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CodeSignal

PredictHealth's Insurance Data & Basic Regression

via CodeSignal

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Overview

Master the foundational skills needed for insurance cost prediction. Learn to explore and visualize insurance data, identify key cost factors, build simple regression models, and evaluate prediction accuracy.

Syllabus

  • Unit 1: Exploring and Preparing the PredictHealth Insurance Dataset
    • Loading Your First Insurance Dataset
    • Creating Your First Dataset Overview Function
    • Fixing Bugs and Checking Data Quality
    • Filtering High Risk Insurance Clients
  • Unit 2: Visualizing PredictHealth's Customer Profiles
    • Enhancing Histograms with Custom Parameters
    • Comparing Gender Costs with Bar Charts
    • Fixing Scatter Plot Axes and Labels
    • Polishing Boxplots with Professional Labels
    • Creating a Customer Insights Dashboard
  • Unit 3: Discovering Key Cost Factors: Correlation and Visualization in Insurance Data
    • Selecting Features for Correlation Analysis
    • Visualizing Correlations with Colorful Heatmaps
    • Visualizing Key Factors with Regression Lines
    • Building a Boxplot Comparison Dashboard
    • Finding Typical Costs with Median Analysis
  • Unit 4: Building Your First Insurance Cost Prediction Model with Simple Linear Regression
    • Preparing Data for Regression Modeling
    • Splitting Data for Model Evaluation
    • Training Your First Regression Model
    • Measuring Your Model's Prediction Accuracy
    • Visualizing and Predicting Insurance Costs
  • Unit 5: Evaluating PredictHealth’s Prediction Accuracy: Comparing Regression Models and Metrics
    • Adding MAE to Model Evaluation Toolkit
    • Building a Feature Comparison System
    • Spotting Patterns in BMI Prediction Errors

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