text_plain_output_1.png image_output_1.png
import matplotlib.pyplot as plt

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

import seaborn as sns

train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')

train_df = train.drop('id', axis=1)

num_data = train_df.select_dtypes('number')
cat_data = train_df.select_dtypes('object')

fig = plt.figure(figsize=(20,15))
for i,j in enumerate(num_data.columns):
    fig.add_subplot(4,4,i+1)
    sns.boxplot(x=j,data=num_data)
    
plt.show()

fig = plt.figure(figsize=(20,15))
for i,j in enumerate(num_data.columns):
    fig.add_subplot(4,4,i+1)
    ax=sns.kdeplot(x=j,data=num_data)
    ax = sns.histplot(x=j,data=num_data)
plt.show()

fig = plt.figure(figsize=(20, 15))
for i, j in enumerate(cat_data.columns):
    fig.add_subplot(4, 4, i + 1)
    ax = sns.barplot(x=j, y=train['target'], data=cat_data)
plt.show()