text_plain_output_1.png
import matplotlib.pyplot as plt

import numpy as np # linear algebra

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/bike-sharing-demand/train.csv')

test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')

import matplotlib.pyplot as plt
import seaborn as sns

fig, axes = plt.subplots(nrows=2,ncols=2)
fig.set_size_inches(12, 10)
sns.boxplot(data=train_df,y="count",orient="v",ax=axes[0][0])
sns.boxplot(data=train_df,y="count",x="season",orient="v",ax=axes[0][1])
sns.boxplot(data=train_df,y="count",x="hour",orient="v",ax=axes[1][0])
sns.boxplot(data=train_df,y="count",x="workingday",orient="v",ax=axes[1][1])

axes[0][0].set(ylabel='Count',title="Box Plot On Count")
axes[0][1].set(xlabel='Season', ylabel='Count',title="Box Plot On Count Across Season")
axes[1][0].set(xlabel='Hour Of The Day', ylabel='Count',title="Box Plot On Count Across Hour Of The Day")
axes[1][1].set(xlabel='Working Day', ylabel='Count',title="Box Plot On Count Across Working Day")

train_df = train_df[np.abs(train_df['count'] - train_df['count'].mean()) <= 3 * train_df['count'].std()]

train_df.columns

train_df = train_df.drop(['datetime'], axis=1)

fig, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(train_df.corr(), annot=True, ax=ax)