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I applied via LinkedIn and was interviewed in May 2024. There was 1 interview round.
I applied via LinkedIn and was interviewed before May 2023. There were 4 interview rounds.
It was easy as compared to my expectatons
Shared case study with complete solution
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I applied via Naukri.com and was interviewed in Nov 2024. There were 2 interview rounds.
SQL, excel, tableau , looker studio
I applied via campus placement at SRM Institute of Science & Technology, Chennai and was interviewed in Apr 2024. There was 1 interview round.
Logistic regression is a linear model used for binary classification, while SVM is a non-linear model that can handle complex decision boundaries.
Logistic regression is a probabilistic model that predicts the probability of a binary outcome based on input features.
SVM aims to find the hyperplane that best separates the classes in a high-dimensional space.
Logistic regression is more interpretable and easier to implement...
I applied via Referral and was interviewed before Apr 2022. There were 3 interview rounds.
I applied via Referral and was interviewed in Jun 2024. There were 3 interview rounds.
50 questions need to do in 15 mins.
I applied via Naukri.com and was interviewed in Nov 2024. There were 2 interview rounds.
SQL, excel, tableau , looker studio
I applied via campus placement at SRM Institute of Science & Technology, Chennai and was interviewed in Apr 2024. There was 1 interview round.
Logistic regression is a linear model used for binary classification, while SVM is a non-linear model that can handle complex decision boundaries.
Logistic regression is a probabilistic model that predicts the probability of a binary outcome based on input features.
SVM aims to find the hyperplane that best separates the classes in a high-dimensional space.
Logistic regression is more interpretable and easier to implement...
I applied via Referral and was interviewed before Apr 2022. There were 3 interview rounds.
I applied via Company Website and was interviewed in Feb 2024. There were 2 interview rounds.
Read about MPIN that is used to access mobile banking apps
Many a times users end up setting an MPIN that is guessable because
1. It is a commonly used MPIN eg 1122
2. It is a combination of easily known demographics of the user. Eg: if the birthdate is 02- Jan-1998 then MPIN could be 0201 or 9802 or 0201 etc. Demographics such as these could be used alone or in a combination
a. DOB
b. Wedding Anniversary
c. Spouse birthday
Solutions Required
1. Part A: Assume that the MPIN is 4-digits. Write a program that suggests if the MPIN is a commonly used one. Ignore the demographics for this part
2. Part B: Enhance the above to take user's demographics as input and provides an output
a. Strength: WEAK or STRONG
3. Part C: Enhance the above to provide the following outputs
a. Strength: WEAK or STRONG
b. If weak then the reason why was it considered weak: It should give from the following the reasons as an array. Array should be empty if Strength is STRONG and non-empty if WEAK
i. COMMONLY_USED
ii. DEMOGRAPHIC_DOB_SELF
iii. DEMOGRAPHIC_DOB_SPOUSE
iv. DEMOGRAPHIC_ANNIVERSARY
4. Part E: Above with a 6-digit PIN
5. Write code that tests the above written code using a set of inputs. Write at least 20 test case in PYTHON
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