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  1. Home/
  2. Sushant Ovhal/
  3. Project 2 - EDA on Vehicle Insurance Customer Data

Project 2 - EDA on Vehicle Insurance Customer Data

1.Question¶ import pandas as pdimport numpy as np Customer_Detail=pd.read_csv('customer_details.csv')Customer_Policy=pd.read_csv('customer_policy_details.csv') Customer_Detail.columns=['Customer_id','Gender','Age','Driving Licence Present','Region Code','Previously Insured','Vehicle age','Vehicle Damage'] Customer_Policy.columns=['Customer_id','Annual…

  • PYTHON

  • Sushant Ovhal

    updated on 20 Aug 2022

1.Question¶

import pandas as pd
import numpy as np

Customer_Detail=pd.read_csv('customer_details.csv')
Customer_Policy=pd.read_csv('customer_policy_details.csv')

Customer_Detail.columns=['Customer_id','Gender','Age','Driving Licence Present','Region Code','Previously Insured','Vehicle age','Vehicle Damage']

Customer_Policy.columns=['Customer_id','Annual Premium(in Rs)','Sales Channel code','Vintage', 'Response']

Customer_Detail

 

Ans:

Customer_id Gender Age Driving Licence Present Region Code Previously Insured Vehicle age Vehicle Damage
0 1.0 Male 44.0 1.0 28.0 0.0 > 2 Years Yes
1 2.0 Male 76.0 1.0 3.0 0.0 1-2 Year No
2 3.0 Male 47.0 1.0 28.0 0.0 > 2 Years Yes
3 4.0 Male 21.0 1.0 11.0 1.0 < 1 Year No
4 5.0 Female 29.0 1.0 41.0 1.0 < 1 Year No
... ... ... ... ... ... ... ... ...
381104 381105.0 Male 74.0 1.0 26.0 1.0 1-2 Year No
381105 381106.0 Male 30.0 1.0 37.0 1.0 < 1 Year No
381106 381107.0 Male 21.0 1.0 30.0 1.0 < 1 Year No
381107 381108.0 Female 68.0 1.0 14.0 0.0 > 2 Years Yes
381108 381109.0 Male 46.0 1.0 29.0 0.0 1-2 Year No

381109 rows × 8 columns

 

Customer_Policy

Ans:

Customer_id Annual Premium(in Rs) Sales Channel code Vintage Response
0 1.0 40454.0 26.0 217.0 1.0
1 2.0 33536.0 26.0 183.0 0.0
2 3.0 38294.0 26.0 27.0 1.0
3 4.0 28619.0 152.0 203.0 0.0
4 5.0 27496.0 152.0 39.0 0.0
... ... ... ... ... ...
381104 381105.0 30170.0 26.0 88.0 0.0
381105 381106.0 40016.0 152.0 131.0 0.0
381106 381107.0 35118.0 160.0 161.0 0.0
381107 381108.0 44617.0 124.0 74.0 0.0
381108 381109.0 41777.0 26.0 237.0 0.0

381109 rows × 5 columns

 

2.Question

Customer_Detail.isnull().sum()

Ans:

Customer_id                386
Gender                     368
Age                        368
Driving Licence Present    393
Region Code                392
Previously Insured         381
Vehicle age                381
Vehicle Damage             407
dtype: int64


Customer_Detail.fillna(Customer_Detail.mean())

Ans:
Customer_id Gender Age Driving Licence Present Region Code Previously Insured Vehicle age Vehicle Damage
0 1.0 Male 44.0 1.0 28.0 0.0 > 2 Years Yes
1 2.0 Male 76.0 1.0 3.0 0.0 1-2 Year No
2 3.0 Male 47.0 1.0 28.0 0.0 > 2 Years Yes
3 4.0 Male 21.0 1.0 11.0 1.0 < 1 Year No
4 5.0 Female 29.0 1.0 41.0 1.0 < 1 Year No
... ... ... ... ... ... ... ... ...
381104 381105.0 Male 74.0 1.0 26.0 1.0 1-2 Year No
381105 381106.0 Male 30.0 1.0 37.0 1.0 < 1 Year No
381106 381107.0 Male 21.0 1.0 30.0 1.0 < 1 Year No
381107 381108.0 Female 68.0 1.0 14.0 0.0 > 2 Years Yes
381108 381109.0 Male 46.0 1.0 29.0 0.0 1-2 Year No

381109 rows × 8 columns

 

Customer_Policy.isnull().sum()
 Ans:
 
Customer_id              387
Annual Premium(in Rs)    346
Sales Channel code       400
Vintage                  388
Response                 361
dtype: int64
 
Customer_Policy.fillna(Customer_Policy.mean())
 
 Ans:
 
  Customer_id Annual Premium(in Rs) Sales Channel code Vintage Response
0 1.0 40454.0 26.0 217.0 1.0
1 2.0 33536.0 26.0 183.0 0.0
2 3.0 38294.0 26.0 27.0 1.0
3 4.0 28619.0 152.0 203.0 0.0
4 5.0 27496.0 152.0 39.0 0.0
... ... ... ... ... ...
381104 381105.0 30170.0 26.0 88.0 0.0
381105 381106.0 40016.0 152.0 131.0 0.0
381106 381107.0 35118.0 160.0 161.0 0.0
381107 381108.0 44617.0 124.0 74.0 0.0
381108 381109.0 41777.0 26.0 237.0 0.0

381109 rows × 5 columns

Customer_Detail['Gender'].fillna(Customer_Detail['Gender'].mode()[0],inplace=True

Customer_Detail['Vehicle age'].fillna(Customer_Detail['Vehicle age'].mode()[0],inplace=True)
Customer_Detail['Region Code'].fillna(Customer_Detail['Region Code'].mode()[0],inplace=True)
Customer_Policy.fillna(Customer_Policy).mode()
 Ans:
 
 
  Customer_id Annual Premium(in Rs) Sales Channel code Vintage Response
0 1.0 2630.0 152.0 256.0 0.0
1 2.0 NaN NaN NaN NaN
2 3.0 NaN NaN NaN NaN
3 4.0 NaN NaN NaN NaN
4 5.0 NaN NaN NaN NaN
... ... ... ... ... ...
380717 381105.0 NaN NaN NaN NaN
380718 381106.0 NaN NaN NaN NaN
380719 381107.0 NaN NaN NaN NaN
380720 381108.0 NaN NaN NaN NaN
380721 381109.0 NaN NaN NaN NaN

380722 rows × 5 columns

Customer_Detail.dropna(subset=["Customer_id"],inplace=True)
Customer_Policy.dropna(subset=["Customer_id"],inplace=True)
 

ii outlier

Q1=Customer_Detail.quantile(0.25)
Q3=Customer_Policy.quantile(0.75)
print(Q1)

Ans:
Customer_id                95269.5
Age                           25.0
Driving Licence Present        1.0
Region Code                   15.0
Previously Insured             0.0
Name: 0.25, dtype: float64 
print(Q3)

Ans:
Customer_id              285818.75
Annual Premium(in Rs)     39401.75
Sales Channel code          152.00
Vintage                     227.00
Response                      0.00
Name: 0.75, dtype: float64

IQR=Q3-Q1
lower=Q1-(1.5*IQR)
upper=Q3+(1.5*IQR) 
Customer_Detail[(Customer_Detail<lower) | (Customer_Detail>upper)].sum()
Customer_Policy[(Customer_Policy<lower) | (Customer_Policy>upper)].sum()
Customer_Detail.describe()
 Ans:
 
 
  Customer_id Age Driving Licence Present Region Code Previously Insured
count 380723.000000 380357.000000 380331.000000 380723.000000 380342.000000
mean 190548.776244 38.822788 0.997868 26.391090 0.458259
std 110016.805160 15.512284 0.046128 13.223772 0.498255
min 1.000000 20.000000 0.000000 0.000000 0.000000
25% 95269.500000 25.000000 1.000000 15.000000 0.000000
50% 190543.000000 36.000000 1.000000 28.000000 0.000000
75% 285822.500000 49.000000 1.000000 35.000000 1.000000
max 381109.000000 85.000000 1.000000 52.000000 1.000000 
Customer_Policy.describe()
 Ans:
 
 
  Customer_id Annual Premium(in Rs) Sales Channel code Vintage Response
count 380722.000000 380378.000000 380322.000000 380334.000000 380361.000000
mean 190547.491663 30563.999774 112.036687 154.347192 0.122526
std 110013.824148 17197.918886 54.205529 83.670742 0.327892
min 1.000000 2630.000000 1.000000 10.000000 0.000000
25% 95276.250000 24407.000000 29.000000 82.000000 0.000000
50% 190536.500000 31667.000000 133.000000 154.000000 0.000000
75% 285818.750000 39401.750000 152.000000 227.000000 0.000000
max 381109.000000 540165.000000 163.000000 299.000000 1.000000
 
Customer_Policy['Annual Premium(in Rs)']=Customer_Policy['Annual Premium(in Rs)'].astype('float') 
Q1=Customer_Policy[['Annual Premium(in Rs)']].quantile(0.25)
Q2=Customer_Policy[['Annual Premium(in Rs)']].quantile(0.75)
((Customer_Policy[['Annual Premium(in Rs)']]<lower) | (Customer_Policy[['Annual Premium(in Rs)']]>upper)).sum
Customer_Policy['Response']=Customer_Policy['Response'].astype('float')
Q1=Customer_Policy[['Response']].quantile(0.25)
Q2=Customer_Policy[['Response']].quantile(0.75)
((Customer_Policy[['Response']]<lower)) | (Customer_Policy[['Response']]>upper).sum()
 
 
 

iii WhiteSpaces 

Customer_Detail.apply(lambda x:x.str.strip() if x.dtype=="object" else x)
Ans: 
  Customer_id Gender Age Driving Licence Present Region Code Previously Insured Vehicle age Vehicle Damage
0 1.0 Male 44.0 1.0 28.0 0.0 > 2 Years Yes
1 2.0 Male 76.0 1.0 3.0 0.0 1-2 Year No
2 3.0 Male 47.0 1.0 28.0 0.0 > 2 Years Yes
3 4.0 Male 21.0 1.0 11.0 1.0 < 1 Year No
4 5.0 Female 29.0 1.0 41.0 1.0 < 1 Year No
... ... ... ... ... ... ... ... ...
381104 381105.0 Male 74.0 1.0 26.0 1.0 1-2 Year No
381105 381106.0 Male 30.0 1.0 37.0 1.0 < 1 Year No
381106 381107.0 Male 21.0 1.0 30.0 1.0 < 1 Year No
381107 381108.0 Female 68.0 1.0 14.0 0.0 > 2 Years Yes
381108 381109.0 Male 46.0 1.0 29.0 0.0 1-2 Year No

380723 rows × 8 columns

Customer_Policy.apply(lambda x: x.str.strip() if x.dtype=="object" else x)
 Ans:
 
  Customer_id Annual Premium(in Rs) Sales Channel code Vintage Response
0 1.0 40454.0 26.0 217.0 1.0
1 2.0 33536.0 26.0 183.0 0.0
2 3.0 38294.0 26.0 27.0 1.0
3 4.0 28619.0 152.0 203.0 0.0
4 5.0 27496.0 152.0 39.0 0.0
... ... ... ... ... ...
381104 381105.0 30170.0 26.0 88.0 0.0
381105 381106.0 40016.0 152.0 131.0 0.0
381106 381107.0 35118.0 160.0 161.0 0.0
381107 381108.0 44617.0 124.0 74.0 0.0
381108 381109.0 41777.0 26.0 237.0 0.0

380722 rows × 5 columns

 

iv Case correction

Customer_Detail.apply(lambda x:x.astype(str).str.upper())
Customer_Detail
Ans:
  Customer_id Gender Age Driving Licence Present Region Code Previously Insured Vehicle age Vehicle Damage
0 1.0 Male 44.0 1.0 28.0 0.0 > 2 Years Yes
1 2.0 Male 76.0 1.0 3.0 0.0 1-2 Year No
2 3.0 Male 47.0 1.0 28.0 0.0 > 2 Years Yes
3 4.0 Male 21.0 1.0 11.0 1.0 < 1 Year No
4 5.0 Female 29.0 1.0 41.0 1.0 < 1 Year No
... ... ... ... ... ... ... ... ...
381104 381105.0 Male 74.0 1.0 26.0 1.0 1-2 Year No
381105 381106.0 Male 30.0 1.0 37.0 1.0 < 1 Year No
381106 381107.0 Male 21.0 1.0 30.0 1.0 < 1 Year No
381107 381108.0 Female 68.0 1.0 14.0 0.0 > 2 Years Yes
381108 381109.0 Male 46.0 1.0 29.0 0.0 1-2 Year No

380723 rows × 8 columns

 

convert nominal data into dummies

Customer_Detail2=pd.get_dummies(Customer_Detail)
Customer_Detail2
 
Ans:
 
  Customer_id Age Driving Licence Present Region Code Previously Insured Gender_Female Gender_Male Vehicle age_1-2 Year Vehicle age_< 1 Year Vehicle age_> 2 Years Vehicle Damage_No Vehicle Damage_Yes
0 1.0 44.0 1.0 28.0 0.0 0 1 0 0 1 0 1
1 2.0 76.0 1.0 3.0 0.0 0 1 1 0 0 1 0
2 3.0 47.0 1.0 28.0 0.0 0 1 0 0 1 0 1
3 4.0 21.0 1.0 11.0 1.0 0 1 0 1 0 1 0
4 5.0 29.0 1.0 41.0 1.0 1 0 0 1 0 1 0
... ... ... ... ... ... ... ... ... ... ... ... ...
381104 381105.0 74.0 1.0 26.0 1.0 0 1 1 0 0 1 0
381105 381106.0 30.0 1.0 37.0 1.0 0 1 0 1 0 1 0
381106 381107.0 21.0 1.0 30.0 1.0 0 1 0 1 0 1 0
381107 381108.0 68.0 1.0 14.0 0.0 1 0 0 0 1 0 1
381108 381109.0 46.0 1.0 29.0 0.0 0 1 1 0 0 1 0

380723 rows × 12 columns

 

Customer_Policy2=pd.get_dummies(Customer_Policy) 
Customer_Policy2
 Ans:
 
  Customer_id Annual Premium(in Rs) Sales Channel code Vintage Response
0 1.0 40454.0 26.0 217.0 1.0
1 2.0 33536.0 26.0 183.0 0.0
2 3.0 38294.0 26.0 27.0 1.0
3 4.0 28619.0 152.0 203.0 0.0
4 5.0 27496.0 152.0 39.0 0.0
... ... ... ... ... ...
381104 381105.0 30170.0 26.0 88.0 0.0
381105 381106.0 40016.0 152.0 131.0 0.0
381106 381107.0 35118.0 160.0 161.0 0.0
381107 381108.0 44617.0 124.0 74.0 0.0
381108 381109.0 41777.0 26.0 237.0 0.0

380722 rows × 5 columns

 

Drop duplicate

Customer_Detail2.drop_duplicates()
 
 
Ans:
 
  Customer_id Age Driving Licence Present Region Code Previously Insured Gender_Female Gender_Male Vehicle age_1-2 Year Vehicle age_< 1 Year Vehicle age_> 2 Years Vehicle Damage_No Vehicle Damage_Yes
0 1.0 44.0 1.0 28.0 0.0 0 1 0 0 1 0 1
1 2.0 76.0 1.0 3.0 0.0 0 1 1 0 0 1 0
2 3.0 47.0 1.0 28.0 0.0 0 1 0 0 1 0 1
3 4.0 21.0 1.0 11.0 1.0 0 1 0 1 0 1 0
4 5.0 29.0 1.0 41.0 1.0 1 0 0 1 0 1 0
... ... ... ... ... ... ... ... ... ... ... ... ...
381104 381105.0 74.0 1.0 26.0 1.0 0 1 1 0 0 1 0
381105 381106.0 30.0 1.0 37.0 1.0 0 1 0 1 0 1 0
381106 381107.0 21.0 1.0 30.0 1.0 0 1 0 1 0 1 0
381107 381108.0 68.0 1.0 14.0 0.0 1 0 0 0 1 0 1
381108 381109.0 46.0 1.0 29.0 0.0 0 1 1 0 0 1 0

380723 rows × 12 columns

Customer_Policy2.drop_duplicates()
 
 
  Customer_id Annual Premium(in Rs) Sales Channel code Vintage Response
0 1.0 40454.0 26.0 217.0 1.0
1 2.0 33536.0 26.0 183.0 0.0
2 3.0 38294.0 26.0 27.0 1.0
3 4.0 28619.0 152.0 203.0 0.0
4 5.0 27496.0 152.0 39.0 0.0
... ... ... ... ... ...
381104 381105.0 30170.0 26.0 88.0 0.0
381105 381106.0 40016.0 152.0 131.0 0.0
381106 381107.0 35118.0 160.0 161.0 0.0
381107 381108.0 44617.0 124.0 74.0 0.0
381108 381109.0 41777.0 26.0 237.0 0.0

380722 rows × 5 columns

 

3.Question

Master_Table=Customer_Detail2.merge(Customer_Policy2,on=["Customer_id"])
Master_Table
 Ans:
 
  Customer_id Age Driving Licence Present Region Code Previously Insured Gender_Female Gender_Male Vehicle age_1-2 Year Vehicle age_< 1 Year Vehicle age_> 2 Years Vehicle Damage_No Vehicle Damage_Yes Annual Premium(in Rs) Sales Channel code Vintage Response
0 1.0 44.0 1.0 28.0 0.0 0 1 0 0 1 0 1 40454.0 26.0 217.0 1.0
1 2.0 76.0 1.0 3.0 0.0 0 1 1 0 0 1 0 33536.0 26.0 183.0 0.0
2 3.0 47.0 1.0 28.0 0.0 0 1 0 0 1 0 1 38294.0 26.0 27.0 1.0
3 4.0 21.0 1.0 11.0 1.0 0 1 0 1 0 1 0 28619.0 152.0 203.0 0.0
4 5.0 29.0 1.0 41.0 1.0 1 0 0 1 0 1 0 27496.0 152.0 39.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
380331 381105.0 74.0 1.0 26.0 1.0 0 1 1 0 0 1 0 30170.0 26.0 88.0 0.0
380332 381106.0 30.0 1.0 37.0 1.0 0 1 0 1 0 1 0 40016.0 152.0 131.0 0.0
380333 381107.0 21.0 1.0 30.0 1.0 0 1 0 1 0 1 0 35118.0 160.0 161.0 0.0
380334 381108.0 68.0 1.0 14.0 0.0 1 0 0 0 1 0 1 44617.0 124.0 74.0 0.0
380335 381109.0 46.0 1.0 29.0 0.0 0 1 1 0 0 1 0 41777.0 26.0 237.0 0.0

380336 rows × 16 columns

 

4.Question 

Master_Table.groupby(['Gender_Male']).mean()['Annual Premium(in Rs)']

Ans:
Gender_Male
0    30491.959528
1    30623.670581
Name: Annual Premium(in Rs), dtype: float64
Master_Table.groupby(['Gender_Female']).mean()['Annual Premium(in Rs)']
Ans:
Gender_Female
0    30623.670581
1    30491.959528
Name: Annual Premium(in Rs), dtype: float64
Master_Table.groupby(['Age']).mean()['Annual Premium(in Rs)']
 Ans:
Age
20.0    26920.517153
21.0    30564.476130
22.0    30824.039423
23.0    30688.699065
24.0    31184.306152
            ...     
81.0    31201.571429
82.0    37705.379310
83.0    31012.727273
84.0    35440.818182
85.0    29792.363636
Name: Annual Premium(in Rs), Length: 66, dtype: float64
Master_Table['Gender_Male'].value_counts()
 Ans:
1    205851
0    174485
Name: Gender_Male, dtype: int64
Master_Table['Gender_Female'].value_counts()
 Ans:
0    205851
1    174485
Name: Gender_Female, dtype: int64
Master_Table.groupby(['Vehicle age_1-2 Year']).mean()['Annual Premium(in Rs)']
 Ans:
Vehicle age_1-2 Year
0    30606.151077
1    30524.590052
Name: Annual Premium(in Rs), dtype: float64
Master_Table.groupby(['Vehicle age_< 1 Year']).mean()['Annual Premium(in Rs)']
 Ans:
Vehicle age_< 1 Year
0    30903.960198
1    30115.361397
Name: Annual Premium(in Rs), dtype: float64
Master_Table.groupby(['Vehicle age_> 2 Years']).mean()['Annual Premium(in Rs)']
Ans:
Vehicle age_> 2 Years
0    30340.043557
1    35661.355606
Name: Annual Premium(in Rs), dtype: float64 

5.Question

import numpy as np
Master_Table.corr()
 Ans:
  Customer_id Age Driving Licence Present Region Code Previously Insured Gender_Female Gender_Male Vehicle age_1-2 Year Vehicle age_< 1 Year Vehicle age_> 2 Years Vehicle Damage_No Vehicle Damage_Yes Annual Premium(in Rs) Sales Channel code Vintage Response
Customer_id 1.000000 0.001669 -0.000475 -0.000727 0.002425 -0.001153 0.001153 0.001489 -0.001337 -0.000406 0.001523 -0.001436 0.003087 -0.002922 -0.000603 -0.001349
Age 0.001669 1.000000 -0.079770 0.042524 -0.254664 -0.145127 0.145127 0.692334 -0.787152 0.220577 -0.267226 0.267260 0.067778 -0.577861 -0.001218 0.111186
Driving Licence Present -0.000475 -0.079770 1.000000 -0.001062 0.014959 0.018318 -0.018318 -0.037506 0.040221 -0.005968 0.016550 -0.016649 -0.012027 0.043864 -0.000851 0.010303
Region Code -0.000727 0.042524 -0.001062 1.000000 -0.024604 -0.000503 0.000503 0.037779 -0.043952 0.014508 -0.027960 0.028062 -0.010700 -0.042414 -0.002752 0.010471
Previously Insured 0.002425 -0.254664 0.014959 -0.024604 1.000000 0.081909 -0.081909 -0.278771 0.358379 -0.191216 0.823324 -0.823328 0.004440 0.219312 0.002397 -0.341191
Gender_Female -0.001153 -0.145127 0.018318 -0.000503 0.081909 1.000000 -1.000000 -0.147153 0.165755 -0.043059 0.091462 -0.091447 -0.003818 0.110926 0.002450 -0.052542
Gender_Male 0.001153 0.145127 -0.018318 0.000503 -0.081909 -1.000000 1.000000 0.147153 -0.165755 0.043059 -0.091462 0.091447 0.003818 -0.110926 -0.002450 0.052542
Vehicle age_1-2 Year 0.001489 0.692334 -0.037506 0.037779 -0.278771 -0.147153 0.147153 1.000000 -0.918807 -0.220431 -0.284118 0.284076 -0.002369 -0.507877 -0.002672 0.164036
Vehicle age_< 1 Year -0.001337 -0.787152 0.040221 -0.043952 0.358379 0.165755 -0.165755 -0.918807 1.000000 -0.182465 0.369986 -0.369995 -0.022725 0.571054 0.002437 -0.209615
Vehicle age_> 2 Years -0.000406 0.220577 -0.005968 0.014508 -0.191216 -0.043059 0.043059 -0.220431 -0.182465 1.000000 -0.206575 0.206703 0.062055 -0.146110 0.000633 0.109413
Vehicle Damage_No 0.001523 -0.267226 0.016550 -0.027960 0.823324 0.091462 -0.091462 -0.284118 0.369986 -0.206575 1.000000 -0.997867 -0.009196 0.223998 0.001908 -0.354015
Vehicle Damage_Yes -0.001436 0.267260 -0.016649 0.028062 -0.823328 -0.091447 0.091447 0.284076 -0.369995 0.206703 -0.997867 1.000000 0.009259 -0.224008 -0.001960 0.353969
Annual Premium(in Rs) 0.003087 0.067778 -0.012027 -0.010700 0.004440 -0.003818 0.003818 -0.002369 -0.022725 0.062055 -0.009196 0.009259 1.000000 -0.113441 -0.000671 0.022295
Sales Channel code -0.002922 -0.577861 0.043864 -0.042414 0.219312 0.110926 -0.110926 -0.507877 0.571054 -0.146110 0.223998 -0.224008 -0.113441 1.000000 -0.000151 -0.139071
Vintage -0.000603 -0.001218 -0.000851 -0.002752 0.002397 0.002450 -0.002450 -0.002672 0.002437 0.000633 0.001908 -0.001960 -0.000671 -0.000151 1.000000 -0.001094
Response -0.001349 0.111186 0.010303 0.010471 -0.341191 -0.052542 0.052542 0.164036 -0.209615 0.

 

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Project 1 - Analyzing the Education trends in Tamilnadu

Objective:

This dashboard empowers mission driven organizations to harness the power of data visualization for social change. Women are tracked away from science and mathematics throughout their education, limiting their training and options to go into these fields as adults. The data set contains the data of women graduated by years,…

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14 Nov 2023 01:32 PM IST

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    Project 1 - English Dictionary App & Library Book Management System

    Objective:

    Project 1) English dictionary app and Library Book Management system

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    06 Nov 2023 04:04 PM IST

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      Project 1 - Implement and deploy CNN model in real-time using python on Fashion MNIST dataset

      Objective:

       Implement and deploy CNN model in real-time using python on Fashion MNIST dataset

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      20 Dec 2022 07:04 AM IST

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        Project 2

        Objective:

        Project 2

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        30 Nov 2022 11:41 AM IST

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