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from sklearn.preprocessing import StandardScaler

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import seaborn as sns

train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')

train.isna().any()

g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6, 
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")

g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6, 
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")

g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6, 
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")

plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(),cmap="BrBG",annot=True, linewidths = 2.0)

scaler = StandardScaler()
X = scaler.fit_transform(train.drop(['price_range'], axis=1))
y = np.ravel(train[['price_range']])

print(X.shape)
print(y.shape)