diff --git a/SWEETVIZ_REPORT (2).html b/SWEETVIZ_REPORT (2).html new file mode 100644 index 0000000..549e847 --- /dev/null +++ b/SWEETVIZ_REPORT (2).html @@ -0,0 +1,4302 @@ + + + + + + + + + + + + + + + +
+ + +
+ + + + + +
+
+ +
Tweets data
+
NO COMPARISON TARGET
+
+
+
7613
+
ROWS
+
+
+
+
0
+
DUPLICATES
+
+
+
+
2.3 MB
+
RAM
+
+
+
+
+ 5 +
+
FEATURES
+
+
+
+
+
1
+
CATEGORICAL
+
+
+
+
1
+
NUMERICAL
+
+
+
+
3
+
TEXT
+
+
+ +
+
+
+ + + +
+ +
+ 2.3.1
+ Get updates, docs & report issues here
+ Created & maintained by Francois Bertrand
+ Graphic design by Jean-Francois Hains +
+
+
+ + + +
+
+
+
+
+ + +
+ 1 +
+
+ id + +
+
+
VALUES:
+
+ 7,613 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 7,613 +
+
+ (100%) +
+
+
+
+
+
ZEROES:
+
+ --- +
+
+ +
+
+
+ +
+
+
MAX
+
10,873
+
+
+
95%
+
10,356
+
+
+
Q3
+
8,146
+
+
+
AVG
+
5,442
+
+
+
MEDIAN
+
5,408
+
+
+
Q1
+
2,734
+
+
+
5%
+
548
+
+
+
MIN
+
1
+
+
+
+
+
RANGE
+
10,872
+
+
+
IQR
+
5,412
+
+
+
STD
+
3,137
+
+
+
VAR
+
9.8M
+
+
+
+
+
KURT.
+
-1.19
+
+
+
SKEW
+
0.008
+
+
+
SUM
+
41.4M
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 2 +
+
+ keyword +
+
+
VALUES:
+
+ 7,552 +
+
+ (>99%) +
+
+
+
MISSING:
+
+ 61 +
+
+
+ (<1%) +
+
+
+
+
+
DISTINCT:
+
+ 221 +
+
+ (3%) +
+
+
+ +
+
+
+
45
+
<1%
+
+
+
-
+
-
+
+
+ fatalities +
+
+
+
+
42
+
<1%
+
+
+
-
+
-
+
+
+ deluge +
+
+
+
+
42
+
<1%
+
+
+
-
+
-
+
+
+ armageddon +
+
+
+
+
41
+
<1%
+
+
+
-
+
-
+
+
+ sinking +
+
+
+
+
41
+
<1%
+
+
+
-
+
-
+
+
+ damage +
+
+
+
+
41
+
<1%
+
+
+
-
+
-
+
+
+ harm +
+
+
+
+
41
+
<1%
+
+
+
-
+
-
+
+
+ body%20bags +
+
+
+
+
7,259
+
96%
+
+
+
-
+
-
+
+
+ (Other) +
+
+
+
+ +
+
+
+
+
+
+ + +
+ 3 +
+
+ location +
+
+
VALUES:
+
+ 5,080 +
+
+ (67%) +
+
+
+
MISSING:
+
+ 2,533 +
+
+
+ (33%) +
+
+
+
+
+
DISTINCT:
+
+ 3,341 +
+
+ (44%) +
+
+
+ +
+
+
+
104
+
2%
+
+
+
-
+
-
+
+
+ USA +
+
+
+
+
71
+
1%
+
+
+
-
+
-
+
+
+ New York +
+
+
+
+
50
+
<1%
+
+
+
-
+
-
+
+
+ United States +
+
+
+
+
45
+
<1%
+
+
+
-
+
-
+
+
+ London +
+
+
+
+
29
+
<1%
+
+
+
-
+
-
+
+
+ Canada +
+
+
+
+
28
+
<1%
+
+
+
-
+
-
+
+
+ Nigeria +
+
+
+
+
27
+
<1%
+
+
+
-
+
-
+
+
+ UK +
+
+
+
+
4,726
+
93%
+
+
+
-
+
-
+
+
+ (Other) +
+
+
+
+ +
+
+
+
+
+
+ + +
+ 4 +
+
+ text +
+
+
VALUES:
+
+ 7,613 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 7,503 +
+
+ (99%) +
+
+
+ +
+
+
+
10
+
<1%
+
+
+
-
+
-
+
+
+ 11-Year-Old Boy Charged With Manslaughter of Toddler: Report: An 11-year-old boy has been charged with manslaughter over the fatal sh... +
+
+
+
+
6
+
<1%
+
+
+
-
+
-
+
+
+ He came to a land which was engulfed in tribal war and turned it into a land of peace i.e. Madinah. #ProphetMuhammad #islam +
+
+
+
+
6
+
<1%
+
+
+
-
+
-
+
+
+ The Prophet (peace be upon him) said 'Save yourself from Hellfire even if it is by giving half a date in charity.' +
+
+
+
+
6
+
<1%
+
+
+
-
+
-
+
+
+ #Bestnaijamade: 16yr old PKK suicide bomber who detonated bomb in ... http://t.co/KSAwlYuX02 bestnaijamade bestnaijamade bestnaijamade be‰Û_ +
+
+
+
+
5
+
<1%
+
+
+
-
+
-
+
+
+ Madhya Pradesh Train Derailment: Village Youth Saved Many Lives +
+
+
+
+
4
+
<1%
+
+
+
-
+
-
+
+
+ that horrible sinking feeling when you‰Ûªve been at home on your phone for a while and you realise its been on 3G this whole time +
+
+
+
+
4
+
<1%
+
+
+
-
+
-
+
+
+ To fight bioterrorism sir. +
+
+
+
+
7,572
+
>99%
+
+
+
-
+
-
+
+
+ (Other) +
+
+
+
+ +
+
+
+
+
+
+ + +
+ 5 +
+
+ target +
+
+
VALUES:
+
+ 7,613 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 2 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+ + +
+
+
+ + Associations +
+ [Only including dataset "Tweets data"]
+ ■ Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical, + (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP). +

• Circles are the symmetrical numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity. +
+ +
+
+ +
+
+ + Associations +
+ [Only including dataset "None"]
+ ■ Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical, + (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP). +

• Circles are the symmetrical numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity. +
+ +
+
+ + + +
+
+ +
+
+ id +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
target
+
0.06
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
1
+
+
1
+
<0.1%
+
+
+
+
7229
+
+
1
+
<0.1%
+
+
+
+
7250
+
+
1
+
<0.1%
+
+
+
+
7248
+
+
1
+
<0.1%
+
+
+
+
7247
+
+
1
+
<0.1%
+
+
+
+
7244
+
+
1
+
<0.1%
+
+
+
+
7242
+
+
1
+
<0.1%
+
+
+
+
7241
+
+
1
+
<0.1%
+
+
+
+
7236
+
+
1
+
<0.1%
+
+
+
+
7235
+
+
1
+
<0.1%
+
+
+
+
7234
+
+
1
+
<0.1%
+
+
+
+
7232
+
+
1
+
<0.1%
+
+
+
+
7231
+
+
1
+
<0.1%
+
+
+
+
7230
+
+
1
+
<0.1%
+
+
+
+
7228
+
+
1
+
<0.1%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
1
+
+
1
+
<0.1%
+
+
+
+
4
+
+
1
+
<0.1%
+
+
+
+
5
+
+
1
+
<0.1%
+
+
+
+
6
+
+
1
+
<0.1%
+
+
+
+
7
+
+
1
+
<0.1%
+
+
+
+
8
+
+
1
+
<0.1%
+
+
+
+
10
+
+
1
+
<0.1%
+
+
+
+
13
+
+
1
+
<0.1%
+
+
+
+
14
+
+
1
+
<0.1%
+
+
+
+
15
+
+
1
+
<0.1%
+
+
+
+
16
+
+
1
+
<0.1%
+
+
+
+
17
+
+
1
+
<0.1%
+
+
+
+
18
+
+
1
+
<0.1%
+
+
+
+
19
+
+
1
+
<0.1%
+
+
+
+
20
+
+
1
+
<0.1%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
10873
+
+
1
+
<0.1%
+
+
+
+
10872
+
+
1
+
<0.1%
+
+
+
+
10871
+
+
1
+
<0.1%
+
+
+
+
10870
+
+
1
+
<0.1%
+
+
+
+
10869
+
+
1
+
<0.1%
+
+
+
+
10867
+
+
1
+
<0.1%
+
+
+
+
10866
+
+
1
+
<0.1%
+
+
+
+
10864
+
+
1
+
<0.1%
+
+
+
+
10863
+
+
1
+
<0.1%
+
+
+
+
10862
+
+
1
+
<0.1%
+
+
+
+
10860
+
+
1
+
<0.1%
+
+
+
+
10859
+
+
1
+
<0.1%
+
+
+
+
10855
+
+
1
+
<0.1%
+
+
+
+
10854
+
+
1
+
<0.1%
+
+
+
+
10853
+
+
1
+
<0.1%
+
+
+
+
+ +
+
+
+
+ +
+
+ keyword +
+
+
+
MISSING:
+
+ 61 +
+
+
+ (<1%) +
+
+
+ +
+
+
+
45
+
<1%
+
+
+
-
+
-
+
+
+ fatalities +
+
+
+
+
42
+
<1%
+
+
+
-
+
-
+
+
+ deluge +
+
+
+
+
42
+
<1%
+
+
+
-
+
-
+
+
+ armageddon +
+
+
+
+
41
+
<1%
+
+
+
-
+
-
+
+
+ sinking +
+
+
+
+
41
+
<1%
+
+
+
-
+
-
+
+
+ damage +
+
+
+
+
41
+
<1%
+
+
+
-
+
-
+
+
+ harm +
+
+
+
+
41
+
<1%
+
+
+
-
+
-
+
+
+ body%20bags +
+
+
+
+
40
+
<1%
+
+
+
-
+
-
+
+
+ evacuate +
+
+
+
+
40
+
<1%
+
+
+
-
+
-
+
+
+ fear +
+
+
+
+
40
+
<1%
+
+
+
-
+
-
+
+
+ outbreak +
+
+
+
+
40
+
<1%
+
+
+
-
+
-
+
+
+ siren +
+
+
+
+
40
+
<1%
+
+
+
-
+
-
+
+
+ twister +
+
+
+
+
40
+
<1%
+
+
+
-
+
-
+
+
+ windstorm +
+
+
+
+
40
+
<1%
+
+
+
-
+
-
+
+
+ collided +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ sinkhole +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ sunk +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ hellfire +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ weapon +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ weapons +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ famine +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ explosion +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ whirlwind +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ earthquake +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ derailment +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ wreckage +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ collision +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ flames +
+
+
+
+
39
+
<1%
+
+
+
-
+
-
+
+
+ wrecked +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ ambulance +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ injury +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ hurricane +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ flooding +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ fire +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ fatal +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ blaze +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ explode +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ drowned +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ thunder +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ derailed +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ typhoon +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ deaths +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ upheaval +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ oil%20spill +
+
+
+
+
38
+
<1%
+
+
+
-
+
-
+
+
+ bombed +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ emergency +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ pandemonium +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ hostages +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ wounded +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ debris +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ ruin +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ burning%20buildings +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ sandstorm +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ wreck +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ blizzard +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ curfew +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ crush +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ fatality +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ bioterror +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ mudslide +
+
+
+
+
37
+
<1%
+
+
+
-
+
-
+
+
+ quarantined +
+
+
+
+
5,233
+
69%
+
+
+
-
+
-
+
+
+ (Other) +
+
+
+
+
+ +
+
+ +
+
+ location +
+
+
+
MISSING:
+
+ 2,533 +
+
+
+ (33%) +
+
+
+ +
+
+
+
104
+
2%
+
+
+
-
+
-
+
+
+ USA +
+
+
+
+
71
+
1%
+
+
+
-
+
-
+
+
+ New York +
+
+
+
+
50
+
<1%
+
+
+
-
+
-
+
+
+ United States +
+
+
+
+
45
+
<1%
+
+
+
-
+
-
+
+
+ London +
+
+
+
+
29
+
<1%
+
+
+
-
+
-
+
+
+ Canada +
+
+
+
+
28
+
<1%
+
+
+
-
+
-
+
+
+ Nigeria +
+
+
+
+
27
+
<1%
+
+
+
-
+
-
+
+
+ UK +
+
+
+
+
26
+
<1%
+
+
+
-
+
-
+
+
+ Los Angeles, CA +
+
+
+
+
24
+
<1%
+
+
+
-
+
-
+
+
+ India +
+
+
+
+
22
+
<1%
+
+
+
-
+
-
+
+
+ Mumbai +
+
+
+
+
21
+
<1%
+
+
+
-
+
-
+
+
+ Washington, DC +
+
+
+
+
20
+
<1%
+
+
+
-
+
-
+
+
+ Kenya +
+
+
+
+
19
+
<1%
+
+
+
-
+
-
+
+
+ Worldwide +
+
+
+
+
18
+
<1%
+
+
+
-
+
-
+
+
+ Chicago, IL +
+
+
+
+
18
+
<1%
+
+
+
-
+
-
+
+
+ Australia +
+
+
+
+
17
+
<1%
+
+
+
-
+
-
+
+
+ California +
+
+
+
+
15
+
<1%
+
+
+
-
+
-
+
+
+ New York, NY +
+
+
+
+
15
+
<1%
+
+
+
-
+
-
+
+
+ Everywhere +
+
+
+
+
15
+
<1%
+
+
+
-
+
-
+
+
+ California, USA +
+
+
+
+
14
+
<1%
+
+
+
-
+
-
+
+
+ San Francisco +
+
+
+
+
14
+
<1%
+
+
+
-
+
-
+
+
+ United Kingdom +
+
+
+
+
14
+
<1%
+
+
+
-
+
-
+
+
+ Florida +
+
+
+
+
13
+
<1%
+
+
+
-
+
-
+
+
+ Indonesia +
+
+
+
+
13
+
<1%
+
+
+
-
+
-
+
+
+ Washington, D.C. +
+
+
+
+
13
+
<1%
+
+
+
-
+
-
+
+
+ Los Angeles +
+
+
+
+
12
+
<1%
+
+
+
-
+
-
+
+
+ NYC +
+
+
+
+
12
+
<1%
+
+
+
-
+
-
+
+
+ Ireland +
+
+
+
+
12
+
<1%
+
+
+
-
+
-
+
+
+ Toronto +
+
+
+
+
11
+
<1%
+
+
+
-
+
-
+
+
+ Earth +
+
+
+
+
11
+
<1%
+
+
+
-
+
-
+
+
+ Seattle +
+
+
+
+
11
+
<1%
+
+
+
-
+
-
+
+
+ Chicago +
+
+
+
+
11
+
<1%
+
+
+
-
+
-
+
+
+ San Francisco, CA +
+
+
+
+
10
+
<1%
+
+
+
-
+
-
+
+
+ London, England +
+
+
+
+
10
+
<1%
+
+
+
-
+
-
+
+
+ Atlanta, GA +
+
+
+
+
10
+
<1%
+
+
+
-
+
-
+
+
+ ss +
+
+
+
+
10
+
<1%
+
+
+
-
+
-
+
+
+ Sacramento, CA +
+
+
+
+
10
+
<1%
+
+
+
-
+
-
+
+
+ New York City +
+
+
+
+
10
+
<1%
+
+
+
-
+
-
+
+
+ Texas +
+
+
+
+
10
+
<1%
+
+
+
-
+
-
+
+
+ London, UK +
+
+
+
+
9
+
<1%
+
+
+
-
+
-
+
+
+ San Diego, CA +
+
+
+
+
9
+
<1%
+
+
+
-
+
-
+
+
+ Dallas, TX +
+
+
+
+
9
+
<1%
+
+
+
-
+
-
+
+
+ 304 +
+
+
+
+
9
+
<1%
+
+
+
-
+
-
+
+
+ Nashville, TN +
+
+
+
+
9
+
<1%
+
+
+
-
+
-
+
+
+ Denver, Colorado +
+
+
+
+
9
+
<1%
+
+
+
-
+
-
+
+
+ World +
+
+
+
+
9
+
<1%
+
+
+
-
+
-
+
+
+ US +
+
+
+
+
9
+
<1%
+
+
+
-
+
-
+
+
+ Manchester +
+
+
+
+
8
+
<1%
+
+
+
-
+
-
+
+
+ Scotland +
+
+
+
+
8
+
<1%
+
+
+
-
+
-
+
+
+ Houston, TX +
+
+
+
+
8
+
<1%
+
+
+
-
+
-
+
+
+ South Africa +
+
+
+
+
7
+
<1%
+
+
+
-
+
-
+
+
+ Sydney +
+
+
+
+
7
+
<1%
+
+
+
-
+
-
+
+
+ Denver, CO +
+
+
+
+
7
+
<1%
+
+
+
-
+
-
+
+
+ Memphis, TN +
+
+
+
+
7
+
<1%
+
+
+
-
+
-
+
+
+ Austin, TX +
+
+
+
+
7
+
<1%
+
+
+
-
+
-
+
+
+ Pennsylvania, USA +
+
+
+
+
7
+
<1%
+
+
+
-
+
-
+
+
+ Tennessee +
+
+
+
+
7
+
<1%
+
+
+
-
+
-
+
+
+ worldwide +
+
+
+
+
7
+
<1%
+
+
+
-
+
-
+
+
+ Seattle, WA +
+
+
+
+
6
+
<1%
+
+
+
-
+
-
+
+
+ Morioh, Japan +
+
+
+
+
6
+
<1%
+
+
+
-
+
-
+
+
+ Pedophile hunting ground +
+
+
+
+
4,101
+
81%
+
+
+
-
+
-
+
+
+ (Other) +
+
+
+
+
+ +
+
+ +
+
+ text +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ +
+
+
+
10
+
<1%
+
+
+
-
+
-
+
+
+ 11-Year-Old Boy Charged With Manslaughter of Toddler: Report: An 11-year-old boy has been charged with manslaughter over the fatal sh... +
+
+
+
+
6
+
<1%
+
+
+
-
+
-
+
+
+ He came to a land which was engulfed in tribal war and turned it into a land of peace i.e. Madinah. #ProphetMuhammad #islam +
+
+
+
+
6
+
<1%
+
+
+
-
+
-
+
+
+ The Prophet (peace be upon him) said 'Save yourself from Hellfire even if it is by giving half a date in charity.' +
+
+
+
+
6
+
<1%
+
+
+
-
+
-
+
+
+ #Bestnaijamade: 16yr old PKK suicide bomber who detonated bomb in ... http://t.co/KSAwlYuX02 bestnaijamade bestnaijamade bestnaijamade be‰Û_ +
+
+
+
+
5
+
<1%
+
+
+
-
+
-
+
+
+ Madhya Pradesh Train Derailment: Village Youth Saved Many Lives +
+
+
+
+
4
+
<1%
+
+
+
-
+
-
+
+
+ that horrible sinking feeling when you‰Ûªve been at home on your phone for a while and you realise its been on 3G this whole time +
+
+
+
+
4
+
<1%
+
+
+
-
+
-
+
+
+ To fight bioterrorism sir. +
+
+
+
+
4
+
<1%
+
+
+
-
+
-
+
+
+ VIDEO: 'We're picking up bodies from water': Rescuers are searching for hundreds of migrants in the Mediterranean after a boat carryi... +
+
+
+
+
4
+
<1%
+
+
+
-
+
-
+
+
+ .POTUS #StrategicPatience is a strategy for #Genocide; refugees; IDP Internally displaced people; horror; etc. https://t.co/rqWuoy1fm4 +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ Who is bringing the tornadoes and floods. Who is bringing the climate change. God is after America He is plaguing her + +#FARRAKHAN #QUOTE +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ Beware of your temper and a loose tongue! These two dangerous weapons combined can lead a person to the Hellfire #islam! +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ Hellfire is surrounded by desires so be careful and don‰Ûªt let your desires control you! #Afterlife +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ Madhya Pradesh Train Derailment: Village Youth Saved Many Lives: A group of villagers saved over 70 passengers' lives after two train... +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ Wreckage 'Conclusively Confirmed' as From MH370: Malaysia PM +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ #Allah describes piling up #wealth thinking it would last #forever as the description of the people of #Hellfire in Surah Humaza. #Reflect +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ @TinyJecht Are you another Stand-user? If you are I will have to detonate you with my Killer Queen. +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ CLEARED:incident with injury:I-495 inner loop Exit 31 - MD 97/Georgia Ave Silver Spring +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ #foodscare #offers2go #NestleIndia slips into loss after #Magginoodle #ban unsafe and hazardous for #humanconsumption +
+
+
+
+
3
+
<1%
+
+
+
-
+
-
+
+
+ @spinningbot Are you another Stand-user? If you are I will have to detonate you with my Killer Queen. +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ @aria_ahrary @TheTawniest The out of control wild fires in California even in the Northern part of the state. Very troubling. +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Caution: breathing may be hazardous to your health. +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ @_minimehh @cjoyner I must be overlooking the burning buildings? #BlackLivesMatter +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ @batfanuk we enjoyed the show today. Great fun. The emergency non evacuation was interesting. Have a great run. +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ USGS reports a M1.94 #earthquake 5km S of Volcano Hawaii on 8/6/15 @ 1:04:01 UTC http://t.co/Njd28pg9Xv #quake +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ @themagickidraps not upset with a rally upset with burning buildings businesses executing cops that have nothing to do with it etc +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Top insurer blasts lack of Australian Govt action on disaster mitigation http://t.co/sDgOUtWNtb via @smh +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ (#LosDelSonido) Obama Declares Disaster for Typhoon-Devastated Saipan: Obama signs disaster declaration for Northern Ma... (#IvanBerroa) +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Japan on Thursday marks the 70th anniversary of the atomic bombing of Hiroshima with the most senior official from Washington ever schedule‰Û_ +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ UD: Rescue (Structural Collapse) - Scott Road @ Ypres Road York (14 Trucks) +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ 320 [IR] ICEMOON [AFTERSHOCK] | http://t.co/THyzOMVWU0 | @djicemoon | #Dubstep #TrapMusic #DnB #EDM #Dance #Ices‰Û_ http://t.co/83jOO0xk29 +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ incident with injury:I-495 inner loop Exit 31 - MD 97/Georgia Ave Silver Spring +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ RT NotExplained: The only known image of infamous hijacker D.B. Cooper. http://t.co/JlzK2HdeTG +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ General News ‰Û¢åÊ'Demolition of houses on waterways begins at Achimota Mile 7 ' via @233liveOnline. Full story at http://t.co/iO7kUUg1uq +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Families to sue over Legionnaires: More than 40 families affected by the fatal outbreak of Legionnaires' disease in Edinburgh are to ... +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ wowo--=== 12000 Nigerian refugees repatriated from Cameroon +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Police investigating after an e-bike collided with a car in Little Portugal. E-bike rider suffered serious non-life threatening injuries. +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ #Newswatch: 2 vehicles collided at Lock and Lansdowne Sts in #Ptbo. Emerg crews on their way +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ 'Suicide bombing at [location named]...' #premonitions http://t.co/iIkSsJGBDn +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ @fewmoretweets all lives matter. Just not a fan of burning down buildings and stealing from your neighbors to 'protest' +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Evacuation order lifted for town of Roosevelt: http://t.co/EDyfo6E2PU http://t.co/M5KxLPKFA1 +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Warne shocked over Australia's epic collapse at Trent Bridge: Johannesburg Aug 06 (ANI): Legendary Australian... http://t.co/LwwoJXtTIV +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ abcnews - Obama Declares Disaster for Typhoon-Devastated Saipan: Obama signs disaster declaration for Northern... http://t.co/mg5eAJElul +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Traffic Collision - No Injury: I5 S at I5 S 43rd Ave offramp South Sac http://t.co/cT9ejXoLpu +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Mmmmmm I'm burning.... I'm burning buildings I'm building.... Oooooohhhh oooh ooh... +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Hellfire! We don‰Ûªt even want to think about it or mention it so let‰Ûªs not do anything that leads to it! +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ In #islam saving a person is equal in reward to saving all humans! Islam is the opposite of terrorism! +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Burning buildings? Media outrage? http://t.co/pHixZnv1YN +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ like for the music video I want some real action shit like burning buildings and police chases not some weak ben winston shit +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Swansea ‰Û÷plot hijack transfer move for Southampton target Virgil van Dijk‰Ûª http://t.co/PVmr38LnvA +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ New evacuation ordered for 25 homes in danger of Hwy. 8 fire near Roosevelt Wash. http://t.co/SQsyUeh4yI #KOIN6News http://t.co/199t7ND0pm +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ RT AbbsWinston: #Zionist #Terrorist kidnapped 15 #Palestinians in overnight terror on Palestinian Villages ‰Û_ http://t.co/J5mKcbKcov +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ #stormchase Violent Record Breaking EF-5 El Reno Oklahoma Tornado Nearly Runs Over ... - http://t.co/3SICroAaNz http://t.co/I27Oa0HISp +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ I Pledge Allegiance To The P.O.P.E. And The Burning Buildings of Epic City. ?????? +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Elem Pomo helping the displaced from the Rocky Fire. Please consider! +Elem Evacuation Center http://t.co/dYDFvz7amj via @gofundme +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ Schools in Western Uganda still Burning down Buildings during Strikes....Strikes in Western Uganda always Lit literally.. +
+
+
+
+
2
+
<1%
+
+
+
-
+
-
+
+
+ that exploded &amp; brought about the +beginning of universe matches what's +mentioned in the versethe heaven and Earth +(thus the universe) +
+
+
+
+
2
+
<1%
+
+
+
-
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+ Check these out: http://t.co/rOI2NSmEJJ http://t.co/3Tj8ZjiN21 http://t.co/YDUiXEfIpE http://t.co/LxTjc87KLS #nsfw +
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2
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<1%
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+ Hellfire! We don‰Ûªt even want to think about it or mention it so let‰Ûªs not do anything that leads to it #islam! +
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<1%
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+ #KCA #VoteJKT48ID 12News: UPDATE: A family of 3 has been displaced after fired damaged housed near 90th and Osborn. Fire extinguished no i‰Û_ +
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+ Experts in France begin examining airplane debris found on Reunion Island: French air accident experts on Wednesday began examining t... +
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98%
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-
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+ (Other) +
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+ target +
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MISSING:
+
+ --- +
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TOP CATEGORIES
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57%
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43%
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ALL
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100%
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+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
target
+ PROVIDES INFORMATION ON...
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+
+
THESE FEATURES
GIVE INFORMATION
+ ON target:
+
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+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
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target
+ CORRELATION RATIO WITH...
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"base_uri": "https://localhost:8080/" + }, + "id": "uuNq22kvzi69", + "executionInfo": { + "status": "ok", + "timestamp": 1702749964141, + "user_tz": 360, + "elapsed": 10233, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "c7fcdc0c-0e19-42be-96e2-5a33ef9eba42" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting sweetviz\n", + " Downloading sweetviz-2.3.1-py3-none-any.whl (15.1 MB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m15.1/15.1 MB\u001B[0m \u001B[31m49.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (1.5.3)\n", + "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (1.23.5)\n", + "Requirement already satisfied: matplotlib>=3.1.3 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (3.7.1)\n", + "Requirement already satisfied: tqdm>=4.43.0 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (4.66.1)\n", + "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (1.11.4)\n", + "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (3.1.2)\n", + "Requirement already satisfied: importlib-resources>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (6.1.1)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.11.1->sweetviz) (2.1.3)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (1.2.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (4.46.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (23.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (9.4.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2023.3.post1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.1.3->sweetviz) (1.16.0)\n", + "Installing collected packages: sweetviz\n", + "Successfully installed sweetviz-2.3.1\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import sweetviz as sv\n", + "import pandas as pd\n", + "\n", + "# Load your dataset (replace 'your_dataset.csv' with your file path)\n", + "data = pd.read_csv('/content/train.csv')\n", + "\n", + "# Analyze the dataset and generate a report\n", + "report = sv.analyze([data, 'Tweets data'])\n", + "\n", + "# Show the report in the default web browser\n", + "report.show_html()\n", + "\n", + "# Save the report as an HTML file\n", + "report.show_html('sweetviz_report.html')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104, + "referenced_widgets": [ + "f8590f16a7b843e8885d1680133c108d", + "ddbb58a283e449c2bd40a9055d55bc41", + 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+ "text": [ + "Report SWEETVIZ_REPORT.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n", + "Report sweetviz_report.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import re\n", + "\n", + "# Load your dataset (replace 'your_dataset.csv' with your file path)\n", + "data = pd.read_csv('/content/train.csv')\n", + "\n", + "# Function to clean text data\n", + "def clean_text(text):\n", + " # Remove special characters, links, emojis, etc.\n", + " text = re.sub(r'http\\S+', '', text) # Remove links\n", + " text = re.sub(r'[^a-zA-Z\\s]', '', text) # Remove special characters except whitespace\n", + " # Add more cleaning steps as needed\n", + "\n", + " return text\n", + "\n", + "# Apply cleaning function to the 'text' column\n", + "data['text'] = data['text'].apply(clean_text)\n", + "\n", + "# Display the cleaned text data\n", + "print(data['text'].head(10))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oOct1hHUCa9T", + "executionInfo": { + "status": "ok", + "timestamp": 1702760696752, + "user_tz": 360, + "elapsed": 151, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "510ea92f-eca4-40d9-d8b8-a69fa7e736e6" + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0 Our Deeds are the Reason of this earthquake Ma...\n", + "1 Forest fire near La Ronge Sask Canada\n", + "2 All residents asked to shelter in place are be...\n", + "3 people receive wildfires evacuation orders in...\n", + "4 Just got sent this photo from Ruby Alaska as s...\n", + "5 RockyFire Update California Hwy closed in bo...\n", + "6 flood disaster Heavy rain causes flash floodin...\n", + "7 Im on top of the hill and I can see a fire in ...\n", + "8 Theres an emergency evacuation happening now i...\n", + "9 Im afraid that the tornado is coming to our area\n", + "Name: text, dtype: object\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "data.head()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "Ulxbhu0f7TKK", + "executionInfo": { + "status": "ok", + "timestamp": 1702760699970, + "user_tz": 360, + "elapsed": 145, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "07607e8d-40ad-46b5-b7cf-9979e42be969" + }, + "execution_count": 36, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id keyword location text \\\n", + "0 1 NaN NaN Our Deeds are the Reason of this earthquake Ma... \n", + "1 4 NaN NaN Forest fire near La Ronge Sask Canada \n", + "2 5 NaN NaN All residents asked to shelter in place are be... \n", + "3 6 NaN NaN people receive wildfires evacuation orders in... \n", + "4 7 NaN NaN Just got sent this photo from Ruby Alaska as s... \n", + "\n", + " target \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 " + ], + "text/html": [ + "\n", + "
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14NaNNaNForest fire near La Ronge Sask Canada1
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36NaNNaNpeople receive wildfires evacuation orders in...1
47NaNNaNJust got sent this photo from Ruby Alaska as s...1
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Assuming tfidf_df contains the TF-IDF DataFrame\n", + "print(tfidf_df.info())\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "chNmT5IV7JUC", + "executionInfo": { + "status": "ok", + "timestamp": 1702760716111, + "user_tz": 360, + "elapsed": 494, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "cbcdefe5-a524-4694-c447-7b2f630ce2f5" + }, + "execution_count": 41, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 7613 entries, 0 to 7612\n", + "Columns: 5000 entries, aa to zouma\n", + "dtypes: float64(5000)\n", + "memory usage: 290.4 MB\n", + "None\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(tfidf_df.describe())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 339 + }, + "id": "p2wHDV7d8P9Q", + "executionInfo": { + "status": "error", + "timestamp": 1702760722867, + "user_tz": 360, + "elapsed": 5211, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "156cfa5a-8a42-4c34-f106-130b9a642a38" + }, + "execution_count": 42, + "outputs": [ + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", + "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtfidf_df\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdescribe\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m", + "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001B[0m in \u001B[0;36mdescribe\u001B[0;34m(self, percentiles, include, exclude, datetime_is_numeric)\u001B[0m\n\u001B[1;32m 10938\u001B[0m \u001B[0mmax\u001B[0m \u001B[0mNaN\u001B[0m \u001B[0;36m3.0\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 10939\u001B[0m \"\"\"\n\u001B[0;32m> 10940\u001B[0;31m return describe_ndframe(\n\u001B[0m\u001B[1;32m 10941\u001B[0m \u001B[0mobj\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 10942\u001B[0m \u001B[0minclude\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0minclude\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/describe.py\u001B[0m in \u001B[0;36mdescribe_ndframe\u001B[0;34m(obj, include, exclude, datetime_is_numeric, percentiles)\u001B[0m\n\u001B[1;32m 99\u001B[0m )\n\u001B[1;32m 100\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 101\u001B[0;31m \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdescriber\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdescribe\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mpercentiles\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mpercentiles\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 102\u001B[0m \u001B[0;32mreturn\u001B[0m \u001B[0mcast\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mNDFrameT\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mresult\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 103\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/describe.py\u001B[0m in \u001B[0;36mdescribe\u001B[0;34m(self, percentiles)\u001B[0m\n\u001B[1;32m 179\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0m_\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mseries\u001B[0m 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2681\u001B[0m \u001B[0mdf\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mto_frame\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 2682\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 2683\u001B[0;31m \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mquantile\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mq\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mq\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minterpolation\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0minterpolation\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnumeric_only\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mFalse\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 2684\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mresult\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mndim\u001B[0m \u001B[0;34m==\u001B[0m 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1632\u001B[0m \u001B[0mblk\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mquantile\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0maxis\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mqs\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mqs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minterpolation\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0minterpolation\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1633\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mblk\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mblocks\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001B[0m in \u001B[0;36m\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m 1630\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1631\u001B[0m blocks = [\n\u001B[0;32m-> 1632\u001B[0;31m 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\u001B[0;31m# is ndarray, e.g. IntegerArray, SparseArray\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/array_algos/quantile.py\u001B[0m in \u001B[0;36mquantile_compat\u001B[0;34m(values, qs, interpolation)\u001B[0m\n\u001B[1;32m 35\u001B[0m \u001B[0mfill_value\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mna_value_for_dtype\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcompat\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mFalse\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 36\u001B[0m \u001B[0mmask\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0misna\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 37\u001B[0;31m \u001B[0;32mreturn\u001B[0m 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93\u001B[0m \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrepeat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mflat\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreshape\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mqs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 94\u001B[0m \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 95\u001B[0;31m result = _nanpercentile(\n\u001B[0m\u001B[1;32m 96\u001B[0m \u001B[0mvalues\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 97\u001B[0m 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in \u001B[0;36mpercentile\u001B[0;34m(*args, **kwargs)\u001B[0m\n", + "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001B[0m in \u001B[0;36mpercentile\u001B[0;34m(a, q, axis, out, overwrite_input, method, keepdims, interpolation)\u001B[0m\n\u001B[1;32m 4164\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0m_quantile_is_valid\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mq\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 4165\u001B[0m \u001B[0;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"Percentiles must be in the range [0, 100]\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 4166\u001B[0;31m return _quantile_unchecked(\n\u001B[0m\u001B[1;32m 4167\u001B[0m a, q, axis, out, overwrite_input, method, keepdims)\n\u001B[1;32m 4168\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001B[0m in \u001B[0;36m_quantile_unchecked\u001B[0;34m(a, q, axis, out, overwrite_input, method, keepdims)\u001B[0m\n\u001B[1;32m 4422\u001B[0m keepdims=False):\n\u001B[1;32m 4423\u001B[0m \u001B[0;34m\"\"\"Assumes that q is in [0, 1], and is an ndarray\"\"\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 4424\u001B[0;31m r, k = _ureduce(a,\n\u001B[0m\u001B[1;32m 4425\u001B[0m \u001B[0mfunc\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0m_quantile_ureduce_func\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 4426\u001B[0m \u001B[0mq\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mq\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001B[0m in \u001B[0;36m_ureduce\u001B[0;34m(a, func, **kwargs)\u001B[0m\n\u001B[1;32m 3723\u001B[0m 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\u001B[0;36m_quantile_ureduce_func\u001B[0;34m(a, q, axis, out, overwrite_input, method)\u001B[0m\n\u001B[1;32m 4590\u001B[0m \u001B[0marr\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0ma\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mflatten\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 4591\u001B[0m \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 4592\u001B[0;31m \u001B[0marr\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0ma\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcopy\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 4593\u001B[0m result = _quantile(arr,\n\u001B[1;32m 4594\u001B[0m \u001B[0mquantiles\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mq\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;31mKeyboardInterrupt\u001B[0m: " + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Plot a heatmap for the TF-IDF DataFrame\n", + "sns.heatmap(tfidf_df.iloc[0:50, 0:50], cmap='viridis') # Adjust rows/columns for visualization\n", + "plt.title('TF-IDF Heatmap (Sample)')\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 500 + }, + "id": "elLlyK7v8kmo", + "executionInfo": { + "status": "ok", + "timestamp": 1702753636438, + "user_tz": 360, + "elapsed": 784, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "d891e49b-3214-4194-c20e-ef810bba9f95" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "# Assuming 'tfidf_df' is your TF-IDF DataFrame\n", + "# Create PCA object specifying the number of components\n", + "n_components = 100 # Example number of components (adjust as needed)\n", + "pca = PCA(n_components=n_components)\n", + "\n", + "# Fit PCA to your TF-IDF data\n", + "pca.fit(tfidf_df)\n", + "\n", + "# Transform your TF-IDF data into the reduced feature space\n", + "tfidf_reduced = pca.transform(tfidf_df)\n" + ], + "metadata": { + "id": "gxYI6pkD_q9P", + "executionInfo": { + "status": "ok", + "timestamp": 1702760744883, + "user_tz": 360, + "elapsed": 10099, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + } + }, + "execution_count": 43, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Assuming 'tfidf_reduced' is your transformed dataset after PCA\n", + "import pandas as pd\n", + "\n", + "# Convert the transformed array into a DataFrame for better inspection (if not already in DataFrame format)\n", + "df_reduced = pd.DataFrame(tfidf_reduced)\n", + "\n", + "# Display the first few rows to examine the reduced dimensions\n", + "print(df_reduced.head())\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3qw6t7B_DTC3", + "executionInfo": { + "status": "ok", + "timestamp": 1702760750127, + "user_tz": 360, + "elapsed": 141, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "3d3a7a1a-1473-46a4-df66-d623739b750a" + }, + "execution_count": 44, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " 0 1 2 3 4 5 6 \\\n", + "0 0.090500 -0.040606 0.028588 0.001397 -0.022889 -0.022007 0.005762 \n", + "1 -0.081842 -0.035420 -0.008939 0.031627 -0.031679 -0.022951 0.007635 \n", + "2 -0.028373 0.004141 0.056549 -0.100895 0.071917 0.037702 -0.005294 \n", + "3 -0.071760 -0.066717 0.056913 -0.059761 0.051206 -0.024110 0.008698 \n", + "4 -0.062248 -0.008604 -0.045037 0.028408 -0.066850 0.091550 0.143935 \n", + "\n", + " 7 8 9 ... 90 91 92 93 \\\n", + "0 -0.001022 -0.019084 0.084638 ... 0.033719 -0.036980 0.010754 0.009529 \n", + "1 -0.043015 -0.040318 -0.001499 ... -0.021709 -0.030041 -0.011191 -0.002487 \n", + "2 0.009985 -0.020544 -0.005841 ... 0.043701 0.016814 0.011782 -0.076484 \n", + "3 0.013605 -0.003424 -0.089122 ... 0.030630 -0.001283 -0.031379 0.011318 \n", + "4 0.046578 0.002926 0.040478 ... 0.043946 0.076858 -0.004490 0.052821 \n", + "\n", + " 94 95 96 97 98 99 \n", + "0 -0.030139 0.055519 0.007553 0.062215 0.082322 -0.054760 \n", + "1 -0.004505 0.003536 -0.011148 0.025443 -0.013441 -0.005027 \n", + "2 0.041769 0.054022 -0.031860 -0.047841 0.023010 -0.010721 \n", + "3 0.003456 0.013922 0.020616 -0.047895 0.026548 0.017618 \n", + "4 0.058650 0.082879 -0.035490 0.021270 0.078490 0.014967 \n", + "\n", + "[5 rows x 100 columns]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "\n", + "# Extracting features (all columns except the last one assumed as target)\n", + "X_features_df = df_reduced.iloc[:, :-1] # Exclude the last column\n", + "\n", + "# Extracting the target variable (last column)\n", + "y_target_df = df_reduced.iloc[:, -1] # Select the last column\n", + "\n", + "# Optionally, convert DataFrames to arrays if needed\n", + "X_features = X_features_df.values\n", + "y_target = y_target_df.values\n" + ], + "metadata": { + "id": "4GFW2IjPE-Cd", + "executionInfo": { + "status": "ok", + "timestamp": 1702760767399, + "user_tz": 360, + "elapsed": 130, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + } + }, + "execution_count": 45, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "\n", + "# Splitting data into train and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X_features, y_target, test_size=0.2, random_state=42)\n", + "\n", + "# Initialize regression models\n", + "linear_reg = LinearRegression()\n", + "rf_regressor = RandomForestRegressor()\n", + "\n", + "# Train regression models on the training data\n", + "linear_reg.fit(X_train, y_train)\n", + "rf_regressor.fit(X_train, y_train)\n", + "\n", + "# Predict using the trained regression models\n", + "linear_reg_preds = linear_reg.predict(X_test)\n", + "rf_preds = rf_regressor.predict(X_test)\n", + "\n", + "# Evaluation metrics for regression\n", + "def evaluate_regression_model(y_true, y_pred):\n", + " mse = mean_squared_error(y_true, y_pred)\n", + " mae = mean_absolute_error(y_true, y_pred)\n", + " r2 = r2_score(y_true, y_pred)\n", + " return mse, mae, r2\n", + "\n", + "# Evaluate Linear Regression\n", + "mse_lr, mae_lr, r2_lr = evaluate_regression_model(y_test, linear_reg_preds)\n", + "\n", + "# Evaluate Random Forest Regressor\n", + "mse_rf, mae_rf, r2_rf = evaluate_regression_model(y_test, rf_preds)\n", + "\n", + "# Display evaluation metrics\n", + "print(\"Linear Regression Metrics:\")\n", + "print(f\"Mean Squared Error: {mse_lr:.4f}, Mean Absolute Error: {mae_lr:.4f}, R^2 Score: {r2_lr:.4f}\\n\")\n", + "\n", + "print(\"Random Forest Regressor Metrics:\")\n", + "print(f\"Mean Squared Error: {mse_rf:.4f}, Mean Absolute Error: {mae_rf:.4f}, R^2 Score: {r2_rf:.4f}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S2d_CGwMH0tX", + "executionInfo": { + "status": "ok", + "timestamp": 1702755394613, + "user_tz": 360, + "elapsed": 132832, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "74e12447-c500-4f4d-c04b-9dd00fe24fb1" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Linear Regression Metrics:\n", + "Mean Squared Error: 0.0012, Mean Absolute Error: 0.0255, R^2 Score: -0.0405\n", + "\n", + "Random Forest Regressor Metrics:\n", + "Mean Squared Error: 0.0005, Mean Absolute Error: 0.0161, R^2 Score: 0.5482\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "import nltk\n", + "from nltk.corpus import stopwords\n", + "from nltk.tokenize import word_tokenize\n", + "from nltk.stem import WordNetLemmatizer\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# Download NLTK resources (if not downloaded)\n", + "nltk.download('punkt')\n", + "nltk.download('wordnet')\n", + "nltk.download('stopwords')\n", + "\n", + "\n", + "# Assuming 'tfidf_df' is your TF-IDF DataFrame\n", + "# Create PCA object specifying the number of components\n", + "n_components = 100 # Example number of components (adjust as needed)\n", + "pca = PCA(n_components=n_components)\n", + "\n", + "# Fit PCA to your TF-IDF data\n", + "pca.fit(tfidf_df)\n", + "\n", + "# Transform your TF-IDF data into the reduced feature space\n", + "tfidf_reduced = pca.transform(tfidf_df)\n", + "\n", + "# Convert the transformed array into a DataFrame for better inspection (if not already in DataFrame format)\n", + "df_reduced = pd.DataFrame(tfidf_reduced)\n", + "\n", + "# Assuming 'pca_df' is your DataFrame after PCA transformation\n", + "# 'pca_df' contains both features and target variable\n", + "\n", + "# Extracting features (all columns except the last one assumed as target)\n", + "X_features_df = df_reduced.iloc[:, :-1] # Exclude the last column\n", + "\n", + "# Extracting the target variable (last column)\n", + "y_target_df = df_reduced.iloc[:, -1] # Select the last column\n", + "\n", + "# Optionally, convert DataFrames to arrays if needed\n", + "X_features = X_features_df.values\n", + "y_target = y_target_df.values\n", + "\n", + "# Splitting data into train and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X_features, y_target, test_size=0.2, random_state=42)\n", + "\n", + "# Initialize regression models\n", + "linear_reg = LinearRegression()\n", + "rf_regressor = RandomForestRegressor()\n", + "\n", + "# Train regression models on the training data\n", + "linear_reg.fit(X_train, y_train)\n", + "rf_regressor.fit(X_train, y_train)\n", + "\n", + "# Placeholder function for preprocessing a single tweet\n", + "def preprocess(tweet):\n", + "\n", + " tokens = word_tokenize(tweet)\n", + "\n", + " # Remove punctuation and convert to lowercase\n", + " tokens = [word.lower() for word in tokens if word.isalpha()]\n", + "\n", + " # Remove stopwords\n", + " stop_words = set(stopwords.words('english'))\n", + " tokens = [word for word in tokens if word not in stop_words]\n", + "\n", + " # Lemmatization\n", + " lemmatizer = WordNetLemmatizer()\n", + " tokens = [lemmatizer.lemmatize(word) for word in tokens]\n", + "\n", + " preprocessed_tweet = ' '.join(tokens) #\n", + " # Your preprocessing steps here\n", + " # Tokenization, cleaning, removing stop words, etc.\n", + " return preprocessed_tweet\n", + "\n", + "\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.decomposition import PCA\n", + "import pandas as pd\n", + "\n", + "# Example preprocessed tweets (replace this with your preprocessed tweets)\n", + "preprocessed_tweets = [\n", + " \"this is an example tweet\",\n", + " \"another example of preprocessed tweet\"\n", + "]\n", + "\n", + "# Initialize a TF-IDF Vectorizer\n", + "vectorizer = TfidfVectorizer()\n", + "\n", + "# Fit and transform the preprocessed tweets to TF-IDF features\n", + "tfidf_features = vectorizer.fit_transform(preprocessed_tweets)\n", + "\n", + "# Convert TF-IDF features to a DataFrame (optional for visualization or inspection)\n", + "tfidf_df = pd.DataFrame(tfidf_features.toarray(), columns=vectorizer.get_feature_names_out())\n", + "\n", + "# Apply PCA transformation\n", + "n_components = 100 # Example number of components\n", + "pca = PCA(n_components=n_components)\n", + "\n", + "# Fit PCA to TF-IDF features\n", + "pca.fit(tfidf_features.toarray())\n", + "\n", + "# Transform TF-IDF features into the reduced feature space using PCA\n", + "tfidf_reduced = pca.transform(tfidf_features.toarray())\n", + "\n", + "# Now, 'tfidf_reduced' contains the TF-IDF features in a reduced dimensionality using PCA\n", + "# You can use 'tfidf_reduced' for further analysis or modeling\n", + "\n", + "\n", + "# Placeholder function for PCA transformation of a preprocessed tweet\n", + "def pca_transform(preprocessed_tweet):\n", + "\n", + " transformed_tweet = pca.transform(preprocessed_tweet)\n", + " # Your PCA transformation steps here\n", + " # Transform the preprocessed tweet into the reduced feature space\n", + " return transformed_tweet\n", + "\n", + "# Function to predict category for a given tweet\n", + "def predict_category_for_tweet(tweet):\n", + " # Preprocess the tweet\n", + " preprocessed_tweet = preprocess(tweet)\n", + "\n", + " # Transform the preprocessed tweet into the reduced feature space\n", + " transformed_tweet = pca_transform(preprocessed_tweet)\n", + "\n", + " # Predict category using the trained regression models\n", + " linear_reg_category = linear_reg.predict(transformed_tweet)\n", + " rf_category = rf_regressor.predict(transformed_tweet)\n", + "\n", + " return linear_reg_category, rf_category\n", + "\n", + "# Input a tweet from the user\n", + "user_tweet = input(\"Enter a tweet: \")\n", + "\n", + "# Get predictions for the entered tweet\n", + "linear_reg_pred, rf_pred = predict_category_for_tweet(user_tweet)\n", + "\n", + "# Display the predicted categories\n", + "print(\"Linear Regression Predicted Category:\", linear_reg_pred)\n", + "print(\"Random Forest Predicted Category:\", rf_pred)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 584 + }, + "id": "ERfAn6RMZu4G", + "executionInfo": { + "status": "error", + "timestamp": 1702761096910, + "user_tz": 360, + "elapsed": 208402, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "010ae0e9-d9e1-4749-be97-4af8064392e6" + }, + "execution_count": 46, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package punkt to /root/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n", + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter a tweet: able\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "error", + "ename": "ValueError", + "evalue": "ignored", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", + "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[1;32m 102\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 103\u001B[0m \u001B[0;31m# Get predictions for the entered tweet\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 104\u001B[0;31m \u001B[0mlinear_reg_pred\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mrf_pred\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpredict_category_for_tweet\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0muser_tweet\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 105\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 106\u001B[0m \u001B[0;31m# Display the predicted categories\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m\u001B[0m in \u001B[0;36mpredict_category_for_tweet\u001B[0;34m(tweet)\u001B[0m\n\u001B[1;32m 90\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 91\u001B[0m \u001B[0;31m# Transform the preprocessed tweet into the reduced feature space\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 92\u001B[0;31m \u001B[0mtransformed_tweet\u001B[0m \u001B[0;34m=\u001B[0m 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pd\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Load your dataset (replace 'dataset.csv' with your actual file)\n", + "data = pd.read_csv('/content/train.csv')\n", + "\n", + "# Assuming 'text' column contains tweet text and 'target' contains categories\n", + "\n", + "# Split data into features (text) and target (categories)\n", + "X = data['text']\n", + "y = data['target']\n", + "\n", + "# Vectorize the text using TF-IDF\n", + "vectorizer = TfidfVectorizer()\n", + "X_vectorized = vectorizer.fit_transform(X)\n", + "\n", + "# Split data into train and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X_vectorized, y, test_size=0.2, random_state=42)\n", + "\n", + "# Initialize and train a Logistic Regression model\n", + "model = LogisticRegression(max_iter=1000)\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Predict on the test set\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate model performance\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuracy: {accuracy:.2f}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rIgb0bxbghy8", + "executionInfo": { + "status": "ok", + "timestamp": 1702761747743, + "user_tz": 360, + "elapsed": 919, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "b2450659-d4b7-4af0-9af1-8618bd6da65b" + }, + "execution_count": 47, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Accuracy: 0.80\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Assuming 'new_tweet' is the new tweet entered by the user\n", + "new_tweet = \"disaster\"\n", + "\n", + "# Preprocess the new tweet and vectorize it using the same vectorizer used during training\n", + "new_tweet_vectorized = vectorizer.transform([new_tweet])\n", + "\n", + "# Predict category of the new tweet\n", + "predicted_category = model.predict(new_tweet_vectorized)\n", + "print(f\"Predicted Category: {predicted_category[0]}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "47HhXN_3gmOO", + "executionInfo": { + "status": "ok", + "timestamp": 1702761801049, + "user_tz": 360, + "elapsed": 140, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "75fd8d20-52ff-4385-f6cb-b7587d2867e8" + }, + "execution_count": 50, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicted Category: 1\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Assuming 'new_tweet' is the new tweet entered by the user\n", + "new_tweet = \"wonderful\"\n", + "\n", + "# Preprocess the new tweet and vectorize it using the same vectorizer used during training\n", + "new_tweet_vectorized = vectorizer.transform([new_tweet])\n", + "\n", + "# Predict category of the new tweet\n", + "predicted_category = model.predict(new_tweet_vectorized)\n", + "print(f\"Predicted Category: {predicted_category[0]}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RtCSwnJgg0Kn", + "executionInfo": { + "status": "ok", + "timestamp": 1702761921246, + "user_tz": 360, + "elapsed": 146, + "user": { + "displayName": "pavanmahaveer singara", + "userId": "17551489007904463854" + } + }, + "outputId": "f80ad5b5-3741-4690-addf-3f8121e5f9d5" + }, + "execution_count": 51, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicted Category: 0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "wxlGUVyYhPl7" + }, + "execution_count": null, + "outputs": [] + } + ] +}