1.Calculating Rolling Sum for the Last 30 Days
2.Enhancing Fraud Detection: Efficient Challenge Authentication Methods
3.Detecting Fraudulent Transactions: Strategies and Techniques
4.Finding the First Missing Value in a Consecutive List.
5.Precision and Recall
6.Bias-Variance Tradeoff in Machine Learning
7.Handling Multi-class Problems with Logistic Regression
8.Difference Between Multi-class and Multi-label Problems
9.ML System Design for Fraud Detection
10.Handling Imbalanced Data
11.Simple SQL Query on Subscription Data
12.Find the First Unique Character in a String
13.Count Strings Starting and Ending with a Vowel
14.Counting Valid Strings from String Array Based on Vowel Criteria
15.Counting Song Pairs with Sum Divisible by 60
16.Vowel Prefix Count in Queries
17.Max Positive Prefix Sum
18.Closest Numbers
19.Playlist Song Pairing
20.First Unique Character Function
21.Vowels Query Function
22.Maximize Positive Prefixes
23.Closest Numbers
24.Playlist Pairing
25.Whole Minute Dilemma
26.Maximize Positive Prefix Sums
27.Vowels Prefix Sum
28.Minute Dilemma
29.Vowels
30.Positive Prefixes
31.First Unique Character
32.Closest Number
33.Whole Minute Dilemma
34.SQL and coding interview round
35.SQL-focused interview round
36.Find the Xth Largest Element in an Array
37.How do you evaluate a model?
38.How do you deal with imbalanced data?
39.What is TFIDF and how is it used?
40.Difference between Bias and Variance?
41.What is the difference between Boosting and Bagging?
42.Explain the difference between a decision tree and RandomForest/LightGBM.
43.Sanity Check for Campaign Results
44.Designing an Experiment
45.SQL Window Functions
46.Implement a Transaction Filter Function
1. Calculating Rolling Sum for the Last 30 Days
2. Enhancing Fraud Detection: Efficient Challenge Authentication Methods
detect fraud以后，需要给user send challenges：sms, email, ivr。brainstorm一下其他更好的challenge authentication的方法，从cost,effective考虑。.
3. Detecting Fraudulent Transactions: Strategies and Techniques
给一些transactional的信息，怎么判断是不是fraud
4. Finding the First Missing Value in a Consecutive List.
find 1st missing value in a consecutive list, like [1,2,4,5] --->3
5. Precision and Recall
Define precision and recall. When is it best to use these metrics, and how do they help evaluate the performance of a classification model?
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