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<speak> Head is the method that will display the first five rows in the data by default. <break strength="strong"/>Here the serial number is the number of the student. <break strength="strong"/>Each row indicates the data of a single student. The index starts at 0. Serial number one means it is the first student data. <break strength="strong"/>The student achieved a GRE score of 337, a TOEFL score of 118, a University rating is four, a statement of purpose of 4.5, LOR 4.5, CGPA 9.65 and research experience 1.<break strength="x-strong"/> For him, the chance of admittance is 0.92. Already, he admitted into some University, that data was collected. <break strength="strong"/>With this student profile starting from GRE Score to research, his chance of admission in getting University admission is 0.92. <break strength="strong"/>He has a 92% chance of getting admission. <break strength="strong"/>Here we see five students' data. <break strength="strong"/>Except for the serial number, all the remaining are useful for us. <break strength="strong"/>These are the columns. <break strength="strong"/>So we have to train our model on this given data.<break strength="x-strong"/> We want to see further details of the data.<break strength="strong"/> Here we see available variables. <break strength="strong"/>Here we see GRE Score, TOEFL score, CGPA research in several columns.<break strength="x-strong"/> Except for the serial number, there are seven variables.<break strength="strong"/> If you exclude the serial number, there are seven variables. <break strength="strong"/>We want to see statistical details of these variables. <break strength="strong"/>You can get statistical information by using the describe function available in the Pandas.<break strength="x-strong"/> </speak>