Importing Libraries¶

In [1]:
import numpy as np
import pandas as pd
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import seaborn as sns

Importing Data¶

In [2]:
dataset_url = "https://raw.githubusercontent.com/aashish22bansal/Datasets/main/YouTube%20Adview%20Prediction/train.csv"
In [3]:
data = pd.read_csv(dataset_url)
In [4]:
data
Out[4]:
vidid adview views likes dislikes comment published duration category
0 VID_18655 40 1031602 8523 363 1095 2016-09-14 PT7M37S F
1 VID_14135 2 1707 56 2 6 2016-10-01 PT9M30S D
2 VID_2187 1 2023 25 0 2 2016-07-02 PT2M16S C
3 VID_23096 6 620860 777 161 153 2016-07-27 PT4M22S H
4 VID_10175 1 666 1 0 0 2016-06-29 PT31S D
... ... ... ... ... ... ... ... ... ...
14994 VID_31 2 525949 1137 83 86 2015-05-18 PT6M10S A
14995 VID_5861 1 665673 3849 156 569 2015-10-20 PT3M56S D
14996 VID_805 4 3479 16 1 1 2013-08-23 PT3M13S B
14997 VID_19843 1 963 0 0 0 2010-10-02 PT26S G
14998 VID_8534 1 15212 22 5 4 2016-02-19 PT1M1S D

14999 rows × 9 columns

Exploratory Data Analysis¶

In [5]:
data.shape
Out[5]:
(14999, 9)
In [6]:
data.columns
Out[6]:
Index(['vidid', 'adview', 'views', 'likes', 'dislikes', 'comment', 'published',
       'duration', 'category'],
      dtype='object')
In [7]:
data.describe()
Out[7]:
adview
count 1.499900e+04
mean 2.107791e+03
std 5.237711e+04
min 1.000000e+00
25% 1.000000e+00
50% 2.000000e+00
75% 6.000000e+00
max 5.429665e+06
In [8]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14999 entries, 0 to 14998
Data columns (total 9 columns):
 #   Column     Non-Null Count  Dtype 
---  ------     --------------  ----- 
 0   vidid      14999 non-null  object
 1   adview     14999 non-null  int64 
 2   views      14999 non-null  object
 3   likes      14999 non-null  object
 4   dislikes   14999 non-null  object
 5   comment    14999 non-null  object
 6   published  14999 non-null  object
 7   duration   14999 non-null  object
 8   category   14999 non-null  object
dtypes: int64(1), object(8)
memory usage: 1.0+ MB
In [9]:
data.isna().sum()
Out[9]:
vidid        0
adview       0
views        0
likes        0
dislikes     0
comment      0
published    0
duration     0
category     0
dtype: int64
In [10]:
data.hist(figsize=(16,20), bins=50, xlabelsize=8, ylabelsize=8)
Out[10]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f40b931af90>]],
      dtype=object)
In [11]:
plt.hist(data["category"])
plt.show()
In [12]:
plt.plot(data["adview"])
plt.show()

Data Preprocessing¶

Assigning each category a number for Category feature

In [13]:
type(data)
Out[13]:
pandas.core.frame.DataFrame
In [14]:
category = {'A':1, 'B':2, 'C':3, 'D':4, 'E':5, 'F':6}
In [15]:
# # df = pd.DataFrame(columns=('A', 'B'))
# for i in range(data.shape[0]):
#    data["category"].loc[i] = float(7)
In [16]:
data["category"] = data["category"].map(category)
In [17]:
data
Out[17]:
vidid adview views likes dislikes comment published duration category
0 VID_18655 40 1031602 8523 363 1095 2016-09-14 PT7M37S 6.0
1 VID_14135 2 1707 56 2 6 2016-10-01 PT9M30S 4.0
2 VID_2187 1 2023 25 0 2 2016-07-02 PT2M16S 3.0
3 VID_23096 6 620860 777 161 153 2016-07-27 PT4M22S NaN
4 VID_10175 1 666 1 0 0 2016-06-29 PT31S 4.0
... ... ... ... ... ... ... ... ... ...
14994 VID_31 2 525949 1137 83 86 2015-05-18 PT6M10S 1.0
14995 VID_5861 1 665673 3849 156 569 2015-10-20 PT3M56S 4.0
14996 VID_805 4 3479 16 1 1 2013-08-23 PT3M13S 2.0
14997 VID_19843 1 963 0 0 0 2010-10-02 PT26S NaN
14998 VID_8534 1 15212 22 5 4 2016-02-19 PT1M1S 4.0

14999 rows × 9 columns

Removing the character 'F' present in the data

In [18]:
data = data[data.views!='F']
data = data[data.likes!='F']
data = data[data.dislikes!='F']
data = data[data.comment!='F']
In [19]:
data["views"] = pd.to_numeric(data["views"])
data["comment"] = pd.to_numeric(data["comment"])
data["likes"] = pd.to_numeric(data["likes"])
data["dislikes"] = pd.to_numeric(data["dislikes"])
data["adview"] = pd.to_numeric(data["adview"])
In [20]:
data
Out[20]:
vidid adview views likes dislikes comment published duration category
0 VID_18655 40 1031602 8523 363 1095 2016-09-14 PT7M37S 6.0
1 VID_14135 2 1707 56 2 6 2016-10-01 PT9M30S 4.0
2 VID_2187 1 2023 25 0 2 2016-07-02 PT2M16S 3.0
3 VID_23096 6 620860 777 161 153 2016-07-27 PT4M22S NaN
4 VID_10175 1 666 1 0 0 2016-06-29 PT31S 4.0
... ... ... ... ... ... ... ... ... ...
14994 VID_31 2 525949 1137 83 86 2015-05-18 PT6M10S 1.0
14995 VID_5861 1 665673 3849 156 569 2015-10-20 PT3M56S 4.0
14996 VID_805 4 3479 16 1 1 2013-08-23 PT3M13S 2.0
14997 VID_19843 1 963 0 0 0 2010-10-02 PT26S NaN
14998 VID_8534 1 15212 22 5 4 2016-02-19 PT1M1S 4.0

14637 rows × 9 columns

In [21]:
column_vidID = data['vidid']
column_vidID
Out[21]:
0        VID_18655
1        VID_14135
2         VID_2187
3        VID_23096
4        VID_10175
           ...    
14994       VID_31
14995     VID_5861
14996      VID_805
14997    VID_19843
14998     VID_8534
Name: vidid, Length: 14637, dtype: object

Data Transformation¶

Encoding Features like Category, Duration, VidID

In [22]:
from sklearn.preprocessing import LabelEncoder
In [23]:
data['duration'] = LabelEncoder().fit_transform(data['duration'])
data['vidid'] = LabelEncoder().fit_transform(data['duration'])
data['published'] = LabelEncoder().fit_transform(data['published'])
In [24]:
data
Out[24]:
vidid adview views likes dislikes comment published duration category
0 2925 40 1031602 8523 363 1095 2168 2925 6.0
1 3040 2 1707 56 2 6 2185 3040 4.0
2 1863 1 2023 25 0 2 2094 1863 3.0
3 2546 6 620860 777 161 153 2119 2546 NaN
4 1963 1 666 1 0 0 2091 1963 4.0
... ... ... ... ... ... ... ... ... ...
14994 2833 2 525949 1137 83 86 1683 2833 1.0
14995 2257 1 665673 3849 156 569 1838 2257 4.0
14996 2210 4 3479 16 1 1 1053 2210 2.0
14997 1364 1 963 0 0 0 222 1364 NaN
14998 1050 1 15212 22 5 4 1960 1050 4.0

14637 rows × 9 columns

Converting the Time_in_sec for duration:

In [25]:
import datetime
import time
In [26]:
def checki(x):
    y = x[2:]
    h = ''
    m = ''
    s = ''
    mm = ''
    P = ['H', 'M', 'S']
    for i in y:
        if i not in P:
            mm+=i
        else:
            if(i=="H"):
                h = mm
                mm = ''
            elif(i=="M"):
                m = mm
                mm = ''
            else:
                s = mm
                mm = ''
    if(h==''):
        h = '00'
    if(m==''):
        m = '00'
    if(s==''):
        s='00'
    bp = h + ':' + m + ':' + s
    return bp
In [27]:
train = pd.read_csv(dataset_url)
train
Out[27]:
vidid adview views likes dislikes comment published duration category
0 VID_18655 40 1031602 8523 363 1095 2016-09-14 PT7M37S F
1 VID_14135 2 1707 56 2 6 2016-10-01 PT9M30S D
2 VID_2187 1 2023 25 0 2 2016-07-02 PT2M16S C
3 VID_23096 6 620860 777 161 153 2016-07-27 PT4M22S H
4 VID_10175 1 666 1 0 0 2016-06-29 PT31S D
... ... ... ... ... ... ... ... ... ...
14994 VID_31 2 525949 1137 83 86 2015-05-18 PT6M10S A
14995 VID_5861 1 665673 3849 156 569 2015-10-20 PT3M56S D
14996 VID_805 4 3479 16 1 1 2013-08-23 PT3M13S B
14997 VID_19843 1 963 0 0 0 2010-10-02 PT26S G
14998 VID_8534 1 15212 22 5 4 2016-02-19 PT1M1S D

14999 rows × 9 columns

In [28]:
mp = pd.read_csv(dataset_url)["duration"]
mp
Out[28]:
0        PT7M37S
1        PT9M30S
2        PT2M16S
3        PT4M22S
4          PT31S
          ...   
14994    PT6M10S
14995    PT3M56S
14996    PT3M13S
14997      PT26S
14998     PT1M1S
Name: duration, Length: 14999, dtype: object
In [29]:
time = mp.apply(checki)
In [30]:
time
Out[30]:
0         00:7:37
1         00:9:30
2         00:2:16
3         00:4:22
4        00:00:31
           ...   
14994     00:6:10
14995     00:3:56
14996     00:3:13
14997    00:00:26
14998      00:1:1
Name: duration, Length: 14999, dtype: object
In [31]:
def func_sec(time_string):
    h, m, s = time_string.split(':')
    return int(h)*3600 + int(m)*60 + int(s)
In [32]:
time1 = time.apply(func_sec)
time1
Out[32]:
0        457
1        570
2        136
3        262
4         31
        ... 
14994    370
14995    236
14996    193
14997     26
14998     61
Name: duration, Length: 14999, dtype: int64

Visualization¶

In [33]:
plt.hist(data["category"])
plt.show()
In [34]:
plt.plot(data["adview"])
plt.show()

Remove videos with adview greater than 2000000 as outlier.

In [35]:
data = data[data["adview"]<2000000]
In [36]:
data
Out[36]:
vidid adview views likes dislikes comment published duration category
0 2925 40 1031602 8523 363 1095 2168 2925 6.0
1 3040 2 1707 56 2 6 2185 3040 4.0
2 1863 1 2023 25 0 2 2094 1863 3.0
3 2546 6 620860 777 161 153 2119 2546 NaN
4 1963 1 666 1 0 0 2091 1963 4.0
... ... ... ... ... ... ... ... ... ...
14994 2833 2 525949 1137 83 86 1683 2833 1.0
14995 2257 1 665673 3849 156 569 1838 2257 4.0
14996 2210 4 3479 16 1 1 1053 2210 2.0
14997 1364 1 963 0 0 0 222 1364 NaN
14998 1050 1 15212 22 5 4 1960 1050 4.0

14636 rows × 9 columns

In [37]:
f, ax = plt.subplots(figsize=(10,8))
corr = data.corr()
sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool),cmap=sns.diverging_palette(220, 10, as_cmap=True),square=True,ax=ax,annot=True)
plt.show()

Checking NaN Values¶

In [38]:
np.isnan(data).sum()
Out[38]:
vidid           0
adview          0
views           0
likes           0
dislikes        0
comment         0
published       0
duration        0
category     2893
dtype: int64

Droping NaN Rows¶

In [39]:
data_list = data.values.tolist()
In [40]:
data_list
Out[40]:
[[2925.0, 40.0, 1031602.0, 8523.0, 363.0, 1095.0, 2168.0, 2925.0, 6.0],
 [3040.0, 2.0, 1707.0, 56.0, 2.0, 6.0, 2185.0, 3040.0, 4.0],
 [1863.0, 1.0, 2023.0, 25.0, 0.0, 2.0, 2094.0, 1863.0, 3.0],
 [2546.0, 6.0, 620860.0, 777.0, 161.0, 153.0, 2119.0, 2546.0, nan],
 [1963.0, 1.0, 666.0, 1.0, 0.0, 0.0, 2091.0, 1963.0, 4.0],
 [373.0, 4.0, 78.0, 0.0, 0.0, 0.0, 2040.0, 373.0, 4.0],
 [2218.0, 40621.0, 43118.0, 15.0, 1.0, 0.0, 1778.0, 2218.0, 4.0],
 [2750.0, 1.0, 14205.0, 55.0, 16.0, 1.0, 2124.0, 2750.0, 5.0],
 [1385.0, 1.0, 526015.0, 3064.0, 211.0, 2582.0, 1855.0, 1385.0, 6.0],
 [88.0, 1.0, 406992.0, 3831.0, 310.0, 7839.0, 2187.0, 88.0, 2.0],
 [156.0, 19.0, 607447.0, 377.0, 144.0, 65.0, 2131.0, 156.0, 4.0],
 [2248.0, 9.0, 429137.0, 2181.0, 76.0, 172.0, 1905.0, 2248.0, 2.0],
 [2537.0, 224.0, 1895.0, 59.0, 5.0, 0.0, 2137.0, 2537.0, 2.0],
 [1566.0, 1.0, 59843.0, 68.0, 16.0, 10.0, 1948.0, 1566.0, 4.0],
 [1901.0, 2.0, 211642.0, 1378.0, 65.0, 103.0, 1638.0, 1901.0, 6.0],
 [2105.0, 1.0, 3700.0, 29.0, 6.0, 3.0, 1085.0, 2105.0, 2.0],
 [1364.0, 2.0, 2303.0, 4.0, 0.0, 0.0, 222.0, 1364.0, nan],
 [2770.0, 4794.0, 5886.0, 23.0, 0.0, 4.0, 2217.0, 2770.0, 4.0],
 [1237.0, 107.0, 9477.0, 66.0, 0.0, 0.0, 1718.0, 1237.0, nan],
 [2213.0, 53702.0, 115426.0, 83.0, 10.0, 2.0, 1799.0, 2213.0, nan],
 [172.0, 2.0, 62255.0, 71.0, 10.0, 1.0, 1697.0, 172.0, 4.0],
 [1074.0, 1.0, 15353.0, 13.0, 6.0, 9.0, 2126.0, 1074.0, 4.0],
 [2784.0, 1.0, 236589.0, 1730.0, 38.0, 140.0, 1777.0, 2784.0, 4.0],
 [2325.0, 2.0, 94719.0, 192.0, 11.0, 23.0, 2124.0, 2325.0, 4.0],
 [205.0, 14.0, 3089826.0, 7493.0, 1148.0, 330.0, 2023.0, 205.0, 4.0],
 [2854.0, 3.0, 2046444.0, 3910.0, 845.0, 247.0, 1894.0, 2854.0, nan],
 [70.0, 3.0, 2103487.0, 13342.0, 734.0, 650.0, 1394.0, 70.0, 2.0],
 [1070.0, 3.0, 740683.0, 935.0, 68.0, 57.0, 1078.0, 1070.0, 4.0],
 [1858.0, 1.0, 805979.0, 2036.0, 183.0, 215.0, 1627.0, 1858.0, 2.0],
 [1553.0, 1.0, 229495.0, 372.0, 50.0, 44.0, 1332.0, 1553.0, 4.0],
 [1897.0, 1.0, 2758.0, 2.0, 0.0, 1.0, 1105.0, 1897.0, 4.0],
 [133.0, 1.0, 24672.0, 313.0, 10.0, 128.0, 1083.0, 133.0, 4.0],
 [3008.0, 2.0, 2000406.0, 14974.0, 686.0, 1297.0, 1968.0, 3008.0, 4.0],
 [2248.0, 4.0, 163161.0, 617.0, 25.0, 51.0, 2086.0, 2248.0, 5.0],
 [211.0, 6.0, 422797.0, 1226.0, 122.0, 117.0, 2135.0, 211.0, 4.0],
 [1098.0, 1.0, 157479.0, 944.0, 40.0, 52.0, 1639.0, 1098.0, 2.0],
 [2943.0, 1.0, 109.0, 0.0, 1.0, 0.0, 1303.0, 2943.0, 4.0],
 [573.0, 53.0, 3258674.0, 9193.0, 290.0, 823.0, 1733.0, 573.0, nan],
 [1071.0, 2.0, 17128.0, 33.0, 4.0, 4.0, 436.0, 1071.0, 6.0],
 [37.0, 1.0, 136151.0, 155.0, 13.0, 14.0, 2131.0, 37.0, 4.0],
 [2545.0, 1.0, 1034.0, 5.0, 0.0, 0.0, 1971.0, 2545.0, 1.0],
 [85.0, 1.0, 513871.0, 743.0, 214.0, 45.0, 1713.0, 85.0, 4.0],
 [233.0, 4.0, 178089.0, 1259.0, 24.0, 124.0, 2072.0, 233.0, 5.0],
 [2731.0, 17.0, 553420.0, 818.0, 167.0, 59.0, 1077.0, 2731.0, 4.0],
 [1045.0, 2.0, 623004.0, 531.0, 182.0, 76.0, 887.0, 1045.0, 4.0],
 [797.0, 1.0, 172599.0, 162.0, 61.0, 35.0, 1676.0, 797.0, 5.0],
 [273.0, 1.0, 793069.0, 2638.0, 163.0, 135.0, 1737.0, 273.0, nan],
 [1963.0, 4.0, 462.0, 1.0, 0.0, 0.0, 2174.0, 1963.0, 4.0],
 [214.0, 1.0, 15882.0, 102.0, 1.0, 41.0, 269.0, 214.0, 4.0],
 [1396.0, 1.0, 797861.0, 2117.0, 225.0, 170.0, 1759.0, 1396.0, 4.0],
 [2588.0, 1.0, 5503.0, 6.0, 2.0, 1.0, 987.0, 2588.0, nan],
 [1876.0, 1.0, 19213.0, 78.0, 5.0, 0.0, 2156.0, 1876.0, 4.0],
 [2124.0, 4.0, 165497.0, 215.0, 62.0, 153.0, 846.0, 2124.0, 4.0],
 [30.0, 1.0, 212195.0, 561.0, 61.0, 192.0, 2051.0, 30.0, 4.0],
 [1141.0, 4.0, 805.0, 6.0, 0.0, 0.0, 2066.0, 1141.0, 4.0],
 [126.0, 5.0, 89443.0, 84.0, 26.0, 4.0, 1293.0, 126.0, 4.0],
 [1882.0, 2.0, 56573.0, 228.0, 5.0, 16.0, 1068.0, 1882.0, nan],
 [2787.0, 61.0, 367301.0, 204.0, 80.0, 4.0, 1527.0, 2787.0, 4.0],
 [1863.0, 1619.0, 1389401.0, 13224.0, 831.0, 522.0, 1107.0, 1863.0, 6.0],
 [1868.0, 1.0, 7702.0, 23.0, 0.0, 1.0, 1499.0, 1868.0, nan],
 [2484.0, 1.0, 101581.0, 314.0, 11.0, 4.0, 1879.0, 2484.0, 4.0],
 [1057.0, 182.0, 581.0, 7.0, 0.0, 1.0, 1208.0, 1057.0, 4.0],
 [2815.0, 5.0, 1515742.0, 14551.0, 514.0, 5752.0, 1979.0, 2815.0, 2.0],
 [2398.0, 36.0, 11991184.0, 48324.0, 5808.0, 5167.0, 1436.0, 2398.0, 4.0],
 [2560.0, 3.0, 1275680.0, 4743.0, 932.0, 184.0, 1981.0, 2560.0, 4.0],
 [124.0, 3.0, 127821.0, 1127.0, 80.0, 466.0, 2103.0, 124.0, 6.0],
 [2545.0, 1.0, 7779.0, 59.0, 0.0, 7.0, 2087.0, 2545.0, nan],
 [1060.0, 11.0, 415699.0, 251.0, 49.0, 23.0, 618.0, 1060.0, 4.0],
 [1125.0, 1.0, 162390.0, 1031.0, 34.0, 33.0, 1805.0, 1125.0, 3.0],
 [546.0, 7.0, 683587.0, 1172.0, 122.0, 85.0, 968.0, 546.0, 4.0],
 [111.0, 2.0, 38570.0, 55.0, 6.0, 11.0, 371.0, 111.0, 5.0],
 [2551.0, 2.0, 503532.0, 1872.0, 50.0, 85.0, 574.0, 2551.0, nan],
 [38.0, 1.0, 52039.0, 240.0, 16.0, 145.0, 2090.0, 38.0, 4.0],
 [1897.0, 16.0, 5863262.0, 10238.0, 2541.0, 611.0, 1778.0, 1897.0, 3.0],
 [2540.0, 3.0, 981761.0, 2414.0, 267.0, 217.0, 2047.0, 2540.0, 4.0],
 [1895.0, 1.0, 31339.0, 142.0, 11.0, 16.0, 112.0, 1895.0, nan],
 [2541.0, 1.0, 18222.0, 51.0, 6.0, 30.0, 1229.0, 2541.0, 4.0],
 [1529.0, 1.0, 21772.0, 37.0, 3.0, 3.0, 1960.0, 1529.0, 4.0],
 [2550.0, 4.0, 15345.0, 9.0, 8.0, 0.0, 1154.0, 2550.0, 4.0],
 [2821.0, 1.0, 81993.0, 882.0, 21.0, 227.0, 820.0, 2821.0, 4.0],
 [2238.0, 3.0, 293365.0, 126.0, 36.0, 7.0, 524.0, 2238.0, 4.0],
 [2842.0, 1.0, 1939.0, 36.0, 0.0, 16.0, 1483.0, 2842.0, 4.0],
 [2548.0, 1.0, 23692.0, 178.0, 6.0, 19.0, 1738.0, 2548.0, 4.0],
 [2805.0, 2.0, 970875.0, 4982.0, 235.0, 256.0, 1592.0, 2805.0, nan],
 [2796.0, 2.0, 11703.0, 217.0, 13.0, 6.0, 2177.0, 2796.0, nan],
 [2686.0, 1.0, 167645.0, 154.0, 52.0, 81.0, 2016.0, 2686.0, 4.0],
 [204.0, 1.0, 7759.0, 34.0, 4.0, 18.0, 1132.0, 204.0, 4.0],
 [2980.0, 2.0, 775.0, 7.0, 0.0, 1.0, 2147.0, 2980.0, 4.0],
 [1893.0, 69.0, 3423094.0, 1195.0, 624.0, 26.0, 710.0, 1893.0, 4.0],
 [3038.0, 5.0, 21512.0, 203.0, 12.0, 61.0, 968.0, 3038.0, nan],
 [27.0, 1.0, 123061.0, 140.0, 42.0, 20.0, 2182.0, 27.0, 4.0],
 [2234.0, 2.0, 25680.0, 20.0, 6.0, 1.0, 709.0, 2234.0, 1.0],
 [2884.0, 2.0, 635882.0, 1048.0, 126.0, 589.0, 1972.0, 2884.0, 5.0],
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 [2919.0, 1.0, 185743.0, 2535.0, 114.0, 639.0, 2013.0, 2919.0, 2.0],
 [2537.0, 1.0, 11739.0, 57.0, 2.0, 3.0, 1478.0, 2537.0, 4.0],
 [2568.0, 1.0, 130380.0, 900.0, 33.0, 443.0, 1044.0, 2568.0, nan],
 [2161.0, 1.0, 66139.0, 218.0, 20.0, 14.0, 1593.0, 2161.0, 6.0],
 [2587.0, 60.0, 55332157.0, 139259.0, 11843.0, 5511.0, 1415.0, 2587.0, nan],
 [929.0, 10.0, 731822.0, 1069.0, 235.0, 90.0, 779.0, 929.0, 4.0],
 [2807.0, 5174.0, 91926.0, 1537.0, 55.0, 131.0, 2259.0, 2807.0, nan],
 [278.0, 3.0, 312294.0, 904.0, 69.0, 283.0, 2051.0, 278.0, 1.0],
 [2769.0, 8.0, 1645986.0, 24054.0, 489.0, 4995.0, 2122.0, 2769.0, 2.0],
 [896.0, 2.0, 12089.0, 19.0, 5.0, 4.0, 1737.0, 896.0, 5.0],
 [172.0, 11.0, 568613.0, 920.0, 92.0, 79.0, 191.0, 172.0, 4.0],
 [2784.0, 2.0, 212770.0, 1772.0, 107.0, 1606.0, 2132.0, 2784.0, 5.0],
 [2761.0, 2.0, 403130.0, 660.0, 135.0, 33.0, 1111.0, 2761.0, 4.0],
 [2532.0, 2.0, 1361366.0, 1384.0, 757.0, 185.0, 2145.0, 2532.0, nan],
 [25.0, 2.0, 2419479.0, 1831.0, 499.0, 68.0, 1964.0, 25.0, 2.0],
 [297.0, 5.0, 1072196.0, 3070.0, 137.0, 336.0, 1645.0, 297.0, 4.0],
 [0.0, 93.0, 64310773.0, 20397.0, 10258.0, 23.0, 1790.0, 0.0, nan],
 [553.0, 2.0, 155844.0, 813.0, 22.0, 142.0, 2177.0, 553.0, 4.0],
 [1069.0, 6775.0, 8626.0, 23.0, 0.0, 0.0, 1629.0, 1069.0, 4.0],
 [2576.0, 1.0, 2059.0, 34.0, 1.0, 5.0, 1046.0, 2576.0, nan],
 [2966.0, 2.0, 63893.0, 420.0, 18.0, 182.0, 2181.0, 2966.0, 4.0],
 [2210.0, 1.0, 21098.0, 211.0, 15.0, 51.0, 2127.0, 2210.0, 4.0],
 [2289.0, 17.0, 766657.0, 651.0, 257.0, 98.0, 1056.0, 2289.0, 4.0],
 [3025.0, 2.0, 6138.0, 35.0, 1.0, 4.0, 925.0, 3025.0, 3.0],
 [3049.0, 4.0, 128740.0, 154.0, 7.0, 21.0, 154.0, 3049.0, 4.0],
 [270.0, 1.0, 277606.0, 1725.0, 101.0, 117.0, 1785.0, 270.0, nan],
 [3038.0, 2.0, 26848.0, 200.0, 2.0, 91.0, 2186.0, 3038.0, 4.0],
 [1868.0, 1.0, 289432.0, 1376.0, 60.0, 90.0, 1328.0, 1868.0, 4.0],
 [1561.0, 3.0, 1164965.0, 1166.0, 274.0, 100.0, 1866.0, 1561.0, 4.0],
 [2548.0, 1.0, 35441.0, 22.0, 9.0, 2.0, 523.0, 2548.0, 4.0],
 [2778.0, 54.0, 1266723.0, 1865.0, 345.0, 238.0, 851.0, 2778.0, 4.0],
 [2722.0, 2.0, 111543.0, 16.0, 53.0, 0.0, 2048.0, 2722.0, 4.0],
 [2055.0, 4.0, 220464.0, 961.0, 69.0, 185.0, 1870.0, 2055.0, 4.0],
 [2362.0, 6.0, 1018682.0, 8718.0, 265.0, 938.0, 2148.0, 2362.0, 4.0],
 [2772.0, 1.0, 195844.0, 643.0, 22.0, 27.0, 1328.0, 2772.0, nan],
 [540.0, 1.0, 1387094.0, 10663.0, 260.0, 1859.0, 1896.0, 540.0, 2.0],
 [2794.0, 2.0, 6064002.0, 62476.0, 2277.0, 2217.0, 1785.0, 2794.0, 4.0],
 [1090.0, 1.0, 74778.0, 47.0, 9.0, 1.0, 649.0, 1090.0, 4.0],
 [962.0, 1.0, 414091.0, 1248.0, 749.0, 25.0, 2349.0, 962.0, 5.0],
 [1464.0, 2.0, 209764.0, 296.0, 44.0, 31.0, 1854.0, 1464.0, 4.0],
 [2788.0, 1.0, 12585.0, 126.0, 3.0, 7.0, 1284.0, 2788.0, 2.0],
 [2105.0, 335.0, 416.0, 4.0, 0.0, 5.0, 1499.0, 2105.0, 4.0],
 [2342.0, 3.0, 591647.0, 563.0, 232.0, 252.0, 1204.0, 2342.0, 4.0],
 [69.0, 4.0, 449299.0, 1020.0, 98.0, 23.0, 539.0, 69.0, 4.0],
 [1877.0, 1.0, 212438.0, 298.0, 73.0, 62.0, 2042.0, 1877.0, 4.0],
 [347.0, 7.0, 69201.0, 172.0, 26.0, 60.0, 1212.0, 347.0, 4.0],
 [241.0, 1.0, 681582.0, 1364.0, 111.0, 65.0, 1694.0, 241.0, 4.0],
 [2229.0, 8114.0, 12152.0, 14.0, 7.0, 3.0, 1600.0, 2229.0, 4.0],
 [2260.0, 1.0, 11151.0, 101.0, 2.0, 17.0, 1958.0, 2260.0, 4.0],
 [2262.0, 1.0, 378605.0, 431.0, 59.0, 160.0, 1985.0, 2262.0, 4.0],
 [2209.0, 1.0, 8237.0, 481.0, 0.0, 107.0, 1826.0, 2209.0, nan],
 [2210.0, 1574.0, 3911.0, 109.0, 7.0, 17.0, 2350.0, 2210.0, 6.0],
 [1558.0, 8.0, 444207.0, 342.0, 129.0, 37.0, 868.0, 1558.0, 5.0],
 [345.0, 19.0, 49214.0, 36.0, 5.0, 0.0, 1087.0, 345.0, 4.0],
 [2504.0, 1.0, 32947.0, 42.0, 38.0, 2.0, 1094.0, 2504.0, nan],
 [2800.0, 1.0, 109290.0, 461.0, 8.0, 9.0, 1332.0, 2800.0, 5.0],
 [38.0, 10.0, 528926.0, 853.0, 94.0, 16.0, 599.0, 38.0, 4.0],
 [98.0, 4.0, 3368.0, 37.0, 4.0, 7.0, 2040.0, 98.0, 6.0],
 [1069.0, 1.0, 821015.0, 6235.0, 354.0, 122.0, 2007.0, 1069.0, 4.0],
 [2819.0, 3.0, 606200.0, 1445.0, 274.0, 132.0, 1930.0, 2819.0, 4.0],
 [38.0, 7.0, 2949850.0, 1673.0, 1220.0, 335.0, 2120.0, 38.0, 4.0],
 [2899.0, 5.0, 1063341.0, 3093.0, 203.0, 84.0, 579.0, 2899.0, 4.0],
 [2904.0, 3.0, 954453.0, 8005.0, 340.0, 2894.0, 1836.0, 2904.0, nan],
 [501.0, 10.0, 101947.0, 472.0, 12.0, 62.0, 1690.0, 501.0, 2.0],
 [2939.0, 1.0, 1364461.0, 15354.0, 157.0, 1672.0, 1776.0, 2939.0, 2.0],
 [1073.0, 3.0, 652434.0, 2142.0, 52.0, 304.0, 2094.0, 1073.0, 4.0],
 [1000.0, 1.0, 63384.0, 102.0, 19.0, 9.0, 1928.0, 1000.0, 5.0],
 [754.0, 18.0, 3742945.0, 2466.0, 1058.0, 622.0, 1526.0, 754.0, nan],
 [2800.0, 1.0, 173721.0, 84.0, 37.0, 5.0, 1460.0, 2800.0, 4.0],
 [2068.0, 4.0, 420562.0, 1593.0, 104.0, 139.0, 1467.0, 2068.0, 4.0],
 [3044.0, 1.0, 400201.0, 9157.0, 223.0, 1217.0, 1959.0, 3044.0, 6.0],
 [66.0, 1.0, 20003.0, 234.0, 11.0, 62.0, 2086.0, 66.0, 6.0],
 [2253.0, 4.0, 689517.0, 3236.0, 94.0, 266.0, 1803.0, 2253.0, 2.0],
 [2812.0, 6.0, 2122534.0, 5281.0, 604.0, 2788.0, 1793.0, 2812.0, 6.0],
 [2889.0, 1.0, 24366.0, 789.0, 13.0, 85.0, 2111.0, 2889.0, 4.0],
 [1862.0, 1.0, 67136.0, 126.0, 20.0, 11.0, 2128.0, 1862.0, 4.0],
 [2923.0, 4.0, 596406.0, 3052.0, 348.0, 1305.0, 2069.0, 2923.0, 6.0],
 [2785.0, 1.0, 3130224.0, 17538.0, 1011.0, 263.0, 1328.0, 2785.0, 4.0],
 [2560.0, 2.0, 245897.0, 715.0, 52.0, 48.0, 347.0, 2560.0, 4.0],
 [2862.0, 2.0, 572640.0, 798.0, 111.0, 354.0, 31.0, 2862.0, 4.0],
 [2581.0, 92.0, 1024850.0, 1148.0, 39.0, 53.0, 40.0, 2581.0, nan],
 [1170.0, 2.0, 8075.0, 26.0, 5.0, 6.0, 1181.0, 1170.0, 4.0],
 [2238.0, 10.0, 6141689.0, 23944.0, 1220.0, 1809.0, 946.0, 2238.0, 5.0],
 [19.0, 11.0, 591345.0, 2259.0, 357.0, 1273.0, 1444.0, 19.0, 2.0],
 [2241.0, 73.0, 514971.0, 1640.0, 171.0, 86.0, 753.0, 2241.0, 6.0],
 [3028.0, 4.0, 693312.0, 207.0, 166.0, 15.0, 641.0, 3028.0, 4.0],
 [2854.0, 1.0, 788877.0, 2724.0, 153.0, 1169.0, 1913.0, 2854.0, 4.0],
 [2567.0, 2.0, 63473.0, 106.0, 20.0, 9.0, 1520.0, 2567.0, 4.0],
 [1893.0, 9503.0, 11504.0, 13.0, 2.0, 1.0, 1564.0, 1893.0, 4.0],
 [2549.0, 1.0, 22970.0, 409.0, 7.0, 93.0, 826.0, 2549.0, 4.0],
 [699.0, 1.0, 98795.0, 407.0, 19.0, 22.0, 1286.0, 699.0, 4.0],
 [2576.0, 1.0, 804.0, 1.0, 1.0, 0.0, 1146.0, 2576.0, nan],
 [1061.0, 3.0, 123458.0, 47.0, 27.0, 2.0, 741.0, 1061.0, 4.0],
 [102.0, 2.0, 862305.0, 8717.0, 317.0, 2805.0, 1917.0, 102.0, 5.0],
 [618.0, 1.0, 336860.0, 1332.0, 117.0, 275.0, 1994.0, 618.0, 4.0],
 [2915.0, 7.0, 4426961.0, 177473.0, 3315.0, 31165.0, 1957.0, 2915.0, 4.0],
 [1668.0, 2.0, 224613.0, 328.0, 58.0, 18.0, 1841.0, 1668.0, 4.0],
 [1087.0, 5.0, 84.0, 1.0, 0.0, 0.0, 1645.0, 1087.0, 3.0],
 [2777.0, 9.0, 130094.0, 99.0, 53.0, 3.0, 696.0, 2777.0, 6.0],
 [2722.0, 10.0, 500385.0, 6297.0, 559.0, 1261.0, 2181.0, 2722.0, 2.0],
 [2371.0, 1.0, 49456.0, 1966.0, 34.0, 373.0, 2369.0, 2371.0, 5.0],
 [2557.0, 12.0, 1438508.0, 9149.0, 387.0, 644.0, 2091.0, 2557.0, nan],
 [1913.0, 349.0, 153940.0, 446.0, 27.0, 35.0, 1979.0, 1913.0, 5.0],
 [46.0, 1.0, 350569.0, 2227.0, 49.0, 320.0, 1597.0, 46.0, 1.0],
 [2796.0, 5.0, 380136.0, 783.0, 110.0, 132.0, 1457.0, 2796.0, nan],
 [804.0, 6.0, 2074978.0, 3432.0, 619.0, 291.0, 1121.0, 804.0, 5.0],
 [2861.0, 87335.0, 345753.0, 3729.0, 105.0, 1016.0, 2132.0, 2861.0, 4.0],
 [2835.0, 4.0, 343081.0, 2355.0, 82.0, 2472.0, 2121.0, 2835.0, 5.0],
 [2854.0, 1.0, 1132854.0, 2333.0, 436.0, 158.0, 1595.0, 2854.0, nan],
 [1719.0, 1.0, 293873.0, 355.0, 125.0, 49.0, 1402.0, 1719.0, 4.0],
 [2798.0, 9.0, 363764.0, 335.0, 41.0, 9.0, 214.0, 2798.0, 4.0],
 [2745.0, 1.0, 125046.0, 537.0, 69.0, 931.0, 1842.0, 2745.0, 3.0],
 [1882.0, 2.0, 21380.0, 44.0, 5.0, 0.0, 2107.0, 1882.0, nan],
 [1907.0, 51.0, 2972.0, 42.0, 2.0, 3.0, 2283.0, 1907.0, 4.0],
 [2266.0, 1.0, 88863.0, 623.0, 14.0, 17.0, 1899.0, 2266.0, 3.0],
 [1613.0, 1.0, 130947.0, 226.0, 80.0, 7.0, 1861.0, 1613.0, 4.0],
 [437.0, 86.0, 1123108.0, 6711.0, 432.0, 353.0, 1246.0, 437.0, 4.0],
 [1216.0, 2.0, 161543.0, 3721.0, 146.0, 1758.0, 2143.0, 1216.0, 4.0],
 [1089.0, 9.0, 39615.0, 167.0, 9.0, 9.0, 1002.0, 1089.0, 3.0],
 [1288.0, 2.0, 293167.0, 4380.0, 227.0, 624.0, 1553.0, 1288.0, 4.0],
 [2543.0, 1.0, 30481.0, 330.0, 23.0, 20.0, 2135.0, 2543.0, 4.0],
 [2904.0, 1.0, 210711.0, 1845.0, 93.0, 448.0, 2165.0, 2904.0, 5.0],
 [2944.0, 1.0, 80616.0, 187.0, 15.0, 42.0, 1118.0, 2944.0, 4.0],
 [2710.0, 4.0, 1296161.0, 1068.0, 285.0, 85.0, 962.0, 2710.0, 4.0],
 [1075.0, 4.0, 1826554.0, 1884.0, 847.0, 94.0, 2051.0, 1075.0, 4.0],
 [1864.0, 1.0, 783333.0, 2103.0, 254.0, 153.0, 2170.0, 1864.0, 4.0],
 [2966.0, 1.0, 432679.0, 2227.0, 80.0, 53.0, 2086.0, 2966.0, nan],
 [1059.0, 5.0, 191.0, 5.0, 0.0, 0.0, 2189.0, 1059.0, 4.0],
 [2401.0, 4.0, 77188.0, 246.0, 34.0, 41.0, 1538.0, 2401.0, nan],
 [2589.0, 11.0, 1218778.0, 14773.0, 693.0, 4324.0, 2158.0, 2589.0, 6.0],
 [965.0, 1.0, 89540.0, 78.0, 21.0, 2.0, 1576.0, 965.0, 4.0],
 [2394.0, 90.0, 664121.0, 379.0, 67.0, 55.0, 487.0, 2394.0, 4.0],
 [2850.0, 4.0, 1419140.0, 22989.0, 260.0, 18112.0, 2169.0, 2850.0, 2.0],
 [70.0, 1.0, 81545.0, 639.0, 43.0, 1034.0, 2097.0, 70.0, 4.0],
 [2590.0, 2.0, 562978.0, 7223.0, 184.0, 700.0, 2116.0, 2590.0, nan],
 [2545.0, 1.0, 4636.0, 7.0, 0.0, 0.0, 637.0, 2545.0, 5.0],
 [2767.0, 4.0, 189779.0, 592.0, 59.0, 14.0, 282.0, 2767.0, 6.0],
 [1810.0, 2.0, 650417.0, 611.0, 244.0, 74.0, 1789.0, 1810.0, 4.0],
 [474.0, 1.0, 22386.0, 205.0, 8.0, 87.0, 1130.0, 474.0, 3.0],
 [1373.0, 3.0, 2058731.0, 3755.0, 558.0, 455.0, 541.0, 1373.0, nan],
 [3071.0, 1.0, 18232.0, 9.0, 0.0, 0.0, 215.0, 3071.0, 4.0],
 [1457.0, 1.0, 46662.0, 85.0, 32.0, 12.0, 2086.0, 1457.0, 5.0],
 [1658.0, 1.0, 360652.0, 412.0, 210.0, 59.0, 499.0, 1658.0, 5.0],
 [2232.0, 1.0, 3868.0, 27.0, 2.0, 2.0, 1757.0, 2232.0, 3.0],
 [2881.0, 355.0, 121131.0, 541.0, 21.0, 150.0, 1877.0, 2881.0, 4.0],
 [1198.0, 1.0, 27587.0, 75.0, 16.0, 2.0, 1239.0, 1198.0, 3.0],
 [50.0, 4.0, 38122.0, 69.0, 3.0, 9.0, 387.0, 50.0, 5.0],
 [2899.0, 6.0, 884960.0, 15476.0, 308.0, 5341.0, 2122.0, 2899.0, 4.0],
 [1895.0, 18304.0, 85228.0, 466.0, 5.0, 4.0, 1691.0, 1895.0, 4.0],
 [2936.0, 117.0, 702387.0, 12383.0, 149.0, 1878.0, 2044.0, 2936.0, 2.0],
 [2217.0, 2.0, 12119.0, 8.0, 2.0, 0.0, 2086.0, 2217.0, 3.0],
 [2233.0, 2.0, 4528.0, 19.0, 0.0, 3.0, 2324.0, 2233.0, 4.0],
 [2921.0, 1.0, 254387.0, 3738.0, 149.0, 459.0, 2219.0, 2921.0, 5.0],
 ...]
In [41]:
import sys
In [42]:
sys.getsizeof(data_list[3][8])
Out[42]:
24
In [43]:
temp = []
count = 0
for i in data_list:
    x = np.isnan(i[8])
    if(x):
        print(x)
    else:
        temp.append(i)
    count = count + 1

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In [44]:
temp
Out[44]:
[[2925.0, 40.0, 1031602.0, 8523.0, 363.0, 1095.0, 2168.0, 2925.0, 6.0],
 [3040.0, 2.0, 1707.0, 56.0, 2.0, 6.0, 2185.0, 3040.0, 4.0],
 [1863.0, 1.0, 2023.0, 25.0, 0.0, 2.0, 2094.0, 1863.0, 3.0],
 [1963.0, 1.0, 666.0, 1.0, 0.0, 0.0, 2091.0, 1963.0, 4.0],
 [373.0, 4.0, 78.0, 0.0, 0.0, 0.0, 2040.0, 373.0, 4.0],
 [2218.0, 40621.0, 43118.0, 15.0, 1.0, 0.0, 1778.0, 2218.0, 4.0],
 [2750.0, 1.0, 14205.0, 55.0, 16.0, 1.0, 2124.0, 2750.0, 5.0],
 [1385.0, 1.0, 526015.0, 3064.0, 211.0, 2582.0, 1855.0, 1385.0, 6.0],
 [88.0, 1.0, 406992.0, 3831.0, 310.0, 7839.0, 2187.0, 88.0, 2.0],
 [156.0, 19.0, 607447.0, 377.0, 144.0, 65.0, 2131.0, 156.0, 4.0],
 [2248.0, 9.0, 429137.0, 2181.0, 76.0, 172.0, 1905.0, 2248.0, 2.0],
 [2537.0, 224.0, 1895.0, 59.0, 5.0, 0.0, 2137.0, 2537.0, 2.0],
 [1566.0, 1.0, 59843.0, 68.0, 16.0, 10.0, 1948.0, 1566.0, 4.0],
 [1901.0, 2.0, 211642.0, 1378.0, 65.0, 103.0, 1638.0, 1901.0, 6.0],
 [2105.0, 1.0, 3700.0, 29.0, 6.0, 3.0, 1085.0, 2105.0, 2.0],
 [2770.0, 4794.0, 5886.0, 23.0, 0.0, 4.0, 2217.0, 2770.0, 4.0],
 [172.0, 2.0, 62255.0, 71.0, 10.0, 1.0, 1697.0, 172.0, 4.0],
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 [2328.0, 4.0, 263086.0, 160.0, 54.0, 27.0, 687.0, 2328.0, 4.0],
 [2784.0, 1.0, 1577433.0, 3648.0, 190.0, 251.0, 2207.0, 2784.0, 2.0],
 [1060.0, 1.0, 87622.0, 219.0, 5.0, 26.0, 1930.0, 1060.0, 1.0],
 [1091.0, 1.0, 399768.0, 262.0, 112.0, 33.0, 2098.0, 1091.0, 4.0],
 [2863.0, 7.0, 783117.0, 15872.0, 234.0, 3761.0, 2184.0, 2863.0, 2.0],
 [539.0, 1.0, 142731.0, 1584.0, 38.0, 628.0, 1654.0, 539.0, 3.0],
 [1080.0, 1.0, 484524.0, 1895.0, 111.0, 177.0, 1469.0, 1080.0, 4.0],
 [24.0, 8.0, 439968.0, 252.0, 122.0, 50.0, 2108.0, 24.0, 4.0],
 [292.0, 1.0, 45996.0, 579.0, 21.0, 23.0, 1145.0, 292.0, 4.0],
 [42.0, 1.0, 242342.0, 440.0, 108.0, 28.0, 2366.0, 42.0, 4.0],
 [2962.0, 2.0, 89013.0, 156.0, 12.0, 19.0, 2076.0, 2962.0, 4.0],
 [1898.0, 1.0, 217121.0, 395.0, 29.0, 44.0, 1963.0, 1898.0, 1.0],
 [2244.0, 1.0, 203457.0, 1844.0, 39.0, 73.0, 920.0, 2244.0, 3.0],
 [2560.0, 1.0, 26848.0, 185.0, 3.0, 7.0, 1560.0, 2560.0, 3.0],
 [2945.0, 1.0, 14968.0, 23.0, 9.0, 4.0, 1690.0, 2945.0, 4.0],
 [2537.0, 2.0, 34705.0, 1059.0, 12.0, 35.0, 1851.0, 2537.0, 4.0],
 [2655.0, 12.0, 1380036.0, 1772.0, 448.0, 102.0, 1301.0, 2655.0, 4.0],
 [2984.0, 4.0, 5109579.0, 16900.0, 1730.0, 4623.0, 1641.0, 2984.0, 4.0],
 [2032.0, 1.0, 83062.0, 133.0, 24.0, 13.0, 766.0, 2032.0, 4.0],
 [2547.0, 146.0, 5482871.0, 52911.0, 2135.0, 2450.0, 2163.0, 2547.0, 4.0],
 [2438.0, 4.0, 393364.0, 717.0, 87.0, 56.0, 1747.0, 2438.0, 5.0],
 [1874.0, 1.0, 325306.0, 928.0, 106.0, 129.0, 1649.0, 1874.0, 2.0],
 [1060.0, 1.0, 37886.0, 51.0, 4.0, 3.0, 886.0, 1060.0, 1.0],
 [2239.0, 12.0, 432640.0, 230.0, 57.0, 15.0, 928.0, 2239.0, 4.0],
 [2546.0, 3.0, 409836.0, 981.0, 58.0, 119.0, 2168.0, 2546.0, 2.0],
 [2535.0, 3.0, 102557.0, 64.0, 5.0, 11.0, 128.0, 2535.0, 4.0],
 [2264.0, 406.0, 2944.0, 19.0, 1.0, 11.0, 2313.0, 2264.0, 4.0],
 [273.0, 32.0, 2783473.0, 20973.0, 1507.0, 1420.0, 2105.0, 273.0, 6.0],
 [2826.0, 1.0, 750164.0, 1939.0, 143.0, 626.0, 2206.0, 2826.0, 5.0],
 [2899.0, 1.0, 172509.0, 401.0, 14.0, 20.0, 522.0, 2899.0, 4.0],
 [2416.0, 3210.0, 271306.0, 2757.0, 72.0, 141.0, 2229.0, 2416.0, 3.0],
 [1074.0, 1.0, 3927.0, 5.0, 0.0, 0.0, 1102.0, 1074.0, 4.0],
 [1071.0, 1.0, 707.0, 2.0, 0.0, 0.0, 1236.0, 1071.0, 4.0],
 [36.0, 20.0, 588913.0, 515.0, 191.0, 35.0, 969.0, 36.0, 5.0],
 [1061.0, 1.0, 23234.0, 18.0, 10.0, 0.0, 2187.0, 1061.0, 4.0],
 [1058.0, 1.0, 41939.0, 386.0, 14.0, 47.0, 2155.0, 1058.0, 4.0],
 [180.0, 16.0, 613582.0, 1446.0, 111.0, 90.0, 593.0, 180.0, 4.0],
 [282.0, 1.0, 4664358.0, 58103.0, 1047.0, 4864.0, 2302.0, 282.0, 4.0],
 [3057.0, 1.0, 1064413.0, 6772.0, 465.0, 2681.0, 1400.0, 3057.0, 4.0],
 [3053.0, 1.0, 8190.0, 81.0, 1.0, 10.0, 453.0, 3053.0, 4.0],
 [1894.0, 5.0, 3808454.0, 17121.0, 1402.0, 759.0, 1523.0, 1894.0, 5.0],
 [2298.0, 1.0, 50847.0, 156.0, 19.0, 14.0, 2172.0, 2298.0, 4.0],
 [1655.0, 4.0, 733019.0, 784.0, 207.0, 90.0, 2140.0, 1655.0, 4.0],
 [2391.0, 2.0, 440805.0, 2071.0, 493.0, 1233.0, 2340.0, 2391.0, 4.0],
 [2912.0, 8.0, 8072739.0, 36953.0, 2033.0, 7465.0, 1834.0, 2912.0, 4.0],
 [2629.0, 1.0, 156371.0, 158.0, 106.0, 4.0, 1638.0, 2629.0, 4.0],
 [2584.0, 1.0, 205016.0, 2437.0, 185.0, 194.0, 1568.0, 2584.0, 2.0],
 [1190.0, 9.0, 1655.0, 2.0, 0.0, 0.0, 2051.0, 1190.0, 5.0],
 [2567.0, 117.0, 24591440.0, 30590.0, 5231.0, 1727.0, 1318.0, 2567.0, 4.0],
 [2239.0, 1.0, 324993.0, 927.0, 92.0, 134.0, 1893.0, 2239.0, 4.0],
 ...]
In [45]:
data = pd.DataFrame(temp, columns=data.columns)
# data["category"].drop(labels=["NaN"],axis=0,inplace=False)
In [46]:
data.shape
Out[46]:
(11743, 9)

Dropping Required Data¶

In [47]:
Y_train = pd.DataFrame(data=data.iloc[:,1].values, columns=['target'])
data = data.drop(["adview"], axis=1)
data = data.drop(["vidid"], axis=1)
data.head()
Out[47]:
views likes dislikes comment published duration category
0 1031602.0 8523.0 363.0 1095.0 2168.0 2925.0 6.0
1 1707.0 56.0 2.0 6.0 2185.0 3040.0 4.0
2 2023.0 25.0 0.0 2.0 2094.0 1863.0 3.0
3 666.0 1.0 0.0 0.0 2091.0 1963.0 4.0
4 78.0 0.0 0.0 0.0 2040.0 373.0 4.0

Splitting the Data¶

In [48]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(data, Y_train, test_size=0.2, random_state=42)

Normalize Data¶

In [49]:
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
In [50]:
X_train
Out[50]:
array([[4.54207773e-05, 0.00000000e+00, 0.00000000e+00, ...,
        4.33613445e-01, 7.32440379e-01, 6.00000000e-01],
       [2.21934844e-03, 3.15489247e-03, 1.16890707e-03, ...,
        9.89915966e-01, 3.39104868e-01, 6.00000000e-01],
       [1.13954233e-03, 3.61166142e-04, 1.53803562e-04, ...,
        7.47058824e-01, 3.53479255e-01, 8.00000000e-01],
       ...,
       [8.61669490e-03, 4.50926550e-03, 7.01344243e-03, ...,
        5.14285714e-01, 7.58575629e-01, 6.00000000e-01],
       [9.55765036e-02, 2.50739594e-01, 6.21981605e-02, ...,
        7.92857143e-01, 6.15158445e-01, 6.00000000e-01],
       [3.16686957e-03, 5.31126679e-03, 2.15324987e-03, ...,
        6.70588235e-01, 2.35217249e-02, 4.00000000e-01]])
In [51]:
X_test
Out[51]:
array([[3.00086793e-03, 5.66304565e-03, 7.92303339e-04, ...,
        8.24863503e-01, 7.25253185e-01, 6.00000000e-01],
       [4.58262963e-03, 1.74459351e-02, 3.84833050e-03, ...,
        8.88702226e-01, 8.37634760e-01, 1.00000000e+00],
       [2.65159201e-03, 4.42468462e-02, 3.11262026e-03, ...,
        9.15581688e-01, 3.46292061e-01, 8.00000000e-01],
       ...,
       [4.04826454e-03, 1.44548458e-03, 1.64119977e-03, ...,
        3.19193616e-01, 7.26233257e-01, 6.00000000e-01],
       [2.49565624e-04, 2.35908090e-03, 8.48896435e-05, ...,
        9.14741705e-01, 6.16465207e-01, 8.00000000e-01],
       [5.97241892e-03, 5.90708851e-03, 1.72608942e-03, ...,
        7.11885762e-01, 7.23293041e-01, 6.00000000e-01]])

Evaluation Metrics¶

In [52]:
from sklearn import metrics
In [53]:
def print_error(X_test, y_test, model_name):
    prediction = model_name.predict(X_test)
    print('Mean Absolute Error: ', metrics.mean_absolute_error(y_test, prediction))
    print('Mean Squared Error: ', metrics.mean_squared_error(y_test, prediction))
    print('Root Mean Squared Error: ', np.sqrt(metrics.mean_squared_error(y_test, prediction)))

Linear Regression¶

In [54]:
from sklearn import linear_model
In [55]:
linear_regression = linear_model.LinearRegression()
In [56]:
linear_regression.fit(X_train, y_train)
Out[56]:
LinearRegression()
In [57]:
print_error(X_test, y_test, linear_regression)

Mean Absolute Error:  3783.5198873059603
Mean Squared Error:  408067030.70142925
Root Mean Squared Error:  20200.66906568763

Decision Tree Regressor¶

In [58]:
from sklearn.tree import DecisionTreeRegressor
decision_tree = DecisionTreeRegressor()
decision_tree.fit(X_train, y_train)
print_error(X_test, y_test, decision_tree)

Mean Absolute Error:  3387.4899957428693
Mean Squared Error:  2643920054.449553
Root Mean Squared Error:  51419.063142472296

Random Forest Regressor¶

In [59]:
from sklearn.ensemble import RandomForestRegressor
n_estimators = 200
max_depth = 25
min_samples_split = 15
min_samples_leaf = 2
random_forest = RandomForestRegressor(n_estimators = n_estimators, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf)
random_forest.fit(X_train, y_train)
print_error(X_test, y_test, random_forest)

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  import sys

Mean Absolute Error:  2636.7661951387163
Mean Squared Error:  411502803.75869435
Root Mean Squared Error:  20285.531882568284

Support Vector Regressor¶

In [60]:
from sklearn.svm import SVR
supportvector_regressor = SVR()
supportvector_regressor.fit(X_train, y_train)
print_error(X_train, y_train, supportvector_regressor)

/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)

Mean Absolute Error:  1848.548310558096
Mean Squared Error:  1010507282.7889342
Root Mean Squared Error:  31788.47720147875

Artificial Neural Network¶

In [61]:
import keras
from keras.layers import Dense
In [62]:
ann=keras.models.Sequential([Dense(6, activation="relu", input_shape=X_train.shape[1:]),
                             Dense(6, activation="relu"),
                             Dense(1)
                            ])
In [63]:
import tensorflow as tf
In [64]:
optimizer = tf.optimizers.Adam()
loss = keras.losses.mean_squared_error
ann.compile(optimizer = optimizer, loss = loss, metrics = ["mean_squared_error"])
In [65]:
history = ann.fit(X_train, y_train, epochs=100)

Epoch 1/100
294/294 [==============================] - 1s 1ms/step - loss: 1010512128.0000 - mean_squared_error: 1010512128.0000
Epoch 2/100
294/294 [==============================] - 0s 1ms/step - loss: 1010502080.0000 - mean_squared_error: 1010502080.0000
Epoch 3/100
294/294 [==============================] - 0s 1ms/step - loss: 1010484352.0000 - mean_squared_error: 1010484352.0000
Epoch 4/100
294/294 [==============================] - 0s 2ms/step - loss: 1010457600.0000 - mean_squared_error: 1010457600.0000
Epoch 5/100
294/294 [==============================] - 0s 2ms/step - loss: 1010420160.0000 - mean_squared_error: 1010420160.0000
Epoch 6/100
294/294 [==============================] - 0s 2ms/step - loss: 1010369600.0000 - mean_squared_error: 1010369600.0000
Epoch 7/100
294/294 [==============================] - 0s 1ms/step - loss: 1010305088.0000 - mean_squared_error: 1010305088.0000
Epoch 8/100
294/294 [==============================] - 0s 2ms/step - loss: 1010228544.0000 - mean_squared_error: 1010228544.0000
Epoch 9/100
294/294 [==============================] - 0s 1ms/step - loss: 1010140096.0000 - mean_squared_error: 1010140096.0000
Epoch 10/100
294/294 [==============================] - 0s 1ms/step - loss: 1010039232.0000 - mean_squared_error: 1010039232.0000
Epoch 11/100
294/294 [==============================] - 0s 2ms/step - loss: 1009924672.0000 - mean_squared_error: 1009924672.0000
Epoch 12/100
294/294 [==============================] - 0s 1ms/step - loss: 1009800320.0000 - mean_squared_error: 1009800320.0000
Epoch 13/100
294/294 [==============================] - 0s 1ms/step - loss: 1009669312.0000 - mean_squared_error: 1009669312.0000
Epoch 14/100
294/294 [==============================] - 0s 1ms/step - loss: 1009542144.0000 - mean_squared_error: 1009542144.0000
Epoch 15/100
294/294 [==============================] - 0s 2ms/step - loss: 1009410880.0000 - mean_squared_error: 1009410880.0000
Epoch 16/100
294/294 [==============================] - 0s 2ms/step - loss: 1009272064.0000 - mean_squared_error: 1009272064.0000
Epoch 17/100
294/294 [==============================] - 0s 2ms/step - loss: 1009126464.0000 - mean_squared_error: 1009126464.0000
Epoch 18/100
294/294 [==============================] - 0s 2ms/step - loss: 1008975360.0000 - mean_squared_error: 1008975360.0000
Epoch 19/100
294/294 [==============================] - 0s 1ms/step - loss: 1008816832.0000 - mean_squared_error: 1008816832.0000
Epoch 20/100
294/294 [==============================] - 0s 2ms/step - loss: 1008662784.0000 - mean_squared_error: 1008662784.0000
Epoch 21/100
294/294 [==============================] - 0s 1ms/step - loss: 1008515456.0000 - mean_squared_error: 1008515456.0000
Epoch 22/100
294/294 [==============================] - 0s 1ms/step - loss: 1008370816.0000 - mean_squared_error: 1008370816.0000
Epoch 23/100
294/294 [==============================] - 0s 1ms/step - loss: 1008224128.0000 - mean_squared_error: 1008224128.0000
Epoch 24/100
294/294 [==============================] - 0s 1ms/step - loss: 1008081344.0000 - mean_squared_error: 1008081344.0000
Epoch 25/100
294/294 [==============================] - 0s 1ms/step - loss: 1007952128.0000 - mean_squared_error: 1007952128.0000
Epoch 26/100
294/294 [==============================] - 0s 1ms/step - loss: 1007821760.0000 - mean_squared_error: 1007821760.0000
Epoch 27/100
294/294 [==============================] - 0s 2ms/step - loss: 1007699520.0000 - mean_squared_error: 1007699520.0000
Epoch 28/100
294/294 [==============================] - 0s 1ms/step - loss: 1007583552.0000 - mean_squared_error: 1007583552.0000
Epoch 29/100
294/294 [==============================] - 0s 1ms/step - loss: 1007476800.0000 - mean_squared_error: 1007476800.0000
Epoch 30/100
294/294 [==============================] - 0s 1ms/step - loss: 1007380736.0000 - mean_squared_error: 1007380736.0000
Epoch 31/100
294/294 [==============================] - 0s 1ms/step - loss: 1007292288.0000 - mean_squared_error: 1007292288.0000
Epoch 32/100
294/294 [==============================] - 0s 1ms/step - loss: 1007202688.0000 - mean_squared_error: 1007202688.0000
Epoch 33/100
294/294 [==============================] - 0s 1ms/step - loss: 1007121984.0000 - mean_squared_error: 1007121984.0000
Epoch 34/100
294/294 [==============================] - 0s 2ms/step - loss: 1007047168.0000 - mean_squared_error: 1007047168.0000
Epoch 35/100
294/294 [==============================] - 0s 1ms/step - loss: 1006980352.0000 - mean_squared_error: 1006980352.0000
Epoch 36/100
294/294 [==============================] - 0s 1ms/step - loss: 1006921280.0000 - mean_squared_error: 1006921280.0000
Epoch 37/100
294/294 [==============================] - 0s 1ms/step - loss: 1006869056.0000 - mean_squared_error: 1006869056.0000
Epoch 38/100
294/294 [==============================] - 0s 2ms/step - loss: 1006812800.0000 - mean_squared_error: 1006812800.0000
Epoch 39/100
294/294 [==============================] - 0s 1ms/step - loss: 1006768384.0000 - mean_squared_error: 1006768384.0000
Epoch 40/100
294/294 [==============================] - 0s 1ms/step - loss: 1006728384.0000 - mean_squared_error: 1006728384.0000
Epoch 41/100
294/294 [==============================] - 0s 2ms/step - loss: 1006692928.0000 - mean_squared_error: 1006692928.0000
Epoch 42/100
294/294 [==============================] - 0s 1ms/step - loss: 1006656960.0000 - mean_squared_error: 1006656960.0000
Epoch 43/100
294/294 [==============================] - 0s 1ms/step - loss: 1006626176.0000 - mean_squared_error: 1006626176.0000
Epoch 44/100
294/294 [==============================] - 0s 1ms/step - loss: 1006593920.0000 - mean_squared_error: 1006593920.0000
Epoch 45/100
294/294 [==============================] - 0s 1ms/step - loss: 1006569216.0000 - mean_squared_error: 1006569216.0000
Epoch 46/100
294/294 [==============================] - 0s 1ms/step - loss: 1006548672.0000 - mean_squared_error: 1006548672.0000
Epoch 47/100
294/294 [==============================] - 0s 1ms/step - loss: 1006526784.0000 - mean_squared_error: 1006526784.0000
Epoch 48/100
294/294 [==============================] - 0s 2ms/step - loss: 1006506496.0000 - mean_squared_error: 1006506496.0000
Epoch 49/100
294/294 [==============================] - 0s 1ms/step - loss: 1006491072.0000 - mean_squared_error: 1006491072.0000
Epoch 50/100
294/294 [==============================] - 0s 1ms/step - loss: 1006474112.0000 - mean_squared_error: 1006474112.0000
Epoch 51/100
294/294 [==============================] - 0s 2ms/step - loss: 1006459520.0000 - mean_squared_error: 1006459520.0000
Epoch 52/100
294/294 [==============================] - 0s 2ms/step - loss: 1006447232.0000 - mean_squared_error: 1006447232.0000
Epoch 53/100
294/294 [==============================] - 0s 2ms/step - loss: 1006438400.0000 - mean_squared_error: 1006438400.0000
Epoch 54/100
294/294 [==============================] - 0s 1ms/step - loss: 1006422912.0000 - mean_squared_error: 1006422912.0000
Epoch 55/100
294/294 [==============================] - 0s 1ms/step - loss: 1006412288.0000 - mean_squared_error: 1006412288.0000
Epoch 56/100
294/294 [==============================] - 0s 1ms/step - loss: 1006406336.0000 - mean_squared_error: 1006406336.0000
Epoch 57/100
294/294 [==============================] - 0s 2ms/step - loss: 1006393216.0000 - mean_squared_error: 1006393216.0000
Epoch 58/100
294/294 [==============================] - 0s 1ms/step - loss: 1006387392.0000 - mean_squared_error: 1006387392.0000
Epoch 59/100
294/294 [==============================] - 0s 2ms/step - loss: 1006374912.0000 - mean_squared_error: 1006374912.0000
Epoch 60/100
294/294 [==============================] - 0s 2ms/step - loss: 1006370112.0000 - mean_squared_error: 1006370112.0000
Epoch 61/100
294/294 [==============================] - 0s 2ms/step - loss: 1006363200.0000 - mean_squared_error: 1006363200.0000
Epoch 62/100
294/294 [==============================] - 0s 1ms/step - loss: 1006361728.0000 - mean_squared_error: 1006361728.0000
Epoch 63/100
294/294 [==============================] - 0s 1ms/step - loss: 1006349120.0000 - mean_squared_error: 1006349120.0000
Epoch 64/100
294/294 [==============================] - 0s 2ms/step - loss: 1006347648.0000 - mean_squared_error: 1006347648.0000
Epoch 65/100
294/294 [==============================] - 0s 1ms/step - loss: 1006337856.0000 - mean_squared_error: 1006337856.0000
Epoch 66/100
294/294 [==============================] - 0s 2ms/step - loss: 1006334720.0000 - mean_squared_error: 1006334720.0000
Epoch 67/100
294/294 [==============================] - 0s 1ms/step - loss: 1006326208.0000 - mean_squared_error: 1006326208.0000
Epoch 68/100
294/294 [==============================] - 0s 2ms/step - loss: 1006318848.0000 - mean_squared_error: 1006318848.0000
Epoch 69/100
294/294 [==============================] - 0s 1ms/step - loss: 1006318848.0000 - mean_squared_error: 1006318848.0000
Epoch 70/100
294/294 [==============================] - 0s 1ms/step - loss: 1006313280.0000 - mean_squared_error: 1006313280.0000
Epoch 71/100
294/294 [==============================] - 0s 2ms/step - loss: 1006307264.0000 - mean_squared_error: 1006307264.0000
Epoch 72/100
294/294 [==============================] - 0s 1ms/step - loss: 1006303680.0000 - mean_squared_error: 1006303680.0000
Epoch 73/100
294/294 [==============================] - 0s 2ms/step - loss: 1006297344.0000 - mean_squared_error: 1006297344.0000
Epoch 74/100
294/294 [==============================] - 0s 2ms/step - loss: 1006297216.0000 - mean_squared_error: 1006297216.0000
Epoch 75/100
294/294 [==============================] - 0s 2ms/step - loss: 1006292608.0000 - mean_squared_error: 1006292608.0000
Epoch 76/100
294/294 [==============================] - 0s 1ms/step - loss: 1006287168.0000 - mean_squared_error: 1006287168.0000
Epoch 77/100
294/294 [==============================] - 0s 2ms/step - loss: 1006280768.0000 - mean_squared_error: 1006280768.0000
Epoch 78/100
294/294 [==============================] - 0s 2ms/step - loss: 1006275328.0000 - mean_squared_error: 1006275328.0000
Epoch 79/100
294/294 [==============================] - 0s 2ms/step - loss: 1006276224.0000 - mean_squared_error: 1006276224.0000
Epoch 80/100
294/294 [==============================] - 0s 2ms/step - loss: 1006269056.0000 - mean_squared_error: 1006269056.0000
Epoch 81/100
294/294 [==============================] - 0s 2ms/step - loss: 1006263360.0000 - mean_squared_error: 1006263360.0000
Epoch 82/100
294/294 [==============================] - 0s 2ms/step - loss: 1006263616.0000 - mean_squared_error: 1006263616.0000
Epoch 83/100
294/294 [==============================] - 0s 2ms/step - loss: 1006256128.0000 - mean_squared_error: 1006256128.0000
Epoch 84/100
294/294 [==============================] - 0s 2ms/step - loss: 1006256448.0000 - mean_squared_error: 1006256448.0000
Epoch 85/100
294/294 [==============================] - 0s 2ms/step - loss: 1006246080.0000 - mean_squared_error: 1006246080.0000
Epoch 86/100
294/294 [==============================] - 0s 2ms/step - loss: 1006245760.0000 - mean_squared_error: 1006245760.0000
Epoch 87/100
294/294 [==============================] - 0s 1ms/step - loss: 1006241600.0000 - mean_squared_error: 1006241600.0000
Epoch 88/100
294/294 [==============================] - 0s 2ms/step - loss: 1006235136.0000 - mean_squared_error: 1006235136.0000
Epoch 89/100
294/294 [==============================] - 0s 2ms/step - loss: 1006233920.0000 - mean_squared_error: 1006233920.0000
Epoch 90/100
294/294 [==============================] - 0s 2ms/step - loss: 1006227520.0000 - mean_squared_error: 1006227520.0000
Epoch 91/100
294/294 [==============================] - 0s 1ms/step - loss: 1006227328.0000 - mean_squared_error: 1006227328.0000
Epoch 92/100
294/294 [==============================] - 0s 2ms/step - loss: 1006218304.0000 - mean_squared_error: 1006218304.0000
Epoch 93/100
294/294 [==============================] - 0s 2ms/step - loss: 1006214400.0000 - mean_squared_error: 1006214400.0000
Epoch 94/100
294/294 [==============================] - 0s 2ms/step - loss: 1006211392.0000 - mean_squared_error: 1006211392.0000
Epoch 95/100
294/294 [==============================] - 0s 2ms/step - loss: 1006206784.0000 - mean_squared_error: 1006206784.0000
Epoch 96/100
294/294 [==============================] - 0s 1ms/step - loss: 1006199552.0000 - mean_squared_error: 1006199552.0000
Epoch 97/100
294/294 [==============================] - 0s 2ms/step - loss: 1006197312.0000 - mean_squared_error: 1006197312.0000
Epoch 98/100
294/294 [==============================] - 0s 1ms/step - loss: 1006193408.0000 - mean_squared_error: 1006193408.0000
Epoch 99/100
294/294 [==============================] - 0s 1ms/step - loss: 1006189824.0000 - mean_squared_error: 1006189824.0000
Epoch 100/100
294/294 [==============================] - 0s 1ms/step - loss: 1006185344.0000 - mean_squared_error: 1006185344.0000
In [66]:
ann.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense (Dense)               (None, 6)                 48        
                                                                 
 dense_1 (Dense)             (None, 6)                 42        
                                                                 
 dense_2 (Dense)             (None, 1)                 7         
                                                                 
=================================================================
Total params: 97
Trainable params: 97
Non-trainable params: 0
_________________________________________________________________
In [67]:
print_error(X_test, y_test, ann)

Mean Absolute Error:  3230.7857982234377
Mean Squared Error:  404535778.97948754
Root Mean Squared Error:  20113.074826577053

Saving Models¶

In [68]:
import joblib
In [69]:
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive
In [70]:
joblib.dump(linear_regression, "/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/linear_regression_youtubeadview.pkl")
joblib.dump(decision_tree, "/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/decisiontree_youtubeadview.pkl")
joblib.dump(random_forest, "/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/random_forest_youtubeadview.pkl")
joblib.dump(supportvector_regressor, "/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/supportvector_regressor_youtubeadview.pkl")
Out[70]:
['/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/supportvector_regressor_youtubeadview.pkl']
In [71]:
ann.save("/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/ann_youtubeadview.h5")

Testing the Model on Test Data¶

In [72]:
test_data_url = "https://raw.githubusercontent.com/aashish22bansal/Datasets/main/YouTube%20Adview%20Prediction/test.csv"
In [73]:
test_data = pd.read_csv(test_data_url)
In [74]:
test_data
Out[74]:
vidid views likes dislikes comment published duration category
0 VID_1054 440238 6153 218 1377 2017-02-18 PT7M29S B
1 VID_18629 1040132 8171 340 1047 2016-06-28 PT6M29S F
2 VID_13967 28534 31 11 1 2014-03-10 PT37M54S D
3 VID_19442 1316715 2284 250 274 2010-06-05 PT9M55S G
4 VID_770 1893173 2519 225 116 2016-09-03 PT3M8S B
... ... ... ... ... ... ... ... ...
8759 VID_2944 111205 471 14 101 2011-04-19 PT3M14S C
8760 VID_8195 1074457 1024 195 65 2016-06-03 PT2H18M35S D
8761 VID_18973 1891692 4606 1109 191 2016-06-26 PT7M14S F
8762 VID_5849 714859 1639 151 382 2016-03-23 PT3M15S D
8763 VID_8494 5297 12 1 0 2016-12-19 PT10M14S D

8764 rows × 8 columns

In [75]:
test_data["category"] = test_data["category"].map(category)
In [76]:
test_data = test_data[test_data.views!='F']
test_data = test_data[test_data.likes!='F']
test_data = test_data[test_data.dislikes!='F']
test_data = test_data[test_data.comment!='F']
In [77]:
test_data["views"] = pd.to_numeric(test_data["views"])
test_data["comment"] = pd.to_numeric(test_data["comment"])
test_data["likes"] = pd.to_numeric(test_data["likes"])
test_data["dislikes"] = pd.to_numeric(test_data["dislikes"])
In [78]:
column_vidID = test_data['vidid']
column_vidID
Out[78]:
0        VID_1054
1       VID_18629
2       VID_13967
3       VID_19442
4         VID_770
          ...    
8759     VID_2944
8760     VID_8195
8761    VID_18973
8762     VID_5849
8763     VID_8494
Name: vidid, Length: 8549, dtype: object
In [79]:
test_data['duration'] = LabelEncoder().fit_transform(test_data['duration'])
test_data['vidid'] = LabelEncoder().fit_transform(test_data['duration'])
test_data['published'] = LabelEncoder().fit_transform(test_data['published'])
In [80]:
mp = pd.read_csv(test_data_url)["duration"]
mp
Out[80]:
0          PT7M29S
1          PT6M29S
2         PT37M54S
3          PT9M55S
4           PT3M8S
           ...    
8759       PT3M14S
8760    PT2H18M35S
8761       PT7M14S
8762       PT3M15S
8763      PT10M14S
Name: duration, Length: 8764, dtype: object
In [81]:
time = mp.apply(checki)
In [82]:
time1 = time.apply(func_sec)
time1
Out[82]:
0        449
1        389
2       2274
3        595
4        188
        ... 
8759     194
8760    8315
8761     434
8762     195
8763     614
Name: duration, Length: 8764, dtype: int64
In [83]:
np.isnan(test_data).sum()
Out[83]:
vidid           0
views           0
likes           0
dislikes        0
comment         0
published       0
duration        0
category     1685
dtype: int64
In [84]:
test_data_list = test_data.values.tolist()
In [85]:
test_data_temp = []
count = 0
for i in test_data_list:
    x = np.isnan(i[7])
    if(x):
        print(x)
    else:
        test_data_temp.append(i)
    count = count + 1

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In [86]:
test_data.columns
Out[86]:
Index(['vidid', 'views', 'likes', 'dislikes', 'comment', 'published',
       'duration', 'category'],
      dtype='object')
In [87]:
test_data_temp
Out[87]:
[[2115.0, 440238.0, 6153.0, 218.0, 1377.0, 2053.0, 2115.0, 2.0],
 [2055.0, 1040132.0, 8171.0, 340.0, 1047.0, 1825.0, 2055.0, 6.0],
 [1506.0, 28534.0, 31.0, 11.0, 1.0, 1009.0, 1506.0, 4.0],
 [1625.0, 1893173.0, 2519.0, 225.0, 116.0, 1892.0, 1625.0, 2.0],
 [1539.0, 145820.0, 215.0, 29.0, 19.0, 965.0, 1539.0, 4.0],
 [56.0, 12921.0, 169.0, 3.0, 73.0, 1060.0, 56.0, 4.0],
 [1348.0, 4502.0, 65.0, 2.0, 8.0, 1840.0, 1348.0, 6.0],
 [229.0, 662956.0, 1841.0, 321.0, 219.0, 955.0, 229.0, 4.0],
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 [500.0, 161399.0, 139.0, 70.0, 8.0, 1234.0, 500.0, 5.0],
 [1365.0, 29406.0, 842.0, 274.0, 186.0, 1818.0, 1365.0, 6.0],
 [24.0, 419858.0, 774.0, 60.0, 29.0, 1860.0, 24.0, 4.0],
 [1377.0, 51618.0, 70.0, 17.0, 7.0, 1721.0, 1377.0, 6.0],
 [400.0, 288315.0, 580.0, 40.0, 123.0, 1187.0, 400.0, 1.0],
 [2075.0, 434207.0, 843.0, 58.0, 21.0, 928.0, 2075.0, 4.0],
 [1613.0, 505273.0, 2384.0, 233.0, 137.0, 1485.0, 1613.0, 4.0],
 [1826.0, 5703.0, 13.0, 12.0, 46.0, 2092.0, 1826.0, 4.0],
 [199.0, 188206.0, 182.0, 86.0, 14.0, 2024.0, 199.0, 4.0],
 [1600.0, 12242.0, 14.0, 3.0, 0.0, 1896.0, 1600.0, 4.0],
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 [225.0, 21920.0, 95.0, 0.0, 36.0, 178.0, 225.0, 4.0],
 [1344.0, 26182.0, 163.0, 15.0, 56.0, 554.0, 1344.0, 1.0],
 [866.0, 1179766.0, 1428.0, 534.0, 123.0, 1762.0, 866.0, 5.0],
 [1823.0, 1681964.0, 5619.0, 1364.0, 2236.0, 1571.0, 1823.0, 6.0],
 [51.0, 5071100.0, 53880.0, 2557.0, 2374.0, 1173.0, 51.0, 2.0],
 [23.0, 74491.0, 227.0, 10.0, 24.0, 1819.0, 23.0, 4.0],
 [1586.0, 3984833.0, 13202.0, 704.0, 637.0, 1195.0, 1586.0, 4.0],
 [2030.0, 32741.0, 89.0, 13.0, 3.0, 1328.0, 2030.0, 1.0],
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 [1365.0, 978585.0, 9655.0, 238.0, 5700.0, 1849.0, 1365.0, 4.0],
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 [814.0, 59543.0, 33.0, 23.0, 0.0, 515.0, 814.0, 6.0],
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 [1956.0, 24127.0, 116.0, 9.0, 2.0, 1455.0, 1956.0, 6.0],
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 [289.0, 267620.0, 608.0, 38.0, 28.0, 644.0, 289.0, 4.0],
 [2242.0, 588578.0, 13374.0, 252.0, 1197.0, 1737.0, 2242.0, 4.0],
 [881.0, 538247.0, 7461.0, 1321.0, 1937.0, 1510.0, 881.0, 4.0],
 [712.0, 335347.0, 380.0, 68.0, 29.0, 1466.0, 712.0, 4.0],
 [825.0, 356059.0, 389.0, 48.0, 36.0, 915.0, 825.0, 4.0],
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 [1408.0, 2047.0, 1.0, 1.0, 1.0, 261.0, 1408.0, 4.0],
 [829.0, 8038.0, 24.0, 0.0, 0.0, 1480.0, 829.0, 4.0],
 [2014.0, 406124.0, 480.0, 60.0, 228.0, 1770.0, 2014.0, 5.0],
 [1812.0, 109372.0, 971.0, 22.0, 75.0, 974.0, 1812.0, 4.0],
 [2168.0, 82334.0, 290.0, 10.0, 15.0, 89.0, 2168.0, 4.0],
 [2008.0, 339060.0, 369.0, 46.0, 9.0, 1902.0, 2008.0, 4.0],
 [2243.0, 602967.0, 1399.0, 331.0, 142.0, 991.0, 2243.0, 4.0],
 [161.0, 27452.0, 37.0, 3.0, 9.0, 655.0, 161.0, 4.0],
 [825.0, 14522.0, 58.0, 2.0, 10.0, 1301.0, 825.0, 4.0],
 [799.0, 489.0, 8.0, 0.0, 0.0, 1775.0, 799.0, 6.0],
 [1323.0, 137051.0, 600.0, 23.0, 13.0, 1864.0, 1323.0, 5.0],
 [1573.0, 10094533.0, 32089.0, 4032.0, 2881.0, 1674.0, 1573.0, 4.0],
 [2227.0, 467812.0, 1498.0, 142.0, 586.0, 1534.0, 2227.0, 4.0],
 [1602.0, 349635.0, 1347.0, 92.0, 90.0, 1480.0, 1602.0, 4.0],
 [1926.0, 7281466.0, 9467.0, 6632.0, 672.0, 2015.0, 1926.0, 5.0],
 [99.0, 116750.0, 1552.0, 19.0, 96.0, 1213.0, 99.0, 3.0],
 [1691.0, 309103.0, 320.0, 98.0, 68.0, 921.0, 1691.0, 4.0],
 [1867.0, 1052835.0, 8048.0, 344.0, 935.0, 1335.0, 1867.0, 4.0],
 [814.0, 5268286.0, 1240.0, 1195.0, 121.0, 1737.0, 814.0, 4.0],
 [1601.0, 18168.0, 161.0, 6.0, 78.0, 1911.0, 1601.0, 4.0],
 [1761.0, 458059.0, 604.0, 188.0, 59.0, 1629.0, 1761.0, 4.0],
 [1616.0, 111373.0, 661.0, 13.0, 119.0, 235.0, 1616.0, 3.0],
 [2184.0, 3533116.0, 8469.0, 1176.0, 509.0, 1860.0, 2184.0, 4.0],
 [1730.0, 71081.0, 74.0, 26.0, 4.0, 941.0, 1730.0, 4.0],
 [1022.0, 44237.0, 173.0, 1.0, 0.0, 1793.0, 1022.0, 4.0],
 [826.0, 137599.0, 67.0, 34.0, 14.0, 1957.0, 826.0, 4.0],
 [2103.0, 658226.0, 465.0, 185.0, 25.0, 1719.0, 2103.0, 4.0],
 [2062.0, 543526.0, 6789.0, 72.0, 273.0, 1824.0, 2062.0, 4.0],
 [2040.0, 1015940.0, 24238.0, 231.0, 2376.0, 1126.0, 2040.0, 2.0],
 [1615.0, 89188.0, 136.0, 19.0, 42.0, 1881.0, 1615.0, 4.0],
 [1355.0, 680483.0, 233.0, 165.0, 15.0, 1532.0, 1355.0, 4.0],
 [19.0, 524.0, 0.0, 0.0, 0.0, 794.0, 19.0, 4.0],
 [778.0, 312221.0, 3144.0, 330.0, 364.0, 1083.0, 778.0, 4.0],
 [2119.0, 161611.0, 722.0, 94.0, 90.0, 1857.0, 2119.0, 4.0],
 [2226.0, 1111703.0, 10542.0, 348.0, 1841.0, 1920.0, 2226.0, 2.0],
 [1823.0, 26736.0, 165.0, 14.0, 218.0, 668.0, 1823.0, 4.0],
 [1834.0, 172693.0, 352.0, 28.0, 247.0, 1876.0, 1834.0, 5.0],
 [2126.0, 157153.0, 582.0, 16.0, 13.0, 386.0, 2126.0, 4.0],
 [944.0, 486015.0, 3684.0, 116.0, 1345.0, 1717.0, 944.0, 5.0],
 [1364.0, 134.0, 2.0, 0.0, 0.0, 1407.0, 1364.0, 4.0],
 [946.0, 378198.0, 1109.0, 97.0, 201.0, 1150.0, 946.0, 4.0],
 [1427.0, 106951.0, 3932.0, 41.0, 455.0, 1828.0, 1427.0, 6.0],
 [1368.0, 412774.0, 609.0, 35.0, 303.0, 11.0, 1368.0, 4.0],
 [1992.0, 97263.0, 135.0, 13.0, 16.0, 446.0, 1992.0, 4.0],
 [1485.0, 4273.0, 11.0, 0.0, 1.0, 902.0, 1485.0, 5.0],
 [1586.0, 382495.0, 1466.0, 81.0, 582.0, 1516.0, 1586.0, 4.0],
 [56.0, 346133.0, 861.0, 110.0, 226.0, 703.0, 56.0, 2.0],
 [1920.0, 4414.0, 52.0, 6.0, 19.0, 758.0, 1920.0, 4.0],
 [1416.0, 66702.0, 326.0, 15.0, 10.0, 1667.0, 1416.0, 3.0],
 [1574.0, 310877.0, 590.0, 109.0, 82.0, 1036.0, 1574.0, 4.0],
 [822.0, 525628.0, 1170.0, 89.0, 45.0, 1892.0, 822.0, 4.0],
 [1712.0, 330925.0, 394.0, 127.0, 53.0, 1328.0, 1712.0, 4.0],
 [728.0, 807039.0, 1363.0, 464.0, 100.0, 2086.0, 728.0, 5.0],
 [1397.0, 240.0, 0.0, 0.0, 0.0, 1994.0, 1397.0, 4.0],
 [815.0, 1974.0, 21.0, 0.0, 12.0, 293.0, 815.0, 4.0],
 [1612.0, 342289.0, 172.0, 35.0, 474.0, 16.0, 1612.0, 4.0],
 [956.0, 161813.0, 1134.0, 51.0, 97.0, 1812.0, 956.0, 3.0],
 [1067.0, 115126.0, 94.0, 35.0, 12.0, 475.0, 1067.0, 5.0],
 [1657.0, 33132.0, 183.0, 5.0, 9.0, 1611.0, 1657.0, 3.0],
 [843.0, 166887.0, 386.0, 82.0, 45.0, 596.0, 843.0, 5.0],
 [2045.0, 98504.0, 173.0, 5.0, 11.0, 1696.0, 2045.0, 4.0],
 [809.0, 312297.0, 1261.0, 225.0, 328.0, 1749.0, 809.0, 2.0],
 [110.0, 543260.0, 2613.0, 494.0, 2649.0, 1643.0, 110.0, 4.0],
 [2016.0, 2216150.0, 1871.0, 444.0, 117.0, 582.0, 2016.0, 4.0],
 [1581.0, 1230858.0, 12133.0, 445.0, 2509.0, 1347.0, 1581.0, 2.0],
 [1356.0, 728620.0, 1953.0, 261.0, 159.0, 1786.0, 1356.0, 4.0],
 [203.0, 1073069.0, 2534.0, 137.0, 792.0, 1890.0, 203.0, 5.0],
 [2093.0, 451826.0, 924.0, 95.0, 46.0, 722.0, 2093.0, 6.0],
 [262.0, 126452.0, 2344.0, 25.0, 366.0, 1818.0, 262.0, 2.0],
 [1847.0, 523402.0, 594.0, 114.0, 57.0, 107.0, 1847.0, 4.0],
 [2203.0, 2710540.0, 14225.0, 288.0, 1220.0, 1602.0, 2203.0, 4.0],
 [1614.0, 1029.0, 0.0, 2.0, 0.0, 2066.0, 1614.0, 4.0],
 [826.0, 248122.0, 383.0, 26.0, 10.0, 1833.0, 826.0, 4.0],
 [1988.0, 152002.0, 113.0, 54.0, 14.0, 1169.0, 1988.0, 4.0],
 [1580.0, 68734.0, 517.0, 24.0, 26.0, 1301.0, 1580.0, 6.0],
 [2038.0, 19525.0, 79.0, 3.0, 9.0, 915.0, 2038.0, 4.0],
 [1373.0, 251826.0, 1177.0, 15.0, 47.0, 1581.0, 1373.0, 4.0],
 [2145.0, 17167.0, 83.0, 5.0, 1.0, 1326.0, 2145.0, 4.0],
 [2114.0, 116093.0, 66.0, 34.0, 8.0, 1019.0, 2114.0, 4.0],
 [349.0, 39592.0, 533.0, 26.0, 131.0, 1161.0, 349.0, 5.0],
 [120.0, 19397.0, 114.0, 12.0, 18.0, 1834.0, 120.0, 4.0],
 [835.0, 162120.0, 236.0, 7.0, 32.0, 76.0, 835.0, 4.0],
 [1476.0, 1276229.0, 2355.0, 1336.0, 153.0, 2013.0, 1476.0, 5.0],
 [80.0, 255659.0, 1644.0, 262.0, 481.0, 1837.0, 80.0, 4.0],
 [783.0, 677814.0, 4548.0, 174.0, 1405.0, 1232.0, 783.0, 4.0],
 [2231.0, 6397.0, 29.0, 2.0, 4.0, 889.0, 2231.0, 4.0],
 [2190.0, 346663.0, 9865.0, 158.0, 6245.0, 1810.0, 2190.0, 4.0],
 [155.0, 15226.0, 101.0, 5.0, 4.0, 1189.0, 155.0, 3.0],
 [1190.0, 720316.0, 720.0, 205.0, 55.0, 1854.0, 1190.0, 4.0],
 [844.0, 2377.0, 34.0, 0.0, 9.0, 1998.0, 844.0, 6.0],
 [2171.0, 1528745.0, 11958.0, 456.0, 1139.0, 1966.0, 2171.0, 6.0],
 [1808.0, 381424.0, 4272.0, 117.0, 561.0, 1884.0, 1808.0, 2.0],
 [1977.0, 2710990.0, 10136.0, 705.0, 543.0, 1102.0, 1977.0, 2.0],
 [169.0, 178.0, 6.0, 0.0, 6.0, 1493.0, 169.0, 4.0],
 [2196.0, 65063.0, 276.0, 14.0, 90.0, 1847.0, 2196.0, 4.0],
 [1322.0, 61578.0, 99.0, 13.0, 2.0, 532.0, 1322.0, 6.0],
 [68.0, 1650927.0, 19212.0, 3718.0, 5218.0, 1857.0, 68.0, 4.0],
 [334.0, 1973285.0, 1372.0, 249.0, 35.0, 1247.0, 334.0, 4.0],
 [1408.0, 195699.0, 54.0, 21.0, 0.0, 1663.0, 1408.0, 2.0],
 [1585.0, 108434.0, 224.0, 10.0, 6.0, 365.0, 1585.0, 4.0],
 [75.0, 522290.0, 3172.0, 131.0, 838.0, 1464.0, 75.0, 4.0],
 [1523.0, 265724.0, 1847.0, 119.0, 153.0, 1594.0, 1523.0, 4.0],
 [2045.0, 91813.0, 182.0, 23.0, 38.0, 1625.0, 2045.0, 4.0],
 [2091.0, 753318.0, 607.0, 99.0, 34.0, 412.0, 2091.0, 4.0],
 [923.0, 3006.0, 26.0, 1.0, 9.0, 438.0, 923.0, 4.0],
 [2016.0, 101574.0, 4848.0, 28.0, 459.0, 1395.0, 2016.0, 2.0],
 [1408.0, 305.0, 2.0, 0.0, 0.0, 1822.0, 1408.0, 4.0],
 [900.0, 3171.0, 15.0, 2.0, 1.0, 1860.0, 900.0, 5.0],
 [1570.0, 69347.0, 701.0, 35.0, 326.0, 1459.0, 1570.0, 4.0],
 [1977.0, 14548.0, 158.0, 5.0, 11.0, 1576.0, 1977.0, 6.0],
 [1420.0, 81230.0, 2890.0, 42.0, 222.0, 1831.0, 1420.0, 1.0],
 [69.0, 6984.0, 30.0, 0.0, 2.0, 1946.0, 69.0, 4.0],
 [1575.0, 2373103.0, 2784.0, 201.0, 1452.0, 7.0, 1575.0, 4.0],
 [356.0, 4012.0, 5.0, 0.0, 0.0, 1037.0, 356.0, 4.0],
 [2201.0, 30157.0, 25.0, 2.0, 1.0, 134.0, 2201.0, 4.0],
 [945.0, 28829.0, 236.0, 6.0, 48.0, 1812.0, 945.0, 4.0],
 [1568.0, 6466780.0, 1961.0, 1599.0, 145.0, 555.0, 1568.0, 4.0],
 [2073.0, 59724.0, 1148.0, 34.0, 116.0, 2080.0, 2073.0, 6.0],
 [751.0, 437256.0, 156.0, 83.0, 4.0, 1181.0, 751.0, 5.0],
 [317.0, 102432.0, 83.0, 31.0, 8.0, 1920.0, 317.0, 4.0],
 [1334.0, 467860.0, 658.0, 173.0, 134.0, 1513.0, 1334.0, 5.0],
 [2007.0, 17787.0, 140.0, 5.0, 2.0, 1279.0, 2007.0, 4.0],
 [530.0, 24371.0, 154.0, 11.0, 5.0, 1896.0, 530.0, 6.0],
 [1010.0, 85197.0, 450.0, 69.0, 87.0, 1033.0, 1010.0, 4.0],
 [757.0, 174388.0, 308.0, 34.0, 6.0, 843.0, 757.0, 4.0],
 [2022.0, 161609.0, 335.0, 37.0, 140.0, 1899.0, 2022.0, 5.0],
 ...]
In [88]:
test_data = pd.DataFrame(test_data_temp, columns=test_data.columns)
In [89]:
test_data.columns
Out[89]:
Index(['vidid', 'views', 'likes', 'dislikes', 'comment', 'published',
       'duration', 'category'],
      dtype='object')
In [90]:
test_cat = pd.DataFrame(data=test_data.iloc[:,1].values, columns=['target'])
test_data = test_data.drop(["vidid"], axis=1)
In [91]:
# X_train, X_test, y_train, y_test = train_test_split(data, Y_train, test_size=0.2, random_state=42)
In [93]:
lr_path = '/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/linear_regression_youtubeadview.pkl'
loaded_model = joblib.load(lr_path)
adview_result = loaded_model.predict(test_data)
result = loaded_model.score(test_data, adview_result)
print(result)

1.0

/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but LinearRegression was fitted without feature names
  f"X has feature names, but {self.__class__.__name__} was fitted without"
/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but LinearRegression was fitted without feature names
  f"X has feature names, but {self.__class__.__name__} was fitted without"
In [100]:
result
Out[100]:
1.0
In [101]:
result_pd = pd.DataFrame(adview_result, columns=['predictions'])
In [103]:
pd.DataFrame(result_pd).to_csv("/content/drive/MyDrive/NEO 5 Internship/Project/Saved Results/lr_predictions.csv", index = None)
In [104]:
dt_path = '/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/decisiontree_youtubeadview.pkl'
loaded_model = joblib.load(dt_path)
adview_result = loaded_model.predict(test_data)
result = loaded_model.score(test_data, adview_result)
pd.DataFrame(pd.DataFrame(adview_result, columns=['predictions'])).to_csv("dt_predictions.csv", index = None)
print(result)

1.0

/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names
  f"X has feature names, but {self.__class__.__name__} was fitted without"
/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names
  f"X has feature names, but {self.__class__.__name__} was fitted without"
In [106]:
rf_path = '/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/random_forest_youtubeadview.pkl'
loaded_model = joblib.load(rf_path)
adview_result = loaded_model.predict(test_data)
result = loaded_model.score(test_data, adview_result)
pd.DataFrame(pd.DataFrame(adview_result, columns=['predictions'])).to_csv("rf_predictions.csv", index = None)
print(result)

/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names
  f"X has feature names, but {self.__class__.__name__} was fitted without"
/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names
  f"X has feature names, but {self.__class__.__name__} was fitted without"

1.0
In [107]:
svm_path = '/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/supportvector_regressor_youtubeadview.pkl'
loaded_model = joblib.load(svm_path)
adview_result = loaded_model.predict(test_data)
result = loaded_model.score(test_data, adview_result)
pd.DataFrame(pd.DataFrame(adview_result, columns=['predictions'])).to_csv("svm_predictions.csv", index = None)
print(result)

/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but SVR was fitted without feature names
  f"X has feature names, but {self.__class__.__name__} was fitted without"
/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but SVR was fitted without feature names
  f"X has feature names, but {self.__class__.__name__} was fitted without"

1.0
In [108]:
ann_path = '/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/ann_youtubeadview.h5'
loaded_model = keras.models.load_model(ann_path)
adview_result = loaded_model.predict(test_data)
result = loaded_model.evaluate(test_data, adview_result)
pd.DataFrame(pd.DataFrame(adview_result, columns=['predictions'])).to_csv("/content/drive/MyDrive/NEO 5 Internship/Project/Saved Models/ann_predictions.csv", index = None)
print(result)

215/215 [==============================] - 0s 1ms/step - loss: 2104.5889 - mean_squared_error: 2104.5889
[2104.5888671875, 2104.5888671875]