Life Expectancy Analysis Modeling Using WHO UN Data
LINK TO NB
This report investigates modifiable environmental factors that may contribute to life expectancy in the hopes of finding a mathematical model. Two questions are answered here:
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Which variables are significant in modeling and predicting Life Expectancy?
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What model best describes the relationship(s)?
..
├── 3_EDA
│ ├── 3_Life_Expectancy_Exploratory_Data_Analysis.ipynb
│ ├── a_Life_Expectancy_Exploratory_Data_Analysis_PART2.ipynb
│ ├── LE_by_country_and_3D_Seaborn_Plots .ipynb
│ ├── NEED_LE_country_8regions.ipynb
│ ├── NEED_Violin_Plots.ipynb
│ ├── Seaborn_Graphics_Income_Education_age.ipynb
│ ├── Seaborn_Plots .ipynb
│ ├── WorkOnThis_Plotly_FACETING.ipynb
│ └── WorkOnThis_QQ Plots.ipynb
├── 7_Models
│ ├── 7_Life_Expectancy_Modeling.ipynb
│ ├── 8-Life_Expectancy_Linear_Coefficients.ipynb
│ ├── Linear Model 1 - WHO L.E..ipynb
│ ├── Linear Model Only.ipynb
│ └── test statsmodel api.ipynb
├── 8_Groupings
│ └── What_are_means_by_region.ipynb
├── 9_RATE_Changes
│ ├── Countrt_Feature_Rates.ipynb
│ ├── EXCELLENT_Life Expectancy Vs Yearly Age Rate.ipynb
│ └── LE_by_Countrfy_BMI Vs Rate Change of BMI.ipynb
├── assets
│ ├── hover-text-and-formatting.py
│ ├── le_yr_prog.txt
│ ├── life_expectancy_related
│ │ ├── Life_Expectancy_By_Country.svg
│ │ ├── Life_Expectancy_Educagtion_BMI.svg
│ │ ├── Life_Expectancy_vs_8_Regions.svg
│ │ └── Screenshot at 2023-02-01 10-12-34.png
│ ├── logo
│ │ ├── un-logo.png
│ │ └── who-logo.png
│ ├── Make Yr Own DF.ipynb
│ ├── notes2
│ ├── Pandas_Profiling_material
│ │ ├── config_default.yaml
│ │ ├── default_ON.yaml
│ │ ├── Life_Expectancy_Profiling_Test.html
│ │ ├── Life Expecteancy: IDA_EDA Report Using Pandas ProfilingBeta.html
│ │ └── mcc_config.yaml
│ ├── preventing_disease_through_healthy_environments_who.pdf
│ └── rate_graphics
│ ├── age_avs_yearly_age_rate.png
│ └── test_age_avs_yearly_age_rate.png
├── data
│ ├── processed
│ │ ├── all_processed_data.zip
│ │ ├── Clean_LE_Data_Post_Correlation_2.csv
│ │ ├── Clean_LE_Data_w_Means_1.csv
│ │ ├── Clean_LE_Regions_3.csv
│ │ └── regional_data_2-2023-01-18 13:18:55.451573.csv
│ └── raw
│ ├── gdp
│ │ ├── data.csv
│ │ ├── data.csv.zip
│ │ └── gdp.per.capita.csv
│ ├── Life_Expectancy_Data.csv
│ ├── Life_Expectancy_Data.zip
│ ├── README.md
│ └── README.md~
├── docs
│ ├── 1_MAIN_Life_Expectancy_WHO_UN_Analysis_Modeling_MAIN.pdf
│ ├── 2_Life_Expectancy_Initial_Data_Analysis.pdf
│ ├── 3_Life_Expectancy_EDA.pdf
│ ├── 4_Life_Expectancy_Exploratory_Data_Analysis_PART2.pdf
│ ├── 5_Life_Expectancy_Feature_Engineering.pdf
│ ├── 6_Life_Expectancy_Recursive_Feature_Elimination.pdf
│ ├── 7_Life_Expectancy_Modeling.pdf
│ ├── 8-Life_Expectancy_Linear_Coefficients.pdf
│ ├── forward.odt
│ ├── jan29.odp
│ ├── REport_Section.odt
│ └── updated-2016-data-tables_preventing_disease_deaths_dalys_pafs_sept_2019_rev.xlsx
├── jupy_notebook_notes
│ ├── Discretization of BMI data.ipynb
│ ├── Feature_table.ipynb
│ ├── Produce_file_names_w_date_time_stamp.ipynb
│ ├── traces_chat_gpt.ipynb
│ ├── Variable_table.ipynb
│ └── What_makes a developed_country.ipynb
├── notebooks
│ ├── 1_MAIN_Life_Expectancy_WHO_UN_Analysis_Modeling.ipynb
│ ├── 2_Initial_Data_Analysis.ipynb
│ ├── 3_What exactly is missing from life expectancy data.ipynb
│ ├── 4_Test_Pandas_Profiling_Report.ipynb
│ ├── 5_How_to_drop_highly_correlated_features.ipynb
│ ├── 6a_Feature_Engineering_catgorize_nations_2_Named_regions.ipynb
│ ├── 6b_Feature_Engineering_catgorize_nations_2_regions.ipynb
│ ├── 7_Plotly_Life_Expectancy_183_countries.ipynb
│ └── 8_Recursive_Feature_Elimination.ipynb
├── README.md
└── requirements.txt
16 directories, 78 files