Predict bitcoin price using gold and S&P 500 data implementing LSTM, Gradient Boosting Regression, and Random Forest
This project aims to develop a predictive model estimating insurance coverage costs for customers based on their attributes and product choices. The dataset includes transaction and quote details for policy purchasers. The objective is to predict quoted coverage costs, considering customer traits and 7 customizable product options.
All things around ... Regression
This project develops a machine learning model to predict the salaries of baseball players based on their past performance.
🇵🇱🏠 The project predicts an apartment price for Warsaw, Krakow and Poznan. Distributed apartments by districts using geopandas; built XGBoost model with MAPE = 9% (the best of others).
In this notebook, we'll build from scratch a gradient boosted trees regression model that includes a learning rate hyperparameter, and then use it to fit a noisy nonlinear function.
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