Remove EDA Remove Exploratory Data Analysis Remove Support Vector Machines
article thumbnail

Enhancing Customer Churn Prediction with Continuous Experiment Tracking

Heartbeat

In a typical MLOps project, similar scheduling is essential to handle new data and track model performance continuously. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). Support Vector Machine (svm): Versatile model for linear and non-linear data.

article thumbnail

Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. Here is a brief description of the same.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

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.

article thumbnail

Forecasting Carbon Emission Across Continents Research & Data Challenge Review

Ocean Protocol

Here we use data science to diagnose the issues and propose better practices to treat our planet better than the last 30 years. Exploratory Data Analysis (EDA) In Asia, the surge in CO2 and GHG emissions is closely linked to rapid population growth, industrialization, and the rise of emerging economies.

article thumbnail

Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data. E Ensemble Learning: A technique combining multiple models to improve a Machine Learning system’s overall performance and robustness.