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JPMorgan Chase & Co.
Proud winner of ABECA 2024 - AmbitionBox Employee Choice Awards
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I was asked Python, sql, coding questions
Case study on how would you identify the total number of footfall on a airport
I applied via campus placement at Birla Institute of Technology and Science (BITS), Pilani
Developed machine learning models to predict customer churn and optimize marketing campaigns.
Built predictive models using Python and scikit-learn
Utilized SQL to extract and manipulate data for analysis
Collaborated with cross-functional teams to implement data-driven solutions
I applied via Referral and was interviewed in Nov 2024. There were 2 interview rounds.
posted on 6 Jan 2025
SQL & aptitude question
1 coding question for 45 min
I applied via LinkedIn and was interviewed in Jul 2024. There were 3 interview rounds.
Assignment on credit risk
Model Gini is a measure of statistical dispersion used to evaluate the performance of classification models.
Model Gini is calculated as twice the area between the ROC curve and the diagonal line (random model).
It ranges from 0 (worst model) to 1 (best model), with higher values indicating better model performance.
A Gini coefficient of 0.5 indicates a model that is no better than random guessing.
Commonly used in credit
XGBoost model is trained by specifying parameters, splitting data into training and validation sets, fitting the model, and tuning hyperparameters.
Specify parameters for XGBoost model such as learning rate, max depth, and number of trees
Split data into training and validation sets using train_test_split function
Fit the XGBoost model on training data using fit method
Tune hyperparameters using techniques like grid search
I applied via Company Website and was interviewed before Aug 2023. There were 2 interview rounds.
Bert and transformer are models used in natural language processing for tasks like text classification and language generation.
Bert (Bidirectional Encoder Representations from Transformers) is a transformer-based model developed by Google for NLP tasks.
Transformer is a deep learning model architecture that uses self-attention mechanisms to process sequential data like text.
Both Bert and transformer have been widely use...
NLP pre processing techniques involve cleaning and preparing text data for analysis.
Tokenization: breaking text into words or sentences
Stopword removal: removing common words that do not add meaning
Lemmatization: reducing words to their base form
Normalization: converting text to lowercase
Removing special characters and punctuation
I was interviewed before Apr 2023.
Cross validation is a technique used to assess the performance of a predictive model by splitting the data into training and testing sets multiple times.
Cross validation helps to evaluate how well a model generalizes to new data.
It involves splitting the data into k subsets, training the model on k-1 subsets, and testing it on the remaining subset.
Common types of cross validation include k-fold cross validation and lea...
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