7_Logistic_Regression_By_Binary_Classification.ipynb

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      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/insurance_data.csv\")\n",
        "df.head()"
      ],
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          "data": {
            "text/plain": [
              "   age  bought_insurance\n",
              "0   22                 0\n",
              "1   25                 0\n",
              "2   47                 1\n",
              "3   52                 0\n",
              "4   46                 1"
            ],
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      "source": [
        "plt.scatter(df.age, df.bought_insurance, marker='+', color='red')"
      ],
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      "outputs": [
        {
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              "<matplotlib.collections.PathCollection at 0x7f5471da5850>"
            ]
          },
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        },
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              "<Figure size 432x288 with 1 Axes>"
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z6PuBZ9H773uvraovTaTIiyzJ84E/oHex7ycA+6vqpiTfRm93+nTgfuD6qvrq5CqdjCQ7gJ+pqldN85p0v/uHu+Z64INV9WtJnsGUPncAklwBvBe4BDgBvJbuecQaWZNmA12Spk2zp1wkadoY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakR/w+oGVyw4/IRsgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOpPljgbUDtR",
        "outputId": "f385a0e8-da74-4f6b-b742-cc270a16088a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "LpB3u5juUStN"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZykBN0SrVBZ9",
        "outputId": "8e0ebd86-140a-4dd7-80a3-d865c2f57ae2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20    0\n",
              "9     1\n",
              "17    1\n",
              "7     1\n",
              "12    0\n",
              "14    1\n",
              "24    1\n",
              "15    1\n",
              "6     0\n",
              "0     0\n",
              "8     1\n",
              "4     1\n",
              "3     0\n",
              "1     0\n",
              "25    1\n",
              "5     1\n",
              "16    1\n",
              "21    0\n",
              "10    0\n",
              "13    0\n",
              "22    1\n",
              "2     1\n",
              "18    0\n",
              "23    1\n",
              "Name: bought_insurance, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df[['age']], df.bought_insurance, test_size = 0.1)"
      ],
      "metadata": {
        "id": "8wu6ikYqU2mX"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "X_test"
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        "outputId": "cc28c5b9-2774-44e2-a9bf-2fb149c89888"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    age\n",
              "24   50\n",
              "19   18\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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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    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "PIHxb0GhV_uG",
        "outputId": "27bcc76c-e1d8-44c4-b885-7f80bc1b1abf"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age\n",
              "12   27\n",
              "10   18\n",
              "26   23\n",
              "8    62\n",
              "19   18\n",
              "4    46\n",
              "9    61\n",
              "11   28\n",
              "14   49\n",
              "18   19\n",
              "24   50\n",
              "5    56\n",
              "2    47\n",
              "16   25\n",
              "3    52\n",
              "1    25\n",
              "23   45\n",
              "13   29\n",
              "25   54\n",
              "7    60\n",
              "20   21\n",
              "22   40\n",
              "0    22\n",
              "17   58"
            ],
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              "      <th>3</th>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>45</td>\n",
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              "      <td>58</td>\n",
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              "</table>\n",
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              "\n",
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              "    }\n",
              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e7911d60-ec47-4d32-8df7-ab006b079385 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e7911d60-ec47-4d32-8df7-ab006b079385');\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",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "4liI44vgWprf"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "8O7hFSBlXDYs"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBo4IiGYXHAe",
        "outputId": "a75b8e04-a95e-4043-c5e3-c5e659435e6a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWFyOGdpXNKH",
        "outputId": "b9bc40bb-1666-434c-e47d-b572e2a24b7e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qM2O4MGXbhW",
        "outputId": "50260541-a564-4083-f8a2-e146eaa8751d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6666666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict_proba(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KnZRQQkTXvXW",
        "outputId": "e7a4d8d7-1d96-4865-f088-6df79712e81f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.84748721, 0.15251279],\n",
              "       [0.04187548, 0.95812452],\n",
              "       [0.04187548, 0.95812452]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[25]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eU4XjuwdYQns",
        "outputId": "6f183832-73cd-4657-d7fd-75512dd0b67d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[55]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ydr0SRbAYhOo",
        "outputId": "38b98b65-0229-4bac-88ac-0bbab2052ff2"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3b-EkpRPY13g"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}