DRAFT

Project Overview

  • Goal: Help CharityML maximize the likelihood of receiving dontations

  • How: Construct a model that predicts whether an individual makes more than 50k/yr, a value associated with being a candidate for giving donations

  • Data Source: 1994 US Census Data UCI Machine Learning Repository

Note: Datset donated by Ron Kohavi and Barry Becker, from the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". Small changes to the dataset have been made, such as removing the 'fnlwgt' feature and records with missing or ill-formatted entries.

Table of Contents:

1.Exploratory Data Analysis

1.1 Data Dictionary
1.2 Simple Cleaning
1.3 Summary Statistics
1.4 Distributions
1.5 Skew and Variance
1.6 Relationships

2.Data Engineering

2.1 Separate Labels from Factors
2.2 Transformation

2.2.1 Indicator Variables
2.2.2 Impact
2.2.3 Logarithmic Transform
2.2.4 Normalization and Standardization

2.3 Shuffling and Splitting

2.4 Pipeline

3.Metrics

3.1 Accuracy
3.2 Precision
3.3 Recall
3.4 F$\beta$-Score

4.Models

4.1 Selection

4.2 Benchmark: Naive Bayes

4.2.1 Application

4.3 Model Application Pipeline

4.4 Logistic Regression

4.4.1 Application
4.4.2 Tuning

4.5 Random Forest

4.5.1 Application
4.5.2 Tuning

4.6 Ada Boost

4.6.1 Application
4.6.2 Tuning

4.7 Gradient Boost

4.7.1 Application
4.7.2 Tuning

4.8 Extreme Gradient Boosting

4.8.1 Application
4.8.2 Tuning

4.9 K-Nearest Neighbors

4.9.1 Application
4.9.2 Tuning

4.10 Comparison

4.10.1 Feature Importance
4.10.2 Selection
4.10.3 Comp: Reduced Feature Model Performance

5.Summary

1. Exploratory Data Analysis

1.1 Data Dictionary
1.2 Simple Cleaning and Engineering
1.3 Summary Statistics
1.4 Distributions
1.5 Skew and Variance
1.6 Correlations

1.1 EDA: Data Dictionary

  • age: continuous.
  • workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
  • education_level: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
  • education-num: continuous.
  • marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
  • occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
  • relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
  • race: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other.
  • sex: Female, Male.
  • capital-gain: continuous.
  • capital-loss: continuous.
  • hours_per-week: continuous.
  • native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.

1.2 EDA: Simple Cleaning and Engineering

Standardizing factor names by PEP8 Naming Convention Standards can be helpful.

There are a number of categorical variables. Handling those with one-hot encoding can be helpful.

1.3 EDA:Summary Statistics

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45222 entries, 0 to 45221
Data columns (total 14 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   age              45222 non-null  int64  
 1   workclass        45222 non-null  object 
 2   education_level  45222 non-null  object 
 3   education_num    45222 non-null  float64
 4   marital_status   45222 non-null  object 
 5   occupation       45222 non-null  object 
 6   relationship     45222 non-null  object 
 7   race             45222 non-null  object 
 8   sex              45222 non-null  object 
 9   capital_gain     45222 non-null  float64
 10  capital_loss     45222 non-null  float64
 11  hours_per_week   45222 non-null  float64
 12  native_country   45222 non-null  object 
 13  income           45222 non-null  object 
dtypes: float64(4), int64(1), object(9)
memory usage: 4.8+ MB
Out[8]:
count unique top freq mean std min 25% 50% 75% max
age 45222 NaN NaN NaN 38.5479 13.2179 17 28 37 47 90
workclass 45222 7 Private 33307 NaN NaN NaN NaN NaN NaN NaN
education_level 45222 16 HS-grad 14783 NaN NaN NaN NaN NaN NaN NaN
education_num 45222 NaN NaN NaN 10.1185 2.55288 1 9 10 13 16
marital_status 45222 7 Married-civ-spouse 21055 NaN NaN NaN NaN NaN NaN NaN
occupation 45222 14 Craft-repair 6020 NaN NaN NaN NaN NaN NaN NaN
relationship 45222 6 Husband 18666 NaN NaN NaN NaN NaN NaN NaN
race 45222 5 White 38903 NaN NaN NaN NaN NaN NaN NaN
sex 45222 2 Male 30527 NaN NaN NaN NaN NaN NaN NaN
capital_gain 45222 NaN NaN NaN 1101.43 7506.43 0 0 0 0 99999
capital_loss 45222 NaN NaN NaN 88.5954 404.956 0 0 0 0 4356
hours_per_week 45222 NaN NaN NaN 40.938 12.0075 1 40 40 45 99
native_country 45222 41 United-States 41292 NaN NaN NaN NaN NaN NaN NaN
income 45222 2 <=50K 34014 NaN NaN NaN NaN NaN NaN NaN 
Number of observations: 45222
Number of people with income > 50k: 11208
Number of people with income <= 50k: 34014
Percent of people with income > 50k: 24.78

1.4 EDA:Distributions

1.4 Dist: Income

1.4 Dist: Age

1.4 Dist: Workclass

1.4 Dist: Education