Exercise \#1: Principle Component Analysis ( 60 points): This exercise is based on the attached file 'soil,data' which includes three types of soils (represented as 1,2,3 ) and the quantities of 13 constituents of these soils. 1. Read the file titled 'soil.data' (uploaded to this homework) into your Jupyter Notebook as a DataFrame using the pd.read_csv0 0 command, and name it 'data'. (1 point) Note: make sure you imported the needed packages first 2. Show the descriptive statistics of the data ( 1 point). Hint: use the describo().T method 3. How many observations are in the dataset? (1 point) Hint: use the shape method 4. Does the dataset contain missing values? ( 1 point) Hint: use the isnull() sum() method 5. a. How many Soil Types do we have and how are they represented in the data? (2 points) Hint: use the -value counts() method on the 'Soil Type' variable OR use the .unique0) method on the 'Soil Type' variable b. Convert 'Soil Type' integer values into strings by using the following mapping ( 2 points): 1: 'Soil Type 1' 2: 'Soil Type 2' 3: 'Soil Type 3' And replace it in the original 'Soil Type' column/variable. Hint: use the datapSoil Type'] = data[ [Soil Type"] replace(...) method c. Save the new 'Soil Type' column into a separate variable (not in the dataframe), and name it: 'soil_type'. (2 points) 6. Drop the 'Soil Type' column from the dataframe (because we already saved it into a sperate variable). Note: make sure your drop it from the original dataframe named 'data'. (2 points) Hint: use the data-data.drop(columis= ...) method 7. a. Get the correlation matrix between the remaining features/variables in the dataframe. (2 points) Hint use the corr() method b. Using seaborn, plot a heatmap of the obtained correlation matrix in part 7(a), with a cmap = 'seismic' color parameter, vmin =1, and vmax=1. (2 points) Hint: use the sns heatmap(...) function c. By a visual inspection of the obtained heatmap in part 7(b), what do red colors mean and what do blue colors mean? (2 points) d. By a visual inspection of the obtained heatmap in part 7(b), are the Exercise \#1: Principle Component Analysis ( 60 points): This exercise is based on the attached file 'soil,data' which includes three types of soils (represented as 1,2,3 ) and the quantities of 13 constituents of these soils. 1. Read the file titled 'soil.data' (uploaded to this homework) into your Jupyter Notebook as a DataFrame using the pd.read_csv0 0 command, and name it 'data'. (1 point) Note: make sure you imported the needed packages first 2. Show the descriptive statistics of the data ( 1 point). Hint: use the describo().T method 3. How many observations are in the dataset? (1 point) Hint: use the shape method 4. Does the dataset contain missing values? ( 1 point) Hint: use the isnull() sum() method 5. a. How many Soil Types do we have and how are they represented in the data? (2 points) Hint: use the -value counts() method on the 'Soil Type' variable OR use the .unique0) method on the 'Soil Type' variable b. Convert 'Soil Type' integer values into strings by using the following mapping ( 2 points): 1: 'Soil Type 1' 2: 'Soil Type 2' 3: 'Soil Type 3' And replace it in the original 'Soil Type' column/variable. Hint: use the datapSoil Type'] = data[ [Soil Type"] replace(...) method c. Save the new 'Soil Type' column into a separate variable (not in the dataframe), and name it: 'soil_type'. (2 points) 6. Drop the 'Soil Type' column from the dataframe (because we already saved it into a sperate variable). Note: make sure your drop it from the original dataframe named 'data'. (2 points) Hint: use the data-data.drop(columis= ...) method 7. a. Get the correlation matrix between the remaining features/variables in the dataframe. (2 points) Hint use the corr() method b. Using seaborn, plot a heatmap of the obtained correlation matrix in part 7(a), with a cmap = 'seismic' color parameter, vmin =1, and vmax=1. (2 points) Hint: use the sns heatmap(...) function c. By a visual inspection of the obtained heatmap in part 7(b), what do red colors mean and what do blue colors mean? (2 points) d. By a visual inspection of the obtained heatmap in part 7(b), are the
