Logistic_Regression.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27OPLjn73LVf",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# Iris Species Classification\n",
        "***Classify the Iris species using Logistic Regression***\n",
        "\n",
        "![iris Virginica](https://upload.wikimedia.org/wikipedia/commons/3/38/Iris_virginica_-_NRCS.jpg)\n",
        "\n",
        "This is a simple logistic regression program to classify an iris species as either ( virginica, setosa, or versicolor) based off of the pedal length, pedal height, sepal length, and sepal height using a machine learning algorithm called Logistic Regression.\n",
        "\n",
        "\n",
        "\n",
        "**Resources:**\n",
        "\n",
        " (1) https://towardsdatascience.com/building-a-logistic-regression-in-python-301d27367c24\n",
        " \n",
        " (2) https://towardsdatascience.com/logistic-regression-a-simplified-approach-using-python-c4bc81a87c31\n",
        " \n",
        " (3) https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wv9Yp1dSs9Zn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "This Is A Simple Logistic Regression Program To Classify Iris Species\n",
        "\n",
        "Resources:\n",
        " (1) https://towardsdatascience.com/building-a-logistic-regression-in-python-301d27367c24\n",
        " (2) https://towardsdatascience.com/logistic-regression-a-simplified-approach-using-python-c4bc81a87c31\n",
        " (3) https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Import the dependencies\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QykzHudMt480",
        "colab_type": "code",
        "outputId": "179af60b-22a8-457e-db34-62fa8795f350",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "#Load the data set\n",
        "data = sns.load_dataset(\"iris\")\n",
        "data.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SkmpiAj_uE4w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Prepare the training set\n",
        "\n",
        "# X = feature values, all the columns except the last column\n",
        "X = data.iloc[:, :-1]\n",
        "\n",
        "# y = target values, last column of the data frame\n",
        "y = data.iloc[:, -1]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DkNWmIQNumf1",
        "colab_type": "code",
        "outputId": "548670c1-b325-4a64-c625-4bec2039723c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# Plot the relation of each feature with each species\n",
        "\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Species')\n",
        "\n",
        "pltX = data.loc[:, 'sepal_length']\n",
        "pltY = data.loc[:,'species']\n",
        "plt.scatter(pltX, pltY, color='blue', label='sepal_length')\n",
        "\n",
        "pltX = data.loc[:, 'sepal_width']\n",
        "pltY = data.loc[:,'species']\n",
        "plt.scatter(pltX, pltY, color='green', label='sepal_width')\n",
        "\n",
        "pltX = data.loc[:, 'petal_length']\n",
        "pltY = data.loc[:,'species']\n",
        "plt.scatter(pltX, pltY, color='red', label='petal_length')\n",
        "\n",
        "pltX = data.loc[:, 'petal_width']\n",
        "pltY = data.loc[:,'species']\n",
        "plt.scatter(pltX, pltY, color='black', label='petal_width')\n",
        "\n",
        "plt.legend(loc=4, prop={'size':8})\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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bdM+iMs7r7qvN7HAzmwhMAP4MTCF4bcbqsNihwMcIgqnd3X8djn8D+EgYIj8nCJhkxwFv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\nYQxYjIKT7AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JpxxN8UKwBOh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Split the data into 80% training and 20% testing\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yI7TRVRAwaPU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Train the model\n",
        "model = LogisticRegression()\n",
        "model.fit(x_train, y_train) #Training the model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDKGcVVSwmul",
        "colab_type": "code",
        "outputId": "b6fe702d-71a7-4a17-b282-28d6f328acdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "#Test the model\n",
        "predictions = model.predict(x_test)\n",
        "print(predictions)# printing predictions\n",
        "\n",
        "print()# Printing new line\n",
        "\n",
        "#Check precision, recall, f1-score\n",
        "print( classification_report(y_test, predictions) )\n",
        "\n",
        "print( accuracy_score(y_test, predictions))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['versicolor' 'setosa' 'virginica' 'versicolor' 'versicolor' 'setosa'\n",
            " 'versicolor' 'virginica' 'versicolor' 'versicolor' 'virginica' 'setosa'\n",
            " 'setosa' 'setosa' 'setosa' 'versicolor' 'virginica' 'versicolor'\n",
            " 'versicolor' 'virginica' 'setosa' 'virginica' 'setosa' 'virginica'\n",
            " 'virginica' 'virginica' 'virginica' 'virginica' 'setosa' 'setosa']\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        10\n",
            "  versicolor       1.00      1.00      1.00         9\n",
            "   virginica       1.00      1.00      1.00        11\n",
            "\n",
            "    accuracy                           1.00        30\n",
            "   macro avg       1.00      1.00      1.00        30\n",
            "weighted avg       1.00      1.00      1.00        30\n",
            "\n",
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}