article thumbnail

Pima Indian Diabetes Prediction

Heartbeat

I will start by looking at the data distribution, followed by the relationship between the target variable and independent variables. #replacing the missing values with the mean variables = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI'] for i in variables: df[i].replace(0,df[i].mean(),inplace=True)

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

Predicting Heart Failure Survival with Machine Learning Models — Part II

Towards AI

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. Preface In the previous post, we looked at the heart failure dataset of 299 patients, which included several lifestyle and clinical features. among supervised models and k-nearest neighbors, DBSCAN, etc.,

article thumbnail

2024 Tech breakdown: Understanding Data Science vs ML vs AI

Pickl AI

Key Components In Data Science, key components include data cleaning, Exploratory Data Analysis, and model building using statistical techniques. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition.

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

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

Five machine learning types to know

IBM Journey to AI blog

Classification algorithms —predict categorical output variables (e.g., “junk” or “not junk”) by labeling pieces of input data. Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others.