Analytics Vidhya

JULY 8, 2023

Among these trailblazers stands an exceptional individual, Mr. Nirmal, a visionary in the realm of data science, who has risen to become a driving […] The post The Success Story of Microsoft’s Senior Data Scientist appeared first on Analytics Vidhya.

Data Scientist 291

Data Scientist Data Science Analytics Analytics 291

Ocean Protocol

SEPTEMBER 29, 2023

Fantasy Football is a popular pastime for a large amount of the world, we gathered data around the past 6 seasons of player performance data to see what our community of data scientists could create. By leveraging cross-validation, we ensured the model’s assessment wasn’t reliant on a singular data split.

Cross Validation 59

Cross Validation Predictive Analytics Exploratory Data Analysis EDA 59

Pickl AI

JANUARY 3, 2024

Key takeaways Feature engineering transforms raw data for ML, enhancing model performance and significance. EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models. Steps of Feature Engineering 1.

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Pickl AI

FEBRUARY 12, 2024

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. What is cross-validation, and why is it used in Machine Learning? Here is a brief description of the same.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

DrivenData Labs

APRIL 10, 2024

Summary of approach: In the end I managed to create two submissions, both employing an ensemble of models trained across all 10-fold cross-validation (CV) splits, achieving a private leaderboard (LB) score of 0.7318.

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

Pickl AI

JULY 12, 2024

Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset. Techniques such as statistical summaries, data visualisation, and correlation analysis help uncover patterns, anomalies, and relationships within the data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

DECEMBER 16, 2024

Making Data Stationary: Many forecasting models assume stationarity. If the data is non-stationary, apply transformations like differencing or logarithmic scaling to stabilize its statistical properties. Exploratory Data Analysis (EDA): Conduct EDA to identify trends, seasonal patterns, and correlations within the dataset.

AI 52

AI AI Machine Learning Machine Learning 52