<jupyter_start><jupyter_text>Introduction data set intro The data was created by university of california irvine, The datas main goal is to Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset. the Extraction of data was done by <NAME> from the 1994 Census database. The pupose of the data collected is mainliy for public intrest.The data goes into detail about income in America giving different information about each individual and their income. the information it gives on each individual is age, workclass, education level, marital status occupation realationship, race, sex, hours per week of work, and what country an individual is from. The main prediction the data makes based on the related information is whether or not the individual has an income greater than or less than 50k a year. I hope to use this dataset to find information about income in america as well as the different factors and correlations I can make in the data set. I am interested in the opportunities that the data set presents in relation to the different categories that are available to interpret.  Reserch Questions RQ 1: What race on average works the most hours per week?RQ 2: Is there a pattern in relation to age on whether or not an age group is predicted to make less than 50k?<jupyter_code>from scripts.project_functions import *

df = project_functions.load_and_process(r"..\data\raw\adult.csv")
df<jupyter_output><empty_output><jupyter_text>Data Set this data set has 10 unique collums 1. **age** represents the individuals age.2. **workclass** represent type of buisness an individual works for, for example Private means privately owned company local-goverment means they work for the local goverment and so on. 3. **education** represent the highest level of education an indivual has completed4. **marital.status** represents the last know status of an indivduals relationship in relation to mariage 5. **occupation** represents the specific feild an indivdual works in 6. **relationship** represents an indiviuals relathionship in regards to family status 7. **race** represents an individuals race 8. **sex** represents an indivuals gender 9. **hours.per.week** represents on average how many hours per week an individual works 10. **native.country** represents where an individual is originaly from   Analysis<jupyter_code>df.plot(kind='scatter', x='hours.per.week', y='race')<jupyter_output><empty_output><jupyter_text>Graph 1 analysis this graph shows hours per week of work in relation to race. White race is the most common in the data set with the majority of races workingon average similar hours. scatterplot alllows us to see the differnece in total individuals per week as well as the aveage hours.<jupyter_code>df['hours.per.week'].plot(kind='hist', bins=10, figsize=(12,6), facecolor='green',edgecolor='black')<jupyter_output><empty_output><jupyter_text>Graph 2 analysis This histogram allows us to more clearly see the average hours worked per individual than the privious scatterplot. this graph plots avaerage hourts worked against how often the specific number is applied to an individual<jupyter_code>sns.displot(df['age'], kde=False, bins=72)<jupyter_output><empty_output><jupyter_text>Graph 3 analysis this histogram is used to show the age distraabution of the dataset it finds that the average age range of the data is skewed to the right with the most common ages being between 30-40.<jupyter_code>sns.catplot(x="income", y="age", data=df)<jupyter_output><empty_output><jupyter_text>Graph 4 analysis this graph allows us to see that at eiother end of the age range there is a steady drop off in number of individuals that are predicted to make above 50k. this graph is made by compairing the age of indivduals against weather or not they are preedicted to make above or below 50k<jupyter_code>df.groupby(['race']).mean().sort_values('hours.per.week', ascending=False)<jupyter_output><empty_output>
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