/Medical-Cost-Personal-Dataset-Analysis

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

Primary LanguageJupyter Notebook

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)