├── Data cleaning using Python
└── README.md


/Data cleaning using Python:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "3631d4f9",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 3,
 16 |    "id": "85c18b1c",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "import matplotlib.pyplot as plt"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 4,
 26 |    "id": "0f16a430",
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "data=pd.read_csv(r\"D:\\dataset for ML\\Employee.csv\")"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 5,
 36 |    "id": "68f1e690",
 37 |    "metadata": {},
 38 |    "outputs": [
 39 |     {
 40 |      "data": {
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 55 |        "</style>\n",
 56 |        "<table border=\"1\" class=\"dataframe\">\n",
 57 |        "  <thead>\n",
 58 |        "    <tr style=\"text-align: right;\">\n",
 59 |        "      <th></th>\n",
 60 |        "      <th>Education</th>\n",
 61 |        "      <th>JoiningYear</th>\n",
 62 |        "      <th>City</th>\n",
 63 |        "      <th>PaymentTier</th>\n",
 64 |        "      <th>Age</th>\n",
 65 |        "      <th>Gender</th>\n",
 66 |        "      <th>EverBenched</th>\n",
 67 |        "      <th>ExperienceInCurrentDomain</th>\n",
 68 |        "      <th>LeaveOrNot</th>\n",
 69 |        "    </tr>\n",
 70 |        "  </thead>\n",
 71 |        "  <tbody>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>0</th>\n",
 74 |        "      <td>Bachelors</td>\n",
 75 |        "      <td>2017</td>\n",
 76 |        "      <td>Bangalore</td>\n",
 77 |        "      <td>3</td>\n",
 78 |        "      <td>34</td>\n",
 79 |        "      <td>Male</td>\n",
 80 |        "      <td>No</td>\n",
 81 |        "      <td>0</td>\n",
 82 |        "      <td>0</td>\n",
 83 |        "    </tr>\n",
 84 |        "    <tr>\n",
 85 |        "      <th>1</th>\n",
 86 |        "      <td>Bachelors</td>\n",
 87 |        "      <td>2013</td>\n",
 88 |        "      <td>Pune</td>\n",
 89 |        "      <td>1</td>\n",
 90 |        "      <td>28</td>\n",
 91 |        "      <td>Female</td>\n",
 92 |        "      <td>No</td>\n",
 93 |        "      <td>3</td>\n",
 94 |        "      <td>1</td>\n",
 95 |        "    </tr>\n",
 96 |        "    <tr>\n",
 97 |        "      <th>2</th>\n",
 98 |        "      <td>Bachelors</td>\n",
 99 |        "      <td>2014</td>\n",
100 |        "      <td>New Delhi</td>\n",
101 |        "      <td>3</td>\n",
102 |        "      <td>38</td>\n",
103 |        "      <td>Female</td>\n",
104 |        "      <td>No</td>\n",
105 |        "      <td>2</td>\n",
106 |        "      <td>0</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <th>3</th>\n",
110 |        "      <td>Masters</td>\n",
111 |        "      <td>2016</td>\n",
112 |        "      <td>Bangalore</td>\n",
113 |        "      <td>3</td>\n",
114 |        "      <td>27</td>\n",
115 |        "      <td>Male</td>\n",
116 |        "      <td>No</td>\n",
117 |        "      <td>5</td>\n",
118 |        "      <td>1</td>\n",
119 |        "    </tr>\n",
120 |        "    <tr>\n",
121 |        "      <th>4</th>\n",
122 |        "      <td>Masters</td>\n",
123 |        "      <td>2017</td>\n",
124 |        "      <td>Pune</td>\n",
125 |        "      <td>3</td>\n",
126 |        "      <td>24</td>\n",
127 |        "      <td>Male</td>\n",
128 |        "      <td>Yes</td>\n",
129 |        "      <td>2</td>\n",
130 |        "      <td>1</td>\n",
131 |        "    </tr>\n",
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140 |        "      <td>...</td>\n",
141 |        "      <td>...</td>\n",
142 |        "      <td>...</td>\n",
143 |        "    </tr>\n",
144 |        "    <tr>\n",
145 |        "      <th>4648</th>\n",
146 |        "      <td>Bachelors</td>\n",
147 |        "      <td>2013</td>\n",
148 |        "      <td>Bangalore</td>\n",
149 |        "      <td>3</td>\n",
150 |        "      <td>26</td>\n",
151 |        "      <td>Female</td>\n",
152 |        "      <td>No</td>\n",
153 |        "      <td>4</td>\n",
154 |        "      <td>0</td>\n",
155 |        "    </tr>\n",
156 |        "    <tr>\n",
157 |        "      <th>4649</th>\n",
158 |        "      <td>Masters</td>\n",
159 |        "      <td>2013</td>\n",
160 |        "      <td>Pune</td>\n",
161 |        "      <td>2</td>\n",
162 |        "      <td>37</td>\n",
163 |        "      <td>Male</td>\n",
164 |        "      <td>No</td>\n",
165 |        "      <td>2</td>\n",
166 |        "      <td>1</td>\n",
167 |        "    </tr>\n",
168 |        "    <tr>\n",
169 |        "      <th>4650</th>\n",
170 |        "      <td>Masters</td>\n",
171 |        "      <td>2018</td>\n",
172 |        "      <td>New Delhi</td>\n",
173 |        "      <td>3</td>\n",
174 |        "      <td>27</td>\n",
175 |        "      <td>Male</td>\n",
176 |        "      <td>No</td>\n",
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178 |        "      <td>1</td>\n",
179 |        "    </tr>\n",
180 |        "    <tr>\n",
181 |        "      <th>4651</th>\n",
182 |        "      <td>Bachelors</td>\n",
183 |        "      <td>2012</td>\n",
184 |        "      <td>Bangalore</td>\n",
185 |        "      <td>3</td>\n",
186 |        "      <td>30</td>\n",
187 |        "      <td>Male</td>\n",
188 |        "      <td>Yes</td>\n",
189 |        "      <td>2</td>\n",
190 |        "      <td>0</td>\n",
191 |        "    </tr>\n",
192 |        "    <tr>\n",
193 |        "      <th>4652</th>\n",
194 |        "      <td>Bachelors</td>\n",
195 |        "      <td>2015</td>\n",
196 |        "      <td>Bangalore</td>\n",
197 |        "      <td>3</td>\n",
198 |        "      <td>33</td>\n",
199 |        "      <td>Male</td>\n",
200 |        "      <td>Yes</td>\n",
201 |        "      <td>4</td>\n",
202 |        "      <td>0</td>\n",
203 |        "    </tr>\n",
204 |        "  </tbody>\n",
205 |        "</table>\n",
206 |        "<p>4653 rows × 9 columns</p>\n",
207 |        "</div>"
208 |       ],
209 |       "text/plain": [
210 |        "      Education  JoiningYear       City  PaymentTier  Age  Gender EverBenched  \\\n",
211 |        "0     Bachelors         2017  Bangalore            3   34    Male          No   \n",
212 |        "1     Bachelors         2013       Pune            1   28  Female          No   \n",
213 |        "2     Bachelors         2014  New Delhi            3   38  Female          No   \n",
214 |        "3       Masters         2016  Bangalore            3   27    Male          No   \n",
215 |        "4       Masters         2017       Pune            3   24    Male         Yes   \n",
216 |        "...         ...          ...        ...          ...  ...     ...         ...   \n",
217 |        "4648  Bachelors         2013  Bangalore            3   26  Female          No   \n",
218 |        "4649    Masters         2013       Pune            2   37    Male          No   \n",
219 |        "4650    Masters         2018  New Delhi            3   27    Male          No   \n",
220 |        "4651  Bachelors         2012  Bangalore            3   30    Male         Yes   \n",
221 |        "4652  Bachelors         2015  Bangalore            3   33    Male         Yes   \n",
222 |        "\n",
223 |        "      ExperienceInCurrentDomain  LeaveOrNot  \n",
224 |        "0                             0           0  \n",
225 |        "1                             3           1  \n",
226 |        "2                             2           0  \n",
227 |        "3                             5           1  \n",
228 |        "4                             2           1  \n",
229 |        "...                         ...         ...  \n",
230 |        "4648                          4           0  \n",
231 |        "4649                          2           1  \n",
232 |        "4650                          5           1  \n",
233 |        "4651                          2           0  \n",
234 |        "4652                          4           0  \n",
235 |        "\n",
236 |        "[4653 rows x 9 columns]"
237 |       ]
238 |      },
239 |      "execution_count": 5,
240 |      "metadata": {},
241 |      "output_type": "execute_result"
242 |     }
243 |    ],
244 |    "source": [
245 |     "data"
246 |    ]
247 |   },
248 |   {
249 |    "cell_type": "code",
250 |    "execution_count": 6,
251 |    "id": "7e30784e",
252 |    "metadata": {},
253 |    "outputs": [],
254 |    "source": [
255 |     "data=data.dropna()"
256 |    ]
257 |   },
258 |   {
259 |    "cell_type": "code",
260 |    "execution_count": 7,
261 |    "id": "c60e9f00",
262 |    "metadata": {},
263 |    "outputs": [
264 |     {
265 |      "data": {
266 |       "text/html": [
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271 |        "    }\n",
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288 |        "      <th>PaymentTier</th>\n",
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299 |        "      <td>Bachelors</td>\n",
300 |        "      <td>2017</td>\n",
301 |        "      <td>Bangalore</td>\n",
302 |        "      <td>3</td>\n",
303 |        "      <td>34</td>\n",
304 |        "      <td>Male</td>\n",
305 |        "      <td>No</td>\n",
306 |        "      <td>0</td>\n",
307 |        "      <td>0</td>\n",
308 |        "    </tr>\n",
309 |        "    <tr>\n",
310 |        "      <th>1</th>\n",
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312 |        "      <td>2013</td>\n",
313 |        "      <td>Pune</td>\n",
314 |        "      <td>1</td>\n",
315 |        "      <td>28</td>\n",
316 |        "      <td>Female</td>\n",
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318 |        "      <td>3</td>\n",
319 |        "      <td>1</td>\n",
320 |        "    </tr>\n",
321 |        "    <tr>\n",
322 |        "      <th>2</th>\n",
323 |        "      <td>Bachelors</td>\n",
324 |        "      <td>2014</td>\n",
325 |        "      <td>New Delhi</td>\n",
326 |        "      <td>3</td>\n",
327 |        "      <td>38</td>\n",
328 |        "      <td>Female</td>\n",
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330 |        "      <td>2</td>\n",
331 |        "      <td>0</td>\n",
332 |        "    </tr>\n",
333 |        "    <tr>\n",
334 |        "      <th>3</th>\n",
335 |        "      <td>Masters</td>\n",
336 |        "      <td>2016</td>\n",
337 |        "      <td>Bangalore</td>\n",
338 |        "      <td>3</td>\n",
339 |        "      <td>27</td>\n",
340 |        "      <td>Male</td>\n",
341 |        "      <td>No</td>\n",
342 |        "      <td>5</td>\n",
343 |        "      <td>1</td>\n",
344 |        "    </tr>\n",
345 |        "    <tr>\n",
346 |        "      <th>4</th>\n",
347 |        "      <td>Masters</td>\n",
348 |        "      <td>2017</td>\n",
349 |        "      <td>Pune</td>\n",
350 |        "      <td>3</td>\n",
351 |        "      <td>24</td>\n",
352 |        "      <td>Male</td>\n",
353 |        "      <td>Yes</td>\n",
354 |        "      <td>2</td>\n",
355 |        "      <td>1</td>\n",
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365 |        "      <td>...</td>\n",
366 |        "      <td>...</td>\n",
367 |        "      <td>...</td>\n",
368 |        "    </tr>\n",
369 |        "    <tr>\n",
370 |        "      <th>4648</th>\n",
371 |        "      <td>Bachelors</td>\n",
372 |        "      <td>2013</td>\n",
373 |        "      <td>Bangalore</td>\n",
374 |        "      <td>3</td>\n",
375 |        "      <td>26</td>\n",
376 |        "      <td>Female</td>\n",
377 |        "      <td>No</td>\n",
378 |        "      <td>4</td>\n",
379 |        "      <td>0</td>\n",
380 |        "    </tr>\n",
381 |        "    <tr>\n",
382 |        "      <th>4649</th>\n",
383 |        "      <td>Masters</td>\n",
384 |        "      <td>2013</td>\n",
385 |        "      <td>Pune</td>\n",
386 |        "      <td>2</td>\n",
387 |        "      <td>37</td>\n",
388 |        "      <td>Male</td>\n",
389 |        "      <td>No</td>\n",
390 |        "      <td>2</td>\n",
391 |        "      <td>1</td>\n",
392 |        "    </tr>\n",
393 |        "    <tr>\n",
394 |        "      <th>4650</th>\n",
395 |        "      <td>Masters</td>\n",
396 |        "      <td>2018</td>\n",
397 |        "      <td>New Delhi</td>\n",
398 |        "      <td>3</td>\n",
399 |        "      <td>27</td>\n",
400 |        "      <td>Male</td>\n",
401 |        "      <td>No</td>\n",
402 |        "      <td>5</td>\n",
403 |        "      <td>1</td>\n",
404 |        "    </tr>\n",
405 |        "    <tr>\n",
406 |        "      <th>4651</th>\n",
407 |        "      <td>Bachelors</td>\n",
408 |        "      <td>2012</td>\n",
409 |        "      <td>Bangalore</td>\n",
410 |        "      <td>3</td>\n",
411 |        "      <td>30</td>\n",
412 |        "      <td>Male</td>\n",
413 |        "      <td>Yes</td>\n",
414 |        "      <td>2</td>\n",
415 |        "      <td>0</td>\n",
416 |        "    </tr>\n",
417 |        "    <tr>\n",
418 |        "      <th>4652</th>\n",
419 |        "      <td>Bachelors</td>\n",
420 |        "      <td>2015</td>\n",
421 |        "      <td>Bangalore</td>\n",
422 |        "      <td>3</td>\n",
423 |        "      <td>33</td>\n",
424 |        "      <td>Male</td>\n",
425 |        "      <td>Yes</td>\n",
426 |        "      <td>4</td>\n",
427 |        "      <td>0</td>\n",
428 |        "    </tr>\n",
429 |        "  </tbody>\n",
430 |        "</table>\n",
431 |        "<p>4653 rows × 9 columns</p>\n",
432 |        "</div>"
433 |       ],
434 |       "text/plain": [
435 |        "      Education  JoiningYear       City  PaymentTier  Age  Gender EverBenched  \\\n",
436 |        "0     Bachelors         2017  Bangalore            3   34    Male          No   \n",
437 |        "1     Bachelors         2013       Pune            1   28  Female          No   \n",
438 |        "2     Bachelors         2014  New Delhi            3   38  Female          No   \n",
439 |        "3       Masters         2016  Bangalore            3   27    Male          No   \n",
440 |        "4       Masters         2017       Pune            3   24    Male         Yes   \n",
441 |        "...         ...          ...        ...          ...  ...     ...         ...   \n",
442 |        "4648  Bachelors         2013  Bangalore            3   26  Female          No   \n",
443 |        "4649    Masters         2013       Pune            2   37    Male          No   \n",
444 |        "4650    Masters         2018  New Delhi            3   27    Male          No   \n",
445 |        "4651  Bachelors         2012  Bangalore            3   30    Male         Yes   \n",
446 |        "4652  Bachelors         2015  Bangalore            3   33    Male         Yes   \n",
447 |        "\n",
448 |        "      ExperienceInCurrentDomain  LeaveOrNot  \n",
449 |        "0                             0           0  \n",
450 |        "1                             3           1  \n",
451 |        "2                             2           0  \n",
452 |        "3                             5           1  \n",
453 |        "4                             2           1  \n",
454 |        "...                         ...         ...  \n",
455 |        "4648                          4           0  \n",
456 |        "4649                          2           1  \n",
457 |        "4650                          5           1  \n",
458 |        "4651                          2           0  \n",
459 |        "4652                          4           0  \n",
460 |        "\n",
461 |        "[4653 rows x 9 columns]"
462 |       ]
463 |      },
464 |      "execution_count": 7,
465 |      "metadata": {},
466 |      "output_type": "execute_result"
467 |     }
468 |    ],
469 |    "source": [
470 |     "data"
471 |    ]
472 |   },
473 |   {
474 |    "cell_type": "code",
475 |    "execution_count": 8,
476 |    "id": "755c3d9d",
477 |    "metadata": {},
478 |    "outputs": [],
479 |    "source": [
480 |     "data=data.fillna(2)"
481 |    ]
482 |   },
483 |   {
484 |    "cell_type": "code",
485 |    "execution_count": 9,
486 |    "id": "697267b1",
487 |    "metadata": {},
488 |    "outputs": [
489 |     {
490 |      "data": {
491 |       "text/html": [
492 |        "<div>\n",
493 |        "<style scoped>\n",
494 |        "    .dataframe tbody tr th:only-of-type {\n",
495 |        "        vertical-align: middle;\n",
496 |        "    }\n",
497 |        "\n",
498 |        "    .dataframe tbody tr th {\n",
499 |        "        vertical-align: top;\n",
500 |        "    }\n",
501 |        "\n",
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504 |        "    }\n",
505 |        "</style>\n",
506 |        "<table border=\"1\" class=\"dataframe\">\n",
507 |        "  <thead>\n",
508 |        "    <tr style=\"text-align: right;\">\n",
509 |        "      <th></th>\n",
510 |        "      <th>Education</th>\n",
511 |        "      <th>JoiningYear</th>\n",
512 |        "      <th>City</th>\n",
513 |        "      <th>PaymentTier</th>\n",
514 |        "      <th>Age</th>\n",
515 |        "      <th>Gender</th>\n",
516 |        "      <th>EverBenched</th>\n",
517 |        "      <th>ExperienceInCurrentDomain</th>\n",
518 |        "      <th>LeaveOrNot</th>\n",
519 |        "    </tr>\n",
520 |        "  </thead>\n",
521 |        "  <tbody>\n",
522 |        "    <tr>\n",
523 |        "      <th>0</th>\n",
524 |        "      <td>Bachelors</td>\n",
525 |        "      <td>2017</td>\n",
526 |        "      <td>Bangalore</td>\n",
527 |        "      <td>3</td>\n",
528 |        "      <td>34</td>\n",
529 |        "      <td>Male</td>\n",
530 |        "      <td>No</td>\n",
531 |        "      <td>0</td>\n",
532 |        "      <td>0</td>\n",
533 |        "    </tr>\n",
534 |        "    <tr>\n",
535 |        "      <th>1</th>\n",
536 |        "      <td>Bachelors</td>\n",
537 |        "      <td>2013</td>\n",
538 |        "      <td>Pune</td>\n",
539 |        "      <td>1</td>\n",
540 |        "      <td>28</td>\n",
541 |        "      <td>Female</td>\n",
542 |        "      <td>No</td>\n",
543 |        "      <td>3</td>\n",
544 |        "      <td>1</td>\n",
545 |        "    </tr>\n",
546 |        "    <tr>\n",
547 |        "      <th>2</th>\n",
548 |        "      <td>Bachelors</td>\n",
549 |        "      <td>2014</td>\n",
550 |        "      <td>New Delhi</td>\n",
551 |        "      <td>3</td>\n",
552 |        "      <td>38</td>\n",
553 |        "      <td>Female</td>\n",
554 |        "      <td>No</td>\n",
555 |        "      <td>2</td>\n",
556 |        "      <td>0</td>\n",
557 |        "    </tr>\n",
558 |        "    <tr>\n",
559 |        "      <th>3</th>\n",
560 |        "      <td>Masters</td>\n",
561 |        "      <td>2016</td>\n",
562 |        "      <td>Bangalore</td>\n",
563 |        "      <td>3</td>\n",
564 |        "      <td>27</td>\n",
565 |        "      <td>Male</td>\n",
566 |        "      <td>No</td>\n",
567 |        "      <td>5</td>\n",
568 |        "      <td>1</td>\n",
569 |        "    </tr>\n",
570 |        "    <tr>\n",
571 |        "      <th>4</th>\n",
572 |        "      <td>Masters</td>\n",
573 |        "      <td>2017</td>\n",
574 |        "      <td>Pune</td>\n",
575 |        "      <td>3</td>\n",
576 |        "      <td>24</td>\n",
577 |        "      <td>Male</td>\n",
578 |        "      <td>Yes</td>\n",
579 |        "      <td>2</td>\n",
580 |        "      <td>1</td>\n",
581 |        "    </tr>\n",
582 |        "    <tr>\n",
583 |        "      <th>...</th>\n",
584 |        "      <td>...</td>\n",
585 |        "      <td>...</td>\n",
586 |        "      <td>...</td>\n",
587 |        "      <td>...</td>\n",
588 |        "      <td>...</td>\n",
589 |        "      <td>...</td>\n",
590 |        "      <td>...</td>\n",
591 |        "      <td>...</td>\n",
592 |        "      <td>...</td>\n",
593 |        "    </tr>\n",
594 |        "    <tr>\n",
595 |        "      <th>4648</th>\n",
596 |        "      <td>Bachelors</td>\n",
597 |        "      <td>2013</td>\n",
598 |        "      <td>Bangalore</td>\n",
599 |        "      <td>3</td>\n",
600 |        "      <td>26</td>\n",
601 |        "      <td>Female</td>\n",
602 |        "      <td>No</td>\n",
603 |        "      <td>4</td>\n",
604 |        "      <td>0</td>\n",
605 |        "    </tr>\n",
606 |        "    <tr>\n",
607 |        "      <th>4649</th>\n",
608 |        "      <td>Masters</td>\n",
609 |        "      <td>2013</td>\n",
610 |        "      <td>Pune</td>\n",
611 |        "      <td>2</td>\n",
612 |        "      <td>37</td>\n",
613 |        "      <td>Male</td>\n",
614 |        "      <td>No</td>\n",
615 |        "      <td>2</td>\n",
616 |        "      <td>1</td>\n",
617 |        "    </tr>\n",
618 |        "    <tr>\n",
619 |        "      <th>4650</th>\n",
620 |        "      <td>Masters</td>\n",
621 |        "      <td>2018</td>\n",
622 |        "      <td>New Delhi</td>\n",
623 |        "      <td>3</td>\n",
624 |        "      <td>27</td>\n",
625 |        "      <td>Male</td>\n",
626 |        "      <td>No</td>\n",
627 |        "      <td>5</td>\n",
628 |        "      <td>1</td>\n",
629 |        "    </tr>\n",
630 |        "    <tr>\n",
631 |        "      <th>4651</th>\n",
632 |        "      <td>Bachelors</td>\n",
633 |        "      <td>2012</td>\n",
634 |        "      <td>Bangalore</td>\n",
635 |        "      <td>3</td>\n",
636 |        "      <td>30</td>\n",
637 |        "      <td>Male</td>\n",
638 |        "      <td>Yes</td>\n",
639 |        "      <td>2</td>\n",
640 |        "      <td>0</td>\n",
641 |        "    </tr>\n",
642 |        "    <tr>\n",
643 |        "      <th>4652</th>\n",
644 |        "      <td>Bachelors</td>\n",
645 |        "      <td>2015</td>\n",
646 |        "      <td>Bangalore</td>\n",
647 |        "      <td>3</td>\n",
648 |        "      <td>33</td>\n",
649 |        "      <td>Male</td>\n",
650 |        "      <td>Yes</td>\n",
651 |        "      <td>4</td>\n",
652 |        "      <td>0</td>\n",
653 |        "    </tr>\n",
654 |        "  </tbody>\n",
655 |        "</table>\n",
656 |        "<p>4653 rows × 9 columns</p>\n",
657 |        "</div>"
658 |       ],
659 |       "text/plain": [
660 |        "      Education  JoiningYear       City  PaymentTier  Age  Gender EverBenched  \\\n",
661 |        "0     Bachelors         2017  Bangalore            3   34    Male          No   \n",
662 |        "1     Bachelors         2013       Pune            1   28  Female          No   \n",
663 |        "2     Bachelors         2014  New Delhi            3   38  Female          No   \n",
664 |        "3       Masters         2016  Bangalore            3   27    Male          No   \n",
665 |        "4       Masters         2017       Pune            3   24    Male         Yes   \n",
666 |        "...         ...          ...        ...          ...  ...     ...         ...   \n",
667 |        "4648  Bachelors         2013  Bangalore            3   26  Female          No   \n",
668 |        "4649    Masters         2013       Pune            2   37    Male          No   \n",
669 |        "4650    Masters         2018  New Delhi            3   27    Male          No   \n",
670 |        "4651  Bachelors         2012  Bangalore            3   30    Male         Yes   \n",
671 |        "4652  Bachelors         2015  Bangalore            3   33    Male         Yes   \n",
672 |        "\n",
673 |        "      ExperienceInCurrentDomain  LeaveOrNot  \n",
674 |        "0                             0           0  \n",
675 |        "1                             3           1  \n",
676 |        "2                             2           0  \n",
677 |        "3                             5           1  \n",
678 |        "4                             2           1  \n",
679 |        "...                         ...         ...  \n",
680 |        "4648                          4           0  \n",
681 |        "4649                          2           1  \n",
682 |        "4650                          5           1  \n",
683 |        "4651                          2           0  \n",
684 |        "4652                          4           0  \n",
685 |        "\n",
686 |        "[4653 rows x 9 columns]"
687 |       ]
688 |      },
689 |      "execution_count": 9,
690 |      "metadata": {},
691 |      "output_type": "execute_result"
692 |     }
693 |    ],
694 |    "source": [
695 |     "data"
696 |    ]
697 |   },
698 |   {
699 |    "cell_type": "code",
700 |    "execution_count": 10,
701 |    "id": "21ad4911",
702 |    "metadata": {},
703 |    "outputs": [],
704 |    "source": [
705 |     "pay=data['PaymentTier']"
706 |    ]
707 |   },
708 |   {
709 |    "cell_type": "code",
710 |    "execution_count": 11,
711 |    "id": "dc55b7d2",
712 |    "metadata": {},
713 |    "outputs": [
714 |     {
715 |      "data": {
716 |       "text/plain": [
717 |        "0       3\n",
718 |        "1       1\n",
719 |        "2       3\n",
720 |        "3       3\n",
721 |        "4       3\n",
722 |        "       ..\n",
723 |        "4648    3\n",
724 |        "4649    2\n",
725 |        "4650    3\n",
726 |        "4651    3\n",
727 |        "4652    3\n",
728 |        "Name: PaymentTier, Length: 4653, dtype: int64"
729 |       ]
730 |      },
731 |      "execution_count": 11,
732 |      "metadata": {},
733 |      "output_type": "execute_result"
734 |     }
735 |    ],
736 |    "source": [
737 |     "pay"
738 |    ]
739 |   },
740 |   {
741 |    "cell_type": "code",
742 |    "execution_count": 18,
743 |    "id": "ae905dd1",
744 |    "metadata": {},
745 |    "outputs": [],
746 |    "source": [
747 |     "age=data['Age']"
748 |    ]
749 |   },
750 |   {
751 |    "cell_type": "code",
752 |    "execution_count": 19,
753 |    "id": "831979df",
754 |    "metadata": {},
755 |    "outputs": [
756 |     {
757 |      "data": {
758 |       "text/plain": [
759 |        "0       34\n",
760 |        "1       28\n",
761 |        "2       38\n",
762 |        "3       27\n",
763 |        "4       24\n",
764 |        "        ..\n",
765 |        "4648    26\n",
766 |        "4649    37\n",
767 |        "4650    27\n",
768 |        "4651    30\n",
769 |        "4652    33\n",
770 |        "Name: Age, Length: 4653, dtype: int64"
771 |       ]
772 |      },
773 |      "execution_count": 19,
774 |      "metadata": {},
775 |      "output_type": "execute_result"
776 |     }
777 |    ],
778 |    "source": [
779 |     "age"
780 |    ]
781 |   },
782 |   {
783 |    "cell_type": "code",
784 |    "execution_count": 26,
785 |    "id": "3b693527",
786 |    "metadata": {},
787 |    "outputs": [
788 |     {
789 |      "data": {
790 |       "image/png": 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",
791 |       "text/plain": [
792 |        "<Figure size 640x480 with 1 Axes>"
793 |       ]
794 |      },
795 |      "metadata": {},
796 |      "output_type": "display_data"
797 |     }
798 |    ],
799 |    "source": [
800 |     "plt.bar(pay,age)\n",
801 |     "plt.xlabel(\"Employe\")\n",
802 |     "plt.ylabel(\"Growth of employe\")\n",
803 |     "plt.title(\"Employe's salary tire growth\")\n",
804 |     "plt.show()"
805 |    ]
806 |   },
807 |   {
808 |    "cell_type": "code",
809 |    "execution_count": null,
810 |    "id": "31cb7e9c",
811 |    "metadata": {},
812 |    "outputs": [],
813 |    "source": []
814 |   }
815 |  ],
816 |  "metadata": {
817 |   "kernelspec": {
818 |    "display_name": "Python 3 (ipykernel)",
819 |    "language": "python",
820 |    "name": "python3"
821 |   },
822 |   "language_info": {
823 |    "codemirror_mode": {
824 |     "name": "ipython",
825 |     "version": 3
826 |    },
827 |    "file_extension": ".py",
828 |    "mimetype": "text/x-python",
829 |    "name": "python",
830 |    "nbconvert_exporter": "python",
831 |    "pygments_lexer": "ipython3",
832 |    "version": "3.11.5"
833 |   }
834 |  },
835 |  "nbformat": 4,
836 |  "nbformat_minor": 5
837 | }
838 | 


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/README.md:
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1 | # Data-Cleaning-using-Python
2 | This part consist of the Data cleaning which includes the removing the null values and replacing the some values instead of the null values and making it a cleaned data set
3 | 


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