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Case Study on Ride-Hailing App

Case Study: This is an open-ended case study asked around the reliability of ride-hailing service having decreased in a particular area. I had to suggest the data variables I will consider analyzing to solve the problem. Interviewer : The reliability of Ola cars has decreased in a particular area. What data parameters will you consider analyzing the problem? Priyanka : There were some clarifying questions that I asked to get more insights about the problem. How do you measure reliability exactly? Interviewer : Reliability would be a measure of Rides getting requested vs requests getting accepted i.e., Out of the requests accepted how many are actually completed. Priyanka : Ok since the platform has both driver and rider sides do you want me to focus on any specific one. Interviewer : You are free to choose. Priyanka : Ok I would like to think about the data variables that driver considers while accepting a ride: Distance/Length of trip  — Drivers won’t prefer short rides as they might...
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Brain Teasers | Tiger and Sheep

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Question:   One hundred tigers and one sheep arc are put on a magic island that only has grass. Tigers can eat grass, but they would rather eat sheep Assume:    A. Each time only one tiger can eat one sheep, and that tiger itself will become a sheep after it cats the sheep.    B. All tigers are smart and perfectly rational and they want to survive. So will the sheep be eaten?      Solution :     Let no of tigers = n     Let's start with 1 tiger(n=1).   Since there is only one tiger it will definitely eat the sheep as there will be no more tigers to eat him once he becomes a sheep.       With n=2 tigers,   If any of the tigers eat the sheep, it will become a sheep itself so the other tiger who is left will definitely eat him. So none of the tigers will eat the sheep.     With n=3 tigers,   The sheep will be eaten since each tiger realizes that once it becomes a sheep there will be two tige...
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Model Drift in Machine Learning

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  We expect our model to perform the same as it does with the training data. However if the distribution of production data is different from that of the training data, this may lead to Model Drift. Model drift refers to the decay of the model’s predictive power. Model Drift occurs when: Training data is poorly sampled There is a change in the underlying business context Why is important to monitor Model Drift? It is necessary to monitor performance for model drift to ensure accurate predictions and check if retraining is required. Kinds of Drift Data Drift/Feature drift   — When there is a change in the input feature. Ta rget Drift   — When there is a change in the distribution of the target variable. Concept Drift   — When there is a change between the pattern or the relationship between the predictors and the outcome. Data Drift: This is also known as feature drift, population drift or covariate drift. Data drift is observed when there is a change in the distribut...
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