<jupyter_start><jupyter_text>Data Transformation By Z Score In statistics, a z-score tells us how many standard deviations away a value is from the mean.      Z = (X - a)/bWhere:'X' is Single Raw Data Value'a' is mean of the Row'b' is Standard<jupyter_code>import matplotlib.pyplot as plt
import seaborn as sns
from google.colab import files
import io
#Add scipy.stats for z-score of an array/Datasets
import scipy.stats as stats
data = files.upload()
import pandas as pd 

# Load the dataset
df = pd.read_csv(io.StringIO(data['Emotion Dataset.csv'].decode('utf-8')))
df.head
#remove the lable column from dataset
new_df = df
del new_df["labels"]
new_df.head<jupyter_output><empty_output><jupyter_text>Applying Z-scores for dataset<jupyter_code>new_df.apply(stats.zscore)<jupyter_output><empty_output>