Welcome to Correlation Analysis of Library-Exam’s documentation!

• First step
  • Problem Statement
  • Abstract
  • Literature Review
  • Where we started?…
  • Hypothesis
• Collection and Masking the Data
  • Data Collection
  • Data Masking
• BATCH 16
  • Importation of libraries
  • Reading the data
  • This function Represents Assigning the semester details to the lib_tans..
  • the function is assigned to the lib variable…
  • The exam_cell_clean function has done to add subject_code & course_title to exam_cell data.
  • the function is assigned to the exam variable…
  • This function helps in assigning the subject_code in the lib_transaction data..
  • the function is assigned to the new_lib variable…
  • This function is used to calculate mean_score for exam in class and in eah semester…
  • And also Library usage by section and semster
  • So both class and course mean score is calculated and stored in the below variables…
  • As there are no transaction in certain semester and in section, we are replacing the null values with ‘0’:
  • subject_code
  • Adding subject_code and title in the lib_transaction
  • Assigning variables for each semester
  • Importing matplotlib library
  • The below is the correlation plot for course_mean_score & library_freq_course_by_id
  • Correlation based on the Semester and the Section
  • Ploting of boxenplot for each semester and section is hued…
  • sem1 boxplot for both section
  • sem2 boxplot for both section
  • sem3 boxplot for both section
  • sem4 boxplot for both section
  • sem5 boxplot for both section
  • sem6 boxplot for both section
  • sem7 boxplot for both section
• BATCH 17
  • Importation of libraries
  • Reading the data
  • This function Represents Assigning the semester details to the lib_tans..
  • the function is assigned to the lib variable…
  • This function Represents Assigning the semester details to the lib_tans..
  • the function is assigned to the new_lib variable…
  • Library usage by section and semster and subject_code in the below function:
  • Variable for the above function:
  • Replacing the class_mean_score[‘section] to 0:’A’ and 1:’B’
  • Importing matplotlib library
  • subjectcode vs library_id_frequency for each section
  • semester library transaction details:
  • section vs library_id_frequency
  • Correlation of Library_id_frequency in semester
• BATCH 18
  • Importation of libraries
  • Reading the data
  • This function Represents Assigning the semester details to the lib_tans..
  • the function is assigned to the lib variable…
  • This function Represents Assigning the semester details to the lib_tans..
  • the function is assigned to the new_lib variable…
  • Library usage by section and semster and subject_code in the below function:
  • Variable for the above function:
  • Replacing the class_mean_score[‘section] to 0:’A’ and 1:’B’
  • subjectcode vs library_id_frequency for each section
  • section vs library_id_frequency
  • Correlation of Library_id_frequency in semester
• Conclusion
  • Concluding Hypothesis
  • Batch 16
  • Batch 17 & Batch 18
• Improving Further