# ---
# jupyter:
#   jupytext:
#     text_representation:
#       extension: .py
#       format_name: light
#       format_version: '1.5'
#       jupytext_version: 1.14.4
#   kernelspec:
#     display_name: 'Python 3.8.5 64-bit (''anaconda3'': virtualenv)'
#     language: python
#     name: python385jvsc74a57bd072544b4b71f926df8e6550fd872403aeb49cdc47d952f1d1cea45f1032de013d
# ---

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.decomposition import PCA


df_wine=pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data',header=None)
df_wine.head()

df_wine.isnull().sum()

plt.figure(figsize=(10,8))
sns.heatmap(df_wine.corr(),annot=True)

df_wine[0].value_counts()

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(df_wine.iloc[:,1:],df_wine.iloc[:,0],)

StarcoderdataJupyterScriptsDedupFiltered