Random_Forest_Assignment16.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Aditi840/Guvi-Tasks/blob/main/Random_Forest_Assignment16.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqp7Q6rWnX20"
      },
      "source": [
        "#Social_Network_Ads.csv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jSlqZcZnmrD"
      },
      "source": [
        "**Importing the libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACJTKMfInp-E"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.datasets import make_classification\n",
        "from matplotlib.colors import ListedColormap"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fRZkaj-dnxIj"
      },
      "source": [
        "**Importing the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfTR4mVDnqBV"
      },
      "source": [
        "df = pd.read_csv('/content/Social_Network_Ads.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)\n",
        "print(df.columns)\n",
        "print(df.isnull())"
      ],
      "metadata": {
        "id": "498lcR5Dawbr",
        "outputId": "d3444c6d-84de-44c4-92dc-e004603a818b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
            "0    15624510    Male   19            19000          0\n",
            "1    15810944    Male   35            20000          0\n",
            "2    15668575  Female   26            43000          0\n",
            "3    15603246  Female   27            57000          0\n",
            "4    15804002    Male   19            76000          0\n",
            "..        ...     ...  ...              ...        ...\n",
            "395  15691863  Female   46            41000          1\n",
            "396  15706071    Male   51            23000          1\n",
            "397  15654296  Female   50            20000          1\n",
            "398  15755018    Male   36            33000          0\n",
            "399  15594041  Female   49            36000          1\n",
            "\n",
            "[400 rows x 5 columns]\n",
            "Index(['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased'], dtype='object')\n",
            "     User ID  Gender    Age  EstimatedSalary  Purchased\n",
            "0      False   False  False            False      False\n",
            "1      False   False  False            False      False\n",
            "2      False   False  False            False      False\n",
            "3      False   False  False            False      False\n",
            "4      False   False  False            False      False\n",
            "..       ...     ...    ...              ...        ...\n",
            "395    False   False  False            False      False\n",
            "396    False   False  False            False      False\n",
            "397    False   False  False            False      False\n",
            "398    False   False  False            False      False\n",
            "399    False   False  False            False      False\n",
            "\n",
            "[400 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Thl3lvkn9Kr"
      },
      "source": [
        "**Splitting the dataset into the Training set and Test set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sp359H4XnqD3",
        "outputId": "9545477f-d16b-49c8-81c1-34714c72b5ab",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "source": [
        "df = pd.get_dummies(df, columns=['Gender'])\n",
        "print(df)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      User ID  Age  EstimatedSalary  Purchased  Gender_Female  Gender_Male\n",
            "0    15624510   19            19000          0              0            1\n",
            "1    15810944   35            20000          0              0            1\n",
            "2    15668575   26            43000          0              1            0\n",
            "3    15603246   27            57000          0              1            0\n",
            "4    15804002   19            76000          0              0            1\n",
            "..        ...  ...              ...        ...            ...          ...\n",
            "395  15691863   46            41000          1              1            0\n",
            "396  15706071   51            23000          1              0            1\n",
            "397  15654296   50            20000          1              1            0\n",
            "398  15755018   36            33000          0              0            1\n",
            "399  15594041   49            36000          1              1            0\n",
            "\n",
            "[400 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['Age', 'EstimatedSalary']]\n",
        "y = df['Purchased']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "UOk-Bbw7bZDn"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNNxUncRoG6K"
      },
      "source": [
        "**Feature Scaling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loYjT47TnqIG"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VB4b09roPhq"
      },
      "source": [
        "**Fitting Random Forest to the Training set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UxhDc2lnqKu",
        "outputId": "49525b07-5866-43bf-c270-756779461f1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "# Create a Random Forest classifier object\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=0)\n",
        "\n",
        "# Fit the classifier to the training set\n",
        "rf_classifier.fit(X_train, y_train)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=0)"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XOl6_ERWoYh2"
      },
      "source": [
        "**Predicting the Test set results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xFCuAXjnqPN"
      },
      "source": [
        "y_pred = rf_classifier.predict(X_test)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqaQ_HTZogaZ"
      },
      "source": [
        "**Making the Confusion Matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DdqNOMK8nqSs",
        "outputId": "42d4e4fa-70d9-4a57-cc87-72eff9d0b4df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "print(cm)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[55  3]\n",
            " [ 3 19]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ko5EDqqiopBl"
      },
      "source": [
        "**Visualising the Training set results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7C-xZjFnqVC",
        "outputId": "ada09b01-94a1-4409-b765-3c6ab85aa4ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "source": [
        "# Create a color map for the plot\n",
        "colors = ListedColormap(['red', 'green'])\n",
        " \n",
        "# Prepare the mesh grid for the plot\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_train[:, 0].min() - 1, stop = X_train[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_train[:, 1].min() - 1, stop = X_train[:, 1].max() + 1, step = 0.01))\n",
        "\n",
        "# Plot the decision boundary\n",
        "plt.contourf(X1, X2, rf_classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = colors)\n",
        "\n",
        "# Plot the training set points\n",
        "plt.scatter(X_train[:, 0], X_train[:, 1], c = y_train, cmap = colors)\n",
        "\n",
        "# Set the title and axes labels\n",
        "plt.title('Random Forest Classification (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ExFSX84Hou9C"
      },
      "source": [
        "**Visualising the Test set results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHbPz86Noj6d",
        "outputId": "1dac8061-f9af-4d0a-df9c-49c041ad0f52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "source": [
        "# Create a color map for the plot\n",
        "colors = ListedColormap(['red', 'green'])\n",
        "\n",
        "# Prepare the mesh grid for the plot\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_test[:, 0].min() - 1, stop = X_test[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_test[:, 1].min() - 1, stop = X_test[:, 1].max() + 1, step = 0.01))\n",
        "\n",
        "# Plot the decision boundary\n",
        "plt.contourf(X1, X2, rf_classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = colors)\n",
        "\n",
        "# Plot the test set points\n",
        "plt.scatter(X_test[:, 0], X_test[:, 1], c = y_test, cmap = colors)\n",
        "\n",
        "# Set the title and axes labels\n",
        "plt.title('Random Forest Classification (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}