├── README.md
├── Untitled6 (1).py
├── LICENSE
├── .gitignore
└── Untitled37.ipynb


/README.md:
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1 | # data-analysis-and-machine-learning-algorithms-


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/Untitled6 (1).py:
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 1 | #!/usr/bin/env python
 2 | # coding: utf-8
 3 | 
 4 | # In[1]:
 5 | 
 6 | 
 7 | import pandas as pd
 8 | import matplotlib.pyplot as plt
 9 | import seaborn as sns
10 | import numpy as np
11 | 
12 | 
13 | # In[3]:
14 | 
15 | 
16 | df=pd.read_csv("E:\IBM\covid_vaccine_statewise - covid_vaccine_statewise (1).csv")
17 | df
18 | 
19 | 
20 | # In[4]:
21 | 
22 | 
23 | df.head()
24 | 
25 | 
26 | # In[5]:
27 | 
28 | 
29 | df.tail()
30 | 
31 | 
32 | # In[6]:
33 | 
34 | 
35 | df.dtypes
36 | 
37 | 
38 | # In[7]:
39 | 
40 | 
41 | df.isnull()
42 | 
43 | 
44 | # In[10]:
45 | 
46 | 
47 | df["First Dose Administered"].fillna(df["First Dose Administered"].mode()[0],inplace=True)
48 | 
49 | 
50 | # In[11]:
51 | 
52 | 
53 | df.dropna().values
54 | 
55 | 
56 | # In[12]:
57 | 
58 | 
59 | df.describe().T
60 | 
61 | 
62 | # In[13]:
63 | 
64 | 
65 | df.info()
66 | 
67 | 
68 | # In[14]:
69 | 
70 | 
71 | df["Sessions"].hist()
72 | 
73 | 
74 | # In[16]:
75 | 
76 | 
77 | df.groupby("State")
78 | 
79 | 
80 | # In[ ]:
81 | 
82 | 
83 | 
84 | 
85 | 


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/LICENSE:
--------------------------------------------------------------------------------
 1 | BSD 2-Clause License
 2 | 
 3 | Copyright (c) 2024, KISHAN KUMAR SURESH KUMAR 
 4 | 
 5 | Redistribution and use in source and binary forms, with or without
 6 | modification, are permitted provided that the following conditions are met:
 7 | 
 8 | 1. Redistributions of source code must retain the above copyright notice, this
 9 |    list of conditions and the following disclaimer.
10 | 
11 | 2. Redistributions in binary form must reproduce the above copyright notice,
12 |    this list of conditions and the following disclaimer in the documentation
13 |    and/or other materials provided with the distribution.
14 | 
15 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
16 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
17 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
18 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
19 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
20 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
21 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
22 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
23 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
24 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
25 | 


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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 | share/python-wheels/
 24 | *.egg-info/
 25 | .installed.cfg
 26 | *.egg
 27 | MANIFEST
 28 | 
 29 | # PyInstaller
 30 | #  Usually these files are written by a python script from a template
 31 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 32 | *.manifest
 33 | *.spec
 34 | 
 35 | # Installer logs
 36 | pip-log.txt
 37 | pip-delete-this-directory.txt
 38 | 
 39 | # Unit test / coverage reports
 40 | htmlcov/
 41 | .tox/
 42 | .nox/
 43 | .coverage
 44 | .coverage.*
 45 | .cache
 46 | nosetests.xml
 47 | coverage.xml
 48 | *.cover
 49 | *.py,cover
 50 | .hypothesis/
 51 | .pytest_cache/
 52 | cover/
 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 | .pybuilder/
 76 | target/
 77 | 
 78 | # Jupyter Notebook
 79 | .ipynb_checkpoints
 80 | 
 81 | # IPython
 82 | profile_default/
 83 | ipython_config.py
 84 | 
 85 | # pyenv
 86 | #   For a library or package, you might want to ignore these files since the code is
 87 | #   intended to run in multiple environments; otherwise, check them in:
 88 | # .python-version
 89 | 
 90 | # pipenv
 91 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 92 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 93 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 94 | #   install all needed dependencies.
 95 | #Pipfile.lock
 96 | 
 97 | # poetry
 98 | #   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
 99 | #   This is especially recommended for binary packages to ensure reproducibility, and is more
100 | #   commonly ignored for libraries.
101 | #   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
102 | #poetry.lock
103 | 
104 | # pdm
105 | #   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
106 | #pdm.lock
107 | #   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
108 | #   in version control.
109 | #   https://pdm.fming.dev/#use-with-ide
110 | .pdm.toml
111 | 
112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
113 | __pypackages__/
114 | 
115 | # Celery stuff
116 | celerybeat-schedule
117 | celerybeat.pid
118 | 
119 | # SageMath parsed files
120 | *.sage.py
121 | 
122 | # Environments
123 | .env
124 | .venv
125 | env/
126 | venv/
127 | ENV/
128 | env.bak/
129 | venv.bak/
130 | 
131 | # Spyder project settings
132 | .spyderproject
133 | .spyproject
134 | 
135 | # Rope project settings
136 | .ropeproject
137 | 
138 | # mkdocs documentation
139 | /site
140 | 
141 | # mypy
142 | .mypy_cache/
143 | .dmypy.json
144 | dmypy.json
145 | 
146 | # Pyre type checker
147 | .pyre/
148 | 
149 | # pytype static type analyzer
150 | .pytype/
151 | 
152 | # Cython debug symbols
153 | cython_debug/
154 | 
155 | # PyCharm
156 | #  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
157 | #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
158 | #  and can be added to the global gitignore or merged into this file.  For a more nuclear
159 | #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
160 | #.idea/
161 | 


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/Untitled37.ipynb:
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   1 | {
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   7 |       "authorship_tag": "ABX9TyN30rMj4HXg7+aQKV4x/K/b",
   8 |       "include_colab_link": true
   9 |     },
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  21 |       "metadata": {
  22 |         "id": "view-in-github",
  23 |         "colab_type": "text"
  24 |       },
  25 |       "source": [
  26 |         "<a href=\"https://colab.research.google.com/github/Kishankumar1328/data-analysis-and-machine-learning-algorithms-/blob/main/Untitled37.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  27 |       ]
  28 |     },
  29 |     {
  30 |       "cell_type": "code",
  31 |       "execution_count": 44,
  32 |       "metadata": {
  33 |         "id": "_GUlGOJI2_6c"
  34 |       },
  35 |       "outputs": [],
  36 |       "source": [
  37 |         "import pandas as pd\n",
  38 |         "import matplotlib.pyplot as plt\n",
  39 |         "import seaborn as sns\n",
  40 |         "import numpy as np\n",
  41 |         "from sklearn.linear_model import LinearRegression\n",
  42 |         "from sklearn.model_selection import train_test_split"
  43 |       ]
  44 |     },
  45 |     {
  46 |       "cell_type": "code",
  47 |       "source": [
  48 |         "df=pd.read_csv(\"/content/covid_19_india _l.csv\")\n",
  49 |         "df"
  50 |       ],
  51 |       "metadata": {
  52 |         "colab": {
  53 |           "base_uri": "https://localhost:8080/",
  54 |           "height": 423
  55 |         },
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  57 |         "outputId": "5aee384b-4fd4-4194-adc0-1fc226f2fc3e"
  58 |       },
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  60 |       "outputs": [
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  67 |               "1             1  31-01-2020         Kerala   10.8505    76.2711          1   \n",
  68 |               "2             2  01-02-2020         Kerala   10.8505    76.2711          2   \n",
  69 |               "3             3  02-02-2020         Kerala   10.8505    76.2711          3   \n",
  70 |               "4             4  03-02-2020         Kerala   10.8505    76.2711          3   \n",
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  72 |               "947         947  15-04-2020      Telengana   18.1124    79.0193        624   \n",
  73 |               "948         948  15-04-2020        Tripura   23.9408    91.9882          2   \n",
  74 |               "949         949  15-04-2020  Uttar Pradesh   26.8467    80.9462        660   \n",
  75 |               "950         950  15-04-2020    Uttarakhand   30.0668    79.0193         37   \n",
  76 |               "951         951  15-04-2020    West Bengal   22.9868    87.8550        213   \n",
  77 |               "\n",
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  83 |               "4         3       0        0.000000      0       0.000000  \n",
  84 |               "..      ...     ...             ...    ...            ...  \n",
  85 |               "947     507      17        0.027244    100       0.160256  \n",
  86 |               "948       2       0        0.000000      0       0.000000  \n",
  87 |               "949     605       5        0.007576     50       0.075758  \n",
  88 |               "950      28       0        0.000000      9       0.243243  \n",
  89 |               "951     169       7        0.032864     37       0.173709  \n",
  90 |               "\n",
  91 |               "[952 rows x 11 columns]"
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 325 |               "\n",
 326 |               "    [theme=dark] .colab-df-convert {\n",
 327 |               "      background-color: #3B4455;\n",
 328 |               "      fill: #D2E3FC;\n",
 329 |               "    }\n",
 330 |               "\n",
 331 |               "    [theme=dark] .colab-df-convert:hover {\n",
 332 |               "      background-color: #434B5C;\n",
 333 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 334 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 335 |               "      fill: #FFFFFF;\n",
 336 |               "    }\n",
 337 |               "  </style>\n",
 338 |               "\n",
 339 |               "    <script>\n",
 340 |               "      const buttonEl =\n",
 341 |               "        document.querySelector('#df-f9743992-b540-415f-8494-0ca01f0ffd2a button.colab-df-convert');\n",
 342 |               "      buttonEl.style.display =\n",
 343 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 344 |               "\n",
 345 |               "      async function convertToInteractive(key) {\n",
 346 |               "        const element = document.querySelector('#df-f9743992-b540-415f-8494-0ca01f0ffd2a');\n",
 347 |               "        const dataTable =\n",
 348 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 349 |               "                                                    [key], {});\n",
 350 |               "        if (!dataTable) return;\n",
 351 |               "\n",
 352 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
 353 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 354 |               "          + ' to learn more about interactive tables.';\n",
 355 |               "        element.innerHTML = '';\n",
 356 |               "        dataTable['output_type'] = 'display_data';\n",
 357 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
 358 |               "        const docLink = document.createElement('div');\n",
 359 |               "        docLink.innerHTML = docLinkHtml;\n",
 360 |               "        element.appendChild(docLink);\n",
 361 |               "      }\n",
 362 |               "    </script>\n",
 363 |               "  </div>\n",
 364 |               "\n",
 365 |               "\n",
 366 |               "<div id=\"df-0e1c1f1c-df7f-4723-84a0-6a18a384162e\">\n",
 367 |               "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0e1c1f1c-df7f-4723-84a0-6a18a384162e')\"\n",
 368 |               "            title=\"Suggest charts\"\n",
 369 |               "            style=\"display:none;\">\n",
 370 |               "\n",
 371 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 372 |               "     width=\"24px\">\n",
 373 |               "    <g>\n",
 374 |               "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
 375 |               "    </g>\n",
 376 |               "</svg>\n",
 377 |               "  </button>\n",
 378 |               "\n",
 379 |               "<style>\n",
 380 |               "  .colab-df-quickchart {\n",
 381 |               "      --bg-color: #E8F0FE;\n",
 382 |               "      --fill-color: #1967D2;\n",
 383 |               "      --hover-bg-color: #E2EBFA;\n",
 384 |               "      --hover-fill-color: #174EA6;\n",
 385 |               "      --disabled-fill-color: #AAA;\n",
 386 |               "      --disabled-bg-color: #DDD;\n",
 387 |               "  }\n",
 388 |               "\n",
 389 |               "  [theme=dark] .colab-df-quickchart {\n",
 390 |               "      --bg-color: #3B4455;\n",
 391 |               "      --fill-color: #D2E3FC;\n",
 392 |               "      --hover-bg-color: #434B5C;\n",
 393 |               "      --hover-fill-color: #FFFFFF;\n",
 394 |               "      --disabled-bg-color: #3B4455;\n",
 395 |               "      --disabled-fill-color: #666;\n",
 396 |               "  }\n",
 397 |               "\n",
 398 |               "  .colab-df-quickchart {\n",
 399 |               "    background-color: var(--bg-color);\n",
 400 |               "    border: none;\n",
 401 |               "    border-radius: 50%;\n",
 402 |               "    cursor: pointer;\n",
 403 |               "    display: none;\n",
 404 |               "    fill: var(--fill-color);\n",
 405 |               "    height: 32px;\n",
 406 |               "    padding: 0;\n",
 407 |               "    width: 32px;\n",
 408 |               "  }\n",
 409 |               "\n",
 410 |               "  .colab-df-quickchart:hover {\n",
 411 |               "    background-color: var(--hover-bg-color);\n",
 412 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 413 |               "    fill: var(--button-hover-fill-color);\n",
 414 |               "  }\n",
 415 |               "\n",
 416 |               "  .colab-df-quickchart-complete:disabled,\n",
 417 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
 418 |               "    background-color: var(--disabled-bg-color);\n",
 419 |               "    fill: var(--disabled-fill-color);\n",
 420 |               "    box-shadow: none;\n",
 421 |               "  }\n",
 422 |               "\n",
 423 |               "  .colab-df-spinner {\n",
 424 |               "    border: 2px solid var(--fill-color);\n",
 425 |               "    border-color: transparent;\n",
 426 |               "    border-bottom-color: var(--fill-color);\n",
 427 |               "    animation:\n",
 428 |               "      spin 1s steps(1) infinite;\n",
 429 |               "  }\n",
 430 |               "\n",
 431 |               "  @keyframes spin {\n",
 432 |               "    0% {\n",
 433 |               "      border-color: transparent;\n",
 434 |               "      border-bottom-color: var(--fill-color);\n",
 435 |               "      border-left-color: var(--fill-color);\n",
 436 |               "    }\n",
 437 |               "    20% {\n",
 438 |               "      border-color: transparent;\n",
 439 |               "      border-left-color: var(--fill-color);\n",
 440 |               "      border-top-color: var(--fill-color);\n",
 441 |               "    }\n",
 442 |               "    30% {\n",
 443 |               "      border-color: transparent;\n",
 444 |               "      border-left-color: var(--fill-color);\n",
 445 |               "      border-top-color: var(--fill-color);\n",
 446 |               "      border-right-color: var(--fill-color);\n",
 447 |               "    }\n",
 448 |               "    40% {\n",
 449 |               "      border-color: transparent;\n",
 450 |               "      border-right-color: var(--fill-color);\n",
 451 |               "      border-top-color: var(--fill-color);\n",
 452 |               "    }\n",
 453 |               "    60% {\n",
 454 |               "      border-color: transparent;\n",
 455 |               "      border-right-color: var(--fill-color);\n",
 456 |               "    }\n",
 457 |               "    80% {\n",
 458 |               "      border-color: transparent;\n",
 459 |               "      border-right-color: var(--fill-color);\n",
 460 |               "      border-bottom-color: var(--fill-color);\n",
 461 |               "    }\n",
 462 |               "    90% {\n",
 463 |               "      border-color: transparent;\n",
 464 |               "      border-bottom-color: var(--fill-color);\n",
 465 |               "    }\n",
 466 |               "  }\n",
 467 |               "</style>\n",
 468 |               "\n",
 469 |               "  <script>\n",
 470 |               "    async function quickchart(key) {\n",
 471 |               "      const quickchartButtonEl =\n",
 472 |               "        document.querySelector('#' + key + ' button');\n",
 473 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
 474 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
 475 |               "      try {\n",
 476 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
 477 |               "            'suggestCharts', [key], {});\n",
 478 |               "      } catch (error) {\n",
 479 |               "        console.error('Error during call to suggestCharts:', error);\n",
 480 |               "      }\n",
 481 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
 482 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
 483 |               "    }\n",
 484 |               "    (() => {\n",
 485 |               "      let quickchartButtonEl =\n",
 486 |               "        document.querySelector('#df-0e1c1f1c-df7f-4723-84a0-6a18a384162e button');\n",
 487 |               "      quickchartButtonEl.style.display =\n",
 488 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 489 |               "    })();\n",
 490 |               "  </script>\n",
 491 |               "</div>\n",
 492 |               "\n",
 493 |               "  <div id=\"id_eac7b5e5-aebc-4a2a-aec6-e20299cd0303\">\n",
 494 |               "    <style>\n",
 495 |               "      .colab-df-generate {\n",
 496 |               "        background-color: #E8F0FE;\n",
 497 |               "        border: none;\n",
 498 |               "        border-radius: 50%;\n",
 499 |               "        cursor: pointer;\n",
 500 |               "        display: none;\n",
 501 |               "        fill: #1967D2;\n",
 502 |               "        height: 32px;\n",
 503 |               "        padding: 0 0 0 0;\n",
 504 |               "        width: 32px;\n",
 505 |               "      }\n",
 506 |               "\n",
 507 |               "      .colab-df-generate:hover {\n",
 508 |               "        background-color: #E2EBFA;\n",
 509 |               "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 510 |               "        fill: #174EA6;\n",
 511 |               "      }\n",
 512 |               "\n",
 513 |               "      [theme=dark] .colab-df-generate {\n",
 514 |               "        background-color: #3B4455;\n",
 515 |               "        fill: #D2E3FC;\n",
 516 |               "      }\n",
 517 |               "\n",
 518 |               "      [theme=dark] .colab-df-generate:hover {\n",
 519 |               "        background-color: #434B5C;\n",
 520 |               "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 521 |               "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 522 |               "        fill: #FFFFFF;\n",
 523 |               "      }\n",
 524 |               "    </style>\n",
 525 |               "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
 526 |               "            title=\"Generate code using this dataframe.\"\n",
 527 |               "            style=\"display:none;\">\n",
 528 |               "\n",
 529 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 530 |               "       width=\"24px\">\n",
 531 |               "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
 532 |               "  </svg>\n",
 533 |               "    </button>\n",
 534 |               "    <script>\n",
 535 |               "      (() => {\n",
 536 |               "      const buttonEl =\n",
 537 |               "        document.querySelector('#id_eac7b5e5-aebc-4a2a-aec6-e20299cd0303 button.colab-df-generate');\n",
 538 |               "      buttonEl.style.display =\n",
 539 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 540 |               "\n",
 541 |               "      buttonEl.onclick = () => {\n",
 542 |               "        google.colab.notebook.generateWithVariable('df');\n",
 543 |               "      }\n",
 544 |               "      })();\n",
 545 |               "    </script>\n",
 546 |               "  </div>\n",
 547 |               "\n",
 548 |               "    </div>\n",
 549 |               "  </div>\n"
 550 |             ]
 551 |           },
 552 |           "metadata": {},
 553 |           "execution_count": 45
 554 |         }
 555 |       ]
 556 |     },
 557 |     {
 558 |       "cell_type": "code",
 559 |       "source": [
 560 |         "sns.lmplot(x=\"Confirmed\",y=\"Cured\",data=df)"
 561 |       ],
 562 |       "metadata": {
 563 |         "colab": {
 564 |           "base_uri": "https://localhost:8080/",
 565 |           "height": 523
 566 |         },
 567 |         "id": "uJVrHDl535CM",
 568 |         "outputId": "1f8aad14-128a-4439-f5af-b22e2ebf36e4"
 569 |       },
 570 |       "execution_count": 46,
 571 |       "outputs": [
 572 |         {
 573 |           "output_type": "execute_result",
 574 |           "data": {
 575 |             "text/plain": [
 576 |               "<seaborn.axisgrid.FacetGrid at 0x7cc99f684340>"
 577 |             ]
 578 |           },
 579 |           "metadata": {},
 580 |           "execution_count": 46
 581 |         },
 582 |         {
 583 |           "output_type": "display_data",
 584 |           "data": {
 585 |             "text/plain": [
 586 |               "<Figure size 500x500 with 1 Axes>"
 587 |             ],
 588 |             "image/png": 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\n"
 589 |           },
 590 |           "metadata": {}
 591 |         }
 592 |       ]
 593 |     },
 594 |     {
 595 |       "cell_type": "code",
 596 |       "source": [
 597 |         "x=np.array(df[[\"Confirmed\"]]).reshape(-1,1)"
 598 |       ],
 599 |       "metadata": {
 600 |         "id": "b5J9DxRR8vGo"
 601 |       },
 602 |       "execution_count": 85,
 603 |       "outputs": []
 604 |     },
 605 |     {
 606 |       "cell_type": "code",
 607 |       "source": [
 608 |         "y=np.array(df[[\"Cured\"]]).reshape(-1,1)"
 609 |       ],
 610 |       "metadata": {
 611 |         "id": "Sqlgxmos89mY"
 612 |       },
 613 |       "execution_count": 86,
 614 |       "outputs": []
 615 |     },
 616 |     {
 617 |       "cell_type": "code",
 618 |       "source": [
 619 |         "lm=LinearRegression()"
 620 |       ],
 621 |       "metadata": {
 622 |         "id": "96vDPSk05OuD"
 623 |       },
 624 |       "execution_count": 87,
 625 |       "outputs": []
 626 |     },
 627 |     {
 628 |       "cell_type": "code",
 629 |       "source": [
 630 |         "lm.fit(x,y)"
 631 |       ],
 632 |       "metadata": {
 633 |         "colab": {
 634 |           "base_uri": "https://localhost:8080/",
 635 |           "height": 74
 636 |         },
 637 |         "id": "MYirZIvs5OyV",
 638 |         "outputId": "f8d813a2-08d5-4114-8790-87c46ec9e5df"
 639 |       },
 640 |       "execution_count": 88,
 641 |       "outputs": [
 642 |         {
 643 |           "output_type": "execute_result",
 644 |           "data": {
 645 |             "text/plain": [
 646 |               "LinearRegression()"
 647 |             ],
 648 |             "text/html": [
 649 |               "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
 650 |             ]
 651 |           },
 652 |           "metadata": {},
 653 |           "execution_count": 88
 654 |         }
 655 |       ]
 656 |     },
 657 |     {
 658 |       "cell_type": "code",
 659 |       "source": [
 660 |         "x_train,y_train,x_test,y_test=train_test_split(x,y,test_size=0.2)"
 661 |       ],
 662 |       "metadata": {
 663 |         "id": "9yOf-c7l4m3h"
 664 |       },
 665 |       "execution_count": 89,
 666 |       "outputs": []
 667 |     },
 668 |     {
 669 |       "cell_type": "code",
 670 |       "source": [
 671 |         "y_predict=lm.predict(x_test)\n",
 672 |         "y_predict"
 673 |       ],
 674 |       "metadata": {
 675 |         "colab": {
 676 |           "base_uri": "https://localhost:8080/"
 677 |         },
 678 |         "id": "6_H579Cz5O1o",
 679 |         "outputId": "0bb79264-8042-43b9-d580-07664a0e531f"
 680 |       },
 681 |       "execution_count": 90,
 682 |       "outputs": [
 683 |         {
 684 |           "output_type": "execute_result",
 685 |           "data": {
 686 |             "text/plain": [
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1436 |               "       [ 0.88592609],\n",
1437 |               "       [ 1.44242717],\n",
1438 |               "       [ 0.80642594],\n",
1439 |               "       [ 6.84843766],\n",
1440 |               "       [ 0.56792547],\n",
1441 |               "       [ 0.56792547],\n",
1442 |               "       [ 2.55542933],\n",
1443 |               "       [ 1.0449264 ],\n",
1444 |               "       [ 0.56792547],\n",
1445 |               "       [ 0.56792547],\n",
1446 |               "       [ 0.56792547],\n",
1447 |               "       [ 0.56792547]])"
1448 |             ]
1449 |           },
1450 |           "metadata": {},
1451 |           "execution_count": 90
1452 |         }
1453 |       ]
1454 |     },
1455 |     {
1456 |       "cell_type": "markdown",
1457 |       "source": [
1458 |         "did not able to create regression because of data is not so clean and we have clean the data"
1459 |       ],
1460 |       "metadata": {
1461 |         "id": "3CwqS8ks_WYT"
1462 |       }
1463 |     }
1464 |   ]
1465 | }


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