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from sklearn.model_selection import train_test_split

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df

import matplotlib.pyplot as plt
import seaborn as sns
cor = train_df.corr()

train_df.dtypes

X = train_df.drop({'price', 'yr_renovated', 'date', 'lat', 'waterfront'}, axis=1)
y = train_df['price']
X_train_valid, X_test, y_train_valid, y_test = train_test_split(X, y, test_size=0.33)
X_train_valid