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Approach involves data preprocessing, model training, evaluation, and interpretation.
Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.
Split the data into training and testing sets.
Train the logistic regression model on the training data.
Evaluate the model using metrics like accuracy, precision, recall, and F1 score.
Interpret the model coefficients to under...
I would seek opportunities to apply my skills in related fields within the company.
Explore other departments or teams within the company that may have projects related to my field of interest
Offer to collaborate with colleagues in different departments to bring a new perspective to their projects
Seek out professional development opportunities to expand my skills and knowledge in related areas
Questions related to basic coding were asked, and some background on projects and discussions alongside maths and statistics concepts
I applied via Campus Placement and was interviewed in Sep 2024. There was 1 interview round.
Decision tree is a tree-like model of decisions and their possible consequences, while random forest is an ensemble learning method that builds multiple decision trees and merges them together.
Decision tree is a flowchart-like structure where each internal node represents a decision based on an attribute, each branch represents the outcome of the decision, and each leaf node represents a class label.
Random forest is a ...
I applied via Campus Placement and was interviewed before May 2023. There were 2 interview rounds.
It been for 45 mins. question asked from python,ML,Deep learning and maths.
Correlation measures the strength and direction of a linear relationship between two variables, while covariance measures the extent to which two variables change together.
Correlation ranges from -1 to 1, where 1 indicates a perfect positive relationship, -1 indicates a perfect negative relationship, and 0 indicates no relationship.
Covariance can be positive, negative, or zero. A positive covariance indicates that as o...
Test 45 mins 30 ques
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.
It assumes a linear relationship between the independent and dependent variables.
The goal of linear regression is to find the best-fitting line that minimi...
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 random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.
Random forest is robust to overfitting and noisy data, and it can handle large datasets...
XGBoost is an optimized distributed gradient boosting library designed for efficient and accurate large-scale machine learning.
XGBoost stands for eXtreme Gradient Boosting.
It is a popular machine learning algorithm known for its speed and performance.
XGBoost is used for regression, classification, ranking, and user-defined prediction problems.
It is based on the gradient boosting framework and uses decision trees as bas...
LSTM and GRU are types of recurrent neural networks used for processing sequential data.
LSTM (Long Short-Term Memory) networks are capable of learning long-term dependencies in data.
GRU (Gated Recurrent Unit) networks are simpler than LSTM and have fewer parameters.
LSTM has three gates (input, output, forget) while GRU has two gates (update, reset).
LSTM is better at capturing long-term dependencies but is more complex,...
Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.
It aims to determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.
Common methods of hypothesis testing include t-tests, chi-square tests, and ANOVA.
The p-value is used to dete...
I applied via Job Portal and was interviewed in Jun 2024. There were 2 interview rounds.
Mcq based test on data science concepts
Precision and recall are metrics used to evaluate the performance of classification models.
Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.
Recall is the ratio of correctly predicted positive observations to the all observations in actual class.
F1 score is the weighted average of precision and recall, where the best value is 1 and the worst is 0.
Precision ...
Dropout is a regularization technique used in neural networks to prevent overfitting by randomly setting some neuron outputs to zero during training.
Dropout is a regularization technique used in neural networks to prevent overfitting.
During training, a fraction of neurons are randomly selected and their outputs are set to zero.
This helps prevent complex co-adaptations in neurons and improves generalization.
Dropout is t...
Simple mediam DSA style questions around ML
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