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posted on 25 Jul 2024
Python arrays loops data structures
posted on 10 May 2024
My favorite algorithm is Random Forest, which I have implemented for predicting customer churn in a telecom company.
Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.
I have implemented Random Forest in Python using scikit-learn library for a telecom company to predict customer churn based on various features like call d...
posted on 29 Mar 2023
I applied via Company Website and was interviewed in Mar 2023. There were 4 interview rounds.
I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.
Context window in LLMs refers to the number of surrounding words considered when predicting the next word in a sequence.
Context window helps LLMs capture dependencies between words in a sentence.
A larger context window allows the model to consider more context but may lead to increased computational complexity.
For example, in a context window of 2, the model considers 2 words before and 2 words after the target word fo
top_k parameter is used to specify the number of top elements to be returned in a result set.
top_k parameter is commonly used in machine learning algorithms to limit the number of predictions or recommendations.
For example, in recommendation systems, setting top_k=5 will return the top 5 recommended items for a user.
In natural language processing tasks, top_k can be used to limit the number of possible next words in a
Regex patterns in Python are sequences of characters that define a search pattern.
Regex patterns are used for pattern matching and searching in strings.
They are created using the 're' module in Python.
Examples of regex patterns include searching for email addresses, phone numbers, or specific words in a text.
Iterators are objects that allow iteration over a sequence of elements. Tuples are immutable sequences of elements.
Iterators are used to loop through elements in a collection, like lists or dictionaries
Tuples are similar to lists but are immutable, meaning their elements cannot be changed
Example of iterator: for item in list: print(item)
Example of tuple: my_tuple = (1, 2, 3)
Yes, I have experience working with REST APIs in various projects.
Developed RESTful APIs using Python Flask framework
Consumed REST APIs in data analysis projects using requests library
Used Postman for testing and debugging REST APIs
Forecasting problem - Predict daily sku level sales
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
Bias is the error introduced by approximating a real-world problem, leading to underfitting.
Variance is the error introduced by modeling the noise in the training data, leading to overfitting.
High bias can cause a model to miss relevant relationships between features and target variable.
High variance can cause a model to ...
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.
Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.
Examples of parametric models inc...
I was interviewed in May 2024.
Maths and stats refer to the study of mathematical concepts and statistical methods for analyzing data.
Maths involves the study of numbers, quantities, shapes, and patterns.
Stats involves collecting, analyzing, interpreting, and presenting data.
Maths is used to solve equations, calculate probabilities, and model real-world phenomena.
Stats is used to make informed decisions, draw conclusions, and test hypotheses.
Both ma...
Confusion matrix what are your job rolls explain me Gradient boosting algorithm?
Count the number of duplicate words in a string.
Split the string into words using a delimiter like space or punctuation.
Create a dictionary to store the count of each word.
Iterate through the words and increment the count in the dictionary.
Count the number of words with count greater than 1 as duplicates.
Chunking in LLM refers to breaking down text into smaller chunks for better processing by the language model.
Chunking helps improve the efficiency of the language model by breaking down large text inputs into smaller segments.
It can help the model better understand the context and relationships within the text.
Chunking is commonly used in natural language processing tasks such as text summarization and sentiment analys
posted on 7 Oct 2023
Basic DP, Array Questions
I applied via Naukri.com and was interviewed in Nov 2022. There were 3 interview rounds.
TF-IDF is a statistical measure used to evaluate the importance of a word in a document.
TF-IDF stands for Term Frequency-Inverse Document Frequency
It is used to weigh a word's importance in a document by considering its frequency in the document and across all documents
The formula for TF-IDF is: TF-IDF = TF * IDF
TF (Term Frequency) measures how frequently a term appears in a document
IDF (Inverse Document Frequency) mea...
Group by is used to group data based on a column while window function is used to perform calculations on a specific window of data.
Group by is used to aggregate data based on a specific column
Window function is used to perform calculations on a specific window of data
Group by is used with aggregate functions like sum, count, avg, etc.
Window function is used with analytical functions like rank, lead, lag, etc.
Group by ...
Developed a predictive model to forecast customer churn for a telecom company.
Used machine learning algorithms like logistic regression and random forest.
Preprocessed and cleaned the dataset by handling missing values and outliers.
Performed feature engineering to create new variables for better model performance.
Evaluated model performance using metrics like accuracy, precision, and recall.
Implemented the model in prod
Seeking new challenges and opportunities for growth.
Looking for a more challenging role that aligns with my career goals.
Seeking a company that values innovation and encourages professional development.
Want to work in a more collaborative and diverse team environment.
Desire to explore new technologies and industries.
Current company lacks opportunities for advancement or career growth.
based on 1 interview
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