<jupyter_start><jupyter_text><jupyter_code>import pandas as pd
import numpy as np
df=pd.read_csv(r'https://raw.githubusercontent.com/chirudukuru/DMDW/main/student-mat.csv')
import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib','inline')
plt.style.use('seaborn-whitegrid')
df
df1=df[['traveltime','studytime']]
df1.head()
x=df1['traveltime']
y=df1['studytime']
sns.lineplot(x,y,dashes=True)
plt.show()
from scipy.stats import norm
correlation=df1.corr()
print(correlation)
sns.heatmap(correlation,cmap='BrBG')
plt.show()
covar=df1.cov()
print(covar)
sns.heatmap(covar)
plt.show()
#Normalization
df
age=np.array(df['age'])
age
print(max(age))
print(min(age))
from scipy import stats
zscore=np.array(stats.zscore (age))
zscore=zscore[0:394]
zscore
#decimal normalization
dn=[]
dn.append(age/pow(10,2))
dn=np.array(dn)
print(dn)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16852678571428573, 'fraction_numerical': 0.014508928571428572, 'mean_word_length': 8.966666666666667, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '590756', 'license': 'Apache-2.0', 'n_tokens_mistral': 392, 'n_tokens_neox': 373, 'n_words': 58, 'path': 'Assignment5.ipynb', 'repo_name': 'DheerajkumarNayak/18CSE019', 'size': '896'}