import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore')

df = pd.read_csv("/work/hotel_bookings 2.csv")

df.head()

df.tail()

df.shape

df.columns

df.info()

df['reservation_status_date'] = pd.to_datetime(df['reservation_status_date'])

df.info()

for col in df.describe(include = 'object').columns: print(col) print(df[col].unique()) print('-'*50)

df.isnull().sum()

df.drop(['company','agent'],axis = 1, inplace = True) df.dropna(inplace = True)

df.isnull().sum()

df.describe()

df = df[df['adr']<5000]

cancelled_perc = df['is_canceled'].value_counts(normalize = True) print(cancelled_perc) plt.figure(figsize = (5,4)) plt.title('Reservation status count') plt.bar(['Not canceled','canceled'],df['is_canceled'].value_counts(), edgecolor = 'k',width = 0.7) plt.show()

plt.figure(figsize = (8,4)) ax1= sns.countplot(x = 'hotel', hue = 'is_canceled',data = df, palette = 'Blues') legend_labels,_ = ax1. get_legend_handles_labels() plt.title('Reservation status in different hotels',size = 20) plt.xlabel('hotel') plt.ylabel('number of reservations')

resort_hotel = df[df['hotel'] == 'Resort Hotel'] resort_hotel['is_canceled'].value_counts(normalize = True)

city_hotel = df[df['hotel'] == 'City Hotel'] city_hotel['is_canceled'].value_counts(normalize = True)

resort_hotel = resort_hotel.groupby('reservation_status_date')[['adr']].mean() city_hotel = city_hotel.groupby('reservation_status_date')[['adr']].mean()

plt.figure(figsize = (20,8)) plt.title('Average Daily Rate in City and Resort Hotel', fontsize = 30) plt.plot(resort_hotel.index,resort_hotel['adr'], label = 'Resort Hotel') plt.plot(city_hotel.index,city_hotel['adr'], label = 'City Hotel') plt.legend(fontsize = 20) plt.show()

df['month'] =df['reservation_status_date'].dt.month plt.figure(figsize = (16,8)) ax1 = sns.countplot(x = 'month',hue = 'is_canceled',data = df,palette = 'bright') legend_labels,_ = ax1. get_legend_handles_labels() plt.title('Reservation status per month',size = 20) plt.xlabel('month') plt.ylabel('number of reservations') plt.legend(['not canceled','canceled']) plt.show()

plt.figure(figsize = (15,8)) plt.title('ADR per month', fontsize = 30) sns.barplot(x='month', y='adr', data=df[df['is_canceled'] == 1].groupby('month')[['adr']].sum().reset_index()) plt.legend(fontsize = 20) plt.show()

cancelled_data = df[df['is_canceled'] == 1] top_10_country = cancelled_data['country'].value_counts()[:10] plt.figure(figsize = (8,8)) plt.title('Top 10 countries with reservation canceled') plt.pie(top_10_country,autopct = '%.2f',labels = top_10_country.index) plt.show()

df['market_segment'].value_counts()

df['market_segment'].value_counts(normalize = True)

cancelled_data['market_segment'].value_counts(normalize = True)