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I applied via Campus Placement and was interviewed in Nov 2023. There were 5 interview rounds.
2 DSA easy to medium question in python language.
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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 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...
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...
Common ways to evaluate Time Series model include AIC, BIC, RMSE, MAE, ACF, PACF, etc.
Use Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to compare models
Calculate Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) to assess model accuracy
Analyze Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to check for autocorrelation in residuals
Use techniques like feature selection, regularization, PCA, and VIF to handle multicollinearity.
Perform feature selection to choose the most relevant variables for the model.
Apply regularization techniques like Lasso or Ridge regression to penalize high coefficients.
Utilize Principal Component Analysis (PCA) to reduce dimensionality and decorrelate variables.
Check for Variance Inflation Factor (VIF) to identify highly
TF IDF is a technique used in NLP to measure the importance of a word in a document within a collection of documents.
TF IDF stands for Term Frequency-Inverse Document Frequency.
It is used to determine how important a word is in a document relative to a collection of documents.
TF IDF is calculated by multiplying the term frequency (TF) of a word in a document by the inverse document frequency (IDF) of the word across al...
I was a test in our college of about 45min revolving around aptitude.
Few basic coding questions.
I applied via Campus Placement and was interviewed before Dec 2023. There were 2 interview rounds.
The first technical round will cover how computer vision works, including the advantages and disadvantages of regression and random forest. It will also include discussions on when to use precision and recall, methods to reduce false positives, and criteria for selecting different models. Additionally, disadvantages of PCA will be addressed, along with project-related questions. The second round will focus on standard aptitude tests, while the third round will involve a casual conversation with the Executive Vice President.
Normal aptitude questions
I applied via Job Portal and was interviewed in Dec 2021. There were 2 interview rounds.
I applied via Naukri.com and was interviewed in Mar 2024. There were 3 interview rounds.
Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions 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 require training data to learn patte...
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
1. Define the problem and objectives of the credit risk model.
2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.
3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.
4. Select a suitable machine learning algorithm such as logi...
AIC and BIC are statistical measures used for model selection in the context of regression analysis.
AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.
BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
XGBoost is known for its accuracy and performance on structured/tabular data.
LightGBM is faster and more memory-efficient, making it suitable for large datasets.
LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
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