After doing data exploraion i came to know that there is no null values and i had also mention that what is TOEFL score, GRE score....etc in data exploration and in data visualization i had use corelation matrix imported from corrmat.py for finding which variable increase the chance of admission in college and also used barplot for counting the particular value in each variable, and then after there is no need of cleaning because there is no null values and then removed the serial number column because it was of no use, finally for model building i had taken 6 different model and also use gridsearchcv for choosing better model and parameters among them linear regression score was highest so i had taken that model for predicting and then finally i had done predicton on some values taken from the data.