Remove Data Analysis Remove EDA 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

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning. classification, regression) and data characteristics.

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

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.