hw04_Williams_Geiger (1).ipynb

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      "source": [
        "#1b: \n",
        "## Homework 4: Capstone \n",
        "\n",
        "## Kobie Williams and Nyles Geiger\n",
        "\n",
        "#### 4/12/2022"
      ],
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      "metadata": {
        "id": "_UnhG1ZC8lxs"
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      "source": [
        "import numpy as np \n",
        "import pandas as pd \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people = pd.read_csv('https://drive.google.com/uc?export=download&id=1Xza-mD7u0w3AHMvntoQMaXu3AlX9BAbA')\n",
        "\n",
        "social_media = pd.read_csv('https://drive.google.com/uc?export=download&id=1XwXv7CoqmqfVe4UCKwQvrkPiwLarOjLo')\n",
        "\n",
        "social_connect = pd.read_csv('https://drive.google.com/uc?export=download&id=1Xue80IWkuKZMkl6AofA4zYslLMKLvRmP')"
      ],
      "metadata": {
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      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "people.describe() "
      ],
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      "execution_count": 3,
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              "                  ID       Weight      ZipCode        SqFt        Shoes  \\\n",
              "count    2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
              "mean   101000.500000   144.492835  3808.212000  2071.45350     7.134000   \n",
              "std       577.494589    31.996841  3132.924239   587.90871     3.807005   \n",
              "min    100001.000000    59.000000   221.000000     5.00000     0.000000   \n",
              "25%    100500.750000   120.642500   221.000000  1740.75000     4.000000   \n",
              "50%    101000.500000   143.790000  3977.000000  2033.50000     7.000000   \n",
              "75%    101500.250000   166.147500  6861.000000  2284.25000    10.000000   \n",
              "max    102000.000000   249.070000  8161.000000  6495.00000    24.000000   \n",
              "\n",
              "           Commute         Income  \n",
              "count  2000.000000    1500.000000  \n",
              "mean     26.112925   66894.393680  \n",
              "std      18.726101   45953.545286  \n",
              "min       0.050000     754.080000  \n",
              "25%      11.045000   34024.247500  \n",
              "50%      22.875000   54956.200000  \n",
              "75%      37.717500   86720.820000  \n",
              "max     117.070000  400211.640000  "
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              "      <th>mean</th>\n",
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              "      <th>25%</th>\n",
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              "      <td>120.642500</td>\n",
              "      <td>221.000000</td>\n",
              "      <td>1740.75000</td>\n",
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              "      <td>11.045000</td>\n",
              "      <td>34024.247500</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>2033.50000</td>\n",
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              "      <td>22.875000</td>\n",
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              "      <td>6861.000000</td>\n",
              "      <td>2284.25000</td>\n",
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              "      <td>37.717500</td>\n",
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              "      <td>8161.000000</td>\n",
              "      <td>6495.00000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>117.070000</td>\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print(df[\"Tweet Text\"])\n",
        "print(\"Cleaning People df... \")\n",
        "print()\n",
        "\n",
        "#Have to add a .replace to some of these places \n",
        "#Maybe even just drop the whole column \n",
        "\n",
        "#We don't wan't to use the mask \n",
        "mask = people[\"Water\"].str.contains('[^\\?]')\n",
        "people[\"Water\"] = people[\"Water\"][mask]\n",
        "\n",
        "#Filling the NaN values with 0's \n",
        "people[\"Water\"] = people[\"Water\"].fillna(0)\n",
        "\n",
        "\n",
        "nmask = people[\"NumKids\"].str.contains('[^None]')\n",
        "people[\"NumKids\"] = people[\"NumKids\"][mask]\n",
        "\n",
        "print(\"Done...\")"
      ],
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        "colab": {
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        },
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      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleaning People df... \n",
            "\n",
            "Done...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people['Water'] = people['Water'].astype(float)"
      ],
      "metadata": {
        "id": "YeoPUEx3PuHU"
      },
      "execution_count": 5,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "people.dtypes"
      ],
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        "colab": {
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        },
        "id": "IgluDOO5PYOm",
        "outputId": "efeba73b-9f4e-4a9e-b30a-769a4b440fca"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID            int64\n",
              "Name         object\n",
              "Gender       object\n",
              "BirthDay     object\n",
              "Weight      float64\n",
              "ZipCode       int64\n",
              "SqFt          int64\n",
              "Pets         object\n",
              "Water       float64\n",
              "Shoes         int64\n",
              "Commute     float64\n",
              "NumKids      object\n",
              "Exercise     object\n",
              "Income      float64\n",
              "Beverage     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mask"
      ],
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        "id": "Xg8zDvteyLGs",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5642c11-5205-47eb-b622-12769906c127"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        True\n",
              "1       False\n",
              "2        True\n",
              "3        True\n",
              "4       False\n",
              "        ...  \n",
              "1995     True\n",
              "1996     True\n",
              "1997     True\n",
              "1998     True\n",
              "1999     True\n",
              "Name: Water, Length: 2000, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "IZzSZu8aK_Sa"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Changing the birthday column to age \n",
        "people['Birthday'] = pd.to_datetime(people.BirthDay) \n"
      ],
      "metadata": {
        "id": "4mE8uiSTKAo-"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "people[\"ZipCode\"].value_counts().plot(kind='bar', title=\"Zip Code\")"
      ],
      "metadata": {
        "id": "aX65JSBaLG99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "94c333f5-fbdc-4da4-bfa8-59ee57dcbadd"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28da66890>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people[\"Beverage\"].value_counts().plot(kind='bar', title=\"Beverage\")"
      ],
      "metadata": {
        "id": "EAg7FjAc3zHE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "5fa822c4-ec75-4c09-ce03-23a556b4bac8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28d974350>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people[\"Exercise\"].value_counts().plot(kind='bar', title=\"Exercise\")"
      ],
      "metadata": {
        "id": "sRbPs3Qu36l1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "5423aa09-6cba-438a-fa33-303379c78733"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28d986cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people[\"NumKids\"].value_counts(dropna=False).plot(kind='bar', title=\"Number of Kids\")"
      ],
      "metadata": {
        "id": "flL3miUU3_Jt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "9237b0fa-31a8-47aa-9660-67d25c442f57"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28d422810>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people[\"Gender\"].value_counts().plot(kind='bar', title=\"Gender\")"
      ],
      "metadata": {
        "id": "Jh0pOGQ54zVz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "05b1251b-bbb6-46a0-a5db-a50a6e6624c2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28d3abf10>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people[\"Weight\"].plot(kind='hist', title=\"Weight\", bins=8)"
      ],
      "metadata": {
        "id": "9M4ygFzq42oZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "52aa2785-e251-40ae-dc78-53af83054d20"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28d306a90>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people[\"Income\"].plot(kind='hist', title=\"Income\", bins=13)"
      ],
      "metadata": {
        "id": "olaw22DP47-d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "05283a68-fee7-4c74-b6ee-390ae39a40bc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28d2a5d90>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people[\"Shoes\"].plot(kind='hist', title=\"Shoes\", bins=8)"
      ],
      "metadata": {
        "id": "8FY2dmwd5Ake",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "40a099b8-bb35-4a46-ce5a-f9178427f702"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28d1c6310>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(people[\"ZipCode\"].value_counts())"
      ],
      "metadata": {
        "id": "88JjR0Mr5DjI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "701e940e-035b-4f4f-916a-f4c4f4f99dfc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "221     596\n",
            "3977    393\n",
            "8161    307\n",
            "6861    208\n",
            "7770    104\n",
            "4532     96\n",
            "849      95\n",
            "445      92\n",
            "6393     90\n",
            "2585     19\n",
            "Name: ZipCode, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations about data:\n",
        "\n",
        "- about 75% of the entries live in the Zipcodes 221, 3977, 8161, and 6861\n",
        "- Most people own 5-10 pairs of shoes\n",
        "- The data is provided by nearly 50% women and 50% men\n",
        "- Most of these entries have kids and earn around $50,000"
      ],
      "metadata": {
        "id": "_TMEHbkr5b9m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3"
      ],
      "metadata": {
        "id": "PwLpNfLh5ewL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### a"
      ],
      "metadata": {
        "id": "pdq3d6Fi5jCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx \n",
        "from operator import itemgetter "
      ],
      "metadata": {
        "id": "-MkukB5r7GKM"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "node_names = social_connect.iloc[:, 0].unique()\n",
        "\n",
        "edges = []\n",
        "\n",
        "for index, row in social_connect.iterrows():\n",
        "  source = row.iloc[0]\n",
        "  target = row.iloc[1]\n",
        "  edges.append((source, target))\n",
        "\n",
        "print(\"num of nodes:\",len(node_names))\n",
        "print(\"num edges:\",len(edges))\n",
        "\n",
        "G = nx.Graph()\n",
        "G.add_nodes_from(node_names)\n",
        "G.add_edges_from(edges)\n",
        "\n",
        "print(nx.info(G))"
      ],
      "metadata": {
        "id": "a7IwfSAF5Y0F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "280aba6a-74f5-4414-ca2d-da3ff184949a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "num of nodes: 1820\n",
            "num edges: 10645\n",
            "Graph with 2000 nodes and 10645 edges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### b"
      ],
      "metadata": {
        "id": "i_Xwo8u87OGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the best position for laying out the nodes graphically.\n",
        "# k controls how far apart nodes are forced to be and will usually need to be\n",
        "# adjusted for each different graph.\n",
        "pos = nx.spring_layout(G, k=1.0)\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "nx.draw(G, pos=pos, with_labels=False,\n",
        "        node_color='#33ddff', node_size=100,\n",
        "        edge_color='#ffaa00', width=0.5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "Nd1D0n4268BM",
        "outputId": "d75ab8bc-78fb-41a0-a7a5-e1957a751990"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### c"
      ],
      "metadata": {
        "id": "v5M0Tk7X7Whu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('- In Degree Centrality:\\n', sorted(nx.degree_centrality(G).items(), key=itemgetter(1))[:20])\n",
        "print('\\n- Betweenness Centrality:\\n', (sorted(nx.betweenness_centrality(G).items(), key=itemgetter(1)))[:20])\n",
        "print('\\n- Closeness Centrality:\\n', (sorted(nx.closeness_centrality(G).items(), key=itemgetter(1)))[:20])\n",
        "print('\\n- Eigenvector Centrality\\n', (sorted(nx.eigenvector_centrality(G).items(), key=itemgetter(1)))[:20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhB2QFd67RtR",
        "outputId": "d32681ec-5a5b-4096-facb-e9b5555155a2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- In Degree Centrality:\n",
            " [(101007, 0.0005002501250625312), (100797, 0.0010005002501250625), (101557, 0.0010005002501250625), (100903, 0.0010005002501250625), (100283, 0.0010005002501250625), (100924, 0.0015007503751875936), (100121, 0.0015007503751875936), (101990, 0.0015007503751875936), (100570, 0.0015007503751875936), (101131, 0.0015007503751875936), (100336, 0.0015007503751875936), (101162, 0.0015007503751875936), (100070, 0.0015007503751875936), (100847, 0.0015007503751875936), (101427, 0.0015007503751875936), (101330, 0.0015007503751875936), (101810, 0.0015007503751875936), (101099, 0.0015007503751875936), (100305, 0.0015007503751875936), (100916, 0.0015007503751875936)]\n",
            "\n",
            "- Betweenness Centrality:\n",
            " [(101007, 0.0), (100797, 6.730330225411099e-07), (100283, 9.359889694516403e-06), (101810, 9.565668510981517e-06), (100070, 1.076912625539971e-05), (100807, 1.611336175945273e-05), (101475, 1.9521668423460423e-05), (101990, 2.523337487269926e-05), (100209, 2.5411758085132305e-05), (100618, 2.5443032353500935e-05), (100924, 2.5907583705715582e-05), (100336, 2.8034027636084786e-05), (101765, 3.044325979419611e-05), (100248, 3.19614477359357e-05), (100991, 3.377185208955045e-05), (101099, 3.645706125730621e-05), (101164, 3.806425960827579e-05), (101346, 3.913146976838009e-05), (101330, 4.063489748876974e-05), (100813, 4.190769333588807e-05)]\n",
            "\n",
            "- Closeness Centrality:\n",
            " [(101007, 0.15930825629582404), (101330, 0.16012495994873438), (100283, 0.1627187627187627), (101099, 0.16464871097932626), (100634, 0.16464871097932626), (101810, 0.16557607885364037), (101189, 0.16630615640599003), (101164, 0.16833684210526315), (100070, 0.16903433113478775), (101707, 0.1697232127695704), (100807, 0.17014213975657502), (100472, 0.17021457765667575), (100847, 0.17065050367082124), (100306, 0.17122055674518202), (101296, 0.1717649080598041), (100119, 0.1718830610490112), (101399, 0.17217915590008614), (100254, 0.1721939874235507), (100236, 0.17220882150241212), (100305, 0.17240189736955586)]\n",
            "\n",
            "- Eigenvector Centrality\n",
            " [(100283, 5.277005717427245e-07), (101330, 9.950401563452415e-07), (101007, 1.102604190350322e-06), (100305, 1.4585655873652346e-06), (101810, 2.3496565103690617e-06), (100870, 2.481687786819948e-06), (101707, 2.9175763657913396e-06), (100390, 3.0318569627742704e-06), (101396, 3.0665453482937328e-06), (100688, 3.1600282529361584e-06), (100835, 3.358720385678768e-06), (101491, 3.447410232568663e-06), (100211, 3.805926503144207e-06), (100306, 3.8110837569833025e-06), (100737, 3.914215370288473e-06), (101970, 3.9598464480273334e-06), (100300, 3.992343733668842e-06), (100671, 4.075533396828809e-06), (100620, 4.163160697675503e-06), (101742, 4.358220318695909e-06)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This library helps find communities within social network graphs\n",
        "import networkx.algorithms.community as nxcom\n",
        "\n",
        "# For large networks, this may take a while (34 seconds)\n",
        "communities = sorted(nxcom.greedy_modularity_communities(G), key=len, reverse=True)\n",
        "\n",
        "# communities is a list of sets of vertices\n",
        "# E.g., communities[0] is a list of the vertices in the first community\n",
        "print(len(communities), 'communities found')\n",
        "\n",
        "# Let's use a colormap to define colors for our communities\n",
        "from matplotlib import cm\n",
        "# this colormap has RGB values for 20 distinct colors\n",
        "cm.tab20.colors\n",
        "\n",
        "\n",
        "# Helper functions to labels nodes/edges with community\n",
        "def set_node_community(G, communities):\n",
        "    '''Add community to node attributes'''\n",
        "    for c, v_c in enumerate(communities):\n",
        "        for v in v_c:\n",
        "            # Add 1 to save 0 for external edges\n",
        "            G.nodes[v]['community'] = c + 1\n",
        "            \n",
        "def set_edge_community(G):\n",
        "    '''Find internal edges and add their community to their attributes'''\n",
        "    for v, w, in G.edges:\n",
        "        if G.nodes[v]['community'] == G.nodes[w]['community']:\n",
        "            # Internal edge, mark with community\n",
        "            G.edges[v, w]['community'] = G.nodes[v]['community']\n",
        "        else:\n",
        "            # External edge, mark as 0\n",
        "            G.edges[v, w]['community'] = 0\n",
        "\n",
        "# Helper function to get distinct colors for display\n",
        "def get_color(i):\n",
        "    colors = cm.tab20.colors\n",
        "\n",
        "    # If i gets too big for how many colors we have, wrap around\n",
        "    if i > len(colors)-5:\n",
        "        i = i % len(colors)\n",
        "\n",
        "    return colors[i]\n",
        "\n",
        "\n",
        "# Set node and edge communities\n",
        "set_node_community(G, communities)\n",
        "set_edge_community(G)\n",
        "\n",
        "\n",
        "# Set community color for internal edges\n",
        "external = [(v, w) for v, w in G.edges if G.edges[v, w]['community'] == 0]\n",
        "internal = [(v, w) for v, w in G.edges if G.edges[v, w]['community'] > 0]\n",
        "internal_color = [get_color(G.edges[e]['community']) for e in internal]\n",
        "\n",
        "\n",
        "\n",
        "# Draw external edges\n",
        "pos = nx.spring_layout(G, k=0.1)\n",
        "nx.draw_networkx(\n",
        "    G, pos=pos, node_size=0,\n",
        "    edgelist=external, edge_color=\"#333333\",\n",
        "    alpha=0.2, with_labels=False)\n",
        "# Draw internal edges\n",
        "nx.draw_networkx(\n",
        "    G, pos=pos, node_size=0,\n",
        "    edgelist=internal, edge_color=internal_color,\n",
        "    alpha=0.05, with_labels=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "KSJN5u2y7Z8i",
        "outputId": "5be5f7b7-f226-405f-ea51-3248b9081d36"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8 communities found\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4"
      ],
      "metadata": {
        "id": "O_UvmfHp8ZOc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "social_media['PosterID'].value_counts().plot(kind='hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "GAa-cOYf8QoC",
        "outputId": "ff7a6daa-0ad9-4ea0-a4af-f1deaf8e04f3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28d18da90>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc_corpus = pd.DataFrame(social_media[\"Post\"].to_numpy(), columns=['doc_text'])\n",
        "# print(sc_corpus)\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# Specify stop words that should be ignored.\n",
        "stop_list = ['a', 'an', 'the', 'and', 'has', 'I', 'in', 'for', 'but', 'of',\n",
        "              'not', 'can', 'are', 'not', 'when', 'it', 'is', 'its', 'comes',\n",
        "              'do', 'be', 'how', 'put', 'able', 'been', 'just', 'into', 'so', 'me',\n",
        "              'you', 'we', 'our', 'on', 'to', 'rt', 'co', 'your', 'this', 'with',\n",
        "              'at', 'about', 'from', 'that', 'great', 'you', 'by', 'with', 'at',\n",
        "              'what', 'as', 'they', 'my', 'them', 'had', 'me', 'was', 'have', 'would',\n",
        "              'their', 'all', 'or', 'no', '00', 'xxxx', 'xx', 'am', 'did', 'which',\n",
        "             'told']\n",
        "\n",
        "count_vect = CountVectorizer(lowercase=True, stop_words=stop_list, max_features=25)\n",
        "sparse_counts = count_vect.fit_transform(sc_corpus['doc_text'])\n",
        "\n",
        "counts = np.asarray(sparse_counts.todense())\n",
        "\n",
        "count_vect.get_feature_names_out()\n",
        "\n",
        "terms = count_vect.get_feature_names_out()\n",
        "total_counts = counts.sum(axis=0)\n",
        "\n",
        "df_counts = pd.DataFrame()\n",
        "df_counts['term'] = terms\n",
        "df_counts['count'] = total_counts\n",
        "df_counts = df_counts.sort_values(by='count', ascending=False)\n",
        "\n",
        "# Just keep the top 10\n",
        "df_counts = df_counts.iloc[:10]\n",
        "\n",
        "df_counts.plot(y='count', x='term', kind='barh', legend=None, figsize=(10, 5))\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Term')\n",
        "plt.title('Most frequently used terms')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "zPUGJcEr8cm4",
        "outputId": "71e070c5-6319-48a8-8694-6385ddaa5bdd"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the text there were a lot of instances where people were using 'xxx' or 'xxxx'. I assume this means seneitive information was being blocked.\n",
        "\n",
        "The demographic must be working individuals because the common words are mostly financial in nature. This could be a forum for people to release their finanical frustrations, hence debt is at the top of the chart\n",
        "\n"
      ],
      "metadata": {
        "id": "Jrwfp7jJ8neB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ZB0QSHfztUKQ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###5"
      ],
      "metadata": {
        "id": "HGh888pr_Bnf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "people.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "4OTALeWdcUdw",
        "outputId": "19778077-6e50-4311-9c02-aa6dbc03d149"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID             Name Gender    BirthDay  Weight  ZipCode  SqFt  \\\n",
              "0  100001  Imogene Hackett      F  1939-10-17  164.24      221  2044   \n",
              "\n",
              "                              Pets  Water  Shoes  Commute NumKids Exercise  \\\n",
              "0  dog,cat,cat,hamster,hamster,dog  114.1      9    42.13       1      Low   \n",
              "\n",
              "     Income Beverage   Birthday  \n",
              "0  89897.46   Coffee 1939-10-17  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2edd04ea-5f5b-4180-ac59-8c3d1aff18a8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>ID</th>\n",
              "      <th>Name</th>\n",
              "      <th>Gender</th>\n",
              "      <th>BirthDay</th>\n",
              "      <th>Weight</th>\n",
              "      <th>ZipCode</th>\n",
              "      <th>SqFt</th>\n",
              "      <th>Pets</th>\n",
              "      <th>Water</th>\n",
              "      <th>Shoes</th>\n",
              "      <th>Commute</th>\n",
              "      <th>NumKids</th>\n",
              "      <th>Exercise</th>\n",
              "      <th>Income</th>\n",
              "      <th>Beverage</th>\n",
              "      <th>Birthday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001</td>\n",
              "      <td>Imogene Hackett</td>\n",
              "      <td>F</td>\n",
              "      <td>1939-10-17</td>\n",
              "      <td>164.24</td>\n",
              "      <td>221</td>\n",
              "      <td>2044</td>\n",
              "      <td>dog,cat,cat,hamster,hamster,dog</td>\n",
              "      <td>114.1</td>\n",
              "      <td>9</td>\n",
              "      <td>42.13</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>89897.46</td>\n",
              "      <td>Coffee</td>\n",
              "      <td>1939-10-17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2edd04ea-5f5b-4180-ac59-8c3d1aff18a8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      gap: 12px;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2edd04ea-5f5b-4180-ac59-8c3d1aff18a8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
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              "          const element = document.querySelector('#df-2edd04ea-5f5b-4180-ac59-8c3d1aff18a8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "people['Exercise'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1tt2FWwJlry",
        "outputId": "618d29e4-a7e2-4696-b178-e60c19de0608"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None      682\n",
              "Low       495\n",
              "High      486\n",
              "Medium    337\n",
              "Name: Exercise, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeRegressor \n",
        "from sklearn.preprocessing import PolynomialFeatures \n",
        "from sklearn.pipeline import make_pipeline \n",
        "\n",
        "#First 1500 rows \n",
        "p1500 = people.head(1500) \n",
        "\n",
        "#Last 500 rows \n",
        "p500 = people.tail(500) \n",
        "\n",
        "#Training data \n",
        "Xtrain = p1500[['Gender','Weight',  'SqFt', 'Water', 'Shoes', 'Commute', 'NumKids', 'Exercise']]\n",
        "\n",
        "#Getting dummies to make data better \n",
        "Xdumb = pd.get_dummies(Xtrain)\n",
        "\n",
        "#normalize the data \n",
        "\n",
        "#Training output \n",
        "ytrain= p1500['Income'] \n",
        "\n",
        "#Test variables from the last 500 rows \n",
        "Xtest = p500[['Gender','Weight', 'SqFt', 'Water', 'Shoes', 'Commute', 'NumKids', 'Exercise']]\n",
        "\n",
        "#Remove ytest because we have to predict it, and we don't have the right answers \n",
        "#ytest = p500['Income']\n",
        "\n",
        "# from sklearn.model_selection import train_test_split\n",
        "#Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=.25, random_state=0)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "i89YdtTk8iDl"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #Getting dummies \n",
        "Xdumb2 = pd.get_dummies(Xtest)"
      ],
      "metadata": {
        "id": "4swrtAiquwCP"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating the model, we have 8 attributes to try "
      ],
      "metadata": {
        "id": "QiaJJFMVl-nT"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model = LinearRegression()\n",
        "model.fit(Xdumb,ytrain) \n",
        "#DT_model = DecisionTreeRegressor(max_depth=5).fit(Xtrain,ytrain)\n",
        "#DT_predict = DT_model.predict(X_test) #Predictions on Testing data "
      ],
      "metadata": {
        "id": "4ek61ERSEJqN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3f07ce7-9c0d-474b-a70d-7303bb20d3b1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting the data \n",
        "ytest = model.predict(Xdumb) \n",
        "#new_ytest = ytest.reshape((-1,1)) \n",
        "print(ytest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4AaJjymWgoRc",
        "outputId": "32f3eb6f-56b5-406e-85ec-693e384c5a8f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 79942.94570832  58864.7564692   68802.79263906 ...  53286.57798346\n",
            "  50089.31205697 -13070.03811068]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred = model.predict(Xdumb2) "
      ],
      "metadata": {
        "id": "8mvO8fZ0O8Yf"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ypred: \n",
        "  print(i) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q2GODADP_xq",
        "outputId": "17fe30f1-2395-4002-ee05-2d69da04ec7b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "-1571.4152775821162\n",
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            "63284.00662834594\n",
            "35208.43403481009\n",
            "79242.60786625999\n",
            "52055.120811793466\n",
            "58121.7866092908\n",
            "49318.563415287026\n",
            "108913.89168912475\n",
            "31653.904854952565\n",
            "93290.51993287445\n",
            "45565.34356475829\n",
            "46522.98627262927\n",
            "69649.55042279934\n",
            "86095.03550830265\n",
            "41874.15358304486\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plt.scatter(Xdumb, ytrain) \n",
        "#plt.plot()"
      ],
      "metadata": {
        "id": "6OU9iwOCObPy"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Randomly splitting the data \n",
        "rXtrain, rXtest, rytrain, rytest = train_test_split(Xdumb, ytrain,test_size=.50, random_state=0)"
      ],
      "metadata": {
        "id": "TK6G393mFNzF"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmodel = LinearRegression()\n",
        "rmodel.fit(rXtrain,rytrain)"
      ],
      "metadata": {
        "id": "GKMPOVbfGIjy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fb0e712-69c9-4a52-c949-c178db727a22"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rypred = rmodel.predict(rXtest) "
      ],
      "metadata": {
        "id": "lCqHMC2bH0o2"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "jVcgCtxkUlpk"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mae = mean_absolute_error(rytest, rypred)"
      ],
      "metadata": {
        "id": "4uinfPWzUtgh"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#printing the mean absolute error \n",
        "mae"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "paT4PI74U13Z",
        "outputId": "37a43bf9-a0f9-4349-b46a-ae6a252a6c4d"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28848.24760726716"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The final mae is 28848.24760726716. "
      ],
      "metadata": {
        "id": "GGaje5ZFVGB4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###6"
      ],
      "metadata": {
        "id": "m6x0W3AQGLyH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Variable to predict beverage \n",
        "class_ytrain = p1500['Beverage'] "
      ],
      "metadata": {
        "id": "vkDTg5m4GOJe"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier \n"
      ],
      "metadata": {
        "id": "LsXHUok6SF3a"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_model = KNeighborsClassifier(n_neighbors=2, metric='euclidean')"
      ],
      "metadata": {
        "id": "RwIdEQvvR-_q"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#1500 training data \n",
        "c_Xtrain = p1500[['Gender','Weight',  'SqFt', 'Water', 'Shoes', 'Commute', 'NumKids', 'Exercise']]\n",
        "c_Xtrain = pd.get_dummies(c_Xtrain) \n",
        "\n",
        "#500 test data \n",
        "c_Xtest = p500[['Gender','Weight', 'SqFt', 'Water', 'Shoes', 'Commute', 'NumKids', 'Exercise']]\n",
        "c_Xtest = pd.get_dummies(c_Xtest) \n"
      ],
      "metadata": {
        "id": "e4XAcu_WXToP"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fit the model \n",
        "class_model.fit(c_Xtrain, class_ytrain) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qd_jWAkzU726",
        "outputId": "076302e2-df44-4eeb-e37c-4037df03dd04"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean', n_neighbors=2)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classPred = class_model.predict(c_Xtest)"
      ],
      "metadata": {
        "id": "io5dzldO37Y0"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Classification data split \n",
        "cXtrain, cXtest, cytrain, cytest = train_test_split(c_Xtrain, class_ytrain, test_size=0.5,random_state=0)"
      ],
      "metadata": {
        "id": "eDkIx6nM4Bx-"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_model.fit(cXtrain,cytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2szUT5NnBQ4O",
        "outputId": "9603b437-78a4-449d-a1e1-f7a752d2a954"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean', n_neighbors=2)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cypred= class_model.predict(cXtest)"
      ],
      "metadata": {
        "id": "tvZHRZKpCAbh"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cypred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3WA8ZEGECMlo",
        "outputId": "4f80d1b6-67df-4ecf-c801-a21755bee515"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Tea', 'Cider', 'Tea', 'Cocoa', 'Tea', 'Coffee', 'Cider', 'Cider',\n",
              "       'Cocoa', 'Cider', 'Cocoa', 'Tea', 'Cider', 'Cider', 'Cider',\n",
              "       'Cocoa', 'Cocoa', 'Cider', 'Cocoa', 'Cider', 'Tea', 'Cocoa', 'Tea',\n",
              "       'Cocoa', 'Cocoa', 'Coffee', 'Cider', 'Cider', 'Cocoa', 'Cider',\n",
              "       'Coffee', 'Coffee', 'Coffee', 'Tea', 'Cocoa', 'Coffee', 'Cider',\n",
              "       'Tea', 'Cider', 'Tea', 'Cider', 'Cider', 'Cocoa', 'Cider',\n",
              "       'Coffee', 'Cider', 'Cocoa', 'Cocoa', 'Coffee', 'Cider', 'Tea',\n",
              "       'Cider', 'Cider', 'Cocoa', 'Coffee', 'Cider', 'Coffee', 'Cocoa',\n",
              "       'None', 'Cider', 'Cider', 'Tea', 'Cider', 'Cider', 'Cocoa',\n",
              "       'Cider', 'Cider', 'None', 'Cider', 'Cocoa', 'Cider', 'Coffee',\n",
              "       'Cocoa', 'Cocoa', 'Coffee', 'Coffee', 'Coffee', 'Cocoa', 'Cider',\n",
              "       'Cider', 'Cider', 'Coffee', 'Coffee', 'Cider', 'Tea', 'Cider',\n",
              "       'Cider', 'Coffee', 'Cider', 'Coffee', 'Cider', 'Cocoa', 'Cocoa',\n",
              "       'Cocoa', 'Cider', 'Coffee', 'Cocoa', 'Coffee', 'Cider', 'Coffee',\n",
              "       'Coffee', 'Coffee', 'Cocoa', 'Coffee', 'Coffee', 'Cocoa', 'Cider',\n",
              "       'Cocoa', 'None', 'Cocoa', 'Cider', 'Cider', 'Cocoa', 'Coffee',\n",
              "       'Cider', 'None', 'Cocoa', 'Cider', 'Coffee', 'Cocoa', 'Cider',\n",
              "       'Cider', 'Cocoa', 'Cocoa', 'Cider', 'Cocoa', 'Cider', 'Cider',\n",
              "       'Cider', 'Cider', 'Coffee', 'Cocoa', 'Cocoa', 'Cider', 'Coffee',\n",
              "       'Cocoa', 'Coffee', 'Cider', 'Cider', 'Cider', 'Cocoa', 'Cocoa',\n",
              "       'Cocoa', 'Coffee', 'Coffee', 'Cider', 'Coffee', 'Cider', 'Tea',\n",
              "       'Cocoa', 'Tea', 'Cocoa', 'Cider', 'Cocoa', 'Coffee', 'Cider',\n",
              "       'Coffee', 'None', 'Coffee', 'Cider', 'Cider', 'Coffee', 'Coffee',\n",
              "       'Cocoa', 'Cider', 'Cocoa', 'Cider', 'Cider', 'Tea', 'Tea', 'Cider',\n",
              "       'Coffee', 'Tea', 'Cider', 'Coffee', 'Cocoa', 'Cocoa', 'Cocoa',\n",
              "       'Cocoa', 'Cider', 'None', 'Cider', 'Cocoa', 'Cider', 'Cider',\n",
              "       'Cocoa', 'Coffee', 'Cider', 'Cider', 'Coffee', 'Cocoa', 'Cider',\n",
              "       'None', 'Cocoa', 'Cider', 'Cider', 'Coffee', 'Cider', 'Cider',\n",
              "       'Cider', 'Cider', 'Coffee', 'Cocoa', 'Cider', 'Cider', 'Cider',\n",
              "       'Coffee', 'Cocoa', 'Coffee', 'Coffee', 'Cocoa', 'Cider', 'Cocoa',\n",
              "       'Tea', 'Cocoa', 'Tea', 'Cider', 'Coffee', 'Cider', 'Cocoa',\n",
              "       'Coffee', 'Cocoa', 'None', 'Coffee', 'Coffee', 'Coffee', 'Coffee',\n",
              "       'Cocoa', 'Coffee', 'None', 'Cider', 'Coffee', 'Cocoa', 'Coffee',\n",
              "       'Cider', 'Cider', 'Coffee', 'Cider', 'Coffee', 'Coffee', 'Cocoa',\n",
              "       'Cider', 'Cider', 'Cider', 'Cocoa', 'Tea', 'Coffee', 'Cocoa',\n",
              "       'Cider', 'Cocoa', 'Cocoa', 'Coffee', 'Coffee', 'Coffee', 'Coffee',\n",
              "       'Coffee', 'None', 'Coffee', 'Cider', 'Cider', 'Cocoa', 'Cider',\n",
              "       'Cider', 'Coffee', 'Cider', 'Coffee', 'Cider', 'Cider', 'Tea',\n",
              "       'Cider', 'Cider', 'None', 'Tea', 'Cider', 'Cocoa', 'Coffee',\n",
              "       'Cider', 'Cider', 'Cocoa', 'Cocoa', 'None', 'Tea', 'Cider',\n",
              "       'Cider', 'Cocoa', 'Cider', 'Cider', 'Cider', 'Tea', 'Cider',\n",
              "       'Cider', 'Coffee', 'Cocoa', 'Tea', 'Cocoa', 'Coffee', 'Cider',\n",
              "       'Cider', 'Tea', 'Cocoa', 'Cider', 'Cider', 'Cider', 'Cocoa',\n",
              "       'Cider', 'Cider', 'Cocoa', 'Coffee', 'Coffee', 'Coffee', 'Cider',\n",
              "       'Cocoa', 'Tea', 'Coffee', 'Coffee', 'Cider', 'Cider', 'Cocoa',\n",
              "       'Cider', 'Cocoa', 'Cocoa', 'Cider', 'None', 'Cider', 'Cocoa',\n",
              "       'Cider', 'Cocoa', 'Coffee', 'Coffee', 'Cocoa', 'None', 'Cider',\n",
              "       'Cider', 'Cider', 'Cocoa', 'Cider', 'Cocoa', 'Cocoa', 'Coffee',\n",
              "       'Cocoa', 'Cider', 'Cider', 'Cocoa', 'Cocoa', 'Coffee', 'Cider',\n",
              "       'Coffee', 'Cider', 'Cider', 'Cider', 'Cider', 'Tea', 'None', 'Tea',\n",
              "       'Cocoa', 'Cocoa', 'Tea', 'Cider', 'Coffee', 'Coffee', 'Coffee',\n",
              "       'Tea', 'Cider', 'Cocoa', 'Cider', 'Cocoa', 'Cocoa', 'Cocoa',\n",
              "       'Cocoa', 'Tea', 'Cocoa', 'Cocoa', 'Cider', 'Tea', 'Cocoa', 'Cocoa',\n",
              "       'Cocoa', 'Cider', 'Coffee', 'Coffee', 'Cider', 'Coffee', 'Cider',\n",
              "       'Cider', 'Cider', 'Coffee', 'Cider', 'None', 'Tea', 'Cider',\n",
              "       'Cider', 'Cider', 'Cider', 'Cider', 'Coffee', 'Cocoa', 'Cider',\n",
              "       'Cocoa', 'Cider', 'Cocoa', 'Cider', 'Cider', 'Cider', 'Cider',\n",
              "       'Tea', 'Cocoa', 'Tea', 'Coffee', 'Cider', 'Coffee', 'Tea', 'Cider',\n",
              "       'Cider', 'Coffee', 'Cider', 'Cider', 'Cocoa', 'Cocoa', 'Coffee',\n",
              "       'Tea', 'Cocoa', 'Cocoa', 'Coffee', 'Coffee', 'Coffee', 'Cocoa',\n",
              "       'Cider', 'Cocoa', 'Tea', 'Cider', 'None', 'Cider', 'Cocoa',\n",
              "       'Coffee', 'Cider', 'Coffee', 'Coffee', 'Cider', 'Cider', 'Cider',\n",
              "       'Cocoa', 'Coffee', 'Cider', 'Cocoa', 'Cider', 'Cocoa', 'Cocoa',\n",
              "       'Cider', 'Cider', 'Coffee', 'Coffee', 'Cider', 'Cocoa', 'Cider',\n",
              "       'Cider', 'Tea', 'Cider', 'Cider', 'Cocoa', 'Cocoa', 'Cider',\n",
              "       'Cocoa', 'Cocoa', 'Coffee', 'Cider', 'Cider', 'Cider', 'Cocoa',\n",
              "       'Cider', 'Cider', 'Tea', 'Cider', 'Cocoa', 'Cocoa', 'Coffee',\n",
              "       'Cocoa', 'Coffee', 'Coffee', 'Cider', 'Cocoa', 'Tea', 'Cider',\n",
              "       'Cider', 'Cider', 'Coffee', 'Cocoa', 'Coffee', 'Cider', 'Cocoa',\n",
              "       'Cocoa', 'Cocoa', 'Tea', 'Coffee', 'Cider', 'Cider', 'Cider',\n",
              "       'Coffee', 'Cocoa', 'Cocoa', 'Coffee', 'Cider', 'Cider', 'Cocoa',\n",
              "       'Cocoa', 'Cocoa', 'Cider', 'Cider', 'Coffee', 'Cider', 'Cider',\n",
              "       'Cocoa', 'Cider', 'Cocoa', 'Coffee', 'Cider', 'Tea', 'Cider',\n",
              "       'Cider', 'Coffee', 'Cocoa', 'Cocoa', 'Cider', 'Cocoa', 'None',\n",
              "       'Cider', 'Cider', 'Cider', 'Cider', 'Coffee', 'Cider', 'Coffee',\n",
              "       'Cider', 'Tea', 'Cider', 'Cider', 'Tea', 'Coffee', 'Cocoa',\n",
              "       'Cocoa', 'Cocoa', 'Coffee', 'Coffee', 'Cocoa', 'Coffee', 'Cider',\n",
              "       'Cider', 'Cocoa', 'Coffee', 'Cider', 'Cider', 'Cider', 'Coffee',\n",
              "       'Cider', 'Cider', 'Cider', 'Cocoa', 'Cider', 'Coffee', 'Cider',\n",
              "       'Coffee', 'Cider', 'Cocoa', 'Cider', 'Tea', 'Coffee', 'Coffee',\n",
              "       'Cider', 'Cocoa', 'Cider', 'Cider', 'Tea', 'Cocoa', 'Tea', 'Cider',\n",
              "       'Coffee', 'Coffee', 'Cider', 'Tea', 'Cider', 'Cider', 'Cider',\n",
              "       'Coffee', 'Cider', 'Cocoa', 'Cocoa', 'Cocoa', 'Coffee', 'Cider',\n",
              "       'Coffee', 'Cider', 'Cider', 'Coffee', 'Cider', 'Cider', 'Coffee',\n",
              "       'Cider', 'Cocoa', 'Tea', 'Coffee', 'Cocoa', 'Cider', 'Coffee',\n",
              "       'Coffee', 'Cocoa', 'Coffee', 'Cocoa', 'Cocoa', 'Cocoa', 'Cider',\n",
              "       'Cider', 'Cider', 'Cocoa', 'None', 'None', 'Cocoa', 'Cider',\n",
              "       'Coffee', 'Cider', 'Cocoa', 'Cocoa', 'Cider', 'Cocoa', 'Cider',\n",
              "       'Tea', 'Cocoa', 'Cider', 'Cocoa', 'Cider', 'Cider', 'Cider',\n",
              "       'Coffee', 'Coffee', 'Cider', 'Coffee', 'Cider', 'Coffee', 'Cider',\n",
              "       'Cocoa', 'Cider', 'Cider', 'Cider', 'Cider', 'Cider', 'Coffee',\n",
              "       'Cider', 'Coffee', 'Cider', 'Cocoa', 'Coffee', 'Cider', 'Cocoa',\n",
              "       'Cider', 'Cider', 'Coffee', 'Cocoa', 'Cider', 'Cocoa', 'Cider',\n",
              "       'Coffee', 'Cider', 'Coffee', 'Coffee', 'Cocoa', 'Cocoa', 'Cocoa',\n",
              "       'Cider', 'Cider', 'Coffee', 'Cider', 'None', 'Coffee', 'Tea',\n",
              "       'Coffee', 'Coffee', 'Coffee', 'Cocoa', 'Coffee', 'Cider', 'Cider',\n",
              "       'Cocoa', 'Cocoa', 'Coffee', 'Cider', 'Cocoa', 'Cider', 'Cocoa',\n",
              "       'Cider', 'Cider', 'Coffee', 'Cider', 'Cocoa', 'Tea', 'Cider',\n",
              "       'Cocoa', 'Cider', 'Coffee', 'Coffee', 'Coffee', 'Tea', 'Coffee',\n",
              "       'Cider', 'Coffee', 'Coffee', 'Tea', 'Tea', 'Cider', 'Cocoa',\n",
              "       'Cider', 'Cider', 'Cocoa', 'Cider', 'Cocoa', 'Coffee', 'Coffee',\n",
              "       'Cocoa', 'Cocoa', 'Coffee', 'Cider', 'Coffee', 'Coffee', 'Cocoa',\n",
              "       'Coffee', 'Cider', 'Coffee', 'Cocoa', 'Cocoa', 'Cider', 'Coffee',\n",
              "       'Coffee', 'Cider', 'Coffee', 'Coffee', 'Cocoa', 'Cider', 'Coffee',\n",
              "       'Cider', 'Cocoa', 'Cider', 'Tea', 'Cider', 'Coffee', 'Coffee',\n",
              "       'Tea', 'Cider', 'Tea', 'Cocoa', 'Cider', 'Cider', 'Tea', 'Cocoa'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in cypred: \n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCqzRSruCcrs",
        "outputId": "97c7c160-7392-4544-abb7-ef1114fe14e3"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tea\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n",
            "Tea\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n",
            "Tea\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Tea\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "None\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "None\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "None\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "None\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n",
            "Tea\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "None\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Tea\n",
            "Cider\n",
            "Coffee\n",
            "Tea\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "None\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "None\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Tea\n",
            "Cocoa\n",
            "Tea\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cocoa\n",
            "None\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "None\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Tea\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "None\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "None\n",
            "Tea\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "None\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Tea\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Tea\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "None\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "None\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "None\n",
            "Tea\n",
            "Cocoa\n",
            "Cocoa\n",
            "Tea\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Tea\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Tea\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "None\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n",
            "Tea\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Tea\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Tea\n",
            "Cider\n",
            "None\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Tea\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "None\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Tea\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n",
            "Tea\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Tea\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "None\n",
            "None\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "None\n",
            "Coffee\n",
            "Tea\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Tea\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Coffee\n",
            "Tea\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Tea\n",
            "Tea\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Cocoa\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Cocoa\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Cocoa\n",
            "Cider\n",
            "Coffee\n",
            "Cider\n",
            "Cocoa\n",
            "Cider\n",
            "Tea\n",
            "Cider\n",
            "Coffee\n",
            "Coffee\n",
            "Tea\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n",
            "Cider\n",
            "Cider\n",
            "Tea\n",
            "Cocoa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score \n",
        "accuracy_score(cytest, cypred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "At-y5k5KFAkp",
        "outputId": "52174ace-da79-4c25-dcf7-8ae86db2be41"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.248"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy is 0.248"
      ],
      "metadata": {
        "id": "BY7-b2WNFLhD"
      }
    }
  ]
}