Pre-Prediction Process on Placements using Python.

Summary sheet of the class of students
Summary sheet of the class of students
no. of rows and columns
  • For this, we use the “.info” function which gives us information about the datatypes of each characteristic and about null characteristics.
  • It becomes easy for us to know what operations can we perform and operations that we can’t because we can easily identify the categorical variables and numerical variables by looking at the data types.
  • Data has float, integer, object values.
  • No variable column has null/missing values.
Data info
  • Here we can notice that the mean value is almost equal to the median value of each column, represented by the 50th percentile.
  • So, we can conclude that it is symmetrical and has zero skewness(approx).
  • It is noticed that there is a considerable difference in the values of 75th percentile and max. This rightly depicts that the graph might have outliers.
Description of the data

Outlier identification:

  • Here we have applied this function to get the uniqueness in the salary which basically gives us the info on how many figures we have in total.
Different salary figures
  • This tells us the vote count of each salary figure in descending order.
  • “value_count” has the most values concentrated in the salary of 300000.
  • Dark shades represent a positive correlation while lighter shades represent a negative correlation.
  • If you set annot=True, you’ll get values by which features are correlated to each other in grid-cells.
Correlation matrix
no. of males who were salaried above 3lac
no. of females who were salaried above 3lac
Mean of the salary
  • Will it depend on your 10th percentage?
  • Will it depend on your 12th percentage?
  • Is it more likely toward the freshers?
  • Or is it more likely towards the people who are already placed?
  • Does it have anything to do with the employability test?
parameters on which placement depends
freshers salary
job experienced salary

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Data Science Aspirant

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Praseeda Saripalle

Praseeda Saripalle

Data Science Aspirant

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