. Pay attention to instructions, no second chance provided under any ground Unclear/incomplete/deficient answers will impact the final grade. Late submissions or wrong templates will not be entertained, may that be w Solutions files are expected to be the same format as this file. Students are expected to answer all the questions. Plagiarised answers shall receive zero points. . . . For Question 1-4, We will load 1000_Companies.csv dataset that contains data spendings and location. We will use this data to build a machine learning based Question 1: 10 Points (Load Data) (A) Load the "1000_Companies.csv" dataset - 5 points (B) Display the first and last 5 rows of this dataset - 5 points In [ ]: pd.read_csv('/content/profit_estimation_of_') . Pay attention to instructions, no second chance provided under any ground Unclear/incomplete/deficient answers will impact the final grade. Late submissions or wrong templates will not be entertained, may that be w Solutions files are expected to be the same format as this file. Students are expected to answer all the questions. Plagiarised answers shall receive zero points. . . . For Question 1-4, We will load 1000_Companies.csv dataset that contains data spendings and location. We will use this data to build a machine learning based Question 1: 10 Points (Load Data) (A) Load the "1000_Companies.csv" dataset - 5 points (B) Display the first and last 5 rows of this dataset - 5 points In [ ]: pd.read_csv('/content/profit_estimation_of_')