Fnlwgt census. br/imnfh/no-such-file-or-directory-meaning.
9%. 0847 May 18, 2021 · We treat ‘Fnlwgt’, ‘Capital_gain’ and ‘Capital_loss’ column for skewness, and use square-root transform and cube-root transform methods (since we cannot apply log and boxcox transform Predict whether income exceeds $50K/yr based on census data Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. education: Bachelors, Some-college, 11th Feb 15, 2019 · census_data[['fnlwgt']] = imputer. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1) && (HRSWK>0)). sql . Census Income dataset is to predict whether the income of a person >$50K/yr (greater than $50K/yr) or <=$50K/yr. frame. 4% low income people were incorrectly classified as high income. The final weight which is the 19-1 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8668 Japan ©1996 Statistics Bureau, Ministry of Internal Affairs and Communications (JCN2000012020001) Context: This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). the class distribution is skewed or imbalanced. workclass May 1, 2023 · a features (age, workclass, fnlwgt, education, etc) that is set. The sets of records were extracted using the following conditions: ((AAGE > 16)&(AGI > 100)&(AFNLWGT > 1)&(HRSWK > 0)) and its predic-tion task is to determine whether a person makes over 50K per year. - tsoumarios/Census_Income_Analysis The dataset used for the analysis is an extraction from the 1994 census data by Barry Becker and donated to the UCI Machine Learning repository. This is an analysis of the Adult data set in the UCI Machine Learning Repository. This notebook is open with private outputs. Data. g. The Adult UCI Dataset's aim is to predict whether a person makes over 50K a year. info() RangeIndex: 48842 entries, 0 to 48841 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ----- ----- ----- 0 age 48842 non-null int64 1 workclass 48842 non-null object 2 fnlwgt 48842 non-null int64 3 education 48842 non-null object 4 educational-num 48842 Mar 24, 2020 · <class 'pandas. k. Box and Whis ker for ‘education "Predicting if income exceeds $50,000 per year based on 1994 US Census Data with Simple Classification Geography Hierarchy Geography Level Example URL Number; 3 examples (default geography) N/A: https://api. Now we get the joint distribution of the of the income (y) and the age (x), P(x,y). เมื่อเราต้องการวิเคราะห์หรือสร้างโมเดลเพื่อใช้ในการทำนายนั้น เรามักจะมีปัญหาที่พบบ่อย ๆ และต้อง The data used is the Adult Census Income which we will fecth age workclass fnlwgt education education-num \\ 0 38. The data was collected by Barry Becker from 1994 Census dataset. Ronny Kohavi and Barry Becker from the UCI Machine Learning Institute extracted data from this census database and used the relevant data and mathematical techniques to predict whether the income of the person from the census was over $50,000 or not. We used regressors instead of classifiers to be able to plot the ROC curves and thus be more flexible on the threshold choice. 3 Dataset 3. This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted Census income dataset Description "Census Income" dataset from UCI machine learning repository. Here is a sample of what the data looks like: uence the income distribution, using the full dataset provided in the US Census Bureau 2. The education is a factor variable of the individuals level of education and the Education_num is the number representation of the highest education level. Making data management decisions. Sep 8, 2020 · Welcome back to Data Every Day!On today's episode, we are looking at a dataset of census data, and trying to predict whether an individual's income is over $ Sep 3, 2020 · Census income dataset UCI Data Set Python. May 31, 2022 · Adult Census Income. Jul 28, 2017 · bubble plot - target vs. Mar 24, 2020 · From understand the problem, to deliver the solution. How it works: We use the pandas library to load the dataset from the URL into a DataFrame. Apr 30, 1996 · Extraction was done by Barry Becker from the 1994 Census database. Geography Hierarchy Geography Level Example URL Number; 3 examples (default geography) N/A: https://api. Mar 6, 2021 · #datascience #model #kaggle #machinelearningCode -https://www. Usage data(adult_total) Format >50K, <=50K. Exploratory Analysis. Sep 27, 2020 · After appropriate application of the test, ‘fnlwgt’ has been dropped which showed negative correlation. colab import widgets Apr 30, 1996 · Extraction was done by Barry Becker from the 1994 Census database. Jun 6, 2022 · fnlwgt: continuous. May 18, 2021 · We treat ‘Fnlwgt’, ‘Capital_gain’ and ‘Capital_loss’ column for skewness, and use square-root transform and cube-root transform methods (since we cannot apply log and boxcox transform Feb 16, 2024 · Decision trees are a popular and intuitive tool used in machine learning and data mining for classification and regression tasks. The US Adult Census dataset is a repository of 48,842 entries extracted from the 1994 US Census database. read 1994-census-summary. testcase -- Edit this file by adding your SQL below each question. df0. 'Gender', and 'Marital status' in Adult Census data do differ by a certain magnitude, when compared against the real data. 6 days ago · Census data covers dozen of topics across 130+ surveys and programs. The data comes from The Census Income Data Set from the UCI Machine Learning Repository. 6% of those with low income were classified correctly, while 11. 1636 F4 education (categorical) 0. With the research question in place and the data source identified, we begin the data storytelling journey. 0966 F9 race (categorical) 0. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)) Prediction task is to determine whether a person makes over 50K a year. Intensive Study – Under census investigation, you must obtain data from each and every unit of the population. This dataset is a collection of demographic information for the adult population as of 1994 in the USA. These are: Predict whether income exceeds $50K/yr based on census data Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. 1 import seaborn as sns import itertools from sklearn. Jul 17, 2016 · Fnlwgt, or final weight, is the number of people that the specific individual represents (this is because the census dataset is reduced to a manageable size). . status occupation relationship race sex Jul 9, 2020 · The dataset we are going to use is the Adult census income ----- ----- 0 age 32561 non-null int64 1 workclass 32561 non-null object 2 fnlwgt 32561 non-null int64 D. [1] The majority of practical machine learning uses… This notebook is open with private outputs. Predict whether income exceeds $50K/yr based on census data Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Column ID Column Name Data type Values type Description; 0: age: int64: Continous: Age of person: 1: workclass: object: Discrete: Workclass of person: 2: fnlwgt Ed. Question: usid age workclass fnlwgt education education_num marital_status occupation relationship race sex capital gain capital_loss hours_per_week native_country label . Categorical values can be a bit trickier, so you should definitely pay attention to your model performance metrics after editing (compare before and after). education: Categorical variable that represents the level of education of the respondent (Doctorate, Prof-school, Masters, Bachelors, Assoc-acdm, Assoc-voc, Some-college, HS-grad, 12th, 11th, 10th, 9th, 7th-8th, 5th-6th, 1st-4th Classifying Income from 1994 Census Data Tracy Nham A0994191 tnham@usd. The top left figure shows a scatter plot of Age (Continuous) vs. gov/data/1990/cps/basic/sep?get=A_LFSR,A_MARITL,A Census income classification with scikit-learn¶ This example uses the standard adult census income dataset from the UCI machine learning data repository. 0144 F10 sex (categiorical) 0. workclass: a general term to represent the employment status of an individual. a Adult data set. Sep 4, 2021 · This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). Predict whether income exceeds $50K/yr based on census data. The prediction task is to predict whether a person is earning a high or low revenue in USD/year. age: continuous. 0329 F5 education-num (continuous) 0. From here we can see that most of the people in our data is within the younger ages (around 20–30), and that a small fraction of the young people (around age 20) group has high income because their bubble is very small. For feature selection, all the numerical columns are selected except ‘fnlwgt’. The dataset was an extraction by Barry Becker and Ronny Kohavi from the 1994 US census data. core. 0604 F7 occupation (categorical) 0. You can disable this in Notebook settings. age — P(x,y). DataFrame'> RangeIndex: 48842 entries, 0 to 48841 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ----- ----- ----- 0 age 48842 non-null int64 1 workclass 46043 non-null object 2 fnlwgt 48842 non-null int64 3 education 48842 non-null object 4 educational-num 48842 non-null int64 5 marital-status 48842 non-null object 6 occupation 46033 non-null Geography Hierarchy Geography Level Example URL Number; 3 examples (default geography) N/A: https://api. 0901 F6 marital-status (categorical) 0. We use 3 sets of controls. census. fnlwgt: an estimate of the number of individuals in the population with the same demographics as this individual, hours_per_week: hours worked per week, marital_status: the marital status of the individual, native_country: the native country of the individual, occupation: the occupation of the individual, race: the individual's race, The columns are: recordiD, age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, income As Adult reads the data file, there is three correctness checks that it must make on the data. DataFrame'> RangeIndex: 48842 entries, 0 to 48841 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ----- ----- ----- 0 age 48842 non-null int64 1 workclass 46043 non-null object 2 fnlwgt 48842 non-null int64 3 education 48842 non-null object 4 educational-num 48842 non-null int64 5 marital-status 48842 non-null object 6 occupation 46033 non-null import os import numpy as np import matplotlib. The naive Bayes model achieves an overall accuracy of 82. Applying machine learning techniques with R to Census Income data set, a. <class 'pandas. 1 Dataset Overview This dataset is a sample from the US Census Database that contains the census result of year 1994. 1994 Census database. 5 million tables of raw data, maps, profiles, and more at data. It is the final sampling weight. -- Sample for question 1 Is provided. With this data, we are tasked of predicting whether a person makes more than $50K/year. S. FNLWGT (Continuous) in the 1994 Census data. fit_transform(census_data[['fnlwgt']]) Categorical NaNs. The data set consists of over 49K records with 14 attributes with missing data. The AdultUCI data set contains the questionnaire data of the Adult database (originally called the Census Income Database) formatted as a data. Introduction The original data for this dataset was the 1994 Census (provided by the census bureau). Throughout this chapter, pre-1996 variable names appear in parentheses following 1996 variable names. The idea is to predict whether income exceeds $50K/yr based on census data. The adult data set is another famous one from the UCI - machine learning repository. [[{"a_lfsr": "3"}, {"a_lfsr": "6"}, {"a_lfsr": "1"}, {"a_lfsr": "2"}, {"a_lfsr": "4"}, {"a_lfsr": "5"}, {"a_lfsr": "7"},"a_maritl"], [49049,0,1005653,49733,6976,0 This project is part of the Knowledge Discovery in Database (ITCS 6162) course from University of North Carolina at Charlotte - Akhil-18/Prediction-of-Adult-Income-based-on-Census-Data In this challenge you must analyze demographic data using Pandas. [3] # Using only data from the training set, I am aiming to predict the test score (or incomes) without knowing the test Oct 25, 2018 · Box and Whis ker for ‘fnlwgt’ attribu te. 17. If an age cohort is undercounted in the Census, that cohort will be under-represented in We would like to show you a description here but the site won’t allow us. Census Income. วิธีการจัดการกับ Outliers ด้วย AzureML. DataFrame'> RangeIndex: 48842 entries, 0 to 48841 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ----- ----- ----- 0 age 48842 non-null int64 1 workclass 46043 non-null object 2 fnlwgt 48842 non-null int64 3 education 48842 non-null object 4 educational-num 48842 non-null int64 5 marital-status บทที่ 12-Outliers. It is a Supervised Binary Classi The dataset I choose is the adult dataset, also called "Census Income" dataset. We chose to benchmark 3 algorithms: - The Linear Regression for its simplicity, quickness and performances - The Random Forest for its global performances and the computation of feature importance - The Gradient Boosting to compare bagging with boosting (and the same Sep 27, 2020 · After appropriate application of the test, ‘fnlwgt’ has been dropped which showed negative correlation. pyplot as plt import pandas as pd %tensorflow_version 1. this is the number of people that census believes the entry represents. According to the data description file, ‘fnlwgt’ is a shortened name for final-weight which is a kind of a measure consists of number of several attributes. Introduction Answer to Solved usid age workclass fnlwgt education education_num | Chegg. The purpose of classification is to predict, whether an income exceeds 50k per year. [U. These are prepared monthly for us by Population Division here at the Census Bureau. gov/data/1989/cps/basic/feb?get=A_LFSR,A_MARITL,A Census Income Dataset. Dec 25, 2018 · A machine learning approach to income prediction using the census data from the UCI database. Get in the weeds with more than 2. UCI Machine Learning Repository. This data set is meant for binary class classification - to predict whether the income of a person exceeds 50K per year based on some census data. 0 1 36. This dataset is popularly called the “Adult” data set. There are 48842 instances with 14 quantitative attributes and 1 qualitative attribute which all clearly describing its meaning. [[{"a_lfsr": "3"}, {"a_lfsr": "6"}, {"a_lfsr": "1"}, {"a_lfsr": "2"}, {"a_lfsr": "4"}, {"a_lfsr": "5"}, {"a_lfsr": "7"},"a_maritl"], [39492,1361,996882,49098,18037 Apr 28, 2023 · Final Weight (the number of people the census believes the entry represents): Discrete Education (the highest level of education obtained): Ordinal (16 categories) Education Number (the number of Sep 2, 2022 · The data presented here contains information collected by census about the salaries of people from different work classes, age groups, locations, etc. Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. We create prediction tasks Or copy & paste this link into an email or IM: Census API: Examples for /data/1991/sipp/core/1991panel/wave5 The data was extracted from 1994 Census bureau ## age workclass fnlwgt education education. gov/data/1990/cps/basic/jul?get=A_LFSR,A_MARITL,A F3 fnlwgt (continuous) 0. Sep 25, 2019 · The data presented here contains information collected by census about the salaries of people from different work-classes, age groups, locations, etc. x import tensorflow as tf import tempfile !pip install seaborn==0. me/akshitmada Geography Hierarchy Geography Level Example URL Number; 3 examples (default geography) N/A: https://api. A census is the procedure of systematically acquiring, recording and calculating population information about the members of a given population. wpi. Mar 24, 2018 · Column ‘fnlwgt’ was the most challenging feature to analyze since it didn’t carry any meaning by itself. 14 quantitative attributes: Jun 9, 2019 · Data Pre-processing on US Census Data; by Luke Perich; Last updated about 5 years ago; Hide Comments (–) Share Hide Toolbars Extraction was done by Barry Becker from the 1994 Census database. This is a feature with continuous values in interval [ 12285, 1484705 ]. { "variables": { "for": { "label": "Census API FIPS 'for' clause", "concept": "Census API Geography Specification", "predicateType": "fips-for", "group": "N/A Apr 30, 1996 · Extraction was done by Barry Becker from the 1994 Census database. The continuous variable fnlwgt represents final weight, which is the number of units in the target population that the responding unit represents. Contribute to vg80/Adult-Census-Income development by creating an account on GitHub. Learn more. fnlwgt: Numerical variable that contains the number of respondents that each row of the data set represents. 1996. status occupation relationship Question: CREATE TABLE `census` ( `usid` int NOT NULL, `age` int NOT NULL, `workclass` varchar(64) DEFAULT NULL, `fnlwgt` int NOT NULL, `education` varchar(64) NOT NULL, `education_num` int NOT NULL, `marital_status` varchar(64) NOT NULL, `occupation` varchar(64) NOT NULL, `relationship` varchar(64) NOT NULL Question: usid age workclass fnlwgt education education_num marital_status occupation relationship race sex capital gain capital_loss hours_per_week native_country label . gov/data/1990/cps/basic/apr?get=A_LFSR,A_MARITL,A Dec 21, 2021 · Supervised learning (SL) is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. Fig 5. INTRODUCTION The adult dataset, hosted by The Machine Learning Group at UCI, contains census information from 1994. They represent a flowchart-like structure where each internal node… Oct 27, 2020 · Many binary classification tasks do not have an equal number of examples from each class, e. education: The highest level of education achieved by an individual. A popular example is the adult income dataset that involves predicting personal income levels as above or below $50,000 per year based on personal details such as relationship and education level. com Predict whether income exceeds $50K/yr based on census data Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. kaggle. Census income classification with scikit-learn¶ This example uses the standard adult census income dataset from the UCI machine learning data repository. Oct 27, 2023 · Getting started: Data cleaning. gov/data/1989/cps/basic/apr?get=A_LFSR,A_MARITL,A May 18, 2021 · We treat ‘Fnlwgt’, ‘Capital_gain’ and ‘Capital_loss’ column for skewness, and use square-root transform and cube-root transform methods (since we cannot apply log and boxcox transform Mar 2, 2017 · The dataset used for the analysis is an extraction from the 1994 census data by Barry Becker and donated to the UCI Machine Learning repository. metrics import roc_curve, roc_auc_score from sklearn. fnlwgt: final weight. We aim to predict whether an individual’s income will be greater than $50,000 per year based on several attributes from the census data. ----- Date: Tue, 15 Feb 2000 12:02:56 -0500 (EST) From: Chris Shoemaker To: cs4341_ta@cs. The data set used in this project is the Census Income Dataset, which is also known as the Adult dataset (“ Census Income ” 1996), and was created in 1996. gov — the Census Bureau’s premiere data dissemination platform. Mar 24, 2020 · <class 'pandas. edu Subject: fnlwgt The census bureau has some information about fnlwgt. - GitHub - renz64/Income_prediction: A machine learning approach to income prediction using the census data from the UCI database. This project involves data preparation, model training, evaluation, and prediction. Census income classification with scikit-learn¶ This example uses the standard adult census income dataset from the UCI machine learning data repository. May 18, 2021 · We treat ‘Fnlwgt’, ‘Capital_gain’ and ‘Capital_loss’ column for skewness, and use square-root transform and cube-root transform methods (since we cannot apply log and boxcox transform The continuous variable fnlwgt represents final weight, which is the number of units in the target population that the responding unit represents. After appropriate application of the test, ‘fnlwgt’ has been dropped which showed negative correlation. 753654 4 179993. The Adult data set contains the data already prepared and coerced to transactions for use with arules . We will be ignoring this variable; education – The highest level of education achieved for that individual; education_num – Highest level of education in numerical form; marital – Marital status of the individual; occupation – The occupation of the individual The adult census dataset#. fnlwgt – The \# of people the census takers believe that observation represents. 565472 8 10. XGBoost is used to perform binary classification. www. Education is divided into Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. Age workclass fnlwgt education education_num marital_status occupation relationship race sex capital_gain capital_loss hours_per_week native_country; 0: 39: State-gov Predict Income Level from Census Data Earl Macalam ## age workclass fnlwgt education education. 0243 F11 capital-gain (continuous) 0. This analysis is about to predict whether income exceeds $50K/yr based on census data, also known as "Census Income" dataset. - BraulioV/Census-Income-Data-Set Adult Census Income is an extract of 1994 Census data for predicting whether a person's income exceeds $50K per year. Details: 88. Dec 17, 2023 · The reason for our actions: The Census Income dataset contains a mix of continuous and categorical data, making it a good fit for Gaussian Naive Bayes after appropriate preprocessing. This dataset was contributed to UCI repository, and It’s openly available at this link. The final weight is the product of: (1) the basic weight, (2) adjustments for special weighting, (3) noninterview adjustment, (4) first stage ratio adjustment factor, and (5) second stage ratio adjustment factor. Merits of a Census Investigation. metrics import confusion_matrix from sklearn. com) This data was extracted from the census bureau database found at US census website by Barry Becker in fnlwgt: continuous. com # 불필요한 변수 제거 df = df. The data used is the Adult Census Income which we will fecth age workclass fnlwgt education education-num \\ 0 38. Each entry contains the following information about the class of individual: age: the age of an individual. gov/data/1993/cps/basic/oct?get=A_LFSR,A_MARITL,A Description of fnlwgt (final weight) The weights on the Current Population Survey (CPS) files are controlled to independent estimates of the civilian non-institutional population of the US. 3. Introduction Abstract For this assignment, we examine the Census Income dataset available at the UC Irvine Machine Learning Repository . to be classified into two mutually exclusive subsets and each. drop(columns = 'fnlwgt') Census income classification with scikit-learn¶ This example uses the standard adult census income dataset from the UCI machine learning data repository. Column ID Column Name Data type Values type Description; 0: age: int64: Continous: Age of person: 1: workclass: object: Discrete: Workclass of person: 2: fnlwgt [[{"a_lfsr": "3"}, {"a_lfsr": "6"}, {"a_lfsr": "1"}, {"a_lfsr": "2"}, {"a_lfsr": "4"}, {"a_lfsr": "5"}, {"a_lfsr": "7"},"a_maritl"], [0,0,15621,2470,1615,0,34249,"4 Adult Census Income is an extract of 1994 Census data for predicting whether a person's income exceeds $50K per year. The data set is made up of 32561 observations and 14 predictor variables for 42 different countries around the world. May 15, 2023 · Github link: techwithreddix/Analyzing-the-Adult-Census-Income-Dataset- (github. 1 Introduction. Census Bureau 2014a). edu I. num ## 0 1836 0 0 0 ##marital. FNLWGT Reference month Feb 15, 2017 · Analysis of the Adult data set from UCI Machine Learning Repository¶. The standard thing to do is to replace the missing entry with the most frequent one: 5 extra credit points were offered in class to the first one finding the meaning of the fnlwgt attribute. The dataset consists of 15 columns of a mix of discrete 'Gender', and 'Marital status' in Adult Census data do differ by a certain magnitude, when compared against the real data. num ## 73 9 21648 16 16 ## marital. 8. Outputs will not be saved. 408844 4 Geography Hierarchy Geography Level Example URL Number; 3 examples (default geography) N/A: https://api. E. The top right figure shows samples produced from the HCBN generative model of Age vs Jul 22, 2020 · Welcome to an end to end case study of Adult census data. We train a k-nearest neighbors classifier using sci-kit learn and then explain the predictions. The variable education_num stands for the number of years of education in total, which is a continuous representation of the discrete variable education. For simplicity, we only consider how the machine learning model tells us about the relationship between features and label. naive Bayes. Is also known as Adult income or adult dataset. You are given a dataset of demographic data that was extracted from the 1994 Census database. Feb 14, 2019 · The Census Bureau’s post-Census population estimates program, which produces yearly national, state, and county population estimates, uses data from the Decennial Census as the starting point to produce post-Census estimates (U. Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning. -- Use 'limit 10' at the end of each line. 0773 F8 relationship (categorical) 0. metrics import precision_recall_curve from google. It was sourced from the UCI Machine Learning Repository and the data was extracted by Barry Becker using the 1994 Census database and details of which could be found here. Now that the census definition is clear, let’s look at the merits of a census investigation. Further, it enables the statistician to study more than one aspect of all items of the population. Introduction Jul 23, 2021 · fnlwgt: Final weight (this is the number of people the census believes the entry represents) education: Highest education done by the person; education-num: Education number done by the person. Chris earns the extra credit. The objective is to predict the salary, specifically predict a binary classification between salary greater than $50K/year and less than $50K/year. May 28, 2020 · A_FNLWGT is the final weight designed for generating population estimates for items on the regular monthly CPS. com/akshitmadan/adult-sensus-income-naive-bayesTelegram Channel- https://t. fnlwgt. First, let’s load the dataset as df0 and look at what the dataset looks like. Extraction was done by Barry Becker from the 1994 Census database. Census Bureau, 2001]. Abstract For this assignment, we examine the Census Income dataset available at the UC Irvine Machine Learning Repository .
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Fnlwgt census. Jul 28, 2017 · bubble plot - target vs.
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