Linear_Regression_Activity.py

#!/usr/bin/env python
# coding: utf-8

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import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.linear_model import LinearRegression
import pandas as pd
import statsmodels.api as sm
get_ipython().run_line_magic('matplotlib', 'inline')


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df = pd.read_csv(r'C:\Users\yunus\Downloads\cereal.csv')

df


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print(df.isnull().sum())


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df.describe()


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df[['sugars','vitamins']].plot(kind='bar',color=['green','orange'])
plt.xlabel('cereals')
plt.ylabel('bins')
plt.show()


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conditions = [(df['mfr'] == 'N'),
    (df['mfr'] == 'Q'), (df['mfr'] == 'K'),
    (df['mfr'] == 'R'), (df['mfr'] == 'G'), (df['mfr'] == 'P'),
    (df['mfr'] == 'A')]
choices = ['Nabisco','Qualer Oats','Kellogs','Raslston Purina','General Mills','Post','American Home Foods Products']
df['full name'] = np.select(conditions,choices, default=0)
print(df)       


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sns.countplot(y='full name', data = df)
plt.show()


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y = df['rating']
x = df[['calories', 'protein', 'fat', 'sodium', 'fiber', 'carbo', 'sugars', 'potass', 'vitamins', 'shelf', 'weight', 'cups']]
x.shape, y.shape


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mod = sm.OLS(y, x)
res = mod.fit()

print(res.summary())


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sns.lmplot(x='rating', y='sugars', data = df)
sns.lmplot(x='rating', y='vitamins', data = df)


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corr = df.corr(method='pearson')
corr


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