├── .gitignore
├── LICENSE
├── README.md
├── data
    ├── journeys.csv.gz
    ├── stations.csv.gz
    ├── winequality-red.csv.gz
    └── winequality-white.csv.gz
├── numpy_histo.py
├── pandas_histo.py
├── pandas_visualisation.ipynb
└── wine_quality_prediction.ipynb


/.gitignore:
--------------------------------------------------------------------------------
  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 | *.egg-info/
 24 | .installed.cfg
 25 | *.egg
 26 | MANIFEST
 27 | 
 28 | # PyInstaller
 29 | #  Usually these files are written by a python script from a template
 30 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 31 | *.manifest
 32 | *.spec
 33 | 
 34 | # Installer logs
 35 | pip-log.txt
 36 | pip-delete-this-directory.txt
 37 | 
 38 | # Unit test / coverage reports
 39 | htmlcov/
 40 | .tox/
 41 | .coverage
 42 | .coverage.*
 43 | .cache
 44 | nosetests.xml
 45 | coverage.xml
 46 | *.cover
 47 | .hypothesis/
 48 | .pytest_cache/
 49 | 
 50 | # Translations
 51 | *.mo
 52 | *.pot
 53 | 
 54 | # Django stuff:
 55 | *.log
 56 | local_settings.py
 57 | db.sqlite3
 58 | 
 59 | # Flask stuff:
 60 | instance/
 61 | .webassets-cache
 62 | 
 63 | # Scrapy stuff:
 64 | .scrapy
 65 | 
 66 | # Sphinx documentation
 67 | docs/_build/
 68 | 
 69 | # PyBuilder
 70 | target/
 71 | 
 72 | # Jupyter Notebook
 73 | .ipynb_checkpoints
 74 | 
 75 | # pyenv
 76 | .python-version
 77 | 
 78 | # celery beat schedule file
 79 | celerybeat-schedule
 80 | 
 81 | # SageMath parsed files
 82 | *.sage.py
 83 | 
 84 | # Environments
 85 | .env
 86 | .venv
 87 | env/
 88 | venv/
 89 | ENV/
 90 | env.bak/
 91 | venv.bak/
 92 | 
 93 | # Spyder project settings
 94 | .spyderproject
 95 | .spyproject
 96 | 
 97 | # Rope project settings
 98 | .ropeproject
 99 | 
100 | # mkdocs documentation
101 | /site
102 | 
103 | # mypy
104 | .mypy_cache/
105 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2019 techcraftco
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # ipad-data-science


--------------------------------------------------------------------------------
/data/journeys.csv.gz:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/techcraftco/ipad-data-science/92badf12d1e54773e6ccf1910d8751aa3d7d2e46/data/journeys.csv.gz


--------------------------------------------------------------------------------
/data/stations.csv.gz:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/techcraftco/ipad-data-science/92badf12d1e54773e6ccf1910d8751aa3d7d2e46/data/stations.csv.gz


--------------------------------------------------------------------------------
/data/winequality-red.csv.gz:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/techcraftco/ipad-data-science/92badf12d1e54773e6ccf1910d8751aa3d7d2e46/data/winequality-red.csv.gz


--------------------------------------------------------------------------------
/data/winequality-white.csv.gz:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/techcraftco/ipad-data-science/92badf12d1e54773e6ccf1910d8751aa3d7d2e46/data/winequality-white.csv.gz


--------------------------------------------------------------------------------
/numpy_histo.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import matplotlib.pyplot as plt
 3 | 
 4 | df = np.loadtxt('data/journeys.csv.gz', skiprows = 1, delimiter= ',')
 5 | start_hours = df[:,11]
 6 | 
 7 | plt.hist(start_hours, 24)
 8 | plt.title("Journey Start Hours")
 9 | plt.xlabel("Count")
10 | plt.ylabel("Hour")
11 | plt.show()


--------------------------------------------------------------------------------
/pandas_histo.py:
--------------------------------------------------------------------------------
 1 | import pandas as pd
 2 | import matplotlib.pyplot as plt
 3 | 
 4 | df = pd.read_csv("data/journeys.csv.gz")
 5 | 
 6 | plt.figure()
 7 | hist = df.hist(column = "Start Hour", bins = 24)
 8 | 
 9 | plt.title("Start Hours")
10 | plt.xlabel("Hour")
11 | plt.ylabel("Count")
12 | plt.show()
13 | 


--------------------------------------------------------------------------------
/pandas_visualisation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "metadata": {
  5 |         "trusted": true
  6 |       },
  7 |       "cell_type": "code",
  8 |       "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt",
  9 |       "execution_count": 2,
 10 |       "outputs": []
 11 |     },
 12 |     {
 13 |       "metadata": {
 14 |         "trusted": true
 15 |       },
 16 |       "cell_type": "code",
 17 |       "source": "df = pd.read_csv(\"data/journeys.csv.gz\")",
 18 |       "execution_count": 3,
 19 |       "outputs": []
 20 |     },
 21 |     {
 22 |       "metadata": {
 23 |         "trusted": true
 24 |       },
 25 |       "cell_type": "code",
 26 |       "source": "df.head()",
 27 |       "execution_count": 4,
 28 |       "outputs": [
 29 |         {
 30 |           "output_type": "execute_result",
 31 |           "execution_count": 4,
 32 |           "data": {
 33 |             "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Journey Duration</th>\n      <th>Journey ID</th>\n      <th>End Date</th>\n      <th>End Month</th>\n      <th>End Year</th>\n      <th>End Hour</th>\n      <th>End Minute</th>\n      <th>End Station ID</th>\n      <th>Start Date</th>\n      <th>Start Month</th>\n      <th>Start Year</th>\n      <th>Start Hour</th>\n      <th>Start Minute</th>\n      <th>Start Station ID</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2040.0</td>\n      <td>953</td>\n      <td>19</td>\n      <td>9</td>\n      <td>17</td>\n      <td>18</td>\n      <td>0</td>\n      <td>478</td>\n      <td>19</td>\n      <td>9</td>\n      <td>17</td>\n      <td>17</td>\n      <td>26</td>\n      <td>251</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1800.0</td>\n      <td>12581</td>\n      <td>19</td>\n      <td>9</td>\n      <td>17</td>\n      <td>15</td>\n      <td>21</td>\n      <td>122</td>\n      <td>19</td>\n      <td>9</td>\n      <td>17</td>\n      <td>14</td>\n      <td>51</td>\n      <td>550</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1140.0</td>\n      <td>1159</td>\n      <td>15</td>\n      <td>9</td>\n      <td>17</td>\n      <td>17</td>\n      <td>1</td>\n      <td>639</td>\n      <td>15</td>\n      <td>9</td>\n      <td>17</td>\n      <td>16</td>\n      <td>42</td>\n      <td>212</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>420.0</td>\n      <td>2375</td>\n      <td>14</td>\n      <td>9</td>\n      <td>17</td>\n      <td>12</td>\n      <td>16</td>\n      <td>755</td>\n      <td>14</td>\n      <td>9</td>\n      <td>17</td>\n      <td>12</td>\n      <td>9</td>\n      <td>163</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1200.0</td>\n      <td>14659</td>\n      <td>13</td>\n      <td>9</td>\n      <td>17</td>\n      <td>19</td>\n      <td>33</td>\n      <td>605</td>\n      <td>13</td>\n      <td>9</td>\n      <td>17</td>\n      <td>19</td>\n      <td>13</td>\n      <td>36</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
 34 |             "text/plain": "   Journey Duration  Journey ID  End Date  End Month  End Year  End Hour  \\\n0            2040.0         953        19          9        17        18   \n1            1800.0       12581        19          9        17        15   \n2            1140.0        1159        15          9        17        17   \n3             420.0        2375        14          9        17        12   \n4            1200.0       14659        13          9        17        19   \n\n   End Minute  End Station ID  Start Date  Start Month  Start Year  \\\n0           0             478          19            9          17   \n1          21             122          19            9          17   \n2           1             639          15            9          17   \n3          16             755          14            9          17   \n4          33             605          13            9          17   \n\n   Start Hour  Start Minute  Start Station ID  \n0          17            26               251  \n1          14            51               550  \n2          16            42               212  \n3          12             9               163  \n4          19            13                36  "
 35 |           },
 36 |           "metadata": {}
 37 |         }
 38 |       ]
 39 |     },
 40 |     {
 41 |       "metadata": {
 42 |         "trusted": true
 43 |       },
 44 |       "cell_type": "code",
 45 |       "source": "plt.figure()\nhist = df.hist(column = \"Start Hour\", bins = 24)\n\nplt.show()",
 46 |       "execution_count": 5,
 47 |       "outputs": [
 48 |         {
 49 |           "output_type": "display_data",
 50 |           "data": {
 51 |             "text/plain": "<matplotlib.figure.Figure at 0x7fefeeae8cc0>"
 52 |           },
 53 |           "metadata": {}
 54 |         },
 55 |         {
 56 |           "output_type": "display_data",
 57 |           "data": {
 58 |             "image/png": 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\n",
 59 |             "text/plain": "<matplotlib.figure.Figure at 0x7fefeeaf8828>"
 60 |           },
 61 |           "metadata": {}
 62 |         }
 63 |       ]
 64 |     },
 65 |     {
 66 |       "metadata": {
 67 |         "trusted": true
 68 |       },
 69 |       "cell_type": "code",
 70 |       "source": "df.plot.scatter(y = \"Journey Duration\", x = \"Start Hour\")",
 71 |       "execution_count": 9,
 72 |       "outputs": [
 73 |         {
 74 |           "output_type": "execute_result",
 75 |           "execution_count": 9,
 76 |           "data": {
 77 |             "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fefee7935c0>"
 78 |           },
 79 |           "metadata": {}
 80 |         },
 81 |         {
 82 |           "output_type": "display_data",
 83 |           "data": {
 84 |             "image/png": 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\n",
 85 |             "text/plain": "<matplotlib.figure.Figure at 0x7fefee795390>"
 86 |           },
 87 |           "metadata": {}
 88 |         }
 89 |       ]
 90 |     },
 91 |     {
 92 |       "metadata": {
 93 |         "trusted": true
 94 |       },
 95 |       "cell_type": "code",
 96 |       "source": "max(df[\"Journey Duration\"])",
 97 |       "execution_count": 7,
 98 |       "outputs": [
 99 |         {
100 |           "output_type": "execute_result",
101 |           "execution_count": 7,
102 |           "data": {
103 |             "text/plain": "1984700.0"
104 |           },
105 |           "metadata": {}
106 |         }
107 |       ]
108 |     },
109 |     {
110 |       "metadata": {
111 |         "trusted": true
112 |       },
113 |       "cell_type": "code",
114 |       "source": "",
115 |       "execution_count": null,
116 |       "outputs": []
117 |     }
118 |   ],
119 |   "metadata": {
120 |     "kernelspec": {
121 |       "name": "python3",
122 |       "display_name": "Python 3",
123 |       "language": "python"
124 |     },
125 |     "language_info": {
126 |       "mimetype": "text/x-python",
127 |       "nbconvert_exporter": "python",
128 |       "name": "python",
129 |       "pygments_lexer": "ipython3",
130 |       "version": "3.5.4",
131 |       "file_extension": ".py",
132 |       "codemirror_mode": {
133 |         "version": 3,
134 |         "name": "ipython"
135 |       }
136 |     }
137 |   },
138 |   "nbformat": 4,
139 |   "nbformat_minor": 2
140 | }


--------------------------------------------------------------------------------
/wine_quality_prediction.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "metadata": {},
  5 |       "cell_type": "markdown",
  6 |       "source": "## A basic linear regression model over the wine quality data set"
  7 |     },
  8 |     {
  9 |       "metadata": {
 10 |         "trusted": true
 11 |       },
 12 |       "cell_type": "code",
 13 |       "source": "import pandas as pd  \nimport numpy as np  \nimport matplotlib.pyplot as plt  \nimport seaborn as sb \nfrom sklearn.model_selection import train_test_split \nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import metrics",
 14 |       "execution_count": 20,
 15 |       "outputs": []
 16 |     },
 17 |     {
 18 |       "metadata": {
 19 |         "trusted": true
 20 |       },
 21 |       "cell_type": "code",
 22 |       "source": "df = pd.read_csv(\"data/winequality-red.csv.gz\", delimiter = ';')\ndf.head()",
 23 |       "execution_count": 21,
 24 |       "outputs": [
 25 |         {
 26 |           "output_type": "execute_result",
 27 |           "execution_count": 21,
 28 |           "data": {
 29 |             "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fixed acidity</th>\n      <th>volatile acidity</th>\n      <th>citric acid</th>\n      <th>residual sugar</th>\n      <th>chlorides</th>\n      <th>free sulfur dioxide</th>\n      <th>total sulfur dioxide</th>\n      <th>density</th>\n      <th>pH</th>\n      <th>sulphates</th>\n      <th>alcohol</th>\n      <th>quality</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7.4</td>\n      <td>0.70</td>\n      <td>0.00</td>\n      <td>1.9</td>\n      <td>0.076</td>\n      <td>11.0</td>\n      <td>34.0</td>\n      <td>0.9978</td>\n      <td>3.51</td>\n      <td>0.56</td>\n      <td>9.4</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>7.8</td>\n      <td>0.88</td>\n      <td>0.00</td>\n      <td>2.6</td>\n      <td>0.098</td>\n      <td>25.0</td>\n      <td>67.0</td>\n      <td>0.9968</td>\n      <td>3.20</td>\n      <td>0.68</td>\n      <td>9.8</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7.8</td>\n      <td>0.76</td>\n      <td>0.04</td>\n      <td>2.3</td>\n      <td>0.092</td>\n      <td>15.0</td>\n      <td>54.0</td>\n      <td>0.9970</td>\n      <td>3.26</td>\n      <td>0.65</td>\n      <td>9.8</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11.2</td>\n      <td>0.28</td>\n      <td>0.56</td>\n      <td>1.9</td>\n      <td>0.075</td>\n      <td>17.0</td>\n      <td>60.0</td>\n      <td>0.9980</td>\n      <td>3.16</td>\n      <td>0.58</td>\n      <td>9.8</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7.4</td>\n      <td>0.70</td>\n      <td>0.00</td>\n      <td>1.9</td>\n      <td>0.076</td>\n      <td>11.0</td>\n      <td>34.0</td>\n      <td>0.9978</td>\n      <td>3.51</td>\n      <td>0.56</td>\n      <td>9.4</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
 30 |             "text/plain": "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n0            7.4              0.70         0.00             1.9      0.076   \n1            7.8              0.88         0.00             2.6      0.098   \n2            7.8              0.76         0.04             2.3      0.092   \n3           11.2              0.28         0.56             1.9      0.075   \n4            7.4              0.70         0.00             1.9      0.076   \n\n   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n0                 11.0                  34.0   0.9978  3.51       0.56   \n1                 25.0                  67.0   0.9968  3.20       0.68   \n2                 15.0                  54.0   0.9970  3.26       0.65   \n3                 17.0                  60.0   0.9980  3.16       0.58   \n4                 11.0                  34.0   0.9978  3.51       0.56   \n\n   alcohol  quality  \n0      9.4        5  \n1      9.8        5  \n2      9.8        5  \n3      9.8        6  \n4      9.4        5  "
 31 |           },
 32 |           "metadata": {}
 33 |         }
 34 |       ]
 35 |     },
 36 |     {
 37 |       "metadata": {
 38 |         "trusted": true
 39 |       },
 40 |       "cell_type": "code",
 41 |       "source": "df.head()",
 42 |       "execution_count": 22,
 43 |       "outputs": [
 44 |         {
 45 |           "output_type": "execute_result",
 46 |           "execution_count": 22,
 47 |           "data": {
 48 |             "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fixed acidity</th>\n      <th>volatile acidity</th>\n      <th>citric acid</th>\n      <th>residual sugar</th>\n      <th>chlorides</th>\n      <th>free sulfur dioxide</th>\n      <th>total sulfur dioxide</th>\n      <th>density</th>\n      <th>pH</th>\n      <th>sulphates</th>\n      <th>alcohol</th>\n      <th>quality</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7.4</td>\n      <td>0.70</td>\n      <td>0.00</td>\n      <td>1.9</td>\n      <td>0.076</td>\n      <td>11.0</td>\n      <td>34.0</td>\n      <td>0.9978</td>\n      <td>3.51</td>\n      <td>0.56</td>\n      <td>9.4</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>7.8</td>\n      <td>0.88</td>\n      <td>0.00</td>\n      <td>2.6</td>\n      <td>0.098</td>\n      <td>25.0</td>\n      <td>67.0</td>\n      <td>0.9968</td>\n      <td>3.20</td>\n      <td>0.68</td>\n      <td>9.8</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7.8</td>\n      <td>0.76</td>\n      <td>0.04</td>\n      <td>2.3</td>\n      <td>0.092</td>\n      <td>15.0</td>\n      <td>54.0</td>\n      <td>0.9970</td>\n      <td>3.26</td>\n      <td>0.65</td>\n      <td>9.8</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11.2</td>\n      <td>0.28</td>\n      <td>0.56</td>\n      <td>1.9</td>\n      <td>0.075</td>\n      <td>17.0</td>\n      <td>60.0</td>\n      <td>0.9980</td>\n      <td>3.16</td>\n      <td>0.58</td>\n      <td>9.8</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7.4</td>\n      <td>0.70</td>\n      <td>0.00</td>\n      <td>1.9</td>\n      <td>0.076</td>\n      <td>11.0</td>\n      <td>34.0</td>\n      <td>0.9978</td>\n      <td>3.51</td>\n      <td>0.56</td>\n      <td>9.4</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
 49 |             "text/plain": "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n0            7.4              0.70         0.00             1.9      0.076   \n1            7.8              0.88         0.00             2.6      0.098   \n2            7.8              0.76         0.04             2.3      0.092   \n3           11.2              0.28         0.56             1.9      0.075   \n4            7.4              0.70         0.00             1.9      0.076   \n\n   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n0                 11.0                  34.0   0.9978  3.51       0.56   \n1                 25.0                  67.0   0.9968  3.20       0.68   \n2                 15.0                  54.0   0.9970  3.26       0.65   \n3                 17.0                  60.0   0.9980  3.16       0.58   \n4                 11.0                  34.0   0.9978  3.51       0.56   \n\n   alcohol  quality  \n0      9.4        5  \n1      9.8        5  \n2      9.8        5  \n3      9.8        6  \n4      9.4        5  "
 50 |           },
 51 |           "metadata": {}
 52 |         }
 53 |       ]
 54 |     },
 55 |     {
 56 |       "metadata": {
 57 |         "trusted": true
 58 |       },
 59 |       "cell_type": "code",
 60 |       "source": "df = df.fillna(method='ffill')",
 61 |       "execution_count": 23,
 62 |       "outputs": []
 63 |     },
 64 |     {
 65 |       "metadata": {
 66 |         "trusted": true
 67 |       },
 68 |       "cell_type": "code",
 69 |       "source": "X = df[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates','alcohol']].values\ny = df['quality'].values\n\n\nsb.distplot(df['quality'])",
 70 |       "execution_count": 24,
 71 |       "outputs": [
 72 |         {
 73 |           "output_type": "stream",
 74 |           "text": "/home/nbuser/anaconda3_501/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
 75 |           "name": "stderr"
 76 |         },
 77 |         {
 78 |           "output_type": "execute_result",
 79 |           "execution_count": 24,
 80 |           "data": {
 81 |             "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f15fa0f6630>"
 82 |           },
 83 |           "metadata": {}
 84 |         },
 85 |         {
 86 |           "output_type": "display_data",
 87 |           "data": {
 88 |             "image/png": 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\n",
 89 |             "text/plain": "<Figure size 432x288 with 1 Axes>"
 90 |           },
 91 |           "metadata": {
 92 |             "needs_background": "light"
 93 |           }
 94 |         }
 95 |       ]
 96 |     },
 97 |     {
 98 |       "metadata": {
 99 |         "trusted": true
100 |       },
101 |       "cell_type": "code",
102 |       "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)",
103 |       "execution_count": 25,
104 |       "outputs": []
105 |     },
106 |     {
107 |       "metadata": {
108 |         "trusted": true
109 |       },
110 |       "cell_type": "code",
111 |       "source": "regressor = LinearRegression()  \nregressor.fit(X_train, y_train)",
112 |       "execution_count": 26,
113 |       "outputs": [
114 |         {
115 |           "output_type": "execute_result",
116 |           "execution_count": 26,
117 |           "data": {
118 |             "text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n         normalize=False)"
119 |           },
120 |           "metadata": {}
121 |         }
122 |       ]
123 |     },
124 |     {
125 |       "metadata": {
126 |         "trusted": true
127 |       },
128 |       "cell_type": "code",
129 |       "source": "y_pred = regressor.predict(X_test)\n\nout = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})",
130 |       "execution_count": 27,
131 |       "outputs": []
132 |     },
133 |     {
134 |       "metadata": {
135 |         "trusted": true
136 |       },
137 |       "cell_type": "code",
138 |       "source": "out.head(25).plot(kind='bar',figsize=(10,8))\nplt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\nplt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\nplt.show()",
139 |       "execution_count": 28,
140 |       "outputs": [
141 |         {
142 |           "output_type": "display_data",
143 |           "data": {
144 |             "image/png": 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\n",
145 |             "text/plain": "<Figure size 720x576 with 1 Axes>"
146 |           },
147 |           "metadata": {
148 |             "needs_background": "light"
149 |           }
150 |         }
151 |       ]
152 |     }
153 |   ],
154 |   "metadata": {
155 |     "kernelspec": {
156 |       "name": "python36",
157 |       "display_name": "Python 3.6",
158 |       "language": "python"
159 |     },
160 |     "language_info": {
161 |       "mimetype": "text/x-python",
162 |       "nbconvert_exporter": "python",
163 |       "name": "python",
164 |       "pygments_lexer": "ipython3",
165 |       "version": "3.6.6",
166 |       "file_extension": ".py",
167 |       "codemirror_mode": {
168 |         "version": 3,
169 |         "name": "ipython"
170 |       }
171 |     }
172 |   },
173 |   "nbformat": 4,
174 |   "nbformat_minor": 2
175 | }


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