LR/LR-Iris.ipynb

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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report,accuracy_score"
      ],
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        "id": "KM9jdpRoiJVC"
      },
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      "source": [
        "df = pd.read_csv(\"https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv\", names=['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width','target'], skiprows=1)"
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      "source": [
        "df.head()"
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            "text/plain": [
              "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width  target\n",
              "0           5.1          3.5           1.4          0.2  Setosa\n",
              "1           4.9          3.0           1.4          0.2  Setosa\n",
              "2           4.7          3.2           1.3          0.2  Setosa\n",
              "3           4.6          3.1           1.5          0.2  Setosa\n",
              "4           5.0          3.6           1.4          0.2  Setosa"
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              "      <th>Sepal_Length</th>\n",
              "      <th>Sepal_Width</th>\n",
              "      <th>Petal_Length</th>\n",
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              "      <th>target</th>\n",
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              "      <td>5.1</td>\n",
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "X = df.iloc[:,:4].values\n",
        "y = df.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "1mMNPq15ielX"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "perm = np.random.permutation(len(X))\n",
        "f= df.loc[perm]\n",
        "x_train, x_test = X[perm][30:], X[perm][:30]\n",
        "y_train, y_test = y[perm][30:], y[perm][:30]"
      ],
      "metadata": {
        "id": "uV-WMb_dkdXh"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
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        "id": "y9kQ7_LTk8Hn",
        "outputId": "e683e40d-d1d0-4810-c96c-972bbca26e6f"
      },
      "execution_count": 8,
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        {
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          "data": {
            "text/plain": [
              "LogisticRegression()"
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model\n",
        "predictions = log_reg.predict(x_test)\n",
        "print(predictions)# printing predictions\n",
        "print()# Printing new line"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fk7D7NpPlAu9",
        "outputId": "f26f25c8-2f40-4b05-b002-679c448273a9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Virginica' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Versicolor'\n",
            " 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Setosa' 'Setosa' 'Setosa'\n",
            " 'Virginica' 'Versicolor' 'Versicolor' 'Versicolor' 'Virginica'\n",
            " 'Versicolor' 'Versicolor' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
            " 'Virginica' 'Versicolor' 'Versicolor' 'Setosa' 'Setosa' 'Virginica']\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, predictions) )\n",
        "print(accuracy_score(y_test, predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NpBfSyT3lhRF",
        "outputId": "1f36d4d3-729d-41f8-d51f-8681bc2660e1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Setosa       1.00      1.00      1.00        11\n",
            "  Versicolor       1.00      0.91      0.95        11\n",
            "   Virginica       0.89      1.00      0.94         8\n",
            "\n",
            "    accuracy                           0.97        30\n",
            "   macro avg       0.96      0.97      0.96        30\n",
            "weighted avg       0.97      0.97      0.97        30\n",
            "\n",
            "0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "marker_map = ['o', 's', '^']\n",
        "unique = np.unique(df['target'])\n",
        "for marker, val in zip(marker_map, unique):\n",
        "    toUse = (df['target'] == val)\n",
        "    plt.scatter(X[toUse,0], X[toUse,1], marker=marker, label=val, s=100)\n",
        "plt.xlabel('Sepal Length cm')\n",
        "plt.ylabel('Sepal Width cm')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "mVSSXPpkliXt",
        "outputId": "362614f1-c80a-4e87-f00b-37a989a72a73"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['target']=='Versicolor'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqGl6ZTRofK4",
        "outputId": "ec88f9b4-b414-41cc-a419-7e896ecde9e5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4      False\n",
              "       ...  \n",
              "145    False\n",
              "146    False\n",
              "147    False\n",
              "148    False\n",
              "149    False\n",
              "Name: target, Length: 150, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "marker_map = ['o', 's', 's']\n",
        "df.loc[((df['target']=='Versicolor') | (df['target']=='Virginica')), 'target']='Non-Setosa'\n",
        "unique = np.unique(df['target'])\n",
        "\n",
        "for marker, val in zip(marker_map, unique):\n",
        "    toUse = (df['target'] == val)\n",
        "    plt.scatter(X[toUse,0], X[toUse,3], marker=marker, label=val)\n",
        "plt.xlabel('Sepal Length cm')\n",
        "plt.ylabel('Petal Width cm')\n",
        "plt.legend()\n",
        "\n",
        "\n",
        "log_reg.fit(np.hstack((X[:,0].reshape(-1, 1), X[:,3].reshape(-1, 1))),df['target'].map({'Setosa':1,'Non-Setosa':0}).values)\n",
        "\n",
        "print(log_reg.coef_, log_reg.intercept_)\n",
        "\n",
        "\n",
        "m1 = log_reg.coef_[0][0]\n",
        "m2 = log_reg.coef_[0][1]\n",
        "m0 = log_reg.intercept_\n",
        "\n",
        "# Generate x1 values\n",
        "x1_values = np.linspace(4, 8, 150)\n",
        "\n",
        "# Calculate corresponding x2 values using the equation m1x1 + m2x2 + m0 = 0\n",
        "x2_values = (-m1 * x1_values - m0) / m2\n",
        "\n",
        "# Plot the line\n",
        "plt.plot(x1_values, x2_values)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "DcswftMSlsRt",
        "outputId": "8545925d-a882-434f-ac32-14c4a0eb0272"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1.3983599  -3.91315269]] [10.48150545]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "marker_map = ['o', 's', 's']\n",
        "unique = np.unique(df['target'])\n",
        "for marker, val in zip(marker_map, unique):\n",
        "    toUse = (df['target'] == val)\n",
        "    plt.scatter(X[toUse,1], X[toUse,3], marker=marker, label=val)\n",
        "plt.xlabel('Sepal Length cm')\n",
        "plt.ylabel('Petal Width cm')\n",
        "plt.legend()\n",
        "\n",
        "log_reg.fit(np.hstack((X[:,1].reshape(-1, 1), X[:,3].reshape(-1, 1))),df['target'].map({'Setosa':1,'Non-Setosa':0}).values)\n",
        "\n",
        "print(log_reg.coef_, log_reg.intercept_)\n",
        "\n",
        "\n",
        "m1 = log_reg.coef_[0][0]\n",
        "m2 = log_reg.coef_[0][1]\n",
        "m0 = log_reg.intercept_\n",
        "\n",
        "# Generate x1 values\n",
        "x1_values = np.linspace(2, 5, 150)\n",
        "\n",
        "# Calculate corresponding x2 values using the equation m1x1 + m2x2 + m0 = 0\n",
        "x2_values = (-m1 * x1_values - m0) / m2\n",
        "\n",
        "# Plot the line\n",
        "plt.plot(x1_values, x2_values)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "c7gCHJuFsnjF",
        "outputId": "a1bcea40-b211-4849-e4c4-37596808be95"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.72135554 -3.98236256]] [-2.23481392]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "abn5zL09tQuP"
      },
      "execution_count": 14,
      "outputs": []
    }
  ]
}