<jupyter_start><jupyter_text>**Probability of deal going through by user_type**<jupyter_code>(train_df.groupby('user_type').deal_probability.sum()/train_df.groupby('user_type').deal_probability.count()).plot.bar()
gc.collect()
train_df.info()<jupyter_output><class 'pandas.core.frame.DataFrame'>
RangeIndex: 1503424 entries, 0 to 1503423
Data columns (total 18 columns):
item_id                 1503424 non-null object
user_id                 1503424 non-null object
region                  1503424 non-null object
city                    1503424 non-null object
parent_category_name    1503424 non-null object
category_name           1503424 non-null object
param_1                 1441848 non-null object
param_2                 848882 non-null object
param_3                 640859 non-null object
title                   1503424 non-null object
description             1387148 non-null object
price                   1418062 non-null float64
item_seq_number         1503424 non-null int64
activation_date         1503424 non-null datetime64[ns]
user_type               1503424 non-null object
image                   1390836 non-null object
image_top_1             1390836 non-null float64
deal_probability        1503424 non-null float64
dtypes: datetim[...]<jupyter_text>temp_series = train_df['region_en'].value_counts()labels = (np.array(temp_series.index))sizes = (np.array((temp_series / temp_series.sum())*100))trace = go.Pie(labels=labels, values=sizes)layout = go.Layout(    title='Region distribution',    width=900,    height=900,)data = [trace]fig = go.Figure(data=data, layout=layout)py.iplot(fig, filename="region")<jupyter_code>region_count = train_df['region'].value_counts()
labels = np.array(region_count.index)
sizes = np.array((region_count / train_df.region.count())*100)
trace = go.Pie(labels=labels, values=sizes)
layout = go.Layout(
    title='Region distribution',
    width=900,
    height=900,
)
data = [trace]
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename="region")<jupyter_output><empty_output>
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