ML regression and model comparison

Fabian Contreras

Data Visualizer

ML Engineer

Data Analyst

Come with me on this journey through the world of machine learning and regression models! This project explores health insurance cost prediction by comparing two regression models. you can check it out at https://projects.falcontreras.com/healthcare_regression.html

Designed to showcase the machine learning workflow, it highlights:

Data preprocessing

Model selection

Model evaluation

Generating insights from the results

This notebook is ideal for those interested in learning about regression techniques and how machine learning can be applied to real-world problems.

✨ Important sections

Exploratory Data Analysis (EDA)

Data Preprocessing

Model Comparisons

Evaluation Metrics

Model implementation

📓 Project Overview

Data Loading and Exploration:

Understand the dataset structure.

Explore relationships between variables and identify key features.

Data Preprocessing:

Handle missing values, outliers, and scaling using StandardScaler.

Prepare data for machine learning models with train_test_split.

Regression Models:

Compare two models:

Linear Regression: A simple, interpretable regression model.

Random Forest Regressor: An ensemble model to capture non-linear relationships.

Use GridSearchCV to optimize hyperparameters for the Random Forest model.

Evaluation and Insights:

Evaluate models using metrics such as:

R²: Proportion of variance explained by the model.

RMSE: Standard deviation of prediction errors.

MAE: Average magnitude of errors.

Visualize results with Matplotlib and Seaborn for better understanding.

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Posted Dec 3, 2024

ML with health insurance cost prediction, comparing regression models. Highlights preprocessing, model selection, evaluation, training and model application.

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Data Visualizer

ML Engineer

Data Analyst

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