<jupyter_start><jupyter_text>Supervised linear regression algorithms follow chisquare distribution(which is square of normal distribution). But to follow normal distribution minimum no.of observations we need to have are 30. Which are not there over here. So going with t distribution test.<jupyter_code>df.columns
df.drop("Unnamed: 0", axis=1, inplace=True)
df.head()
df.info()
df.describe()
df['Hours'].plot.hist()
df['Scores'].plot.hist()
import seaborn as sns
sns.pairplot(df)
sns.lmplot('Hours','Scores',data=df, palette='coolwarm', height=6, aspect=1, fit_reg=True)<jupyter_output><empty_output>
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