Remove EDA Remove Python Remove Support Vector Machines
article thumbnail

Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

article thumbnail

Enhancing Customer Churn Prediction with Continuous Experiment Tracking

Heartbeat

Import Libraries First, import the required Python libraries, such as Comet ML, Optuna, and scikit-learn. 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.

professionals

Sign Up for our Newsletter

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

article thumbnail

Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Examples include linear regression, logistic regression, and support vector machines.

article thumbnail

Forecasting Carbon Emission Across Continents Research & Data Challenge Review

Ocean Protocol

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. The model utilizes the Ordinary Least Squares (OLS) method from the Statsmodels library in Python.

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.