Project - Overview & Results

Overview:

Required Datasets were found from various resources :

School Progress Reports (2010-11)
Average Household Incomes by School Districts (2010)
School Attendance & Enrolment by School District (2010-11)
Graduation Rates by School District After 4,6 Years (2011)
School Districts GeoJSON Data (2010-11)

Analyze these datasets and choose the columns required.
Cleaning the datasets.
Aggregate all the datasets based on School Districts.
Visualize datasets based on School Districts.
Geo Spatial Visualization of different factors based on School Districts.
Find Correlation between different attributes of the aggregated data.
Website for the Project

Hypothesis:

"Schools belonging to a School District with higher Average Household Incomes have higher Overall Score, Graduation Rates, Attendance and Enrolment".

Correlation

Correlation explains how one or more variables are related to each other. These variables can be input data features which have been used to forecast our target variable. Correlation matrix is a table containing correlation coefficients between variables. Each cell in the table represents the correlation between two variables.

Observations:

Average Household Incomes had negligible to weak correlation with Attendance, Enrolment, Overall Score & Graduation Rates.
Graduation Rates had moderate to strong correlation with Attendance.
Overall Score had weak to moderate correlation Enrolment and Attendance.

Pearson (Standard Correlation Coefficient)

Kendall (Kendall Tau Correlation Coefficient)

Spearman (Spearman Rank Correlation)

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