shrek84 Steem Profile | Ecosynthesizer

@shrek84

steemit.com/@shrek84

VOTING POWER100.00%

DOWNVOTE POWER100.00%

RESOURCE CREDITS100.00%

REPUTATION PROGRESS0.00%

Net Worth

0.037USD

STEEM

0.001STEEM

SBD

0.000SBD

Effective Power

5.007SP

├── Own SP

0.631SP

└── Incoming DelegationsDeleg

+4.376SP

Detailed Balance

STEEM
balance	0.001STEEM	STEEM
market_balance	0.000STEEM	STEEM
savings_balance	0.000STEEM	STEEM
reward_steem_balance	0.000STEEM	STEEM
STEEM POWER
Own SP	0.631SP	SP
Delegated Out	0.000SP	SP
Delegation In	4.376SP	SP
Effective Power	5.007SP	SP
Reward SP (pending)	0.000SP	SP
SBD
sbd_balance	0.000SBD	SBD
sbd_conversions	0.000SBD	SBD
sbd_market_balance	0.000SBD	SBD
savings_sbd_balance	0.000SBD	SBD
reward_sbd_balance	0.000SBD	SBD

{
  "balance": "0.001 STEEM",
  "savings_balance": "0.000 STEEM",
  "reward_steem_balance": "0.000 STEEM",
  "vesting_shares": "1026.504803 VESTS",
  "delegated_vesting_shares": "0.000000 VESTS",
  "received_vesting_shares": "7117.155003 VESTS",
  "sbd_balance": "0.000 SBD",
  "savings_sbd_balance": "0.000 SBD",
  "reward_sbd_balance": "0.000 SBD",
  "conversions": []
}

Account Info

name	shrek84
id	462194
rank	433,859
reputation	27565362
created	2017-11-24T20:34:18
recovery_account	steem
proxy	None
post_count	5
comment_count	0
lifetime_vote_count	0
witnesses_voted_for	0
last_post	2017-11-26T20:57:57
last_root_post	2017-11-26T20:57:57
last_vote_time	2017-11-25T18:33:15
proxied_vsf_votes	0, 0, 0, 0
can_vote	1
voting_power	0
delayed_votes	0
balance	0.001 STEEM
savings_balance	0.000 STEEM
sbd_balance	0.000 SBD
savings_sbd_balance	0.000 SBD
vesting_shares	1026.504803 VESTS
delegated_vesting_shares	0.000000 VESTS
received_vesting_shares	7117.155003 VESTS
reward_vesting_balance	0.000000 VESTS
vesting_balance	0.000 STEEM
vesting_withdraw_rate	0.000000 VESTS
next_vesting_withdrawal	1969-12-31T23:59:59
withdrawn	0
to_withdraw	0
withdraw_routes	0
savings_withdraw_requests	0
last_account_recovery	1970-01-01T00:00:00
reset_account	null
last_owner_update	1970-01-01T00:00:00
last_account_update	1970-01-01T00:00:00
mined	No
sbd_seconds	0
sbd_last_interest_payment	1970-01-01T00:00:00
savings_sbd_last_interest_payment	1970-01-01T00:00:00

{
  "active": {
    "account_auths": [],
    "key_auths": [
      [
        "STM58aH1hUJ3gMaT9dDKhd9kNM8wpPyDjeiCzigGTLWnyg1ZL7AWJ",
        1
      ]
    ],
    "weight_threshold": 1
  },
  "balance": "0.001 STEEM",
  "can_vote": true,
  "comment_count": 0,
  "created": "2017-11-24T20:34:18",
  "curation_rewards": 0,
  "delegated_vesting_shares": "0.000000 VESTS",
  "downvote_manabar": {
    "current_mana": 2035914951,
    "last_update_time": 1779085761
  },
  "guest_bloggers": [],
  "id": 462194,
  "json_metadata": "",
  "last_account_recovery": "1970-01-01T00:00:00",
  "last_account_update": "1970-01-01T00:00:00",
  "last_owner_update": "1970-01-01T00:00:00",
  "last_post": "2017-11-26T20:57:57",
  "last_root_post": "2017-11-26T20:57:57",
  "last_vote_time": "2017-11-25T18:33:15",
  "lifetime_vote_count": 0,
  "market_history": [],
  "memo_key": "STM8V7ZgsxrvxgXRTJK8wHwwB3qVtmmCXhS1oj7v9Ju7J2h6xcNXN",
  "mined": false,
  "name": "shrek84",
  "next_vesting_withdrawal": "1969-12-31T23:59:59",
  "other_history": [],
  "owner": {
    "account_auths": [],
    "key_auths": [
      [
        "STM8KNrFyQD2FYBqqh4zYYVdZXMBbQ7nznFnB6Ft3c7swf2LPEe9P",
        1
      ]
    ],
    "weight_threshold": 1
  },
  "pending_claimed_accounts": 0,
  "post_bandwidth": 0,
  "post_count": 5,
  "post_history": [],
  "posting": {
    "account_auths": [],
    "key_auths": [
      [
        "STM8SB9wD2Dw3RnyER6iyTxXuhTTLGZ69nJrFWsyisP5RTciMPvVN",
        1
      ]
    ],
    "weight_threshold": 1
  },
  "posting_json_metadata": "",
  "posting_rewards": 0,
  "proxied_vsf_votes": [
    0,
    0,
    0,
    0
  ],
  "proxy": "",
  "received_vesting_shares": "7117.155003 VESTS",
  "recovery_account": "steem",
  "reputation": 27565362,
  "reset_account": "null",
  "reward_sbd_balance": "0.000 SBD",
  "reward_steem_balance": "0.000 STEEM",
  "reward_vesting_balance": "0.000000 VESTS",
  "reward_vesting_steem": "0.000 STEEM",
  "savings_balance": "0.000 STEEM",
  "savings_sbd_balance": "0.000 SBD",
  "savings_sbd_last_interest_payment": "1970-01-01T00:00:00",
  "savings_sbd_seconds": "0",
  "savings_sbd_seconds_last_update": "1970-01-01T00:00:00",
  "savings_withdraw_requests": 0,
  "sbd_balance": "0.000 SBD",
  "sbd_last_interest_payment": "1970-01-01T00:00:00",
  "sbd_seconds": "0",
  "sbd_seconds_last_update": "1970-01-01T00:00:00",
  "tags_usage": [],
  "to_withdraw": 0,
  "transfer_history": [],
  "vesting_balance": "0.000 STEEM",
  "vesting_shares": "1026.504803 VESTS",
  "vesting_withdraw_rate": "0.000000 VESTS",
  "vote_history": [],
  "voting_manabar": {
    "current_mana": "8143659806",
    "last_update_time": 1779085761
  },
  "voting_power": 0,
  "withdraw_routes": 0,
  "withdrawn": 0,
  "witness_votes": [],
  "witnesses_voted_for": 0,
  "rank": 433859
}

Withdraw Routes

Incoming	Outgoing
Empty	Empty

{
  "incoming": [],
  "outgoing": []
}

From Date

To Date

steemdelegated 4.376 SP to @shrek84

2026/05/18 06:29:21 UTC

106,150,898|768e81f

delegatee	shrek84
delegator	steem
vesting shares	7117.155003 VESTS
Transaction Info	Block #106150898/Trx 768e81f26be60618694381fec910fa9b273f4562

View Raw JSON Data

{
  "block": 106150898,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "7117.155003 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-05-18T06:29:21",
  "trx_id": "768e81f26be60618694381fec910fa9b273f4562",
  "trx_in_block": 1,
  "virtual_op": 0
}

steemdelegated 2.708 SP to @shrek84

2026/05/13 05:13:48 UTC

106,006,106|f1ede54

delegatee	shrek84
delegator	steem
vesting shares	4404.944598 VESTS
Transaction Info	Block #106006106/Trx f1ede5447e76bf1fe299ae85fa6ef752d58a0e5b

View Raw JSON Data

{
  "block": 106006106,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "4404.944598 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-05-13T05:13:48",
  "trx_id": "f1ede5447e76bf1fe299ae85fa6ef752d58a0e5b",
  "trx_in_block": 0,
  "virtual_op": 0
}

steemdelegated 4.383 SP to @shrek84

2026/04/26 05:40:51 UTC

105,518,378|60af777

delegatee	shrek84
delegator	steem
vesting shares	7129.670759 VESTS
Transaction Info	Block #105518378/Trx 60af777631c7f856cc3fae873818d66a92b5b933

View Raw JSON Data

{
  "block": 105518378,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "7129.670759 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-04-26T05:40:51",
  "trx_id": "60af777631c7f856cc3fae873818d66a92b5b933",
  "trx_in_block": 0,
  "virtual_op": 0
}

steemdelegated 2.734 SP to @shrek84

2026/01/24 00:36:39 UTC

102,871,945|ba8acdf

delegatee	shrek84
delegator	steem
vesting shares	4446.491417 VESTS
Transaction Info	Block #102871945/Trx ba8acdf7b63d39ebb975e8560ead9ebd238c5c72

View Raw JSON Data

{
  "block": 102871945,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "4446.491417 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-01-24T00:36:39",
  "trx_id": "ba8acdf7b63d39ebb975e8560ead9ebd238c5c72",
  "trx_in_block": 1,
  "virtual_op": 0
}

steemdelegated 2.835 SP to @shrek84

2024/12/17 19:46:36 UTC

91,318,159|3657a0e

delegatee	shrek84
delegator	steem
vesting shares	4610.710614 VESTS
Transaction Info	Block #91318159/Trx 3657a0ec281accb19eb27378b0736184565c5ebb

View Raw JSON Data

{
  "block": 91318159,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "4610.710614 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2024-12-17T19:46:36",
  "trx_id": "3657a0ec281accb19eb27378b0736184565c5ebb",
  "trx_in_block": 5,
  "virtual_op": 0
}

steemdelegated 2.939 SP to @shrek84

2023/11/14 11:27:36 UTC

79,872,303|9dc4b0c

delegatee	shrek84
delegator	steem
vesting shares	4779.844146 VESTS
Transaction Info	Block #79872303/Trx 9dc4b0c45d2185fce50bb26dd3a7541a33eeab7d

View Raw JSON Data

{
  "block": 79872303,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "4779.844146 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2023-11-14T11:27:36",
  "trx_id": "9dc4b0c45d2185fce50bb26dd3a7541a33eeab7d",
  "trx_in_block": 7,
  "virtual_op": 0
}

steemdelegated 4.744 SP to @shrek84

2023/09/22 10:38:45 UTC

78,363,170|8d7354b

delegatee	shrek84
delegator	steem
vesting shares	7716.752932 VESTS
Transaction Info	Block #78363170/Trx 8d7354b26d1374a31e82c9050b1544535444ce8f

View Raw JSON Data

{
  "block": 78363170,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "7716.752932 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2023-09-22T10:38:45",
  "trx_id": "8d7354b26d1374a31e82c9050b1544535444ce8f",
  "trx_in_block": 4,
  "virtual_op": 0
}

steemdelegated 4.881 SP to @shrek84

2022/11/03 18:03:54 UTC

69,120,859|d0867a5

delegatee	shrek84
delegator	steem
vesting shares	7938.804370 VESTS
Transaction Info	Block #69120859/Trx d0867a5c4864f47211209514812d20f498db5ccf

View Raw JSON Data

{
  "block": 69120859,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "7938.804370 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2022-11-03T18:03:54",
  "trx_id": "d0867a5c4864f47211209514812d20f498db5ccf",
  "trx_in_block": 6,
  "virtual_op": 0
}

steemdelegated 5.016 SP to @shrek84

2022/01/17 23:14:39 UTC

60,824,086|1cd3128

delegatee	shrek84
delegator	steem
vesting shares	8158.911971 VESTS
Transaction Info	Block #60824086/Trx 1cd31281af0cb06a78071261892f047b04af06a3

View Raw JSON Data

{
  "block": 60824086,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "8158.911971 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2022-01-17T23:14:39",
  "trx_id": "1cd31281af0cb06a78071261892f047b04af06a3",
  "trx_in_block": 17,
  "virtual_op": 0
}

steemdelegated 5.129 SP to @shrek84

2021/06/14 06:24:51 UTC

54,614,395|7a3a4e2

delegatee	shrek84
delegator	steem
vesting shares	8343.106259 VESTS
Transaction Info	Block #54614395/Trx 7a3a4e27431ff0c54c26a0d107531d36510528b8

View Raw JSON Data

{
  "block": 54614395,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "8343.106259 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2021-06-14T06:24:51",
  "trx_id": "7a3a4e27431ff0c54c26a0d107531d36510528b8",
  "trx_in_block": 8,
  "virtual_op": 0
}

steemdelegated 5.244 SP to @shrek84

2020/12/11 16:36:48 UTC

49,361,650|5612ad1

delegatee	shrek84
delegator	steem
vesting shares	8530.528233 VESTS
Transaction Info	Block #49361650/Trx 5612ad162b28861c3e65ce506d80807b58b3ebe8

View Raw JSON Data

{
  "block": 49361650,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "8530.528233 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-12-11T16:36:48",
  "trx_id": "5612ad162b28861c3e65ce506d80807b58b3ebe8",
  "trx_in_block": 3,
  "virtual_op": 0
}

steemdelegated 1.176 SP to @shrek84

2020/12/06 10:12:24 UTC

49,213,168|90f3190

delegatee	shrek84
delegator	steem
vesting shares	1912.543513 VESTS
Transaction Info	Block #49213168/Trx 90f3190a7f746475895c2f590007ab6d9bbc52a0

View Raw JSON Data

{
  "block": 49213168,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "1912.543513 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-12-06T10:12:24",
  "trx_id": "90f3190a7f746475895c2f590007ab6d9bbc52a0",
  "trx_in_block": 2,
  "virtual_op": 0
}

steemdelegated 5.248 SP to @shrek84

2020/12/05 20:14:39 UTC

49,196,735|edd13ff

delegatee	shrek84
delegator	steem
vesting shares	8536.736087 VESTS
Transaction Info	Block #49196735/Trx edd13ff81257043941af0dae22a87afa27810407

View Raw JSON Data

{
  "block": 49196735,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "8536.736087 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-12-05T20:14:39",
  "trx_id": "edd13ff81257043941af0dae22a87afa27810407",
  "trx_in_block": 6,
  "virtual_op": 0
}

steemdelegated 1.180 SP to @shrek84

2020/11/03 02:59:36 UTC

48,271,170|2cb40bd

delegatee	shrek84
delegator	steem
vesting shares	1920.017158 VESTS
Transaction Info	Block #48271170/Trx 2cb40bd5b2ac20f97e31a425993f448c37307ffc

View Raw JSON Data

{
  "block": 48271170,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "1920.017158 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-11-03T02:59:36",
  "trx_id": "2cb40bd5b2ac20f97e31a425993f448c37307ffc",
  "trx_in_block": 0,
  "virtual_op": 0
}

steemdelegated 5.373 SP to @shrek84

2020/05/09 11:15:42 UTC

43,223,502|46bf099

delegatee	shrek84
delegator	steem
vesting shares	8739.541446 VESTS
Transaction Info	Block #43223502/Trx 46bf0993aa1922bf2fbe00b20d5c5434f71c178a

View Raw JSON Data

{
  "block": 43223502,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "8739.541446 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-05-09T11:15:42",
  "trx_id": "46bf0993aa1922bf2fbe00b20d5c5434f71c178a",
  "trx_in_block": 6,
  "virtual_op": 0
}

steemdelegated 1.201 SP to @shrek84

2020/05/08 15:41:27 UTC

43,200,572|b78fc27

delegatee	shrek84
delegator	steem
vesting shares	1953.311140 VESTS
Transaction Info	Block #43200572/Trx b78fc27c5206fce698f44346292289685b57a98a

View Raw JSON Data

{
  "block": 43200572,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "1953.311140 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-05-08T15:41:27",
  "trx_id": "b78fc27c5206fce698f44346292289685b57a98a",
  "trx_in_block": 30,
  "virtual_op": 0
}

steemdelegated 5.381 SP to @shrek84

2020/04/16 03:23:09 UTC

42,569,260|e815b66

delegatee	shrek84
delegator	steem
vesting shares	8752.428894 VESTS
Transaction Info	Block #42569260/Trx e815b66c9fcfb5b34d7e7e8c398b7409ff3f92ad

View Raw JSON Data

{
  "block": 42569260,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "shrek84",
      "delegator": "steem",
      "vesting_shares": "8752.428894 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-04-16T03:23:09",
  "trx_id": "e815b66c9fcfb5b34d7e7e8c398b7409ff3f92ad",
  "trx_in_block": 4,
  "virtual_op": 0
}

steemitboardreplied to @shrek84 / steemitboard-notify-shrek84-20191124t220738000z

2019/11/24 22:07:39 UTC

38,466,429|7354faa

author	steemitboard
body	Congratulations @shrek84! You received a personal award! <table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@shrek84/birthday2.png</td><td>Happy Birthday! - You are on the Steem blockchain for 2 years!</td></tr></table> <sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@shrek84) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=shrek84)_</sub> ###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!
json metadata	{"image":["https://steemitboard.com/img/notify.png"]}
parent author	shrek84
parent permlink	7-survival-gears-when-camping
permlink	steemitboard-notify-shrek84-20191124t220738000z
title
Transaction Info	Block #38466429/Trx 7354faa7dcae03fd41f157bd873e2f4e6f37ff0c

View Raw JSON Data

{
  "block": 38466429,
  "op": [
    "comment",
    {
      "author": "steemitboard",
      "body": "Congratulations @shrek84! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@shrek84/birthday2.png</td><td>Happy Birthday! - You are on the Steem blockchain for 2 years!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@shrek84) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=shrek84)_</sub>\n\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
      "json_metadata": "{\"image\":[\"https://steemitboard.com/img/notify.png\"]}",
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steemdelegated 5.501 SP to @shrek84

2019/05/12 20:30:12 UTC

32,852,205|6fd2abc

delegatee	shrek84
delegator	steem
vesting shares	8948.045707 VESTS
Transaction Info	Block #32852205/Trx 6fd2abcd1c1601e4bd755367cab8a9a49d0aafb2

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steemdetectivesent 0.001 STEEM to @shrek84- "Hy @shrek84 check out https://steemdetective.com"

2019/02/14 14:09:45 UTC

30,342,161|1f86b1c

amount	0.001 STEEM
from	steemdetective
memo	Hy @shrek84 check out https://steemdetective.com
to	shrek84
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steemitboardreplied to @shrek84 / steemitboard-notify-shrek84-20181124t221405000z

2018/11/24 22:14:06 UTC

27,992,046|31e154f

author	steemitboard
body	Congratulations @shrek84! You received a personal award! <table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@shrek84/birthday1.png</td><td>1 Year on Steemit</td></tr></table> <sub>_[Click here to view your Board of Honor](https://steemitboard.com/@shrek84)_</sub> Do not miss the last post from @steemitboard: <table><tr><td><a href="https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-the-results-the-winners-and-the-prizes"><img src="https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmeLukvNFRsa7RURqsFpiLGEZZD49MiU52JtWmjS5S2wtW/image.png"></a></td><td><a href="https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-the-results-the-winners-and-the-prizes">Meet the Steemians Contest - The results, the winners and the prizes</a></td></tr><tr><td><a href="https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-special-attendees-revealed"><img src="https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmeLukvNFRsa7RURqsFpiLGEZZD49MiU52JtWmjS5S2wtW/image.png"></a></td><td><a href="https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-special-attendees-revealed">Meet the Steemians Contest - Special attendees revealed</a></td></tr></table> > Support [SteemitBoard's project](https://steemit.com/@steemitboard)! [Vote for its witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) and get one more award!
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steemdelegated 5.624 SP to @shrek84

2018/05/17 02:48:24 UTC

22,497,668|220cfb9

delegatee	shrek84
delegator	steem
vesting shares	9147.560799 VESTS
Transaction Info	Block #22497668/Trx 220cfb9ee799c0ab3e6e8527b9a68410fd1acf34

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kgwupvoted (100.00%) @shrek84 / 6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins

2018/05/08 22:29:36 UTC

22,262,120|ce99509

author	shrek84
permlink	6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins
voter	kgw
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steemdelegated 18.158 SP to @shrek84

2018/04/21 20:52:36 UTC

21,771,340|1ed8fef

delegatee	shrek84
delegator	steem
vesting shares	29535.663145 VESTS
Transaction Info	Block #21771340/Trx 1ed8fef4628c9bf26888b9807636d991d069cc4b

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shrek84followed @msg768

2017/12/28 21:00:36 UTC

18,491,609|8500c5f

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steemdelegated 18.283 SP to @shrek84

2017/12/12 22:19:45 UTC

18,032,578|ae2d47e

delegatee	shrek84
delegator	steem
vesting shares	29739.495197 VESTS
Transaction Info	Block #18032578/Trx ae2d47e70085dc8ef495f9d578457ad060d8184f

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vivekbharadwajreplied to @shrek84 / re-shrek84-6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins-20171201t002104428z

2017/12/01 00:21:03 UTC

17,689,578|27407a5

author	vivekbharadwaj
body	Very cool. I hadn't thought of looking into whitepapers. Did you notice any trends in the whitepapers' time-series as people churn out new ideas?
json metadata	{"tags":["crytocurrency"],"app":"steemit/0.1"}
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permlink	re-shrek84-6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins-20171201t002104428z
title
Transaction Info	Block #17689578/Trx 27407a5f5baf25ebf3315fb2777895ba2726f806

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vivekbharadwajupvoted (100.00%) @shrek84 / 6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins

2017/12/01 00:15:06 UTC

17,689,459|6992a9d

author	shrek84
permlink	6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins
voter	vivekbharadwaj
weight	10000 (100.00%)
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dreamyacornreplied to @shrek84 / re-shrek84-7-survival-gears-when-camping-20171130t032431447z

2017/11/30 03:24:51 UTC

17,664,457|06f1422

author	dreamyacorn
body	@@ -3,16 +3,17 @@ am inter +e sting on
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parent author	shrek84
parent permlink	7-survival-gears-when-camping
permlink	re-shrek84-7-survival-gears-when-camping-20171130t032431447z
title
Transaction Info	Block #17664457/Trx 06f1422fc22b1220bb48102853ce995906ec618c

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dreamyacornreplied to @shrek84 / re-shrek84-7-survival-gears-when-camping-20171130t032431447z

2017/11/30 03:24:30 UTC

17,664,450|4faf6e8

author	dreamyacorn
body	I am intersting on camping the posting is good for me :) thanks!!
json metadata	{"tags":["travel"],"app":"steemit/0.1"}
parent author	shrek84
parent permlink	7-survival-gears-when-camping
permlink	re-shrek84-7-survival-gears-when-camping-20171130t032431447z
title
Transaction Info	Block #17664450/Trx 4faf6e84d19806ecdeaaf7dd78239edcb8f25faa

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shrek84followed @kingscrown

2017/11/27 01:41:45 UTC

17,576,052|c7b3115

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primetimesportsupvoted (0.02%) @shrek84 / 7-survival-gears-when-camping

2017/11/26 21:04:00 UTC

17,570,497|7894dbd

author	shrek84
permlink	7-survival-gears-when-camping
voter	primetimesports
weight	2 (0.02%)
Transaction Info	Block #17570497/Trx 7894dbd486115a2353490a641af6b4c3b5be9cf0

View Raw JSON Data

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shrek84published a new post: 7-survival-gears-when-camping

2017/11/26 20:57:57 UTC

17,570,376|ee7a48a

author	shrek84
body	![camping-2581242_1920.jpg](https://steemitimages.com/DQmUNmHj4fDPrmQALJ8zHYS9c3rWc7xx66NbubX8gq8XD4r/camping-2581242_1920.jpg) If you are planning on going to the wilderness and you don’t have much experience, then you have to inform yourself well about the necessary camping items. Also, surviving gears must be on your list, because you can never know what might happen. Moreover, if you are staying for a longer period, well then items for survival are necessary, because it is impossible to carry with you - the large amounts of water - and you’ll have to drink what nature offers.   1. First Aid Kit In the wilderness, you can face many situations which can cause injuries, so you must have a first aid kit. Every first aid kit contains bandages, alcohol wipes, gauze, gloves, etc. The supplies from the kit help you heal faster and prevent infections that might occur if you have an open wound. This must be on every camping checklist, because you never know what you can experience.   ![aid1.jpg](https://steemitimages.com/DQmPZUhxQLQUh4Grr9KvuqDTpZZJrguxKx9DNC4UqxkDywz/aid1.jpg) [First aid kit](https://www.amazon.com/First-Aid-Kit-Waterproof-Essential/dp/B01E7KBHSM/) 2. Water filtration items In order to survive anywhere, you need water. After some time, you probably won’t have any water left, so you’ll need to drink water from natural resources. You have to filter water before drinking it, so you might consider purchasing a bottle that filter water. There are many filtration bottles on the market, so inform yourself well and choose the best for you. Except for the bottles, there are water purifications tablets that are the other option when it comes to the filtration of water. The best is to have both, because in the wilderness, water is perfectly clear and healthy and you won’t need the tablets. However, if the water is really polluted, then you’ll need them. ![water_filter.jpg](https://steemitimages.com/DQmZedAwmkhMjoNDzuJJuDVPvo8mR8ut8BotVg4YFYdQWdk/water_filter.jpg) [Water Filter](https://www.amazon.com/LifeStraw-Personal-Camping-Emergency-Preparedness/dp/B006QF3TW4) 3. Fire starters If you want to warm yourself at night and cook something, you will definitely need to make a fire. You can use various fire starters, which will make it easier to light it. But the necessary item is definitely a lighter, which will be very useful. It might rain and in the morning, the grass is usually wet, so learn how to start a fire on the wet ground and buy some waterproof matches. Also, if you don’t know how to make a good campfire, make sure you inform yourself, because you don’t want to burn the whole forest. ![spark.jpg](https://steemitimages.com/DQmanY4Actv19zc9L36rKpwj6cKahv78ETso1c9hDmuGisY/spark.jpg) [Fire Starter](https://www.amazon.com/SurvivalSPARK-Magnesium-Survival-Starter-Compass/dp/B016UWWS2O) 4. Make electricity You have to recharge your phone or some other electrical gadget, so you need to have something that can produce electricity. There are products that use heat to produce some electricity, which is called thermoelectric power generation. Also, there are various portable solar recharges, which use solar energy to produce electricity. Portable recharges have an option to charge many gadgets at the same time and are easy to carry.   ![powerbank.jpg](https://steemitimages.com/DQmXDhRLg3xD3EamMX5tagmRpP9V5Yffk1jEfFraH2YhP7Q/powerbank.jpg) [Power Bank](https://www.amazon.com/Elefull-Portable-10000mAh-Speaker-samsung/dp/B01CR4GCAS/) 5. Signaling equipment Nowadays, we all have a mobile phone and it is a necessity. It is important to have an option to call someone if in trouble, so don’t forget to carry it with you. However, there might not be a signal everywhere in the woods. Because of that, you’ll need some signaling equipment such as a whistle, signaling mirror, flashlight, etc. You should have a knowledge of Morse Code so you can signal a message. Moreover, signaling fire is always the best, but you have to use material such as green leaves in order to make a lot of smoke.   If you bought the above fire start it has both whistle and the compass 6. Knife When in the wilderness, a knife is a necessary item. Anyone who likes adventures has an expensive and of good quality fixed blade knife and a folding knife. Fixed blade knives are perfect for cutting thicker rope, branches and other large objects. On the other hand, folding knives are practical and good for cutting smaller objects such as bandages and opening packages. Make sure that you clean your knife after using it in order to avoid rusting. If you are using a folding knife, then be careful when using it, because if loose, it can be very dangerous. When camping in the wilderness, you’ll discover so many situations which will demand a great knife.  ![knife.jpg](https://steemitimages.com/DQmaBSa85Ge4vZCT2adkSPmZcvPygTqmD928en2n4JiobS2/knife.jpg) [Survival Knife](https://www.amazon.com/OutNowTech-VANTAGE-Multi-Purpose-Folding-Pocket/dp/B075K45TXJ/) 7. Map and a compass A map is a must when camping, because you don’t want to get lost in the woods. By having a map, you’ll easily find your way to the road and it will be easier for you to determine your location. However, if you find yourself lost or you cannot manage to read the map well because of being surrounded by wilderness, you’ll need a compass. By using a compass, you’ll easily find out your position and you’ll be able to see what your location is on the map. It is very important to have a sense of direction and location in order to prevent not only getting lost, but also not knowing where to find water. If you bought the above fire start it has both whistle and the compass
json metadata	{"tags":["travel","blog","life","camping"],"image":["https://steemitimages.com/DQmUNmHj4fDPrmQALJ8zHYS9c3rWc7xx66NbubX8gq8XD4r/camping-2581242_1920.jpg","https://steemitimages.com/DQmPZUhxQLQUh4Grr9KvuqDTpZZJrguxKx9DNC4UqxkDywz/aid1.jpg","https://steemitimages.com/DQmZedAwmkhMjoNDzuJJuDVPvo8mR8ut8BotVg4YFYdQWdk/water_filter.jpg","https://steemitimages.com/DQmanY4Actv19zc9L36rKpwj6cKahv78ETso1c9hDmuGisY/spark.jpg","https://steemitimages.com/DQmXDhRLg3xD3EamMX5tagmRpP9V5Yffk1jEfFraH2YhP7Q/powerbank.jpg","https://steemitimages.com/DQmaBSa85Ge4vZCT2adkSPmZcvPygTqmD928en2n4JiobS2/knife.jpg"],"links":["https://www.amazon.com/First-Aid-Kit-Waterproof-Essential/dp/B01E7KBHSM/","https://www.amazon.com/LifeStraw-Personal-Camping-Emergency-Preparedness/dp/B006QF3TW4","https://www.amazon.com/SurvivalSPARK-Magnesium-Survival-Starter-Compass/dp/B016UWWS2O","https://www.amazon.com/Elefull-Portable-10000mAh-Speaker-samsung/dp/B01CR4GCAS/","https://www.amazon.com/OutNowTech-VANTAGE-Multi-Purpose-Folding-Pocket/dp/B075K45TXJ/"],"app":"steemit/0.1","format":"markdown"}
parent author
parent permlink	travel
permlink	7-survival-gears-when-camping
title	7 Survival Gears When Camping
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"body": "![camping-2581242_1920.jpg](https://steemitimages.com/DQmUNmHj4fDPrmQALJ8zHYS9c3rWc7xx66NbubX8gq8XD4r/camping-2581242_1920.jpg)\n\nIf you are planning on going to the wilderness and you don’t have much experience, then you have to inform yourself well about the necessary camping items. Also, surviving gears must be on your list, because you can never know what might happen. Moreover, if you are staying for a longer period, well then items for survival are necessary, because it is impossible to carry with you - the large amounts of water - and you’ll have to drink what nature offers. \n\n**1. First Aid Kit**\nIn the wilderness, you can face many situations which can cause injuries, so you must have a first aid kit. Every first aid kit contains bandages, alcohol wipes, gauze, gloves, etc. The supplies from the kit help you heal faster and prevent infections that might occur if you have an open wound. This must be on every camping checklist, because you never know what you can experience. \n![aid1.jpg](https://steemitimages.com/DQmPZUhxQLQUh4Grr9KvuqDTpZZJrguxKx9DNC4UqxkDywz/aid1.jpg)\n[First aid kit](https://www.amazon.com/First-Aid-Kit-Waterproof-Essential/dp/B01E7KBHSM/)\n\n**2. Water filtration items**\nIn order to survive anywhere, you need water. After some time, you probably won’t have any water left, so you’ll need to drink water from natural resources. You have to filter water before drinking it, so you might consider purchasing a bottle that filter water. There are many filtration bottles on the market, so inform yourself well and choose the best for you. Except for the bottles, there are water purifications tablets that are the other option when it comes to the filtration of water. The best is to have both, because in the wilderness, water is perfectly clear and healthy and you won’t need the tablets. However, if the water is really polluted, then you’ll need them. \n![water_filter.jpg](https://steemitimages.com/DQmZedAwmkhMjoNDzuJJuDVPvo8mR8ut8BotVg4YFYdQWdk/water_filter.jpg)\n[Water Filter](https://www.amazon.com/LifeStraw-Personal-Camping-Emergency-Preparedness/dp/B006QF3TW4)\n\n**3. Fire starters**\nIf you want to warm yourself at night and cook something, you will definitely need to make a fire. You can use various fire starters, which will make it easier to light it. But the necessary item is definitely a lighter, which will be very useful. It might rain and in the morning, the grass is usually wet, so learn how to start a fire on the wet ground and buy some waterproof matches. Also, if you don’t know how to make a good campfire, make sure you inform yourself, because you don’t want to burn the whole forest. \n![spark.jpg](https://steemitimages.com/DQmanY4Actv19zc9L36rKpwj6cKahv78ETso1c9hDmuGisY/spark.jpg)\n[Fire Starter](https://www.amazon.com/SurvivalSPARK-Magnesium-Survival-Starter-Compass/dp/B016UWWS2O)\n\n**4. Make electricity**\nYou have to recharge your phone or some other electrical gadget, so you need to have something that can produce electricity. There are products that use heat to produce some electricity, which is called thermoelectric power generation. Also, there are various portable solar recharges, which use solar energy to produce electricity. Portable recharges have an option to charge many gadgets at the same time and are easy to carry. \n![powerbank.jpg](https://steemitimages.com/DQmXDhRLg3xD3EamMX5tagmRpP9V5Yffk1jEfFraH2YhP7Q/powerbank.jpg)\n[Power Bank](https://www.amazon.com/Elefull-Portable-10000mAh-Speaker-samsung/dp/B01CR4GCAS/)\n\n**5. Signaling equipment**\nNowadays, we all have a mobile phone and it is a necessity. It is important to have an option to call someone if in trouble, so don’t forget to carry it with you. However, there might not be a signal everywhere in the woods. Because of that, you’ll need some signaling equipment such as a whistle, signaling mirror, flashlight, etc. You should have a knowledge of Morse Code so you can signal a message. Moreover, signaling fire is always the best, but you have to use material such as green leaves in order to make a lot of smoke. \nIf you bought the above fire start it has both whistle and the compass\n\n**6. Knife**\nWhen in the wilderness, a knife is a necessary item. Anyone who likes adventures has an expensive and of good quality fixed blade knife and a folding knife. Fixed blade knives are perfect for cutting thicker rope, branches and other large objects. On the other hand, folding knives are practical and good for cutting smaller objects such as bandages and opening packages. Make sure that you clean your knife after using it in order to avoid rusting. If you are using a folding knife, then be careful when using it, because if loose, it can be very dangerous. When camping in the wilderness, you’ll discover so many situations which will demand a great knife. \n\n![knife.jpg](https://steemitimages.com/DQmaBSa85Ge4vZCT2adkSPmZcvPygTqmD928en2n4JiobS2/knife.jpg)\n[Survival Knife](https://www.amazon.com/OutNowTech-VANTAGE-Multi-Purpose-Folding-Pocket/dp/B075K45TXJ/)\n\n**7. Map and a compass**\nA map is a must when camping, because you don’t want to get lost in the woods. By having a map, you’ll easily find your way to the road and it will be easier for you to determine your location. However, if you find yourself lost or you cannot manage to read the map well because of being surrounded by wilderness, you’ll need a compass. By using a compass, you’ll easily find out your position and you’ll be able to see what your location is on the map. It is very important to have a sense of direction and location in order to prevent not only getting lost, but also not knowing where to find water.\nIf you bought the above fire start it has both whistle and the compass",
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shrek84replied to @danizaharie / re-danizaharie-sharpe-capital-a-new-way-to-monetize-your-financial-knowledge-20171125t183430556z

2017/11/25 18:34:54 UTC

17,538,716|2b56694

author	shrek84
body	@@ -45,12 +45,24 @@ to +%5B numer.ai +%5D(numer.ai)
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shrek84replied to @danizaharie / re-danizaharie-sharpe-capital-a-new-way-to-monetize-your-financial-knowledge-20171125t183430556z

2017/11/25 18:34:30 UTC

17,538,708|ac0f3b3

author	shrek84
body	I think it would be fair to say it's similar to numer.ai
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shrek84upvoted (100.00%) @danizaharie / sharpe-capital-a-new-way-to-monetize-your-financial-knowledge

2017/11/25 18:33:15 UTC

17,538,683|7386883

author	danizaharie
permlink	sharpe-capital-a-new-way-to-monetize-your-financial-knowledge
voter	shrek84
weight	10000 (100.00%)
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shrek84followed @techblogger

2017/11/25 18:27:06 UTC

17,538,560|f1adaba

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mr-anatol1y95upvoted (100.00%) @shrek84 / 6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins

2017/11/25 04:35:57 UTC

17,521,940|36671ff

author	shrek84
permlink	6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins
voter	mr-anatol1y95
weight	10000 (100.00%)
Transaction Info	Block #17521940/Trx 36671ff24c3d359d3f27e28a52ce5cef984204d2

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shrek84followed @dantheman

2017/11/25 03:16:33 UTC

17,520,352|a851014

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shrek84followed @ned

2017/11/25 03:10:00 UTC

17,520,221|0a55544

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shrek84followed @steemitboard

2017/11/25 03:00:00 UTC

17,520,021|6782cb6

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shrek84upvoted (100.00%) @shenanigator / the-ultimate-guide-to-steemit-payouts

2017/11/25 02:31:57 UTC

17,519,460|36883d9

author	shenanigator
permlink	the-ultimate-guide-to-steemit-payouts
voter	shrek84
weight	10000 (100.00%)
Transaction Info	Block #17519460/Trx 36883d9ea676dc56ad4d788e79078df26c99b1f7

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  "trx_id": "36883d9ea676dc56ad4d788e79078df26c99b1f7",
  "trx_in_block": 8,
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shrek84upvoted (100.00%) @boxmining / dash-core-ceo-interview-at-10-am-est

2017/11/25 02:28:21 UTC

17,519,388|96a8921

author	boxmining
permlink	dash-core-ceo-interview-at-10-am-est
voter	shrek84
weight	10000 (100.00%)
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shrek84followed @buildawhale

2017/11/25 02:27:03 UTC

17,519,362|cfd5e40

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shrek84upvoted (100.00%) @danizaharie / vehicle-lifecycle-blockchain-gather-all-auto-market-owners-repair-shops-and-vehicle-industry-representatives-in-one-ecosystem

2017/11/25 02:26:42 UTC

17,519,355|29bc06c

author	danizaharie
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2017/11/25 02:22:30 UTC

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kumaranvplupvoted (100.00%) @shrek84 / 6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins

2017/11/25 02:20:24 UTC

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2017/11/25 02:12:03 UTC

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2017/11/25 02:11:36 UTC

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2017/11/25 02:11:33 UTC

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2017/11/25 02:11:27 UTC

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2017/11/25 02:10:51 UTC

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2017/11/25 02:10:24 UTC

17,519,029|9c733cd

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2017/11/25 02:10:09 UTC

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2017/11/25 02:09:54 UTC

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2017/11/25 02:09:36 UTC

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2017/11/25 02:09:24 UTC

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2017/11/25 02:08:27 UTC

17,518,990|aa18a58

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2017/11/25 02:08:21 UTC

17,518,988|624c8e0

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shrek84followed @mor

2017/11/25 02:07:30 UTC

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2017/11/25 02:00:18 UTC

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shrek84followed @danizaharie

2017/11/25 01:58:42 UTC

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shrek84published a new post: 6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins

2017/11/25 01:56:54 UTC

17,518,759|1e3bba8

author	shrek84
body	In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them. Machine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums. This is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same. ### Understanding the need to find similar cryptocurrencies * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc. * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. ### Clustering Clustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together. ### Steps: * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually. * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents. * ``` def tokenize_and_stem(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) stems = [stemmer.stem(t) for t in filtered_tokens] return stems def tokenize_only(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) return filtered_tokens ..... ..... tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000, min_df=0.2, stop_words='english', use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3)) tfidf_matrix = tfidf_vectorizer.fit_transform(texts) .... ... km = KMeans(n_clusters=num_clusters) km.fit(tfidf_matrix) ``` ### Clusters Founds * Cluster 1 ( Ads and consumer privacy) * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/) * Cluster 2 ( Financial domain) * Keywords: minting, white, white, paper, bank, true, holder, liquidity, voting * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/) * Cluster 3 (Prediction market and risks) * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/) * Cluster 4 ( Decentralized applications) * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run * Whitepaper: [Aragon](https://aragon.one/) * Cluster 5 (Storage and mining) * Keywords: data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html) * Cluster 6 (Gaming and mobile related) * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/) * Cluster 7 (Investment platforms) * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/) As you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc. Let me know if you find this post useful and also what you would like to know more about?
json metadata	{"tags":["crytocurrency","technology","machine-learning"],"links":["https://basicattentiontoken.org/","https://ico.encryptotel.com/","https://patientory.com/","https://pillarproject.io/","https://www.scriptdrop.io/","https://chronobank.io/","https://vivaco.in/","https://adelphoi.io/","https://augur.net/","https://www.bancor.network","https://tokensale.civic.com/","https://gnosis.pm/","https://aragon.one/","https://filecoin.io/","https://sonm.io/","https://storj.io/tokensale.html","https://dmarket.io/","https://skincoin.org/ico/","https://mobilego.io/","https://www.coindash.io/","https://ico.ethbits.com/","https://www.iconomi.net/"],"app":"steemit/0.1","format":"markdown"}
parent author
parent permlink	crytocurrency
permlink	6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins
title	Datascience and cryptocurrencies : Finding similar altcoins
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      "author": "shrek84",
      "body": "In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them.\n\nMachine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums.\n\nThis is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same.\n\n### Understanding the need to find similar cryptocurrencies\n\n   * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc.\n   * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. \n\n### Clustering\nClustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together.\n\n### Steps:\n\n   * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually.\n   * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents.\n   *\n```\ndef tokenize_and_stem(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    stems = [stemmer.stem(t) for t in filtered_tokens]\n    return stems\n\n   def tokenize_only(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    return filtered_tokens\n.....\n.....\ntfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000,\n                                 min_df=0.2, stop_words='english',\n                                 use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))\n\ntfidf_matrix = tfidf_vectorizer.fit_transform(texts)\n....\n...\nkm = KMeans(n_clusters=num_clusters)\nkm.fit(tfidf_matrix)\n```\n\n### Clusters Founds\n\n   * Cluster 1 ( Ads and consumer privacy)\n      * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted\n      * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/)\n   * Cluster 2 ( Financial domain)\n      * Keywords:  minting, white, white, paper, bank, true, holder, liquidity, voting\n      * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/)\n   * Cluster 3 (Prediction market and risks)\n      * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks\n      * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/)\n   * Cluster 4 ( Decentralized applications)\n      * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run\n      * Whitepaper: [Aragon](https://aragon.one/)\n   * Cluster 5 (Storage and mining)\n      * Keywords:  data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message\n      * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html)\n   * Cluster 6 (Gaming and mobile related)\n      * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart\n      * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/)\n   * Cluster 7 (Investment platforms)\n      * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits\n      * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/)\n\nAs you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc.\n\n\nLet me know if you find this post useful and also what you would like to know  more about?",
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shrek84published a new post: 6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins

2017/11/25 01:55:24 UTC

17,518,729|ab39973

author	shrek84
body	In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them. Machine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums. This is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same. ### Understanding the need to find similar cryptocurrencies * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc. * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. ### Clustering Clustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together. ### Steps: * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually. * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents. * ``` def tokenize_and_stem(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) stems = [stemmer.stem(t) for t in filtered_tokens] return stems def tokenize_only(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) return filtered_tokens ..... ..... tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000, min_df=0.2, stop_words='english', use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3)) tfidf_matrix = tfidf_vectorizer.fit_transform(texts) .... ... km = KMeans(n_clusters=num_clusters) km.fit(tfidf_matrix) ``` ### Clusters Founds * Cluster 1 ( Ads and consumer privacy) * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/) * Cluster 2 ( Financial domain) * Keywords: minting, white, white, paper, bank, true, holder, liquidity, voting * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/) * Cluster 3 (Prediction market and risks) * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/) * Cluster 4 ( Decentralized applications) * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run * Whitepaper: [Aragon](https://aragon.one/) * Cluster 5 (Storage and mining) * Keywords: data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html) * Cluster 6 (Gaming and mobile related) * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/) * Cluster 7 (Investment platforms) * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/) As you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc. Let me know if you find this post useful and also what you would like to know more about?
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permlink	6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins
title	Datascience and cryptocurrencies : Finding similar altcoins
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      "body": "In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them.\n\nMachine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums.\n\nThis is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same.\n\n### Understanding the need to find similar cryptocurrencies\n\n   * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc.\n   * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. \n\n### Clustering\nClustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together.\n\n### Steps:\n\n   * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually.\n   * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents.\n   *\n```\ndef tokenize_and_stem(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    stems = [stemmer.stem(t) for t in filtered_tokens]\n    return stems\n\n   def tokenize_only(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    return filtered_tokens\n.....\n.....\ntfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000,\n                                 min_df=0.2, stop_words='english',\n                                 use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))\n\ntfidf_matrix = tfidf_vectorizer.fit_transform(texts)\n....\n...\nkm = KMeans(n_clusters=num_clusters)\nkm.fit(tfidf_matrix)\n```\n\n### Clusters Founds\n\n   * Cluster 1 ( Ads and consumer privacy)\n      * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted\n      * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/)\n   * Cluster 2 ( Financial domain)\n      * Keywords:  minting, white, white, paper, bank, true, holder, liquidity, voting\n      * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/)\n   * Cluster 3 (Prediction market and risks)\n      * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks\n      * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/)\n   * Cluster 4 ( Decentralized applications)\n      * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run\n      * Whitepaper: [Aragon](https://aragon.one/)\n   * Cluster 5 (Storage and mining)\n      * Keywords:  data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message\n      * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html)\n   * Cluster 6 (Gaming and mobile related)\n      * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart\n      * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/)\n   * Cluster 7 (Investment platforms)\n      * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits\n      * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/)\n\nAs you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc.\n\n\nLet me know if you find this post useful and also what you would like to know  more about?",
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shrek84deleted a comment or post

2017/11/25 01:54:33 UTC

17,518,712|1abb70c

author	shrek84
permlink	datascience-and-cryptocurrencies
Transaction Info	Block #17518712/Trx 1abb70c7cd405fdc0e3792ea2db0a3fec626a081

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shrek84deleted a comment or post

2017/11/25 01:54:15 UTC

17,518,706|db4a481

author	shrek84
permlink	datascience-and-cryptocurrencies-finding-similar-altcoins
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shrek84published a new post: 6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins

2017/11/25 01:48:45 UTC

17,518,596|eac33eb

author	shrek84
body	In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them. Machine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums. This is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same. ### Understanding the need to find similar cryptocurrencies * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc. * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. ### Clustering Clustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together. ### Steps: * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually. * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents. * ``` def tokenize_and_stem(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) stems = [stemmer.stem(t) for t in filtered_tokens] return stems def tokenize_only(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) return filtered_tokens ..... ..... tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000, min_df=0.2, stop_words='english', use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3)) tfidf_matrix = tfidf_vectorizer.fit_transform(texts) .... ... km = KMeans(n_clusters=num_clusters) km.fit(tfidf_matrix) ``` ### Clusters Founds * Cluster 1 ( Ads and consumer privacy) * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/) * Cluster 2 ( Financial domain) * Keywords: minting, white, white, paper, bank, true, holder, liquidity, voting * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/) * Cluster 3 (Prediction market and risks) * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/) * Cluster 4 ( Decentralized applications) * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run * Whitepaper: [Aragon](https://aragon.one/) * Cluster 5 (Storage and mining) * Keywords: data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html) * Cluster 6 (Gaming and mobile related) * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/) * Cluster 7 (Investment platforms) * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/) As you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc. Let me know if you find this post useful and also what you would like to know more about?
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parent permlink	crytocurrency
permlink	6fnwa6-datascience-and-cryptocurrencies-finding-similar-altcoins
title	Datascience and cryptocurrencies : Finding similar altcoins
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      "body": "In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them.\n\nMachine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums.\n\nThis is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same.\n\n### Understanding the need to find similar cryptocurrencies\n\n   * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc.\n   * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. \n\n### Clustering\nClustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together.\n\n### Steps:\n\n   * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually.\n   * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents.\n   *\n```\ndef tokenize_and_stem(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    stems = [stemmer.stem(t) for t in filtered_tokens]\n    return stems\n\n   def tokenize_only(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    return filtered_tokens\n.....\n.....\ntfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000,\n                                 min_df=0.2, stop_words='english',\n                                 use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))\n\ntfidf_matrix = tfidf_vectorizer.fit_transform(texts)\n....\n...\nkm = KMeans(n_clusters=num_clusters)\nkm.fit(tfidf_matrix)\n```\n\n### Clusters Founds\n\n   * Cluster 1 ( Ads and consumer privacy)\n      * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted\n      * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/)\n   * Cluster 2 ( Financial domain)\n      * Keywords:  minting, white, white, paper, bank, true, holder, liquidity, voting\n      * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/)\n   * Cluster 3 (Prediction market and risks)\n      * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks\n      * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/)\n   * Cluster 4 ( Decentralized applications)\n      * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run\n      * Whitepaper: [Aragon](https://aragon.one/)\n   * Cluster 5 (Storage and mining)\n      * Keywords:  data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message\n      * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html)\n   * Cluster 6 (Gaming and mobile related)\n      * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart\n      * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/)\n   * Cluster 7 (Investment platforms)\n      * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits\n      * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/)\n\nAs you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc.\n\n\nLet me know if you find this post useful and also what you would like to know  more about?",
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      "title": "Datascience and cryptocurrencies : Finding similar altcoins"
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shrek84published a new post: datascience-and-cryptocurrencies

2017/11/25 01:43:24 UTC

17,518,489|a80e8b7

author	shrek84
body	In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them. Machine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums. This is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same. ### Understanding the need to find similar cryptocurrencies * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc. * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. ### Clustering Clustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together. ### Steps: * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually. * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents. * ``` def tokenize_and_stem(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) stems = [stemmer.stem(t) for t in filtered_tokens] return stems def tokenize_only(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) return filtered_tokens ..... ..... tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000, min_df=0.2, stop_words='english', use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3)) tfidf_matrix = tfidf_vectorizer.fit_transform(texts) .... ... km = KMeans(n_clusters=num_clusters) km.fit(tfidf_matrix) ``` ### Clusters Founds * Cluster 1 ( Ads and consumer privacy) * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/) * Cluster 2 ( Financial domain) * Keywords: minting, white, white, paper, bank, true, holder, liquidity, voting * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/) * Cluster 3 (Prediction market and risks) * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/) * Cluster 4 ( Decentralized applications) * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run * Whitepaper: [Aragon](https://aragon.one/) * Cluster 5 (Storage and mining) * Keywords: data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html) * Cluster 6 (Gaming and mobile related) * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/) * Cluster 7 (Investment platforms) * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/) As you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc. Let me know if you find this post useful and also what you would like to know more about?
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      "author": "shrek84",
      "body": "In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them.\n\nMachine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums.\n\nThis is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same.\n\n### Understanding the need to find similar cryptocurrencies\n\n   * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc.\n   * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. \n\n### Clustering\nClustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together.\n\n### Steps:\n\n   * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually.\n   * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents.\n   *\n```\ndef tokenize_and_stem(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    stems = [stemmer.stem(t) for t in filtered_tokens]\n    return stems\n\n   def tokenize_only(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    return filtered_tokens\n.....\n.....\ntfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000,\n                                 min_df=0.2, stop_words='english',\n                                 use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))\n\ntfidf_matrix = tfidf_vectorizer.fit_transform(texts)\n....\n...\nkm = KMeans(n_clusters=num_clusters)\nkm.fit(tfidf_matrix)\n```\n\n### Clusters Founds\n\n   * Cluster 1 ( Ads and consumer privacy)\n      * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted\n      * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/)\n   * Cluster 2 ( Financial domain)\n      * Keywords:  minting, white, white, paper, bank, true, holder, liquidity, voting\n      * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/)\n   * Cluster 3 (Prediction market and risks)\n      * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks\n      * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/)\n   * Cluster 4 ( Decentralized applications)\n      * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run\n      * Whitepaper: [Aragon](https://aragon.one/)\n   * Cluster 5 (Storage and mining)\n      * Keywords:  data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message\n      * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html)\n   * Cluster 6 (Gaming and mobile related)\n      * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart\n      * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/)\n   * Cluster 7 (Investment platforms)\n      * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits\n      * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/)\n\nAs you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc.\n\n\nLet me know if you find this post useful and also what you would like to know  more about?",
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shrek84published a new post: datascience-and-cryptocurrencies-finding-similar-altcoins

2017/11/25 01:37:39 UTC

17,518,374|eb7cbc2

author	shrek84
body	In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them. Machine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums. This is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same. ### Understanding the need to find similar cryptocurrencies * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc. * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. ### Clustering Clustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together. ### Steps: * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually. * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents. * ``` def tokenize_and_stem(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) stems = [stemmer.stem(t) for t in filtered_tokens] return stems def tokenize_only(text): # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] filtered_tokens = [] # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation) for token in tokens: if re.search('[a-zA-Z]', token): filtered_tokens.append(token) return filtered_tokens ..... ..... tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000, min_df=0.2, stop_words='english', use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3)) tfidf_matrix = tfidf_vectorizer.fit_transform(texts) .... ... km = KMeans(n_clusters=num_clusters) km.fit(tfidf_matrix) ``` ### Clusters Founds * Cluster 1 ( Ads and consumer privacy) * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/) * Cluster 2 ( Financial domain) * Keywords: minting, white, white, paper, bank, true, holder, liquidity, voting * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/) * Cluster 3 (Prediction market and risks) * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/) * Cluster 4 ( Decentralized applications) * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run * Whitepaper: [Aragon](https://aragon.one/) * Cluster 5 (Storage and mining) * Keywords: data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html) * Cluster 6 (Gaming and mobile related) * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/) * Cluster 7 (Investment platforms) * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/) As you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc. Let me know if you find this post useful and also what you would like to know more about?
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parent permlink	crytocurrency
permlink	datascience-and-cryptocurrencies-finding-similar-altcoins
title	Datascience and cryptocurrencies : [Finding similar altcoins]
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      "author": "shrek84",
      "body": "In the past few months, the number of cryptocurrencies and ICO's has gone up significantly and it's really hard to keep up with all of the news and hype around them.\n\nMachine learning/Data science has played a really important role in understanding more about text and gaining insights from them. I wanted to use this technique in mining important information from ICO whitepapers and comments from different forums.\n\nThis is part one of the many posts I will be doing in understanding more about cryptocurrencies and automating the extraction of information from the same.\n\n### Understanding the need to find similar cryptocurrencies\n\n   * Diversifying portfolios. I really don't like to bet on a single sector or application. Currently, the blockchain is used for building cryptocurrencies, coins for mining or understanding for user behavior, coins for storage etc.\n   * All if you have missed the train on a coin. You can find similar altcoins in the same area and invest in them. For example, let's say you want to invest in semiconductors stocks and missed an opportunity buying Nvidia. You could find similar stocks like AMD, Intel etc. \n\n### Clustering\nClustering is a traditional method for grouping together similar data. In my example, I have downloaded around 50 to 60 ICO whitepapers and clustered them together.\n\n### Steps:\n\n   * Downloading ICO papers: This is one of the tedious steps but unfortunately, there are no API's which from where we can download the data and need to do this step manually.\n   * Using the scikit libraries TfidfVectorizer we convert text to vectors of number that can be used by algorithms like KMeans to cluster the documents.\n   *\n```\ndef tokenize_and_stem(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    stems = [stemmer.stem(t) for t in filtered_tokens]\n    return stems\n\n   def tokenize_only(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    return filtered_tokens\n.....\n.....\ntfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000,\n                                 min_df=0.2, stop_words='english',\n                                 use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))\n\ntfidf_matrix = tfidf_vectorizer.fit_transform(texts)\n....\n...\nkm = KMeans(n_clusters=num_clusters)\nkm.fit(tfidf_matrix)\n```\n\n### Clusters Founds\n\n   * Cluster 1 ( Ads and consumer privacy)\n      * Keywords: data, cared, advertising, person, costs, communication, publishing, privacy, number, encrypted\n      * Whitepapers: [Basic Attention Token](https://basicattentiontoken.org/), [encryptotel](https://ico.encryptotel.com/), [Patientory](https://patientory.com/), [Pillar Project](https://pillarproject.io/), [ScriptDrop](https://www.scriptdrop.io/)\n   * Cluster 2 ( Financial domain)\n      * Keywords:  minting, white, white, paper, bank, true, holder, liquidity, voting\n      * Whitepapers: [Chronobank](https://chronobank.io/), TrueFlip,[ Vivacoin](https://vivaco.in/)\n   * Cluster 3 (Prediction market and risks)\n      * Keywords: business, rewards, values, event, purchased, applications, price, smart, risks\n      * Whitepapers: [Adel](https://adelphoi.io/), [Augur](https://augur.net/), [Bancor](https://www.bancor.network), [Civic](https://tokensale.civic.com/), [Gnosis](https://gnosis.pm/)\n   * Cluster 4 ( Decentralized applications)\n      * Keywords: organizes, upgradeability, released, decentralized, government, page, voting, ethereum, run\n      * Whitepaper: [Aragon](https://aragon.one/)\n   * Cluster 5 (Storage and mining)\n      * Keywords:  data, miners, nodes, computing, agent, dividends, tasks, storage, obligation, message\n      * Whitepaper: [Filecoin](https://filecoin.io/), [Sonm](https://sonm.io/), [Storj](https://storj.io/tokensale.html)\n   * Cluster 6 (Gaming and mobile related)\n      * Keywords: game, item, players, purchased, mobile, money, sales, eth, monetization, smart\n      * Whitepaper: [Dmarket](https://dmarket.io/), [Skincoin](https://skincoin.org/ico/), [Mobilego](https://mobilego.io/)\n   * Cluster 7 (Investment platforms)\n      * Keywords: trade, investments, ico, investors, assets, crypto, coin, profits\n      * Whitepaper: [Coindash](https://www.coindash.io/), [Ethbits](https://ico.ethbits.com/), [Iconomi](https://www.iconomi.net/)\n\nAs you can see there are some clear sectors that show up for example cluster 1 is all about privacy, ads and cluster 5 is about storage, mining etc.\n\n\nLet me know if you find this post useful and also what you would like to know  more about?",
      "json_metadata": "{\"tags\":[\"crytocurrency\",\"machine-learning\",\"bitcoin\"],\"links\":[\"https://basicattentiontoken.org/\",\"https://ico.encryptotel.com/\",\"https://patientory.com/\",\"https://pillarproject.io/\",\"https://www.scriptdrop.io/\",\"https://chronobank.io/\",\"https://vivaco.in/\",\"https://adelphoi.io/\",\"https://augur.net/\",\"https://www.bancor.network\",\"https://tokensale.civic.com/\",\"https://gnosis.pm/\",\"https://aragon.one/\",\"https://filecoin.io/\",\"https://sonm.io/\",\"https://storj.io/tokensale.html\",\"https://dmarket.io/\",\"https://skincoin.org/ico/\",\"https://mobilego.io/\",\"https://www.coindash.io/\",\"https://ico.ethbits.com/\",\"https://www.iconomi.net/\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "crytocurrency",
      "permlink": "datascience-and-cryptocurrencies-finding-similar-altcoins",
      "title": "Datascience and cryptocurrencies :  [Finding similar altcoins]"
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steemcreated a new account: @shrek84

2017/11/24 20:34:18 UTC

17,512,307|aa77e39

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Manabar

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