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eda's Introduction

Exploratory Data Analysis

Problem Statement

To clean and study the ACT examination data for the year 2018 and 2019 and to visualize the data.

Datasets Used

act_2018.csv - Gives ACT scores for the year 2018.

act_2019.csv - Gives ACT scores for the year 2019.

Data Dictionary

Feature Type Dataset Description
state object final_data name of the state in USA
participation_2018 float Cleaned_Merged_data ACT-2018 particiption rate
participation_2019 float Cleaned_Merged_data ACT-2019 particiption rate
composite_2018 float Cleaned_Merged_data ACT-2018 composite score
composite_2019 float Cleaned_Merged_data ACT-2019 composite score

Summary

The work starts with importing of the above mentioned datasets. The data cleaning is done, which includes null check, renaming of columns, merging of datasets. Then the cleaned dataset is exported as a CSV file.

The cleaned data is then analyzed and visualized. The observations are made, which are,

Maximum composite in 2018 : 25.6

Minimum composite in 2018 : 17.7

Maximum composite in 2019 : 25.5

Minimum composite in 2019 : 17.9

States with maximum participation in 2018 : 17

States with maximum participation in 2018 : 15

States with maximum participation for each year : 15

States with greater than 50% participation each year : 28

States with composite score greater than 19 in 2018 : 46

States with composite score greater than 19 in 2019 : 41

Conclusion And Recommendation

The ACT examination data for the years 2018 and 2019 is studied and required observations and trends were analyzed. From the observation it is found that the trends of participation rate and composite score remain almost the same for each year. Where composite score shows inverse relation to participation rate.

The cleaned data is ready to be an input to a machine learning model which can be used to predict the participation rate and composite score in future.

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