Apply up to 5 tags to help Kaggle users find your dataset. The rich dataset contains detailed information of approximately 3.5 million households about who they are and how they live including ancestry, education, work, transportation, internet use and residency. Tabulations of all surnames occurring 100 or more times in the 2010 Census and Census 2000 returns are provided at the national level only. Dataset Features:Salary, age, workclass, fnlwgt, education, education_num, marital-status, occupation, relationship, race, sex, capital-gain,capital-Loss, hours-per-week, native-country Analytics Vidhya is a community of Analytics and Data…. This data is labeled with whether the person's yearly income is above or below $50K (and you are trying to model and predict this). Explore, If you have a story to tell, knowledge to share, or a perspective to offer — welcome home. The data had redundant columns as well. The dataset is credited to Ronny Kohavi and Barry Becker and was drawn from the 1994 United States Census Bureau data and involves using personal details such as education level to predict whether an individual will earn more or less than $50,000 per year. These datasets provide the aggregated tax, SNAP benefits, and poverty universe data used in producing the SAIPE estimates. The pages below allow you to download public use microdata from various Census surveys and programs in order to conduct your own statistical analysis. Not-in-family White Female 0, 1 Exec-managerial Not-in-family White Female 0, 2 ? The first step towards addressing income gaps is … Census Bureau Releases New American Community Survey 5-Year Estimates For the first time, data from the 2015-2019 ACS will allow users to compare three nonoverlapping sets of 5-year data: 2005-2009, 2010-2014 and 2015-2019. This is a competition for a Kaggle hack night at the Cincinnati machine learning meetup. Since the missing values were represented by ‘?’ , they were replaced by NAN values and removed after detection. Take a look. The following table is a census dataset on income created by the University of California, Irvine: Columns. Adult-Income-Analysis. It might require cleaning, transformation, integration. The training and testing is divided in 80–20 for logistic and naive bayes whereas 70–30 for decision tree and random forest. Extraction was done by Barry Becker from the 1994 Census database. We have all heard that data science is the ‘sexiest job of the 21st century’. The data contains a good blend of categorical, numerical and missing values. Flexible Data Ingestion. - The Random Forest for its global performances and the computation of feature importance The dataset involve 3.5 … We used : to compare ourto have an overhaul quite good predictor, with good f-score. If nothing happens, download GitHub Desktop and try again. The foremost model to predict a dichotomous variable is logistic regression. Our predictions are based on a 30 KFold cross validation. This refers to the age of a person. This problem is handled using SMOTE(Synthetic Minority Oversampling Technique). This refers to the type of employment a person is involved in. Abstract: Predict whether income exceeds $50K/yr based on census data. Housing Dataset, which was derived from by U.S. Census … Census income classification with XGBoost¶ This notebook demonstrates how to use XGBoost to predict the probability of an individual making over $50K a year in annual income. The census income dataset. Hebb wrote, “When one cell repeatedly assists in firing another, the axon of the first cell develops synaptic knobs (or enlarges them if they already exist) in contact with the soma of the second cell.” Translating Hebb’s concepts to artificial neural networks and artificial neurons, his model can be described as a way of altering the relationships between artificial neurons (also referred to as nodes) and the changes to individual neurons. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. Also, according to Ockham’s Razor “the simplest explanation is most likely the right one”. The dataset contained null values, both numerical and categorical values. After fitting the model, we find the model accuracy. It is shown in the following charts. The dataset named Adult Census Income is available in kaggle and UCI repository. To further improve, more complex ensemble methods can be used. The files now … Chi-square estimate is used to measure the correlation between 2 categorical variables. Learn more. What Has Changed? Date Donated. Data Set Characteristics: Multivariate. Do check out. Review our Privacy Policy for more information about our privacy practices. US Adult Census data relating income to social factors such as Age, Education, race etc. The problem is to be accurate in both class (>50k, <=50k) that is why we didn't emphasize on the precision metric. It uses the Census Income dataset; click the Data tab for more information and to download the data. If you are using a screen reader and are having problems accessing data, please call 301-763-3243 for assistance. Census of Population and Housing from the Decennial Census 1790-2010 Historical Census Browser From the University of Virginia Library, has data sets on state and county level topics for individual census years 1790-1960 (including demographics on slave population) and … Prediction task is to determine whether a person makes over 50K a year. I have used one model accuracy measure to form a comparative study between all models. Basically, it’s “naive” because it makes assumptions that may or may not turn out to be correct. A comparative study of the above models with respect to accuracy, precision, recall, ROC score is computed together for better decision. We first transformed and clean the data by : The exploratoring part can be seen using exploratory.ipynb. This visualization part taught us that some features seem to be useful to split the data easily but it would be difficult to be perfect. Also known as "Census Income" dataset. education. Analytics Vidhya is a community of Analytics and Data Science professionals. The categorical values were both nominal and ordinal. The tables below provide income statistics displayed in tables with columns and rows. Arthur Samuel of IBM first came up with the phrase “Machine Learning” in 1952. Number of Attributes: 14. - The Linear Regression for its simplicity, quickness and performances Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. The goal is to train a binary classifier to predict the income which has two possible values ‘>50K’ and ‘<50K’.There are 48842 instances and 14 attributes in the dataset. Exploratory data analysis for the Adult or Census Income dataset from UCI Machine Learning Repository.. Full Analysis : Jupyter Notebook Python Packages: Scikit-learn; Pandas; Numpy; Classification Models Used: Decision Trees We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. We also add an aggregated regressor that uses the previous predictions and average them. Kaggle challenge : Adult Census Income Installation. Using the python language and several visualizations, I have attempted to fit 4 machine learning models and find the best model to describe the data. The basic principle is that a group of “weak learners” can come together to form a “strong learner”. The following code snippet highlights the data preprocessing steps. A decision tree is a branched flowchart showing multiple pathways for potential decisions and outcomes. Got it. The US Census Bureau conducts the American Community Survey generating a massive dataset with millions of data points. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com, Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Kaggle-Census-Income Classification done using Tensorflow and sklearn The data here is for the "Census Income" dataset, which contains data on adults from the 1994 census. This necessity should not be dictated by factors that are out of our control, yet income gaps continue to persist. https://www.kaggle.com/uciml/adult-census-income, download the GitHub extension for Visual Studio, The educational background (education / education.num), Try to transform categorical data into numerical data, Find new features based on the actual one, Be smarter with the prediction aggregation (learning overlay), The data is a bit imbalanced : oversampling / undersampling. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)). The data in this sheet retrieved and collected from Kaggle by Perera (2018) for Boston. work class. We would highly recommend that before the hack night you have some kind of toolchain and development environment already installed and ready. Current Population Survey (CPS) Annual Social and Economic Supplement (ASEC)
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