Analyse various school outcomes in Tennessee using pandas

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Analyse various school outcomes in Tennessee using pandas. Suppose you are a

public school administrator. Some schools in your state of Tennessee are

performing below average academically. Your superintendent, under pressure

from frustrated parents and voters, approached you with the task of understanding

why these schools are under-performing. To improve school performance, you

need to learn more about these schools and their students, just as a business needs

to understand its own strengths and weaknesses and its customers. Though you is

eager to build an impressive explanatory model, you know the importance of

conducting preliminary research to prevent possible pitfalls or blind spots. Thus,

you engages in a thorough exploratory analysis, which includes: a lit review, data

collection, descriptive and inferential statistics, and data visualization.

Phase 1 - Data Collection

Here is a data of every public school in middle Tennessee. The data also includes

various demographic, school faculty, and income variables. You need to convert the

data into useful information.

• Read the data in pandas data frame

• Describe the data to find more details

Phase 2 - Group data by school ratings

Chooses indicators that describe the student body (for example, reduced_lunch) or

school administration (stu_teach_ratio) hoping they will

explain school_rating. reduced_lunch is a variable measuring the average percentage

of students per school enrolled in a federal program that provides lunches for students

from lower-income households. In short, reduced_lunch is is a good proxy for household income.Isolates 'reduced_lunch' and groups the data by 'school_rating' using pandas groupby

method and then uses describe on the re-shaped data

Phase 3 - Correlation analysis

Find the correlation between 'reduced_lunch' and 'school_rating'. The values in the

correlation matrix table will be between -1 and 1. A value of -1 indicates the strongest

possible negative correlation, meaning as one variable decreases the other increases.

And a value of 1 indicates the opposite.

Phase 4 - Scatter Plot

Find the relationship between school_rating and reduced_lunch, Plot a graph with the

two variables on a scatter plot. Each dot represents a school. The placement of the dot

represents that school's rating (Y-axis) and the percentage of its students on reduced

lunch (x-axis). The downward trend line shows the negative correlation

between school_rating and reduced_lunch (as one increases, the other decreases). The

slope of the trend line indicates how much school_rating decreases

as reduced_lunch increases. A steeper slope would indicate that a small change

in reduced_lunch has a big impact on school_rating while a more horizontal slope

would indicate that the same small change in reduced_lunch has a smaller impact

on school_rating.

Phase 5 - Correlation Matrix

An efficient graph for assessing relationships is the correlation matrix, as seen below;

its color-coded cells make it easier to interpret than the tabular correlation matrix

above. Red cells indicate positive correlation; blue cells indicate negative correlation;

white cells indicate no correlation. The darker the colors, the stronger the correlation

(positive or negative) between those two variables. Draw a graph of correlation matrix

having all important fields of data frame.