image_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error

path = '../input/pizza-price-prediction/pizza_v1.csv'
df = pd.read_csv(path)

correlation = df.corr()
plt.figure(figsize=(15, 8))
sns.heatmap(correlation, cbar=True, square=True, fmt='.1f', annot=True, annot_kws={'size': 8}, cmap='YlGnBu')
plt.show()