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.
