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A census as the total process of collecting, compiling, and publishing demographic, economic, and social data pertaining to a specific time to all persons in a country or delimited part of a country. As part of a census count, most countries also include a census of housing. It is the process of collecting, compiling and publishing information on buildings, living quarters and building-related facilities such as sewage systems, bathrooms, and electricity, to name a few.
Possible Uses of Census Information
Total Population Size
When two or more census counts are compared for the same location, planners can determine if locales are increasing or decreasing in size.
Used to help identify segments of the population that require different types of services.
Sex ratios can be calculated by 5-year age groups to crudely observe migration, especially among the working age cohorts.
Used to provide insights into family formation and housing needs.
Household Composition and Size
Used to help determine housing needs for related and unrelated households.
Educational Attainment and Literacy
Used to provide information on the educational skills of the work force. These measures also help planners select the best strategies to communicate with residents.
Location of Residence and Place of Prior Residence
Helps assess changes in rural and urban areas. Place of prior residence helps to identify communities that are experiencing in- or out-migration.
Occupation and Labor Force Participation
Helps to provide insights into the labor force of a given locale. The information can be used to develop economic development strategies.
Living Quarter Characteristics
Can help planners determine housing and community facility needs
In this project, we will use a standard imbalanced machine learning dataset referred to as the “Adult Income” or simply the “adult” dataset.
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.
The dataset provides 14 input variables that are a mixture of categorical, ordinal, and numerical data types. The complete list of variables is as follows:
The dataset contains missing values that are marked with a question mark character (?).
There are a total of 48,842 rows of data, and 3,620 with missing values, leaving 45,222 complete rows.
There are two class values ‘>50K‘ and ‘<=50K‘, meaning it is a binary classification task. The classes are imbalanced, with a skew toward the ‘<=50K‘ class label.
Census Salary Prediction where we have to classify between >50K <=50K.
How Does it help
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.
Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.