├── README.md
├── .gitignore
└── Lets grow more task-2 (3).ipynb


/README.md:
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1 | # LETS-GROW-MORE-TASK-2


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/.gitignore:
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  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | pip-wheel-metadata/
 24 | share/python-wheels/
 25 | *.egg-info/
 26 | .installed.cfg
 27 | *.egg
 28 | MANIFEST
 29 | 
 30 | # PyInstaller
 31 | #  Usually these files are written by a python script from a template
 32 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 33 | *.manifest
 34 | *.spec
 35 | 
 36 | # Installer logs
 37 | pip-log.txt
 38 | pip-delete-this-directory.txt
 39 | 
 40 | # Unit test / coverage reports
 41 | htmlcov/
 42 | .tox/
 43 | .nox/
 44 | .coverage
 45 | .coverage.*
 46 | .cache
 47 | nosetests.xml
 48 | coverage.xml
 49 | *.cover
 50 | *.py,cover
 51 | .hypothesis/
 52 | .pytest_cache/
 53 | 
 54 | # Translations
 55 | *.mo
 56 | *.pot
 57 | 
 58 | # Django stuff:
 59 | *.log
 60 | local_settings.py
 61 | db.sqlite3
 62 | db.sqlite3-journal
 63 | 
 64 | # Flask stuff:
 65 | instance/
 66 | .webassets-cache
 67 | 
 68 | # Scrapy stuff:
 69 | .scrapy
 70 | 
 71 | # Sphinx documentation
 72 | docs/_build/
 73 | 
 74 | # PyBuilder
 75 | target/
 76 | 
 77 | # Jupyter Notebook
 78 | .ipynb_checkpoints
 79 | 
 80 | # IPython
 81 | profile_default/
 82 | ipython_config.py
 83 | 
 84 | # pyenv
 85 | .python-version
 86 | 
 87 | # pipenv
 88 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 89 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 90 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 91 | #   install all needed dependencies.
 92 | #Pipfile.lock
 93 | 
 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 95 | __pypackages__/
 96 | 
 97 | # Celery stuff
 98 | celerybeat-schedule
 99 | celerybeat.pid
100 | 
101 | # SageMath parsed files
102 | *.sage.py
103 | 
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
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111 | venv.bak/
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114 | .spyderproject
115 | .spyproject
116 | 
117 | # Rope project settings
118 | .ropeproject
119 | 
120 | # mkdocs documentation
121 | /site
122 | 
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 | 
128 | # Pyre type checker
129 | .pyre/
130 | 


--------------------------------------------------------------------------------
/Lets grow more task-2 (3).ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "a41a23e5",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# LETS GROW MORE\n",
  9 |     "NAME:ANUSUYA V\n",
 10 |     "\n",
 11 |     "TASK-2:Stock Market Prediction And Forecasting Using Stacked LSTM\n",
 12 |     "\n",
 13 |     "LEVEL:Beginner\n",
 14 |     "\n",
 15 |     "LANGUAGE:Python"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 1,
 21 |    "id": "520bc6ae",
 22 |    "metadata": {},
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "import pandas as pd\n",
 26 |     "import matplotlib.pyplot as plt\n",
 27 |     "import numpy as np"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 2,
 33 |    "id": "d64de5a4",
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "data": {
 38 |       "text/html": [
 39 |        "<div>\n",
 40 |        "<style scoped>\n",
 41 |        "    .dataframe tbody tr th:only-of-type {\n",
 42 |        "        vertical-align: middle;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe tbody tr th {\n",
 46 |        "        vertical-align: top;\n",
 47 |        "    }\n",
 48 |        "\n",
 49 |        "    .dataframe thead th {\n",
 50 |        "        text-align: right;\n",
 51 |        "    }\n",
 52 |        "</style>\n",
 53 |        "<table border=\"1\" class=\"dataframe\">\n",
 54 |        "  <thead>\n",
 55 |        "    <tr style=\"text-align: right;\">\n",
 56 |        "      <th></th>\n",
 57 |        "      <th>Date</th>\n",
 58 |        "      <th>Open</th>\n",
 59 |        "      <th>High</th>\n",
 60 |        "      <th>Low</th>\n",
 61 |        "      <th>Last</th>\n",
 62 |        "      <th>Close</th>\n",
 63 |        "      <th>Total Trade Quantity</th>\n",
 64 |        "      <th>Turnover (Lacs)</th>\n",
 65 |        "    </tr>\n",
 66 |        "  </thead>\n",
 67 |        "  <tbody>\n",
 68 |        "    <tr>\n",
 69 |        "      <th>0</th>\n",
 70 |        "      <td>2018-09-28</td>\n",
 71 |        "      <td>234.05</td>\n",
 72 |        "      <td>235.95</td>\n",
 73 |        "      <td>230.20</td>\n",
 74 |        "      <td>233.50</td>\n",
 75 |        "      <td>233.75</td>\n",
 76 |        "      <td>3069914</td>\n",
 77 |        "      <td>7162.35</td>\n",
 78 |        "    </tr>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>1</th>\n",
 81 |        "      <td>2018-09-27</td>\n",
 82 |        "      <td>234.55</td>\n",
 83 |        "      <td>236.80</td>\n",
 84 |        "      <td>231.10</td>\n",
 85 |        "      <td>233.80</td>\n",
 86 |        "      <td>233.25</td>\n",
 87 |        "      <td>5082859</td>\n",
 88 |        "      <td>11859.95</td>\n",
 89 |        "    </tr>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>2</th>\n",
 92 |        "      <td>2018-09-26</td>\n",
 93 |        "      <td>240.00</td>\n",
 94 |        "      <td>240.00</td>\n",
 95 |        "      <td>232.50</td>\n",
 96 |        "      <td>235.00</td>\n",
 97 |        "      <td>234.25</td>\n",
 98 |        "      <td>2240909</td>\n",
 99 |        "      <td>5248.60</td>\n",
100 |        "    </tr>\n",
101 |        "    <tr>\n",
102 |        "      <th>3</th>\n",
103 |        "      <td>2018-09-25</td>\n",
104 |        "      <td>233.30</td>\n",
105 |        "      <td>236.75</td>\n",
106 |        "      <td>232.00</td>\n",
107 |        "      <td>236.25</td>\n",
108 |        "      <td>236.10</td>\n",
109 |        "      <td>2349368</td>\n",
110 |        "      <td>5503.90</td>\n",
111 |        "    </tr>\n",
112 |        "    <tr>\n",
113 |        "      <th>4</th>\n",
114 |        "      <td>2018-09-24</td>\n",
115 |        "      <td>233.55</td>\n",
116 |        "      <td>239.20</td>\n",
117 |        "      <td>230.75</td>\n",
118 |        "      <td>234.00</td>\n",
119 |        "      <td>233.30</td>\n",
120 |        "      <td>3423509</td>\n",
121 |        "      <td>7999.55</td>\n",
122 |        "    </tr>\n",
123 |        "    <tr>\n",
124 |        "      <th>...</th>\n",
125 |        "      <td>...</td>\n",
126 |        "      <td>...</td>\n",
127 |        "      <td>...</td>\n",
128 |        "      <td>...</td>\n",
129 |        "      <td>...</td>\n",
130 |        "      <td>...</td>\n",
131 |        "      <td>...</td>\n",
132 |        "      <td>...</td>\n",
133 |        "    </tr>\n",
134 |        "    <tr>\n",
135 |        "      <th>2030</th>\n",
136 |        "      <td>2010-07-27</td>\n",
137 |        "      <td>117.60</td>\n",
138 |        "      <td>119.50</td>\n",
139 |        "      <td>112.00</td>\n",
140 |        "      <td>118.80</td>\n",
141 |        "      <td>118.65</td>\n",
142 |        "      <td>586100</td>\n",
143 |        "      <td>694.98</td>\n",
144 |        "    </tr>\n",
145 |        "    <tr>\n",
146 |        "      <th>2031</th>\n",
147 |        "      <td>2010-07-26</td>\n",
148 |        "      <td>120.10</td>\n",
149 |        "      <td>121.00</td>\n",
150 |        "      <td>117.10</td>\n",
151 |        "      <td>117.10</td>\n",
152 |        "      <td>117.60</td>\n",
153 |        "      <td>658440</td>\n",
154 |        "      <td>780.01</td>\n",
155 |        "    </tr>\n",
156 |        "    <tr>\n",
157 |        "      <th>2032</th>\n",
158 |        "      <td>2010-07-23</td>\n",
159 |        "      <td>121.80</td>\n",
160 |        "      <td>121.95</td>\n",
161 |        "      <td>120.25</td>\n",
162 |        "      <td>120.35</td>\n",
163 |        "      <td>120.65</td>\n",
164 |        "      <td>281312</td>\n",
165 |        "      <td>340.31</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>2033</th>\n",
169 |        "      <td>2010-07-22</td>\n",
170 |        "      <td>120.30</td>\n",
171 |        "      <td>122.00</td>\n",
172 |        "      <td>120.25</td>\n",
173 |        "      <td>120.75</td>\n",
174 |        "      <td>120.90</td>\n",
175 |        "      <td>293312</td>\n",
176 |        "      <td>355.17</td>\n",
177 |        "    </tr>\n",
178 |        "    <tr>\n",
179 |        "      <th>2034</th>\n",
180 |        "      <td>2010-07-21</td>\n",
181 |        "      <td>122.10</td>\n",
182 |        "      <td>123.00</td>\n",
183 |        "      <td>121.05</td>\n",
184 |        "      <td>121.10</td>\n",
185 |        "      <td>121.55</td>\n",
186 |        "      <td>658666</td>\n",
187 |        "      <td>803.56</td>\n",
188 |        "    </tr>\n",
189 |        "  </tbody>\n",
190 |        "</table>\n",
191 |        "<p>2035 rows × 8 columns</p>\n",
192 |        "</div>"
193 |       ],
194 |       "text/plain": [
195 |        "            Date    Open    High     Low    Last   Close  \\\n",
196 |        "0     2018-09-28  234.05  235.95  230.20  233.50  233.75   \n",
197 |        "1     2018-09-27  234.55  236.80  231.10  233.80  233.25   \n",
198 |        "2     2018-09-26  240.00  240.00  232.50  235.00  234.25   \n",
199 |        "3     2018-09-25  233.30  236.75  232.00  236.25  236.10   \n",
200 |        "4     2018-09-24  233.55  239.20  230.75  234.00  233.30   \n",
201 |        "...          ...     ...     ...     ...     ...     ...   \n",
202 |        "2030  2010-07-27  117.60  119.50  112.00  118.80  118.65   \n",
203 |        "2031  2010-07-26  120.10  121.00  117.10  117.10  117.60   \n",
204 |        "2032  2010-07-23  121.80  121.95  120.25  120.35  120.65   \n",
205 |        "2033  2010-07-22  120.30  122.00  120.25  120.75  120.90   \n",
206 |        "2034  2010-07-21  122.10  123.00  121.05  121.10  121.55   \n",
207 |        "\n",
208 |        "      Total Trade Quantity  Turnover (Lacs)  \n",
209 |        "0                  3069914          7162.35  \n",
210 |        "1                  5082859         11859.95  \n",
211 |        "2                  2240909          5248.60  \n",
212 |        "3                  2349368          5503.90  \n",
213 |        "4                  3423509          7999.55  \n",
214 |        "...                    ...              ...  \n",
215 |        "2030                586100           694.98  \n",
216 |        "2031                658440           780.01  \n",
217 |        "2032                281312           340.31  \n",
218 |        "2033                293312           355.17  \n",
219 |        "2034                658666           803.56  \n",
220 |        "\n",
221 |        "[2035 rows x 8 columns]"
222 |       ]
223 |      },
224 |      "execution_count": 2,
225 |      "metadata": {},
226 |      "output_type": "execute_result"
227 |     }
228 |    ],
229 |    "source": [
230 |     "a=pd.read_csv(\"https://raw.githubusercontent.com/mwitiderrick/stockprice/master/NSE-TATAGLOBAL.csv\")\n",
231 |     "a"
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "code",
236 |    "execution_count": 3,
237 |    "id": "ca3bbe64",
238 |    "metadata": {},
239 |    "outputs": [
240 |     {
241 |      "data": {
242 |       "text/html": [
243 |        "<div>\n",
244 |        "<style scoped>\n",
245 |        "    .dataframe tbody tr th:only-of-type {\n",
246 |        "        vertical-align: middle;\n",
247 |        "    }\n",
248 |        "\n",
249 |        "    .dataframe tbody tr th {\n",
250 |        "        vertical-align: top;\n",
251 |        "    }\n",
252 |        "\n",
253 |        "    .dataframe thead th {\n",
254 |        "        text-align: right;\n",
255 |        "    }\n",
256 |        "</style>\n",
257 |        "<table border=\"1\" class=\"dataframe\">\n",
258 |        "  <thead>\n",
259 |        "    <tr style=\"text-align: right;\">\n",
260 |        "      <th></th>\n",
261 |        "      <th>Date</th>\n",
262 |        "      <th>Open</th>\n",
263 |        "      <th>High</th>\n",
264 |        "      <th>Low</th>\n",
265 |        "      <th>Last</th>\n",
266 |        "      <th>Close</th>\n",
267 |        "      <th>Total Trade Quantity</th>\n",
268 |        "      <th>Turnover (Lacs)</th>\n",
269 |        "    </tr>\n",
270 |        "  </thead>\n",
271 |        "  <tbody>\n",
272 |        "    <tr>\n",
273 |        "      <th>0</th>\n",
274 |        "      <td>2018-09-28</td>\n",
275 |        "      <td>234.05</td>\n",
276 |        "      <td>235.95</td>\n",
277 |        "      <td>230.20</td>\n",
278 |        "      <td>233.50</td>\n",
279 |        "      <td>233.75</td>\n",
280 |        "      <td>3069914</td>\n",
281 |        "      <td>7162.35</td>\n",
282 |        "    </tr>\n",
283 |        "    <tr>\n",
284 |        "      <th>1</th>\n",
285 |        "      <td>2018-09-27</td>\n",
286 |        "      <td>234.55</td>\n",
287 |        "      <td>236.80</td>\n",
288 |        "      <td>231.10</td>\n",
289 |        "      <td>233.80</td>\n",
290 |        "      <td>233.25</td>\n",
291 |        "      <td>5082859</td>\n",
292 |        "      <td>11859.95</td>\n",
293 |        "    </tr>\n",
294 |        "    <tr>\n",
295 |        "      <th>2</th>\n",
296 |        "      <td>2018-09-26</td>\n",
297 |        "      <td>240.00</td>\n",
298 |        "      <td>240.00</td>\n",
299 |        "      <td>232.50</td>\n",
300 |        "      <td>235.00</td>\n",
301 |        "      <td>234.25</td>\n",
302 |        "      <td>2240909</td>\n",
303 |        "      <td>5248.60</td>\n",
304 |        "    </tr>\n",
305 |        "    <tr>\n",
306 |        "      <th>3</th>\n",
307 |        "      <td>2018-09-25</td>\n",
308 |        "      <td>233.30</td>\n",
309 |        "      <td>236.75</td>\n",
310 |        "      <td>232.00</td>\n",
311 |        "      <td>236.25</td>\n",
312 |        "      <td>236.10</td>\n",
313 |        "      <td>2349368</td>\n",
314 |        "      <td>5503.90</td>\n",
315 |        "    </tr>\n",
316 |        "    <tr>\n",
317 |        "      <th>4</th>\n",
318 |        "      <td>2018-09-24</td>\n",
319 |        "      <td>233.55</td>\n",
320 |        "      <td>239.20</td>\n",
321 |        "      <td>230.75</td>\n",
322 |        "      <td>234.00</td>\n",
323 |        "      <td>233.30</td>\n",
324 |        "      <td>3423509</td>\n",
325 |        "      <td>7999.55</td>\n",
326 |        "    </tr>\n",
327 |        "  </tbody>\n",
328 |        "</table>\n",
329 |        "</div>"
330 |       ],
331 |       "text/plain": [
332 |        "         Date    Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
333 |        "0  2018-09-28  234.05  235.95  230.20  233.50  233.75               3069914   \n",
334 |        "1  2018-09-27  234.55  236.80  231.10  233.80  233.25               5082859   \n",
335 |        "2  2018-09-26  240.00  240.00  232.50  235.00  234.25               2240909   \n",
336 |        "3  2018-09-25  233.30  236.75  232.00  236.25  236.10               2349368   \n",
337 |        "4  2018-09-24  233.55  239.20  230.75  234.00  233.30               3423509   \n",
338 |        "\n",
339 |        "   Turnover (Lacs)  \n",
340 |        "0          7162.35  \n",
341 |        "1         11859.95  \n",
342 |        "2          5248.60  \n",
343 |        "3          5503.90  \n",
344 |        "4          7999.55  "
345 |       ]
346 |      },
347 |      "execution_count": 3,
348 |      "metadata": {},
349 |      "output_type": "execute_result"
350 |     }
351 |    ],
352 |    "source": [
353 |     "a.head()"
354 |    ]
355 |   },
356 |   {
357 |    "cell_type": "code",
358 |    "execution_count": 4,
359 |    "id": "0ee58611",
360 |    "metadata": {},
361 |    "outputs": [
362 |     {
363 |      "data": {
364 |       "text/html": [
365 |        "<div>\n",
366 |        "<style scoped>\n",
367 |        "    .dataframe tbody tr th:only-of-type {\n",
368 |        "        vertical-align: middle;\n",
369 |        "    }\n",
370 |        "\n",
371 |        "    .dataframe tbody tr th {\n",
372 |        "        vertical-align: top;\n",
373 |        "    }\n",
374 |        "\n",
375 |        "    .dataframe thead th {\n",
376 |        "        text-align: right;\n",
377 |        "    }\n",
378 |        "</style>\n",
379 |        "<table border=\"1\" class=\"dataframe\">\n",
380 |        "  <thead>\n",
381 |        "    <tr style=\"text-align: right;\">\n",
382 |        "      <th></th>\n",
383 |        "      <th>Date</th>\n",
384 |        "      <th>Open</th>\n",
385 |        "      <th>High</th>\n",
386 |        "      <th>Low</th>\n",
387 |        "      <th>Last</th>\n",
388 |        "      <th>Close</th>\n",
389 |        "      <th>Total Trade Quantity</th>\n",
390 |        "      <th>Turnover (Lacs)</th>\n",
391 |        "    </tr>\n",
392 |        "  </thead>\n",
393 |        "  <tbody>\n",
394 |        "    <tr>\n",
395 |        "      <th>2030</th>\n",
396 |        "      <td>2010-07-27</td>\n",
397 |        "      <td>117.6</td>\n",
398 |        "      <td>119.50</td>\n",
399 |        "      <td>112.00</td>\n",
400 |        "      <td>118.80</td>\n",
401 |        "      <td>118.65</td>\n",
402 |        "      <td>586100</td>\n",
403 |        "      <td>694.98</td>\n",
404 |        "    </tr>\n",
405 |        "    <tr>\n",
406 |        "      <th>2031</th>\n",
407 |        "      <td>2010-07-26</td>\n",
408 |        "      <td>120.1</td>\n",
409 |        "      <td>121.00</td>\n",
410 |        "      <td>117.10</td>\n",
411 |        "      <td>117.10</td>\n",
412 |        "      <td>117.60</td>\n",
413 |        "      <td>658440</td>\n",
414 |        "      <td>780.01</td>\n",
415 |        "    </tr>\n",
416 |        "    <tr>\n",
417 |        "      <th>2032</th>\n",
418 |        "      <td>2010-07-23</td>\n",
419 |        "      <td>121.8</td>\n",
420 |        "      <td>121.95</td>\n",
421 |        "      <td>120.25</td>\n",
422 |        "      <td>120.35</td>\n",
423 |        "      <td>120.65</td>\n",
424 |        "      <td>281312</td>\n",
425 |        "      <td>340.31</td>\n",
426 |        "    </tr>\n",
427 |        "    <tr>\n",
428 |        "      <th>2033</th>\n",
429 |        "      <td>2010-07-22</td>\n",
430 |        "      <td>120.3</td>\n",
431 |        "      <td>122.00</td>\n",
432 |        "      <td>120.25</td>\n",
433 |        "      <td>120.75</td>\n",
434 |        "      <td>120.90</td>\n",
435 |        "      <td>293312</td>\n",
436 |        "      <td>355.17</td>\n",
437 |        "    </tr>\n",
438 |        "    <tr>\n",
439 |        "      <th>2034</th>\n",
440 |        "      <td>2010-07-21</td>\n",
441 |        "      <td>122.1</td>\n",
442 |        "      <td>123.00</td>\n",
443 |        "      <td>121.05</td>\n",
444 |        "      <td>121.10</td>\n",
445 |        "      <td>121.55</td>\n",
446 |        "      <td>658666</td>\n",
447 |        "      <td>803.56</td>\n",
448 |        "    </tr>\n",
449 |        "  </tbody>\n",
450 |        "</table>\n",
451 |        "</div>"
452 |       ],
453 |       "text/plain": [
454 |        "            Date   Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
455 |        "2030  2010-07-27  117.6  119.50  112.00  118.80  118.65                586100   \n",
456 |        "2031  2010-07-26  120.1  121.00  117.10  117.10  117.60                658440   \n",
457 |        "2032  2010-07-23  121.8  121.95  120.25  120.35  120.65                281312   \n",
458 |        "2033  2010-07-22  120.3  122.00  120.25  120.75  120.90                293312   \n",
459 |        "2034  2010-07-21  122.1  123.00  121.05  121.10  121.55                658666   \n",
460 |        "\n",
461 |        "      Turnover (Lacs)  \n",
462 |        "2030           694.98  \n",
463 |        "2031           780.01  \n",
464 |        "2032           340.31  \n",
465 |        "2033           355.17  \n",
466 |        "2034           803.56  "
467 |       ]
468 |      },
469 |      "execution_count": 4,
470 |      "metadata": {},
471 |      "output_type": "execute_result"
472 |     }
473 |    ],
474 |    "source": [
475 |     "a.tail()"
476 |    ]
477 |   },
478 |   {
479 |    "cell_type": "code",
480 |    "execution_count": 5,
481 |    "id": "a47f945b",
482 |    "metadata": {},
483 |    "outputs": [
484 |     {
485 |      "data": {
486 |       "text/plain": [
487 |        "0       233.75\n",
488 |        "1       233.25\n",
489 |        "2       234.25\n",
490 |        "3       236.10\n",
491 |        "4       233.30\n",
492 |        "         ...  \n",
493 |        "2030    118.65\n",
494 |        "2031    117.60\n",
495 |        "2032    120.65\n",
496 |        "2033    120.90\n",
497 |        "2034    121.55\n",
498 |        "Name: Close, Length: 2035, dtype: float64"
499 |       ]
500 |      },
501 |      "execution_count": 5,
502 |      "metadata": {},
503 |      "output_type": "execute_result"
504 |     }
505 |    ],
506 |    "source": [
507 |     "b=a.reset_index()['Close']\n",
508 |     "b"
509 |    ]
510 |   },
511 |   {
512 |    "cell_type": "code",
513 |    "execution_count": 6,
514 |    "id": "dae6c3d7",
515 |    "metadata": {},
516 |    "outputs": [
517 |     {
518 |      "data": {
519 |       "text/plain": [
520 |        "[<matplotlib.lines.Line2D at 0x197b08dfb50>]"
521 |       ]
522 |      },
523 |      "execution_count": 6,
524 |      "metadata": {},
525 |      "output_type": "execute_result"
526 |     },
527 |     {
528 |      "data": {
529 |       "image/png": 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\n",
530 |       "text/plain": [
531 |        "<Figure size 432x288 with 1 Axes>"
532 |       ]
533 |      },
534 |      "metadata": {
535 |       "needs_background": "light"
536 |      },
537 |      "output_type": "display_data"
538 |     }
539 |    ],
540 |    "source": [
541 |     "plt.plot(b)"
542 |    ]
543 |   },
544 |   {
545 |    "cell_type": "code",
546 |    "execution_count": 7,
547 |    "id": "bed13900",
548 |    "metadata": {},
549 |    "outputs": [
550 |     {
551 |      "name": "stdout",
552 |      "output_type": "stream",
553 |      "text": [
554 |       "[[0.62418301]\n",
555 |       " [0.62214052]\n",
556 |       " [0.62622549]\n",
557 |       " ...\n",
558 |       " [0.1621732 ]\n",
559 |       " [0.16319444]\n",
560 |       " [0.16584967]]\n"
561 |      ]
562 |     }
563 |    ],
564 |    "source": [
565 |     "from sklearn.preprocessing import MinMaxScaler\n",
566 |     "c=MinMaxScaler(feature_range=(0,1))\n",
567 |     "d=c.fit_transform(np.array(b).reshape(-1,1))\n",
568 |     "print(d)"
569 |    ]
570 |   },
571 |   {
572 |    "cell_type": "code",
573 |    "execution_count": 8,
574 |    "id": "5930bd92",
575 |    "metadata": {},
576 |    "outputs": [],
577 |    "source": [
578 |     "X_train = []\n",
579 |     "y_train = []\n",
580 |     "for i in range(60,2035):\n",
581 |     "    X_train.append(d[i-60:i,0])\n",
582 |     "    y_train.append(d[i-50:i,0])\n",
583 |     "X_train,y_train=np.array(X_train),np.array(y_train)\n",
584 |     "\n",
585 |     "\n",
586 |     "X_train = np.reshape(X_train,(X_train.shape[0],X_train.shape[1], 1 ))"
587 |    ]
588 |   },
589 |   {
590 |    "cell_type": "code",
591 |    "execution_count": 9,
592 |    "id": "d928092d",
593 |    "metadata": {},
594 |    "outputs": [
595 |     {
596 |      "name": "stdout",
597 |      "output_type": "stream",
598 |      "text": [
599 |       "Requirement already satisfied: tensorflow in c:\\users\\priya\\anaconda3\\lib\\site-packages (2.7.0)\n",
600 |       "Requirement already satisfied: absl-py>=0.4.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (1.0.0)\n",
601 |       "Requirement already satisfied: tensorboard~=2.6 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (2.7.0)\n",
602 |       "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.0)\n",
603 |       "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (1.6.3)\n",
604 |       "Requirement already satisfied: tensorflow-estimator<2.8,~=2.7.0rc0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (2.7.0)\n",
605 |       "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (3.7.4.3)\n",
606 |       "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (1.12.1)\n",
607 |       "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (1.42.0)\n",
608 |       "Requirement already satisfied: libclang>=9.0.1 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (12.0.0)\n",
609 |       "Requirement already satisfied: gast<0.5.0,>=0.2.1 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (0.4.0)\n",
610 |       "Requirement already satisfied: flatbuffers<3.0,>=1.12 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (2.0)\n",
611 |       "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (0.2.0)\n",
612 |       "Requirement already satisfied: six>=1.12.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (1.15.0)\n",
613 |       "Requirement already satisfied: keras-preprocessing>=1.1.1 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.2)\n",
614 |       "Requirement already satisfied: h5py>=2.9.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (2.10.0)\n",
615 |       "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.21.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (0.22.0)\n",
616 |       "Requirement already satisfied: protobuf>=3.9.2 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (3.19.1)\n",
617 |       "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (3.3.0)\n",
618 |       "Requirement already satisfied: numpy>=1.14.5 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (1.20.1)\n",
619 |       "Requirement already satisfied: keras<2.8,>=2.7.0rc0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (2.7.0)\n",
620 |       "Requirement already satisfied: wheel<1.0,>=0.32.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorflow) (0.36.2)\n",
621 |       "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorboard~=2.6->tensorflow) (0.4.6)\n",
622 |       "Requirement already satisfied: werkzeug>=0.11.15 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorboard~=2.6->tensorflow) (1.0.1)\n",
623 |       "Requirement already satisfied: setuptools>=41.0.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorboard~=2.6->tensorflow) (52.0.0.post20210125)\n",
624 |       "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorboard~=2.6->tensorflow) (1.8.0)\n",
625 |       "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorboard~=2.6->tensorflow) (0.6.1)\n",
626 |       "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorboard~=2.6->tensorflow) (2.3.3)\n",
627 |       "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorboard~=2.6->tensorflow) (3.3.6)\n",
628 |       "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from tensorboard~=2.6->tensorflow) (2.25.1)\n",
629 |       "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (0.2.8)\n",
630 |       "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (4.2.4)\n",
631 |       "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (4.8)\n",
632 |       "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow) (1.3.0)\n",
633 |       "Requirement already satisfied: importlib-metadata>=4.4 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from markdown>=2.6.8->tensorboard~=2.6->tensorflow) (4.8.2)\n",
634 |       "Requirement already satisfied: zipp>=0.5 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard~=2.6->tensorflow) (3.4.1)\n",
635 |       "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (0.4.8)\n",
636 |       "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (2020.12.5)\n",
637 |       "Requirement already satisfied: chardet<5,>=3.0.2 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (4.0.0)\n",
638 |       "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (2.10)\n",
639 |       "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (1.26.4)\n",
640 |       "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\priya\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow) (3.1.1)\n",
641 |       "Note: you may need to restart the kernel to use updated packages.\n"
642 |      ]
643 |     }
644 |    ],
645 |    "source": [
646 |     "pip install tensorflow "
647 |    ]
648 |   },
649 |   {
650 |    "cell_type": "code",
651 |    "execution_count": 10,
652 |    "id": "3ec9ae98",
653 |    "metadata": {},
654 |    "outputs": [],
655 |    "source": [
656 |     "from tensorflow.python import tf2\n",
657 |     "from keras import distribute\n",
658 |     "from keras.models import Sequential\n",
659 |     "from keras.layers import Dense\n",
660 |     "from keras.layers import LSTM\n",
661 |     "from keras.layers import Dropout"
662 |    ]
663 |   },
664 |   {
665 |    "cell_type": "code",
666 |    "execution_count": 11,
667 |    "id": "b1e7d644",
668 |    "metadata": {},
669 |    "outputs": [
670 |     {
671 |      "name": "stdout",
672 |      "output_type": "stream",
673 |      "text": [
674 |       "Epoch 1/5\n",
675 |       "62/62 [==============================] - 19s 138ms/step - loss: 0.0101\n",
676 |       "Epoch 2/5\n",
677 |       "62/62 [==============================] - 9s 142ms/step - loss: 0.0031\n",
678 |       "Epoch 3/5\n",
679 |       "62/62 [==============================] - 9s 138ms/step - loss: 0.0031\n",
680 |       "Epoch 4/5\n",
681 |       "62/62 [==============================] - 9s 144ms/step - loss: 0.0026\n",
682 |       "Epoch 5/5\n",
683 |       "62/62 [==============================] - 9s 140ms/step - loss: 0.0026\n"
684 |      ]
685 |     },
686 |     {
687 |      "data": {
688 |       "text/plain": [
689 |        "<keras.callbacks.History at 0x197bc666820>"
690 |       ]
691 |      },
692 |      "execution_count": 11,
693 |      "metadata": {},
694 |      "output_type": "execute_result"
695 |     }
696 |    ],
697 |    "source": [
698 |     "model = Sequential()\n",
699 |     "\n",
700 |     "model.add(LSTM(units=50,return_sequences=True,input_shape=(X_train.shape[1], 1)))\n",
701 |     "model.add(Dropout(0.2))\n",
702 |     "\n",
703 |     "model.add(LSTM(units=50,return_sequences=True))\n",
704 |     "model.add(Dropout(0.2))\n",
705 |     "\n",
706 |     "model.add(LSTM(units=50,return_sequences=True))\n",
707 |     "model.add(Dropout(0.2))\n",
708 |     "\n",
709 |     "model.add(LSTM(units=50))\n",
710 |     "model.add(Dropout(0.2))\n",
711 |     "\n",
712 |     "model.add(Dense(units=1))\n",
713 |     "\n",
714 |     "model.compile(optimizer='adam',loss='mean_squared_error')\n",
715 |     "\n",
716 |     "model.fit(X_train,y_train,epochs=5,batch_size=32)"
717 |    ]
718 |   },
719 |   {
720 |    "cell_type": "code",
721 |    "execution_count": 12,
722 |    "id": "a287157d",
723 |    "metadata": {},
724 |    "outputs": [],
725 |    "source": [
726 |     "url = 'https://raw.githubusercontent.com/mwitiderrick/stockprice/master/NSE-TATAGLOBAL.csv'\n",
727 |     "dataset_test = pd.read_csv(url)\n",
728 |     "real_stock_price = dataset_test.iloc[:, 1:2].values"
729 |    ]
730 |   },
731 |   {
732 |    "cell_type": "code",
733 |    "execution_count": 13,
734 |    "id": "cdd7c7c2",
735 |    "metadata": {},
736 |    "outputs": [],
737 |    "source": [
738 |     "dataset_total=pd.concat((a['Open'],dataset_test['Open']),axis=0)\n",
739 |     "inputs=dataset_total[len(dataset_total)-len(dataset_test)-60:].values\n",
740 |     "inputs=inputs.reshape(-1,1)\n",
741 |     "inputs=c.transform(inputs)\n",
742 |     "x_test=[]\n",
743 |     "for i in range(60,76):\n",
744 |     "    x_test.append(inputs[i-60:i, 0])\n",
745 |     "x_test=np.array(x_test)\n",
746 |     "x_test=np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))\n",
747 |     "predicted_stock_price=model.predict(x_test)\n",
748 |     "predicted_stock_price=c.inverse_transform(predicted_stock_price)\n",
749 |     "    "
750 |    ]
751 |   },
752 |   {
753 |    "cell_type": "code",
754 |    "execution_count": 15,
755 |    "id": "606966d3",
756 |    "metadata": {},
757 |    "outputs": [
758 |     {
759 |      "data": {
760 |       "image/png": 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\n",
761 |       "text/plain": [
762 |        "<Figure size 432x288 with 1 Axes>"
763 |       ]
764 |      },
765 |      "metadata": {
766 |       "needs_background": "light"
767 |      },
768 |      "output_type": "display_data"
769 |     }
770 |    ],
771 |    "source": [
772 |     "plt.plot(real_stock_price,color='black',label='TATA Stock Price')\n",
773 |     "plt.plot(predicted_stock_price,color='green',label='Predicted TATA Stock Price')\n",
774 |     "plt.title('TATA Stock Price Predication')\n",
775 |     "plt.xlabel('Time')\n",
776 |     "plt.ylabel('TATA Stock Price')\n",
777 |     "plt.legend()\n",
778 |     "plt.show()"
779 |    ]
780 |   },
781 |   {
782 |    "cell_type": "code",
783 |    "execution_count": null,
784 |    "id": "ea4046f8",
785 |    "metadata": {},
786 |    "outputs": [],
787 |    "source": []
788 |   },
789 |   {
790 |    "cell_type": "code",
791 |    "execution_count": null,
792 |    "id": "696dde4c",
793 |    "metadata": {},
794 |    "outputs": [],
795 |    "source": []
796 |   }
797 |  ],
798 |  "metadata": {
799 |   "kernelspec": {
800 |    "display_name": "Python 3",
801 |    "language": "python",
802 |    "name": "python3"
803 |   },
804 |   "language_info": {
805 |    "codemirror_mode": {
806 |     "name": "ipython",
807 |     "version": 3
808 |    },
809 |    "file_extension": ".py",
810 |    "mimetype": "text/x-python",
811 |    "name": "python",
812 |    "nbconvert_exporter": "python",
813 |    "pygments_lexer": "ipython3",
814 |    "version": "3.8.8"
815 |   }
816 |  },
817 |  "nbformat": 4,
818 |  "nbformat_minor": 5
819 | }
820 | 


--------------------------------------------------------------------------------