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posted on 5 Jun 2024
I applied via LinkedIn and was interviewed in Jun 2024. There was 1 interview round.
I applied via Recruitment Consulltant and was interviewed in Apr 2024. There were 2 interview rounds.
You should be familiar with core java features. Collections, converting one collection Array / Vector to other. Removing duplicates. Lambda chaining / Functional programming. Basic SQL - Joins, Aggregates etc.
posted on 18 Feb 2024
I applied via Job Portal and was interviewed in Jan 2024. There were 2 interview rounds.
posted on 22 May 2024
I applied via Referral and was interviewed in Apr 2024. There was 1 interview round.
I applied via AmbitionBox and was interviewed in Jun 2023. There were 3 interview rounds.
I applied via campus placement at Indian Institute of Management (IIM), Kolkatta and was interviewed before Apr 2023. There was 1 interview round.
DBSCAN is a density-based clustering algorithm that groups together points that are closely packed.
DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise.
It groups together points that are closely packed based on two parameters - epsilon (eps) and minimum points (minPts).
Points are classified as core points, border points, or noise points.
Core points have at least minPts points within eps distanc...
KNN models are used for classification and regression tasks based on similarity to nearest neighbors, while K-means is a clustering algorithm based on distance to centroids.
KNN models assign a class label to a new data point based on majority class of its k-nearest neighbors
K-means clusters data points into k clusters based on distance to centroids
KNN is a supervised learning algorithm, while K-means is an unsupervised
Features to consider in designing a time series model
Identifying seasonality and trends in the data
Selecting appropriate lag values for autoregressive components
Choosing the right forecasting method (e.g. ARIMA, Exponential Smoothing)
Evaluating model performance using metrics like RMSE and MAE
I won a Kaggle competition by applying a combination of XGBoost and neural network models.
Won Kaggle competition by predicting housing prices using XGBoost and neural network models
Achieved top 5% ranking in the competition
Used feature engineering techniques to improve model performance
I applied via Recruitment Consulltant
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