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ICICI Securities
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I applied via Campus Placement and was interviewed before Jul 2023. There was 1 interview round.
Developed a predictive model to forecast customer churn for a telecommunications company.
Identified key features such as customer tenure, monthly charges, and service usage
Collected and cleaned data from customer databases
Built a machine learning model using logistic regression or random forest algorithms
Evaluated model performance using metrics like accuracy, precision, and recall
Provided actionable insights to reduce
Types of errors in statistics include sampling error, measurement error, and non-sampling error.
Sampling error occurs when the sample does not represent the population accurately.
Measurement error is caused by inaccuracies in data collection or measurement instruments.
Non-sampling error includes errors in data processing, analysis, and interpretation.
Examples: Sampling error - selecting a biased sample, Measurement err...
Types of machine learning models include supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning: Models learn from labeled data, making predictions based on past examples (e.g. linear regression, support vector machines)
Unsupervised learning: Models find patterns in unlabeled data, clustering similar data points together (e.g. k-means clustering, PCA)
Reinforcement learning: Models le...
get_dummies() function in pandas library is used to convert categorical variables into dummy/indicator variables.
get_dummies() function creates dummy variables for categorical columns in a DataFrame.
It converts categorical variables into numerical representation for machine learning models.
Example: df = pd.get_dummies(df, columns=['column_name'])
I applied via Recruitment Consulltant and was interviewed before May 2023. There were 2 interview rounds.
Machine learning algorithms are used to analyze data and make predictions or decisions without being explicitly programmed.
Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.
Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.
These algorithms learn from data to improve their performance over...
I applied via Naukri.com and was interviewed in Jan 2021. There were 3 interview rounds.
Yes, I have worked on customer segmentation.
I have used clustering algorithms like K-means and hierarchical clustering to segment customers based on their behavior and demographics.
I have also used decision trees and random forests to identify the most important features for segmentation.
I have experience with both supervised and unsupervised learning techniques for customer segmentation.
I have worked on projects where...
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I applied via Referral and was interviewed in Nov 2024. There were 2 interview rounds.
posted on 11 Dec 2024
I applied via Recruitment Consulltant and was interviewed in Nov 2024. There was 1 interview round.
Developed a generative AI model to create realistic images of fictional characters.
Used GANs (Generative Adversarial Networks) to generate new images based on existing data.
Trained the model on a dataset of character images from various sources.
Implemented techniques like style transfer to enhance the diversity and creativity of generated images.
Evaluated the model's performance based on image quality metrics and user
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
posted on 7 May 2024
I applied via Job Portal and was interviewed in Nov 2023. There was 1 interview round.
Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent.
Gradient descent is used to find the minimum of a function by taking steps proportional to the negative of the gradient at the current point.
It is commonly used in machine learning to optimize the parameters of a model by minimizing the loss function.
There are different variants of gradie...
LSTM (Long Short-Term Memory) is a type of recurrent neural network designed to handle long-term dependencies.
LSTM has three gates: input gate, forget gate, and output gate.
Input gate controls the flow of information into the cell state.
Forget gate decides what information to discard from the cell state.
Output gate determines the output based on the cell state.
T-test is a statistical test used to determine if there is a significant difference between the means of two groups.
Mean is the average of a set of numbers, median is the middle value when the numbers are ordered, and mode is the most frequently occurring value.
Mean is sensitive to outliers, median is robust to outliers, and mode is useful for categorical data.
T-test is used to compare means of two groups, mean is used...
Random Forest is an ensemble learning method used for classification and regression tasks.
Random Forest is a collection of decision trees that are trained on random subsets of the data.
Each tree in the forest makes a prediction, and the final prediction is the average (regression) or majority vote (classification) of all trees.
Random Forest helps reduce overfitting and improve accuracy compared to a single decision tre...
I applied via Campus Placement and was interviewed in Nov 2023. There were 2 interview rounds.
Mutual funds are investment vehicles that pool money from multiple investors to invest in a diversified portfolio of securities.
Mutual funds are managed by professional fund managers who make investment decisions on behalf of the investors.
Investors can buy shares of mutual funds, which represent their ownership in the fund's portfolio.
Mutual funds offer diversification, liquidity, and professional management to invest...
I was interviewed before Apr 2023.
based on 2 interviews
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