maroacc / medical-cost-personal-dataset-analysis

Analysis of the Medical Cost Personal Dataset https://www.kaggle.com/datasets/mirichoi0218/insurance

Medical Cost Personal Dataset Analysis

Analysis of the Medical Cost Personal Dataset https://www.kaggle.com/datasets/mirichoi0218/insurance

0. Introduction

0.1 Explanation of the dataset

• Context e.g what is the dataset about, why is studying this dataset important
• Description of the variables e.g bmi : minutes played by the player during that year

0.2 Project Scope

• Summary of what we are going to do in the project

1. Exploratory Analysis

1.1 Description of the dataset

• Variable types e.g qualitative, discrete, continuous (df.info() + insights)
• Variable description (df.describe())

1.2 Variable representation

• Plots of continuous and discrete variables (histograms, bar plots, density curves, box plots, scatter plots etc)
• Identification of outliers

2. Statistical Inference & Hypothesis Testing

• Choose a couple of hypothesis to test based on the exploratory analysis
• confidence intervals
• power
• hypothesis test for the mean
• hypothesis test for the variance
• ANOVA

3. Modeling

• Correlation Analysis of all variables + pairplot()
• linear regression model (Insurance Forecast)
  • error, p-values and confidence intervales for bo and b1
  • confidence bands
  • prediction band
• Diagnostic plots (to check that a regression can be performed)
  • Residual vs fitted plot
  • Residual QQ-plot
  • Scale-location plot
  • Residual vs leverage plot
• linear regression with 3 parameters (beta0, beta1, beta2)
• linear regression with more than one variable ([BMI, age] --> Charges)