In this activity, we used StudentEvent dataset which original dataset has been pivot by Student ID and event context and also has been grouping by same event context. This dataset has been standardized in Rapidminer.

Using the pd.read_excel() function will bring the excel file into a pandas DataFrame. This is our dataset after preprocessing in Excel.

By using info() we can observe the datatype, the total number of rows, the number of rows with a non-null value. After pre-processing, we can see that there is no missing value for all 35 rows, and out of 11 columns, there are only two columns with object datatype. Other than that, one column with int64 datatype and eight columns are float64 type. It is because we are combining the dataset with the same group event context.

data1 = dataf1[['Assignment','Forum','Activity','LectureNote','Tutorial','Questionnaire','Quiz','MarksBin']]

data1

The describe() function is used to generate descriptive statistics that summarize the central tendency, dispersion, and shape of a dataset’s distribution. Since our dataset has been standardized, the standard deviation value for all input columns is the same which is 1.

To create a histogram, we use the pandas hist() method. Calling the hist() method on a pandas data frame will return histograms for all non-nuisance series in the data frame. A histogram allows us to see the distribution for all non-nuisance series variable.

data1.plot()

for i in range(0, len(data1.columns), 4):

sns.pairplot(data=data1,

x_vars=data1.columns[i:i+5],

y_vars=['MarksBin'])

For this dataset, there are 2 variables that have correlation with more than 0.1.

corr = data1.drop('MarksBin', axis=1).corr() # We already examined MarksBin correlations

plt.figure(figsize=(12, 10))

sns.heatmap(corr[(corr >= 0.1) | (corr <= -0.1)],

cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1,

annot=True, annot_kws={"size": 8}, square=True);