostly get placed are Commucation and management , also science and technology students are mostly getting placed and other stream students are not getting that much placements due to less number of students.

Does Percentage in College determine salary ?

plt.figure(figsize =(19,8))
sns.lineplot(dataset["degree_p"], dataset["salary"], hue=dataset["degree_t"], data=dataset)

plt.show()

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

• Percentage does not seem to affect salary.
• Commerce&Mgmt students occasionally get dream placements with high salary

Relation between EmployabilityTest and PlacementStatus

plot = sns.catplot(x="PlacementStatus", y="EmployabilityTestGrade%", data=data)
plt.xlabel("Placement Status")
plt.ylabel("Employability Test Grade (in %)")
plot.savefig("images/EmployabilityTest.png")

Surprisingly, the employability test seems to not be a major factor to the placement rate. There are a lot of students with good scores that did not land a job. It may be necessary for the institution to rethink this test.

Salary Vs Employability Test

sns.regplot(x="EmployabilityTestGrade%", y="Salary", data=data)
plt.xlabel("Employability Test grade")

Text(0.5, 0, 'Employability Test grade')

There is a positive relation between salary and the employability test. It is not huge, but it is visible.

Is the MBAGrade important?

sns.regplot(x="MBAGrade%", y="Salary", data=data)
plt.xlabel("MBA grades")

Text(0.5, 0, 'MBA grades')

The grade indeed is important to the salary

How about work experience?

sns.barplot(x="WorkExperience", y="Salary", data=data)

<AxesSubplot:xlabel='WorkExperience', ylabel='Salary'>

salary_with_experience = np.mean(data[data["WorkExperience"] == "Yes"]["Salary"])
salary_no_experience = np.mean(data[data["WorkExperience"] == "No"]["Salary"])
print("Salary with work experience: ", salary_with_experience)
print("Salary with no work experience: ", salary_no_experience)
print("Percentual difference: ", 100*(salary_with_experience / salary_no_experience - 1))

Salary with work experience:  303265.625
Salary with no work experience:  277523.8095238095
Percentual difference:  9.275534059711731

sns.countplot(x="PlacementStatus", hue="WorkExperience", data=data)
plt.xlabel("Placement status")

Text(0.5, 0, 'Placement status')

placement_with_experience = data[data["WorkExperience"] == "Yes"]["PlacementStatus"] == "Placed"
placement_no_experience = data[data["WorkExperience"] == "No"]["PlacementStatus"] == "Placed"

print("Placement with work experience: ", placement_with_experience.mean())
print("Placement with no work experience: ", placement_no_experience.mean())
print("Percentual difference: ", 100*(placement_with_experience.mean() / placement_no_experience.mean() - 1))

# print("Salary with work experience: ", salary_with_experience)
# print("Salary with no work experience: ", salary_no_experience)
# print("Percentual difference: ", 100*(salary_with_experience / salary_no_experience - 1))

Placement with work experience:  0.8648648648648649
Placement with no work experience:  0.5957446808510638
Percentual difference:  45.173745173745196

Observation

Work Experience is really important! Having working previously leads to a way higher employment rate and salary.

Encoding

We will Label Encode (no new columns) categorical features that only have two unique values. e.g. Gender M/F and Hot Encoded every other categorical column (columns with more than 2 unique values)

def label_encoding(data):
    le = LabelEncoder()
    le.fit(data)
    return le.transform(data)

def one_hot_encoding(data):
    return pd.get_dummies(data)

data["Gender"] = label_encoding(data["Gender"])
data["LowerSecondarySchollBoard"] = label_encoding(data["LowerSecondarySchollBoard"])
data["HigherSecondarySchollBoard"] = label_encoding(data["HigherSecondarySchollBoard"])
data["WorkExperience"] = label_encoding(data["WorkExperience"])
data["MBASpecialization"] = label_encoding(data["MBASpecialization"])
data["PlacementStatus"] = label_encoding(data["PlacementStatus"])

data = one_hot_encoding(data)

data.head(5)

	Gender	LowerSecondarySchollGrade%	LowerSecondarySchollBoard	HigherSecondarySchollGrade%	HigherSecondarySchollBoard	DegreeGrade%	WorkExperience	EmployabilityTestGrade%	MBASpecialization	MBAGrade%	PlacementStatus	Salary	HigherSecondarySchollSpecialization_Arts	HigherSecondarySchollSpecialization_Commerce	HigherSecondarySchollSpecialization_Science	DegreeSpecialization_Comm&Mgmt	DegreeSpecialization_Others	DegreeSpecialization_Sci&Tech
0	1	67.00	1	91.00	1	58.00	0	55.0	1	58.80	1	270000.0	0	1	0	0	0	1
1	1	79.33	0	78.33	1	77.48	1	86.5	0	66.28	1	200000.0	0	0	1	0	0	1
2	1	65.00	0	68.00	0	64.00	0	75.0	0	57.80	1	250000.0	1	0	0	1	0	0
3	1	56.00	0	52.00	0	52.00	0	66.0	1	59.43	0	NaN	0	0	1	0	0	1
4	1	85.80	0	73.60	0	73.30	0	96.8	0	55.50	1	425000.0	0	1	0	1	0	0

Correlations

salary_corr = data[data["PlacementStatus"] == 1].corr()["Salary"].sort_values()

# Display correlations
salary_corr # corr() returns NaN if all values are that same, which is the case with PlacementStatus

DegreeSpecialization_Comm&Mgmt                 -0.160322
MBASpecialization                              -0.146576
HigherSecondarySchollSpecialization_Arts       -0.088253
DegreeGrade%                                   -0.019272
DegreeSpecialization_Others                    -0.016573
HigherSecondarySchollSpecialization_Commerce   -0.014219
HigherSecondarySchollBoard                     -0.007549
LowerSecondarySchollBoard                       0.005539
LowerSecondarySchollGrade%                      0.035330
HigherSecondarySchollSpecialization_Science     0.049547
HigherSecondarySchollGrade%                     0.076819
WorkExperience                                  0.136920
Gender                                          0.158912
DegreeSpecialization_Sci&Tech                   0.172492
MBAGrade%                                       0.175013
EmployabilityTestGrade%                         0.178307
Salary                                          1.000000
PlacementStatus                                      NaN
Name: Salary, dtype: float64

Heat Map for checking correlation

plt.figure(figsize=(20,10))
sns.heatmap(data.corr().abs(), annot=True)

<AxesSubplot:>

Here we are looking at PlacementStatus, so Salary is not important and we will ignore the NaN

placement_corr = data.corr()["PlacementStatus"].sort_values()

# Display correlations
placement_corr # placement_corr = data.corr()["PlacementStatus"].sort_values()

# Display correlations
placement_corr 

MBASpecialization                              -0.250655
DegreeSpecialization_Others                    -0.117232
HigherSecondarySchollSpecialization_Arts       -0.071653
HigherSecondarySchollSpecialization_Science     0.007279
DegreeSpecialization_Sci&Tech                   0.008688
HigherSecondarySchollBoard                      0.016945
HigherSecondarySchollSpecialization_Commerce    0.024414
LowerSecondarySchollBoard                       0.037297
DegreeSpecialization_Comm&Mgmt                  0.046849
MBAGrade%                                       0.076922
Gender                                          0.090670
EmployabilityTestGrade%                         0.127639
WorkExperience                                  0.276060
DegreeGrade%                                    0.479861
HigherSecondarySchollGrade%                     0.491228
LowerSecondarySchollGrade%                      0.607889
PlacementStatus                                 1.000000
Salary                                               NaN
Name: PlacementStatus, dtype: float64

Here we are looking at PlacementStatus, so Salary is not important and we will ignore the NaN

Observations

These results allow us to see what are the features that most relate with what we are trying to predict. In the case o PlacementStatus, having high grades during your education (specially basic education) has a huge factor on employability, alongside work experience and the MBA field of choice. In the case of salary, major factors are the specialization and most recent grades.

Questions

What are the major factors that lead to the person being hired

We already answered this question in the data analysis phase. Things like MBA area of specialization, experience and grades are the most important here.

We will try to predict if someone who just graduated from the MBA is employed.

First, the preprocessing: remove the salary and any other feature that does not seems to be related to the problem. Finally, standardize the data.

placement_corr = data.corr()["PlacementStatus"].sort_values()

# Display correlations
 # Here we are looking at PlacementStatus, so Salary is not important and we will ignore the NaN

columns_to_drop = [
    "Salary", 
    "HigherSecondarySchollSpecialization_Arts",
    "HigherSecondarySchollSpecialization_Science",
    "HigherSecondarySchollSpecialization_Commerce",
    "DegreeSpecialization_Sci&Tech",
    "DegreeSpecialization_Comm&Mgmt",
    "DegreeSpecialization_Others",
    "MBAGrade%"
]
placement_data = data.drop(columns_to_drop, axis=1)

y = placement_data["PlacementStatus"]
X = placement_data.drop(["PlacementStatus"], axis=1)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)

X_train = (X_train - X_train.mean()) / X_train.std()
X_test = (X_test - X_test.mean()) / X_test.std()

X_train.head(5)

	Gender	LowerSecondarySchollGrade%	LowerSecondarySchollBoard	HigherSecondarySchollGrade%	HigherSecondarySchollBoard	DegreeGrade%	WorkExperience	EmployabilityTestGrade%	MBASpecialization
79	-1.367791	0.162888	-0.913101	-0.379024	-1.237537	-0.043779	-0.710761	0.192690	1.100944
25	-1.367791	-1.372102	1.089495	-1.042572	-1.237537	-2.148106	1.399653	0.268148	-0.903605
104	0.727318	0.162888	-0.913101	-0.289355	0.803870	-0.176964	1.399653	-1.316460	1.100944
145	0.727318	2.069940	1.089495	-0.050837	0.803870	0.655444	-0.710761	-0.033682	1.100944
189	-1.367791	-1.239356	-0.913101	1.019806	0.803870	0.382414	-0.710761	1.750888	-0.903605

X_test.head(5)

	Gender	LowerSecondarySchollGrade%	LowerSecondarySchollBoard	HigherSecondarySchollGrade%	HigherSecondarySchollBoard	DegreeGrade%	WorkExperience	EmployabilityTestGrade%	MBASpecialization
200	0.812920	0.106333	0.977008	-0.855848	0.738549	-0.288051	-0.812920	1.379209	-0.738549
212	0.812920	-0.057945	0.977008	-0.031218	0.738549	1.040835	1.174218	-0.751182	-0.738549
138	-1.174218	1.174140	0.977008	-0.384631	0.738549	1.040835	1.174218	2.009745	-0.738549
176	-1.174218	-0.715058	-0.977008	-0.855848	0.738549	-1.783049	-0.812920	-1.049661	1.292461
15	-1.174218	-0.222223	-0.977008	0.911216	-1.292461	0.376392	1.174218	0.218873	-0.738549

What are the major factors that affect the salary?

As we saw in the data analysis phase, the most important features in this problem are MBA and the Employability Test grades, the degree and MBA areas of specialization, gender and work experience. we will build a model that predicts the salary of someone who just graduated from the MBA (assuming this person got hired).

salary_corr = data[data["PlacementStatus"] == 1].corr()["Salary"].sort_values()

# Display correlations
salary_corr

DegreeSpecialization_Comm&Mgmt                 -0.160322
MBASpecialization                              -0.146576
HigherSecondarySchollSpecialization_Arts       -0.088253
DegreeGrade%                                   -0.019272
DegreeSpecialization_Others                    -0.016573
HigherSecondarySchollSpecialization_Commerce   -0.014219
HigherSecondarySchollBoard                     -0.007549
LowerSecondarySchollBoard                       0.005539
LowerSecondarySchollGrade%                      0.035330
HigherSecondarySchollSpecialization_Science     0.049547
HigherSecondarySchollGrade%                     0.076819
WorkExperience                                  0.136920
Gender                                          0.158912
DegreeSpecialization_Sci&Tech                   0.172492
MBAGrade%                                       0.175013
EmployabilityTestGrade%                         0.178307
Salary                                          1.000000
PlacementStatus                                      NaN
Name: Salary, dtype: float64

Correlation returns NaN if all values are that same, which is the case with PlacementStatus

columns_to_keep = [
    "Salary",
    "EmployabilityTestGrade%",
    "MBAGrade%",
    "DegreeSpecialization_Sci&Tech",
    "DegreeSpecialization_Comm&Mgmt",
    "Gender",
    "WorkExperience",
    "MBASpecialization"
]
salary_data = data[columns_to_keep]

salary_data = salary_data[salary_data['Salary'].notna()]

y = salary_data["Salary"]
X = salary_data.drop(["Salary"], axis=1)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)

X_train = (X_train - X_train.mean()) / X_train.std()
X_test = (X_test - X_test.mean()) / X_test.std()

X_train.head(10)

	EmployabilityTestGrade%	MBAGrade%	DegreeSpecialization_Sci&Tech	DegreeSpecialization_Comm&Mgmt	Gender	WorkExperience	MBASpecialization
95	1.605444	-0.038434	-0.606913	0.665057	0.700475	1.098872	-0.736480
27	-0.380194	-0.790559	-0.606913	0.665057	0.700475	-0.903182	1.347601
101	0.342908	-0.327842	-0.606913	0.665057	0.700475	-0.903182	1.347601
177	1.138320	1.417020	-0.606913	0.665057	-1.416869	1.098872	-0.736480
47	0.342908	-1.318897	-0.606913	0.665057	0.700475	1.098872	-0.736480
44	1.138320	1.230250	-0.606913	0.665057	-1.416869	1.098872	-0.736480
146	0.849079	-1.219623	-0.606913	0.665057	0.700475	-0.903182	1.347601
56	-0.380194	0.755756	-0.606913	0.665057	0.700475	-0.903182	-0.736480
164	-1.461954	0.526921	-0.606913	0.665057	-1.416869	-0.903182	-0.736480
80	-0.452504	-0.006465	-0.606913	0.665057	-1.416869	1.098872	1.347601

y_train.head(10)

95     420000.0
27     265000.0
101    380000.0
177    650000.0
47     204000.0
44     200000.0
146    233000.0
56     240000.0
164    250000.0
80     240000.0
Name: Salary, dtype: float64

def trainRegression(X, Y):
    for degree in range(1, 4):
        name = 'Linear Regression degree' + str(degree)
        
        polynomial_features = PolynomialFeatures(degree=degree)
        X_poly = polynomial_features.fit_transform(X)
        
        model = LinearRegression()
        
        cv_results = cross_val_score(model, X_poly, Y, cv=10, scoring='r2')
        print('%s: %.6f (%.6f)' % (name, cv_results.mean(), cv_results.std()))

Linear Regression

trainRegression(X_train, y_train)

Linear Regression degree1: -0.300547 (0.593319)
Linear Regression degree2: -56012702404937373576069120.000000 (167397531279467017331015680.000000)
Linear Regression degree3: -1273200841177254913376256.000000 (2553128309561786567229440.000000)

These results show that it is better to just use Linear Regression instead of polynomial regression

model = LinearRegression()
model.fit(X_train, y_train)
print(model.predict(X_test))
print(y_test.values)
print(r2_score(y_test, model.predict(X_test)))

[303555.88575642 302070.63156475 253695.20532424 306526.57108331
 291716.43907118 241686.34186756 239362.40691229 280510.03222008
 322901.97097687 279396.03079504 319113.87953033 296539.86383578
 322601.86041505 275781.6899594  272699.08542453]
[350000. 336000. 260000. 350000. 400000. 265000. 250000. 250000. 240000.
 200000. 411000. 250000. 400000. 300000. 260000.]
0.23452225966615792

Observation

Results are not great, but the amount of data does not help very much

Can the institution predict if the person will be successfull before they are accepted?

Here, the idea is that the institution would like to know beforehand if someone would fail to find a job after the MBA. The objective is to avoid accepting people that would not find a job and increase the employability rate of the institution. For that, we will keep just the information that are previous to the MBA and see if it is enough to predict the employability of a candidate

columns_to_keep = {
    "Gender",
    "LowerSecondarySchollGrade%",
    "HigherSecondarySchollGrade%",
    "DegreeGrade%",
    "WorkExperience",
    "MBASpecialization", # This one is about the MBA but the institution would already know it beforehand
    "HigherSecondarySchollSpecialization_Arts",
    "HigherSecondarySchollSpecialization_Commerce",
    "HigherSecondarySchollSpecialization_Science",
    "DegreeSpecialization_Comm&Mgmt",
    "DegreeSpecialization_Others",
    "DegreeSpecialization_Sci&Tech",
    "PlacementStatus"
}

before_mba_data = data[columns_to_keep]

The knowlodge from the data analysis phase can tell us what are the best features.

"Things like MBA area of specialization, experience and grades are the most important here."

placement_corr = before_mba_data.corr()["PlacementStatus"].sort_values()

# Display correlations
placement_corr # Here we are looking at PlacementStatus, so Salary is not important and we will ignore the NaN

MBASpecialization                              -0.250655
DegreeSpecialization_Others                    -0.117232
HigherSecondarySchollSpecialization_Arts       -0.071653
HigherSecondarySchollSpecialization_Science     0.007279
DegreeSpecialization_Sci&Tech                   0.008688
HigherSecondarySchollSpecialization_Commerce    0.024414
DegreeSpecialization_Comm&Mgmt                  0.046849
Gender                                          0.090670
WorkExperience                                  0.276060
DegreeGrade%                                    0.479861
HigherSecondarySchollGrade%                     0.491228
LowerSecondarySchollGrade%                      0.607889
PlacementStatus                                 1.000000
Name: PlacementStatus, dtype: float64

Here we are looking at PlacementStatus, so Salary is not important and we will ignore the NaN

columns_to_keep = {
    "Gender",
    "LowerSecondarySchollGrade%",
    "HigherSecondarySchollGrade%",
    "DegreeGrade%",
    "WorkExperience",
    "MBASpecialization", # This one is about the MBA but the institution would already know it beforehand
#     "HigherSecondarySchollSpecialization_Arts",
#     "HigherSecondarySchollSpecialization_Commerce",
#     "HigherSecondarySchollSpecialization_Science",
#     "DegreeSpecialization_Comm&Mgmt",
#     "DegreeSpecialization_Others",
#     "DegreeSpecialization_Sci&Tech",
    "PlacementStatus"
}

before_mba_data = data[columns_to_keep]

models = []
models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('KNN', KNeighborsClassifier()))
models.append(('TREE', DecisionTreeClassifier()))
models.append(('SVM', SVC(gamma='auto', probability=True)))
models.append(('GB', GradientBoostingClassifier()))
models.append(('RF', RandomForestClassifier()))

def runModels(X_train, Y_train, scoring='accuracy'):
    for name, model in models:
        kfold = StratifiedKFold(n_splits=5, random_state=42, shuffle=True)
        cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
        print('%s: %.6f (%.6f)' % (name, cv_results.mean(), cv_results.std()))

runModels(X_train, y_train, scoring='f1')

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names
  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names
  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names
  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names
  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names
  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
  warnings.warn(

LR: nan (nan)
KNN: nan (nan)
TREE: nan (nan)

C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py:771: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_validation.py", line 762, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 103, in __call__
    score = scorer._score(cached_call, estimator, *args, **kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_scorer.py", line 264, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1113, in f1_score
    return fbeta_score(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1251, in fbeta_score
    _, _, f, _ = precision_recall_fscore_support(
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1534, in precision_recall_fscore_support
    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
  File "C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\metrics\_classification.py", line 1355, in _check_set_wise_labels
    raise ValueError(
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

  warnings.warn(
C:\Users\PREETI\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
  warnings.warn(

SVM: nan (nan)

!jupyter nbconvert --to html Campus-placement-data-anal.ipynb

  File "C:\Users\PREETI\AppData\Local\Temp/ipykernel_15924/2096042202.py", line 1
    jupyter nbconvert --execute --to html notebook.ipynb
            ^
SyntaxError: invalid syntax