In [1]:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.svm import LinearSVR
from sklearn.learning_curve import learning_curve
from scipy import stats
from nltk.corpus import stopwords
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
"This module will be removed in 0.20.", DeprecationWarning)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\learning_curve.py:22: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20
DeprecationWarning)
In [2]:
from fastai.imports import *
from fastai.structured import *
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display
from sklearn import metrics
In [3]:
PATH = os.getcwd();
PATH = PATH+"\\AV_Lord\\"
In [4]:
!dir {PATH}
Volume in drive D is Local Disk
Volume Serial Number is B408-A348
Directory of D:\Github\fastai\courses\ml1\AV_Lord
25-Mar-18 12:42 AM <DIR> .
25-Mar-18 12:42 AM <DIR> ..
24-Mar-18 04:08 PM <DIR> .ipynb_checkpoints
24-Mar-18 06:53 PM 11,247,959 av_cat_2.csv
23-Mar-18 04:40 PM 58,415 campaign_data.csv
24-Mar-18 04:35 PM 1,382,961,456 combined.raw
25-Mar-18 09:23 AM 24,181,026 logistic_reg_sub2.csv
25-Mar-18 12:42 AM 3,748,609 logistic_reg_sub2.zip
24-Mar-18 12:07 AM 9,700,243 sample_submission.csv
23-Mar-18 04:42 PM 28,686,790 test_BDIfz5B.csv
23-Mar-18 07:55 PM 42,036,287 train.csv
8 File(s) 1,502,620,785 bytes
3 Dir(s) 168,866,631,680 bytes free
In [5]:
df_raw = pd.read_csv(f'{PATH}train.csv',low_memory=False)
In [24]:
camp = pd.read_csv(f'{PATH}campaign_data.csv',low_memory=False)
In [25]:
df_raw.shape, camp.shape
Out[25]:
((1023191, 6), (52, 9))
In [26]:
df_raw.get_ftype_counts(),\
camp.get_ftype_counts()
Out[26]:
(int64:dense 4
object:dense 2
dtype: int64, int64:dense 5
object:dense 4
dtype: int64)
In [27]:
def display_all(df):
with pd.option_context("display.max_rows", 1000):
with pd.option_context("display.max_columns", 1000):
display(df)
In [28]:
camp.head(2)
Out[28]:
campaign_id
communication_type
total_links
no_of_internal_links
no_of_images
no_of_sections
email_body
subject
email_url
0
29
Newsletter
67
61
12
3
Dear AVians,\r\n \r\nWe are shaping up a super...
Sneak Peek: A look at the emerging data scienc...
http://r.newsletters.analyticsvidhya.com/7um44...
1
30
Upcoming Events
18
14
7
1
Dear AVians,\r\n \r\nAre your eager to know wh...
[July] Data Science Expert Meetups & Competiti...
http://r.newsletters.analyticsvidhya.com/7up0e...
In [ ]:
df[col].apply(lambda x: len(str(x).split(" "))#word count
df[col].str.len()#char count
def avg_word(sentence):
words = sentence.split()
return (sum(len(word) for word in words)/len(words))
df['avg_word'] = df[col].apply(lambda x: avg_word(x))
stop = stopwords.words('english')
df['stopwords'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x in stop]))
df['upper'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x.isupper()]))
df[col] = df[col].apply(lamda x: " ".join(x.lower() for x in split()))
df['removal_of_stopwords'] = df['tweet'].apply(lambda x: " ".join(x for x in x.split() if x not in stop))
In [21]:
freq = pd.Series(' '.join(camp['email_body']).split()).value_counts()[:10]
freq = list(freq.index)
camp['email_body'] = camp['email_body'].apply(lambda x: " ".join(x for x in x.split() if x not in freq))
camp['email_body'].head()
freq = pd.Series(' '.join(camp['email_body']).split()).value_counts()[-10:]
freq = list(freq.index)
camp['email_body'] = camp['email_body'].apply(lambda x: " ".join(x for x in x.split() if x not in freq))
camp['email_body'].head()
Out[21]:
0 Dear AVians, We are shaping up superb science ...
1 Dear AVians, Are your eager know what are upco...
2 Early Bird Pricing Till August 07 Save upto ...
3 Hi ? Before I dive into why should attend this...
4 Fireside Chat with DJ Patil - master is here! ...
Name: email_body, dtype: object
In [ ]:
In [ ]:
In [ ]:
In [11]:
camp['communication_type'].value_counts().plot(kind='barh');
In [28]:
print(df_raw.is_open.value_counts())
df_raw['is_open'].value_counts().plot(kind='bar')
0 920401
1 102790
Name: is_open, dtype: int64
Out[28]:
<matplotlib.axes._subplots.AxesSubplot at 0x19fa3c8c9b0>
In [37]:
add_datepart(df_raw,'send_date')
df_raw.drop('send_Elapsed', axis=1, inplace=True)
df_raw.head(1)
Out[37]:
id
user_id
campaign_id
is_open
is_click
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
send_Dayofyear
send_Is_month_end
send_Is_month_start
send_Is_quarter_end
send_Is_quarter_start
send_Is_year_end
send_Is_year_start
0
42_14051
14051
42
0
0
2017
1
2
9
0
9
False
False
False
False
False
False
In [38]:
train_cats(df_raw)
In [39]:
df_raw['user_id'].value_counts()
Out[39]:
183177 20
145022 20
4118 19
216700 19
114180 19
197188 19
47152 19
163618 18
69970 18
196296 18
122065 18
218458 18
114075 18
38127 18
162109 18
67867 18
163399 18
156029 18
157374 18
36788 18
92799 18
49136 18
28463 18
160443 18
117705 18
86941 18
152991 18
143179 18
185773 18
111959 18
..
9258 1
1070 1
146480 1
60010 1
39524 1
48059 1
43618 1
156523 1
23463 1
21414 1
37685 1
230364 1
17199 1
134125 1
170356 1
182903 1
190842 1
178809 1
124304 1
85381 1
97281 1
171007 1
78593 1
228376 1
173048 1
185334 1
181236 1
2854 1
138223 1
188649 1
Name: user_id, Length: 168236, dtype: int64
In [40]:
df_raw['campaign_id'].value_counts().plot(kind='barh');
In [42]:
df_raw.get_ftype_counts()
Out[42]:
bool:dense 6
category:dense 1
int64:dense 10
dtype: int64
In [ ]:
df_raw['send_Month'].value_counts().plot(kind='bar')
In [43]:
df_raw.groupby('campaign_id')['is_click'].describe()
Out[43]:
count
mean
std
min
25%
50%
75%
max
campaign_id
29
69129.0
0.016549
0.127574
0.0
0.0
0.0
0.0
1.0
30
69756.0
0.012615
0.111608
0.0
0.0
0.0
0.0
1.0
31
3192.0
0.024436
0.154423
0.0
0.0
0.0
0.0
1.0
32
69624.0
0.010600
0.102409
0.0
0.0
0.0
0.0
1.0
33
46815.0
0.011449
0.106388
0.0
0.0
0.0
0.0
1.0
34
73112.0
0.009807
0.098544
0.0
0.0
0.0
0.0
1.0
35
4121.0
0.043921
0.204945
0.0
0.0
0.0
0.0
1.0
36
73415.0
0.004767
0.068882
0.0
0.0
0.0
0.0
1.0
37
7559.0
0.006482
0.080257
0.0
0.0
0.0
0.0
1.0
38
7232.0
0.007605
0.086881
0.0
0.0
0.0
0.0
1.0
39
3487.0
0.046171
0.209886
0.0
0.0
0.0
0.0
1.0
40
4822.0
0.012028
0.109023
0.0
0.0
0.0
0.0
1.0
41
2786.0
0.014716
0.120437
0.0
0.0
0.0
0.0
1.0
42
81253.0
0.012824
0.112516
0.0
0.0
0.0
0.0
1.0
43
67.0
0.029851
0.171460
0.0
0.0
0.0
0.0
1.0
44
39498.0
0.011697
0.107519
0.0
0.0
0.0
0.0
1.0
45
5322.0
0.071402
0.257519
0.0
0.0
0.0
0.0
1.0
46
9831.0
0.027566
0.163734
0.0
0.0
0.0
0.0
1.0
47
14230.0
0.016725
0.128244
0.0
0.0
0.0
0.0
1.0
48
51456.0
0.012166
0.109626
0.0
0.0
0.0
0.0
1.0
49
81358.0
0.012758
0.112231
0.0
0.0
0.0
0.0
1.0
50
39710.0
0.014455
0.119358
0.0
0.0
0.0
0.0
1.0
51
3882.0
0.043792
0.204658
0.0
0.0
0.0
0.0
1.0
52
82160.0
0.014192
0.118282
0.0
0.0
0.0
0.0
1.0
53
85431.0
0.010090
0.099942
0.0
0.0
0.0
0.0
1.0
54
93943.0
0.010251
0.100727
0.0
0.0
0.0
0.0
1.0
In [95]:
df_raw['send_Day'].value_counts().plot(kind='bar')
Out[95]:
<matplotlib.axes._subplots.AxesSubplot at 0x1eb80fbca58>
In [134]:
def code(url): return re.split('/', url)[3][:11]
def lexical_diversity(my_text_data):
word_count = len(my_text_data)
vocab_size = len(set(my_text_data))
diversity_score = vocab_size / word_count
return diversity_score
In [137]:
## Add features on camp df
camp['len_email_body'] = len(camp['email_body'])
camp['diversity_email_body'] = camp['email_body'].apply(lexical_diversity)
camp['diversity_subject'] = camp['subject'].apply(lexical_diversity)
camp['diversity_email_url'] = camp['email_url'].apply(lexical_diversity)
camp['body_per_sec'] = camp['len_email_body']/camp['no_of_sections']
camp['links_per_sec'] = camp['total_links']/camp['no_of_sections']
camp['img_per_sec'] = camp['no_of_images']/ camp['no_of_sections']
camp['code_email_url'] = camp['email_url'].apply(code)
camp['other_links'] = camp['total_links'] - camp['no_of_internal_links']
camp['av_links_percent'] = camp['no_of_internal_links'] / camp['total_links']
In [142]:
camp.drop(['email_body','email_url','subject'], axis=1,inplace=True)
In [ ]:
camp = pd.get_dummies(camp);camp.head(2)
In [154]:
df_raw = df_raw.merge(camp,on='campaign_id');
df_raw.head(1)
Out[154]:
id
user_id
campaign_id
is_open
is_click
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
...
code_email_url_7wra6vb5p4c
code_email_url_7wrjo7b5p4c
code_email_url_7ww0uvb5p4c
code_email_url_7wx2s7b5p4c
code_email_url_7wxlqvb5p4c
code_email_url_7wxv87b5p4c
code_email_url_7wz6mvb5p4c
code_email_url_7wzpljb5p4c
code_email_url_7x08k7b5p4c
code_email_url_o7ohwml8lxh
0
42_14051
14051
42
0
0
2017
1
2
9
0
...
0
0
0
0
0
0
0
0
0
0
1 rows × 89 columns
In [159]:
len(np.unique(df_raw['user_id']))
Out[159]:
168236
In [164]:
test.head(1)
Out[164]:
id
campaign_id
user_id
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
send_Dayofyear
send_Is_month_end
send_Is_month_start
send_Is_quarter_end
send_Is_quarter_start
send_Is_year_end
send_Is_year_start
0
63_122715
63
122715
2018
1
1
2
1
2
False
False
False
False
False
False
In [67]:
df_raw.drop(['user_id', 'campaign_id'], axis=1, inplace=True);
df_raw.get_ftype_counts()
Out[67]:
int32:dense 6
int64:dense 12
object:dense 2
dtype: int64
In [88]:
for s in np.unique(df_raw['communication_type']):
print ( s , '->' , hash ( s ) % 9)
Conference -> 8
Corporate -> 7
Hackathon -> 4
Newsletter -> 1
Others -> 5
Upcoming Events -> 0
Webinar -> 6
In [94]:
sns.set()
corr_matrix = df_raw.corr ()
plt.figure(figsize=(10,10))
sns.heatmap(corr_matrix , annot = True , fmt = ".2f" , cbar = True , cmap = 'PuOr');
Out[94]:
<matplotlib.axes._subplots.AxesSubplot at 0x1eb8b966fd0>
df_raw['start_month'] = df_raw['send_Year']*100 + df_raw['send_Month'] df_raw['noon'] = np.where(np.logical_and(df_raw['start_hour']>11 , df_raw['start_hour']<=19),1,0) df_raw['night'] = np.where(np.logical_and(df_raw['start_hour']>19, df_raw['start_hour']<24),1,0)
span_time = pd.concat([time_df['seconds'], time_df_test['seconds']]) df_raw['session_span'] = span_time
df_raw['session_span_per_n_uniques_sites'] = df_raw['session_span']/df_raw['n_unique_sites']
df_raw['hour_sin_x'] = df_raw['start_hour'].apply(lambda ts: np.sin(2pits/24.)) df_raw['hour_cos_x'] = df_raw['start_hour'].apply(lambda ts: np.cos(2pits/24.))""
In [175]:
y_target = df_raw['is_click'];
df_raw.head(1).T
Out[175]:
0
id
42_14051
user_id
14051
campaign_id
42
is_open
0
is_click
0
send_Year
2017
send_Month
1
send_Week
2
send_Day
9
send_Dayofweek
0
send_Dayofyear
9
send_Is_month_end
False
send_Is_month_start
False
send_Is_quarter_end
False
send_Is_quarter_start
False
send_Is_year_end
False
send_Is_year_start
False
total_links
88
no_of_internal_links
79
no_of_images
13
no_of_sections
4
len_email_body
52
diversity_email_body
0.0419682
diversity_subject
0.386364
body_per_sec
13
diversity_email_url
0.457143
img_per_sec
3.25
other_links
9
av_links_percent
0.897727
links_per_sec
22
...
...
code_email_url_7vtb2vb5p4c
0
code_email_url_7vv5g7b5p4c
0
code_email_url_7vzmmvb5p4c
0
code_email_url_7w2sevb5p4c
0
code_email_url_7w3uc7b5p4c
0
code_email_url_7w43tjb5p4c
0
code_email_url_7w5y6vb5p4c
0
code_email_url_7w6qmvb5p4c
0
code_email_url_7w7047b5p4c
0
code_email_url_7wghg7b5p4c
0
code_email_url_7wh9w7b5p4c
0
code_email_url_7whsuvb5p4c
0
code_email_url_7wjn87b5p4c
0
code_email_url_7wjwpjb5p4c
0
code_email_url_7wkfo7b5p4c
0
code_email_url_7wnbyvb5p4c
0
code_email_url_7wo4evb5p4c
0
code_email_url_7wowuvb5p4c
0
code_email_url_7wppavb5p4c
0
code_email_url_7wqhqvb5p4c
0
code_email_url_7wra6vb5p4c
0
code_email_url_7wrjo7b5p4c
0
code_email_url_7ww0uvb5p4c
0
code_email_url_7wx2s7b5p4c
0
code_email_url_7wxlqvb5p4c
0
code_email_url_7wxv87b5p4c
0
code_email_url_7wz6mvb5p4c
0
code_email_url_7wzpljb5p4c
0
code_email_url_7x08k7b5p4c
0
code_email_url_o7ohwml8lxh
0
89 rows × 1 columns
In [176]:
os.makedirs('tmp', exist_ok=True)
df_raw.to_feather('tmp/av_lord-raw')
In [29]:
df_raw = pd.read_feather('tmp/av_lord-raw')
In [134]:
df, y, nas, mapper = proc_df(df_raw, 'is_click', do_scale=True,max_n_cat=30)
In [135]:
sns.countplot(y)
Out[135]:
<matplotlib.axes._subplots.AxesSubplot at 0x27193e5ff28>
In [139]:
y[-26:] = 0
In [229]:
#df.drop('is_open', axis=1, inplace=True)
m = RandomForestRegressor(n_jobs=-1)
m.fit(df, y)
m.score(df,y)
Out[229]:
0.7547053047196518
In [141]:
def print_score(m):
res = [rmse(m.predict(X_train), y_train), rmse(m.predict(X_valid), y_valid),
m.score(X_train, y_train), m.score(X_valid, y_valid)]
if hasattr(m, 'oob_score_'): res.append(m.oob_score_)
print(res)
In [ ]:
m = RandomForestRegressor(n_jobs=-1)
%%time m.fit(X_train, y_train)
print_score(m)
In [42]:
display_all(df_raw.isnull().sum().sort_index()/len(df_raw))
campaign_id 0.0
id 0.0
is_click 0.0
is_open 0.0
send_Day 0.0
send_Dayofweek 0.0
send_Dayofyear 0.0
send_Is_month_end 0.0
send_Is_month_start 0.0
send_Is_quarter_end 0.0
send_Is_quarter_start 0.0
send_Is_year_end 0.0
send_Is_year_start 0.0
send_Month 0.0
send_Week 0.0
send_Year 0.0
user_id 0.0
dtype: float64
In [178]:
display_all(df.columns)
Index(['id', 'user_id', 'campaign_id', 'send_Year', 'send_Month', 'send_Week',
'send_Day', 'send_Dayofweek', 'send_Dayofyear', 'total_links',
'no_of_internal_links', 'no_of_images', 'no_of_sections', 'email_body',
'subject', 'email_url', 'link_diff', 'img_per_sec', 'link_diff_%',
'img_per_section', 'user_id_na', 'is_open_na', 'send_Year_na',
'send_Month_na', 'send_Week_na', 'send_Day_na', 'send_Dayofweek_na',
'send_Dayofyear_na', 'send_Is_month_end_0.0', 'send_Is_month_end_nan',
'send_Is_month_start_0.0', 'send_Is_month_start_nan',
'send_Is_quarter_end_0.0', 'send_Is_quarter_end_nan',
'send_Is_quarter_start_0.0', 'send_Is_quarter_start_nan',
'send_Is_year_end_0.0', 'send_Is_year_end_nan',
'send_Is_year_start_0.0', 'send_Is_year_start_nan',
'communication_type_Conference', 'communication_type_Corporate',
'communication_type_Hackathon', 'communication_type_Newsletter',
'communication_type_Others', 'communication_type_Upcoming Events',
'communication_type_Webinar', 'communication_type_nan', 'av_links'],
dtype='object')
In [ ]:
In [33]:
test = pd.read_csv(f'{PATH}\\test_BDIfz5B.csv')
In [34]:
test.shape
Out[34]:
(773858, 4)
In [35]:
test.head(2)
Out[35]:
id
campaign_id
user_id
send_date
0
63_122715
63
122715
01-02-2018 22:35
1
56_76206
56
76206
02-01-2018 08:15
In [45]:
add_datepart(test,'send_date');
test.drop('send_Elapsed',axis= 1, inplace=True)
In [46]:
test = test * 1
train_cats(test)
In [47]:
test = test.merge(camp,on='campaign_id');
In [134]:
test.drop(['user_id', 'campaign_id'], axis=1, inplace=True);
test['communication_type'] = mapped;
test.get_ftype_counts()
Out[134]:
bool:dense 6
category:dense 1
int64:dense 10
object:dense 1
dtype: int64
In [137]:
test['link_diff'] = test['total_links'] - test['no_of_internal_links']
test['av_links'] = (test['no_of_internal_links']/ test['total_links'])
test['img_per_section'] = test['no_of_images']/ test['no_of_sections']
test['link_diff_%'] = (test['total_links'] - test['no_of_internal_links'])/test['total_links']
test.head(1)
Out[137]:
id
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
send_Dayofyear
send_Is_month_end
send_Is_month_start
send_Is_quarter_end
...
send_Is_year_start
communication_type
total_links
no_of_internal_links
no_of_images
no_of_sections
link_diff
av_links
img_per_section
link_diff_%
0
63_122715
2018
1
1
2
1
2
False
False
False
...
False
3
68
64
15
5
4
0.941176
3.0
0.058824
1 rows × 22 columns
In [138]:
df_raw['link_diff'] = df_raw['total_links'] - df_raw['no_of_internal_links']
df_raw['av_links'] = (df_raw['no_of_internal_links']/ df_raw['total_links'])
df_raw['img_per_section'] = df_raw['no_of_images']/ df_raw['no_of_sections']
df_raw['link_diff_%'] = (df_raw['total_links'] - df_raw['no_of_internal_links'])/df_raw['total_links']
df_raw.head(1)
Out[138]:
id
is_open
is_click
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
send_Dayofyear
send_Is_month_end
...
send_Is_year_start
communication_type
total_links
no_of_internal_links
no_of_images
no_of_sections
link_diff
av_links
img_per_section
link_diff_%
0
42_14051
0
0
2017
1
2
9
0
9
0
...
0
3
88
79
13
4
9
0.897727
3.25
0.102273
1 rows × 24 columns
In [139]:
test.to_feather('tmp/av_lord_test')
In [30]:
test = pd.read_feather('tmp/av_lord_test')
In [31]:
df_raw.head(1)
Out[31]:
id
user_id
campaign_id
is_open
is_click
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
...
code_email_url_7wra6vb5p4c
code_email_url_7wrjo7b5p4c
code_email_url_7ww0uvb5p4c
code_email_url_7wx2s7b5p4c
code_email_url_7wxlqvb5p4c
code_email_url_7wxv87b5p4c
code_email_url_7wz6mvb5p4c
code_email_url_7wzpljb5p4c
code_email_url_7x08k7b5p4c
code_email_url_o7ohwml8lxh
0
42_14051
14051
42
0
0
2017
1
2
9
0
...
0
0
0
0
0
0
0
0
0
0
1 rows × 89 columns
In [32]:
test.head(1)
Out[32]:
id
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
send_Dayofyear
send_Is_month_end
send_Is_month_start
send_Is_quarter_end
...
send_Is_year_start
communication_type
total_links
no_of_internal_links
no_of_images
no_of_sections
link_diff
av_links
img_per_section
link_diff_%
0
63_122715
2018
1
1
2
1
2
False
False
False
...
False
3
68
64
15
5
4
0.941176
3.0
0.058824
1 rows × 22 columns
In [218]:
test, _, _ = proc_df(test,max_n_cat=30,mapper=mapper,na_dict=nas)
In [219]:
test.columns
Out[219]:
Index(['id', 'campaign_id', 'user_id', 'y', 'send_Year', 'send_Month',
'send_Week', 'send_Day', 'send_Dayofweek', 'send_Dayofyear',
...
'email_url_http://r.newsletters.analyticsvidhya.com/7wra6vb5p4c.html?t=1520942329',
'email_url_http://r.newsletters.analyticsvidhya.com/7wrjo7b5p4c.html?t=1520942329',
'email_url_http://r.newsletters.analyticsvidhya.com/7ww0uvb5p4c.html?t=1520940826',
'email_url_http://r.newsletters.analyticsvidhya.com/7wx2s7b5p4c.html?t=1520940826',
'email_url_http://r.newsletters.analyticsvidhya.com/7wxlqvb5p4c.html?t=1520940826',
'email_url_http://r.newsletters.analyticsvidhya.com/7wxv87b5p4c.html?t=1520940826',
'email_url_http://r.newsletters.analyticsvidhya.com/7wz6mvb5p4c.html?t=1520940826',
'email_url_http://r.newsletters.analyticsvidhya.com/7wzpljb5p4c.html?t=1520935115',
'email_url_http://r.newsletters.analyticsvidhya.com/7x08k7b5p4c.html?t=1520935115',
'email_url_nan'],
dtype='object', length=113)
In [224]:
df.drop(list(set(df.columns) - set(test.columns)), axis=1,inplace=True)
In [228]:
len(test.columns)
Out[228]:
29
In [227]:
len(df.columns)
Out[227]:
29
In [51]:
print(df['img_per_sec'].value_counts())
sns.countplot(df['img_per_sec'],orient='h');
2.000000 211722
7.000000 139380
3.000000 132195
13.000000 85433
2.500000 82163
16.000000 81358
3.250000 81253
4.000000 76361
1.000000 50942
2.833333 39710
3.166667 39498
5.000000 3198
3.750000 2
3.500000 1
9.000000 1
Name: img_per_sec, dtype: int64
In [43]:
print(df['is_open'].value_counts())
sns.countplot(df['is_open']);
0.0 920401
1.0 102790
Name: is_open, dtype: int64
In [38]:
sns.countplot(df['no_of_images']);
In [39]:
sns.countplot(df['no_of_sections']);
In [37]:
sns.countplot(df['link_diff']);
Out[37]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f117841a20>
In [81]:
train_cats(df)
In [82]:
apply_cats(test, df)
In [87]:
df.drop(['id', 'user_id'], axis=1, inplace=True);
test.drop(['id', 'user_id'], axis=1, inplace=True);
In [88]:
df.head(1)
Out[88]:
campaign_id
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
send_Dayofyear
send_Is_month_end
send_Is_month_start
send_Is_quarter_end
...
no_of_internal_links
no_of_images
no_of_sections
email_body
subject
email_url
link_diff
link_diff_%
img_per_section
av_links
0
42
2017.0
1.0
2.0
9.0
0.0
9.0
False
False
False
...
79
13
4
September Newsletter\r\n \r\nDear AVians,\r\n ...
[September] Exciting days ahead with DataHack ...
http://r.newsletters.analyticsvidhya.com/7v3rd...
9
10.227273
3.25
89.772727
1 rows × 25 columns
In [89]:
test.head(1)
Out[89]:
campaign_id
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
send_Dayofyear
send_Is_month_end
send_Is_month_start
send_Is_quarter_end
...
no_of_internal_links
no_of_images
no_of_sections
email_body
subject
email_url
link_diff
av_links
img_per_section
link_diff_%
0
63
2018
1
1
2
1
2
False
False
False
...
64
15
5
\r\nFebruary 2018 Newsletter\r\n \r\nDear AVia...
AVbytes, Ultimate 2018 learning path and aweso...
http://r.newsletters.analyticsvidhya.com/7whsu...
4
94.117647
3.0
5.882353
1 rows × 25 columns
In [90]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1023217 entries, 0 to 1023216
Data columns (total 25 columns):
campaign_id 1023217 non-null int64
send_Year 1023191 non-null float64
send_Month 1023191 non-null float64
send_Week 1023191 non-null float64
send_Day 1023191 non-null float64
send_Dayofweek 1023191 non-null float64
send_Dayofyear 1023191 non-null float64
send_Is_month_end 1023191 non-null category
send_Is_month_start 1023191 non-null category
send_Is_quarter_end 1023191 non-null category
send_Is_quarter_start 1023191 non-null category
send_Is_year_end 1023191 non-null category
send_Is_year_start 1023191 non-null category
communication_type 1023217 non-null category
total_links 1023217 non-null int64
no_of_internal_links 1023217 non-null int64
no_of_images 1023217 non-null int64
no_of_sections 1023217 non-null int64
email_body 1023217 non-null category
subject 1023217 non-null category
email_url 1023217 non-null category
link_diff 1023217 non-null int64
link_diff_% 1023217 non-null float64
img_per_section 1023217 non-null float64
av_links 1023217 non-null float64
dtypes: category(10), float64(9), int64(6)
memory usage: 126.9 MB
In [177]:
df_raw.get_ftype_counts()
Out[177]:
bool:dense 6
category:dense 1
float64:dense 7
int64:dense 16
uint8:dense 59
dtype: int64
In [180]:
df_raw['comb_id'] = df_raw['user_id']*100+ df_raw['campaign_id'];
test['comb_id'] = test['user_id']*100+ test['campaign_id'];
In [181]:
df_raw.drop(['id','user_id','campaign_id'], axis=1,inplace=True);
test.drop(['id','user_id','campaign_id'], axis=1,inplace=True);
In [186]:
test.head(1)
Out[186]:
send_Year
send_Month
send_Week
send_Day
send_Dayofweek
send_Dayofyear
send_Is_month_end
send_Is_month_start
send_Is_quarter_end
send_Is_quarter_start
...
code_email_url_7wrjo7b5p4c
code_email_url_7ww0uvb5p4c
code_email_url_7wx2s7b5p4c
code_email_url_7wxlqvb5p4c
code_email_url_7wxv87b5p4c
code_email_url_7wz6mvb5p4c
code_email_url_7wzpljb5p4c
code_email_url_7x08k7b5p4c
code_email_url_o7ohwml8lxh
comb_id
0
2018
1
1
2
1
2
False
False
False
False
...
0
0
0
0
0
0
0
0
0
12271563
1 rows × 85 columns
In [188]:
df_raw.drop(['is_click'],axis=1,inplace=True)
df_raw.drop('is_open',axis=1,inplace=True)
In [144]:
categorical_features_indices = np.where(df_raw.dtypes == 'category')[0]
In [145]:
categorical_features_indices
Out[145]:
array([], dtype=int64)
In [195]:
def rmse(x,y): return math.sqrt(((x-y)**2).mean())
def print_score(m):
res = [rmse(m.predict(X_train), y_train), rmse(m.predict(X_valid), y_valid),
m.score(X_train, y_train), m.score(X_valid, y_valid)]
if hasattr(m, 'oob_score_'): res.append(m.oob_score_)
print(res)
In [196]:
from sklearn.model_selection import train_test_split
In [197]:
from sklearn.preprocessing import StandardScaler
df_raw_scaled = StandardScaler().fit_transform(df_raw[:])
test_scaled = StandardScaler().fit_transform(test[:])
In [202]:
X_train, X_validation, y_train, y_validation = train_test_split(df_raw, y_target, train_size=0.8, random_state=17)
lr = LogisticRegression(C=1, random_state=17, solver='lbfgs',class_weight='balanced',n_jobs=-1,max_iter=2000).fit(X_train,y_train)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.
FutureWarning)
In [203]:
preds = lr.predict_proba(test_scaled)
In [212]:
#importing library and building model
from catboost import CatBoostClassifier
model=CatBoostClassifier(iterations=1000, depth=10,learning_rate=0.01, loss_function='CrossEntropy')
model.fit(X_train, y_train,eval_set=(X_validation, y_validation))
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bestTest = 0.06604674677
bestIteration = 788
Out[212]:
<catboost.core.CatBoostClassifier at 0x19f8a857cc0>
In [217]:
prediction_proba = model.predict_proba(test)
In [219]:
prediction_proba[:,1]
Out[219]:
array([ 0.01567, 0.01569, 0.0155 , ..., 0.02772, 0.02941, 0.02941])
In [204]:
def make_submission(probs):
sample = pd.read_csv(f'{PATH}//sample_submission.csv')
submit = sample.copy()
submit['is_click'] = probs
return submit
In [221]:
submit = make_submission(prediction_proba[:,1])
In [222]:
submit.head(2)
Out[222]:
id
is_click
0
63_122715
0.015673
1
56_76206
0.015691
In [223]:
submit.to_csv(PATH + '//logistic_reg_sub2.csv', index=False)
In [201]:
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.svm import LinearSVR
from sklearn.learning_curve import learning_curve
from scipy import stats
In [203]:
X_train, X_validation, y_train, y_validation = train_test_split(df_raw, y_target, train_size=0.8, random_state=17)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.
FutureWarning)
In [ ]:
# We teach the basic random forest for the selection of characteristics
rfc_base = RandomForestRegressor ( n_estimators = 500 , random_state = 42,n_jobs=-1 )
rfc_base . fit ( X_train , y_train )
In [ ]:
# Display the significance of features
features = pd . DataFrame ( rfc_base.feature_importances_ , index = X_train . columns ,
columns = [ 'Importance' ]) . sort_values ([ 'Importance' ], ascending = False )
features
In [ ]:
# Let's
start choosing a model # List of regressors regressors
= [ LinearRegression (), GradientBoostingRegressor ( random_state = 17 ), RandomForestRegressor ( random_state = 17 ), LinearSVR ( random_state = 17 )]
regressor_name = [ 'LinearRegression' , 'GradientBoostingRegressor' , 'RandomForestRegressor' , 'LinearSVR' ]
In [ ]:
# Parameters to the regressors
scores = []
fits = []
linear_params = { 'normalize' : ( True , False )}
gbr_params = { 'n_estimators' : [ 100 , 300 , 500 ],
'learning_rate' :( 0.1 , 0.5 , 1 ),
'max_depth' : list ( range ( 3 , 10 , 2 )),
'min_samples_leaf': list ( range ( 10 , 31 , 10 ))}
forest_params = { 'n_estimators' : [ 100 , 300 , 500 ],
'max_depth' : list ( range ( 3 , 10 , 2 )),
'min_samples_leaf' : list ( range ( 10 , 31 , 10 ))}
svm_params = { 'loss' : ( 'epsilon_insensitive' , 'squared_epsilon_insensitive' ), 'C' : ( . 5 , 1 , 2 )}
params = [ linear_params , gbr_params , forest_params , svm_params ]
In [ ]:
# We search the regressors parameters in search of the best (on 5 folds)
np . random . seed ( 42 )
for i , each_regressor in enumerate ( regressors ):
reg = each_regressor
reg_params = params [ i ]
grid = GridSearchCV ( reg , reg_params ,
cv = 5 ,
scoring = 'neg_mean_squared_error' ,
n_jobs = -1 )
grid . fit ( X_train , y_train )
fits . append ( grid . best_params_ )
reg_best_score = grid . best_score_
scores . append ( reg_best_score )
print ( regressor_name [ i ], - reg_best_score , " \ n " , grid . best_params_ , " \ n " )
In [ ]:
# In-depth selection of hyperparameters for a random forest
np . random . seed ( 42 )
forest_params_deep = { 'n_estimators' : [ 100 , 150 , 200 , 300 , 500 ], #n_estimators - number of trees in the random forest
'max_depth' : list ( range ( 3 , 13 , 2 )), #max_depth - maximum depth of the tree
'min_samples_leaf' : list ( range( 5 , 30 , 5 ))} #min_samples_leaf - the minimum number of objects in the tree sheet.
rfr = RandomForestRegressor ( random_state = 42 )
grid_rfr = GridSearchCV ( rfr , forest_params_deep ,
cv = 5 ,
scoring = 'neg_mean_squared_error' ,
n_jobs = -1 )
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grid_rfr . fit ( X_train , y_train )
In [ ]:
# The result is even better!
print ( - grid_rfr . best_score_ , ' \ n ' , grid_rfr . best_params_ )
In [6]:
Out[6]:
0
id
42_14051
user_id
14051
campaign_id
42
is_open
0
is_click
0
send_Year
2017
send_Month
1
send_Week
2
send_Day
9
send_Dayofweek
0
send_Dayofyear
9
send_Is_month_end
False
send_Is_month_start
False
send_Is_quarter_end
False
send_Is_quarter_start
False
send_Is_year_end
False
send_Is_year_start
False
total_links
88
no_of_internal_links
79
no_of_images
13
no_of_sections
4
len_email_body
52
diversity_email_body
0.0419682
diversity_subject
0.386364
body_per_sec
13
diversity_email_url
0.457143
img_per_sec
3.25
other_links
9
av_links_percent
0.897727
links_per_sec
22
...
...
code_email_url_7vtb2vb5p4c
0
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89 rows × 1 columns
In [ ]:
Content source: AdityaSoni19031997/Machine-Learning
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