Project

Predictive Analysis and Optimization with Boosting Algorithms

Challenging

11 completions

~ 29 hours

5.0

Gain a deeper understanding of your data by conducting Exploratory Data Analysis (EDA). Use ColumnTransformer to set up an efficient and streamlined machine-learning workflow. To estimate medical insurance costs, perform predictive analysis with the XGBoost, Catboost, and LightGBM regressors. Optimize your models' hyperparameters with the Optuna library for the best predictions.

Provided by

JetBrains Academy

About

This project uses boosting algorithms to develop and optimize predictive models for medical insurance costs. It encompasses data analysis and feature transformation and provides a basic overview of boosting algorithms. It will guide and support you in implementing and optimizing these algorithms to achieve accurate predictive analytics.

Graduate project

This project covers the core topics of the Data Scientist course, making it sufficiently challenging to be a proud addition to your portfolio.

At least one graduate project is required to complete the course.

What you'll learn

Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:

Split the data into train, test, and validation sets.

Preprocess the numerical, ordinal categorical, and nominal categorical features.

Train a XGBoost, CatBoost, and LightGBM model and evaluate with MAE.

Optimize boosting algorithms hyperparameters with Optuna.

Reviews

Il'dar Kharrasov

7 months ago

5.0

I learned next technologies:1) optuna2) xgboost, lightgbm, catboost regression tasks3) hyperparameters tuningTHANK YOU!!!!

Amirhossein Biglari

2 years ago

5.0

I have learned to train XGBoost, CatBoost and LightGBM model and compare their results, also used Optuna to fined-tune them and improve them.

5.0

Learners who completed this project within the Data Scientist course rated it as follows:

Usefulness

5.0

Fun

5.0

Clarity

5.0