Predicting the Pokemon Card’s Hit Points from its Features

Python
Machine Learning
Data Visualization
A machine learning project that predicts the hit points (HP) of Pokemon cards based on their various features using Python and data visualization techniques.
Published

September 21, 2025

Project Summary

This projects takes most pokemon cards that exists for the first generation Pokemons and create a linear regression with regularization to predict the hit points (HP) of these cards. In total there are 151 first generation pokemons and about 4574 total combined pokemon cards for all of them.

Project Plan

  • Data Collection
  • Clean and preprocess data
  • Seperate and categorize columns
  • Feature scale the data
  • Train the model
  • Evaluate the model

Required Python Packages

pip install matplotlib pandas scikit-learn seaborn

Project Notebooks

You can see my first iteration of this project here.

This project is broken into 4 main sections:

  1. Data Collection and Preprocessing
  2. Feature Engineering
  3. Exploratory Data Analysis
  4. Transforming, Scaling, and Fitting the Data