Decision_Tree_Classification.ipynb

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      "name": "Decision Tree Classification.ipynb",
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      "execution_count": 15,
      "metadata": {
        "id": "QSyGJskiu1rX"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"Social_Network_Ads.csv\")\n",
        "data.head()"
      ],
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              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
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            "text/plain": [
              "   Age  EstimatedSalary  Purchased\n",
              "0   19            19000          0\n",
              "1   35            20000          0\n",
              "2   26            43000          0\n",
              "3   27            57000          0\n",
              "4   19            76000          0"
            ]
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    },
    {
      "cell_type": "code",
      "source": [
        "x = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "Ia23RdHK0tkP"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2)"
      ],
      "metadata": {
        "id": "qgQ_M2fp1E26"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "metadata": {
        "id": "Ei9YY_ac4RBR"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
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              "       [ 0.79787029,  0.54888845],\n",
              "       [-0.27407758,  0.11319066],\n",
              "       [ 0.01827184,  0.05509762],\n",
              "       [-0.76132661,  0.31651629],\n",
              "       [-0.27407758, -0.46773974],\n",
              "       [ 0.79787029,  0.78126061],\n",
              "       [-0.27407758, -0.87439101],\n",
              "       [ 0.70042048,  0.28746977],\n",
              "       [ 0.50552087,  1.73979576],\n",
              "       [-1.05367603, -0.32250714],\n",
              "       [-0.27407758,  0.28746977],\n",
              "       [-1.44347526, -0.61297234],\n",
              "       [-0.17662777, -0.17727454],\n",
              "       [ 0.70042048, -1.36818185],\n",
              "       [ 1.96726797, -1.33913533],\n",
              "       [-0.37152739,  0.08414414],\n",
              "       [-0.76132661, -1.57150749],\n",
              "       [ 0.79787029, -1.33913533],\n",
              "       [-1.15112584, -0.49678626],\n",
              "       [ 1.09021971,  0.49079541],\n",
              "       [-0.85877642, -0.75820494],\n",
              "       [-0.27407758, -0.1191815 ],\n",
              "       [ 0.79787029, -0.81629797],\n",
              "       [ 0.9927699 ,  2.00121444],\n",
              "       [-0.27407758,  1.12981885],\n",
              "       [-0.566427  ,  0.89744669],\n",
              "       [-1.24857565,  0.51984193],\n",
              "       [-0.27407758, -1.33913533],\n",
              "       [-0.07917797,  0.28746977],\n",
              "       [-0.46897719, -1.10676317],\n",
              "       [-0.17662777, -0.5548793 ],\n",
              "       [ 0.8953201 , -1.42627489],\n",
              "       [ 0.11572165,  1.53647012],\n",
              "       [ 0.21317145,  1.10077233],\n",
              "       [ 0.31062126,  0.08414414],\n",
              "       [-1.34602545, -1.33913533],\n",
              "       [-0.66387681,  0.05509762],\n",
              "       [-0.27407758,  0.08414414],\n",
              "       [ 2.16216758,  0.95553973],\n",
              "       [-1.05367603, -1.51341445],\n",
              "       [-0.95622623,  0.46174889],\n",
              "       [-0.95622623,  0.51984193],\n",
              "       [ 0.79787029,  0.28746977],\n",
              "       [ 0.31062126,  0.31651629],\n",
              "       [-1.63837487, -1.54246097],\n",
              "       [-0.07917797, -0.35155366],\n",
              "       [-0.37152739,  1.33314448],\n",
              "       [-1.34602545, -1.45532141],\n",
              "       [ 2.06471778,  1.76884228],\n",
              "       [ 1.09021971,  0.14223718],\n",
              "       [-0.07917797,  2.2335866 ],\n",
              "       [-0.66387681, -0.09013498],\n",
              "       [ 1.77236836,  1.01363277],\n",
              "       [ 0.01827184, -0.23536758],\n",
              "       [-0.95622623, -0.4096467 ],\n",
              "       [ 0.60297068,  2.03026096],\n",
              "       [-1.05367603,  1.97216792],\n",
              "       [-1.15112584, -1.57150749],\n",
              "       [ 2.16216758, -0.78725145],\n",
              "       [-0.66387681, -1.57150749],\n",
              "       [ 0.40807106,  0.31651629],\n",
              "       [-0.27407758, -0.23536758],\n",
              "       [ 0.9927699 ,  1.88502836],\n",
              "       [ 0.9927699 , -1.16485621],\n",
              "       [-0.66387681,  1.42028404],\n",
              "       [-0.76132661, -0.20632106],\n",
              "       [-1.34602545, -0.32250714],\n",
              "       [-1.54092507, -0.17727454],\n",
              "       [-0.95622623,  1.56551664],\n",
              "       [ 1.28511932, -1.33913533],\n",
              "       [ 0.31062126, -0.29346062],\n",
              "       [-0.66387681,  0.57793497],\n",
              "       [ 1.38256913,  2.00121444],\n",
              "       [ 1.48001894, -1.01962361],\n",
              "       [-0.66387681, -0.32250714],\n",
              "       [-0.27407758, -1.28104229],\n",
              "       [ 1.38256913,  1.30409796],\n",
              "       [-0.566427  ,  1.47837708],\n",
              "       [ 0.60297068, -0.87439101],\n",
              "       [ 0.8953201 , -0.64201886],\n",
              "       [ 0.8953201 , -0.75820494],\n",
              "       [ 1.57746874,  1.12981885],\n",
              "       [ 0.11572165,  0.22937674],\n",
              "       [ 0.9927699 ,  1.44933056],\n",
              "       [-0.46897719, -0.81629797],\n",
              "       [ 0.31062126, -0.7001119 ],\n",
              "       [-1.44347526,  0.37460933],\n",
              "       [-0.566427  ,  0.49079541],\n",
              "       [-0.17662777, -0.49678626],\n",
              "       [-0.27407758, -0.35155366],\n",
              "       [ 1.86981816,  0.14223718],\n",
              "       [ 0.01827184, -0.5548793 ],\n",
              "       [ 1.86981816,  1.53647012],\n",
              "       [ 0.21317145,  2.11740052],\n",
              "       [-0.07917797,  1.97216792],\n",
              "       [ 0.21317145, -0.29346062],\n",
              "       [-1.05367603, -1.42627489],\n",
              "       [-1.34602545, -1.22294925],\n",
              "       [-1.15112584, -0.99057709],\n",
              "       [ 0.21317145, -0.35155366],\n",
              "       [ 0.31062126, -0.49678626],\n",
              "       [ 1.09021971, -1.19390273],\n",
              "       [ 2.16216758, -1.01962361],\n",
              "       [ 0.01827184, -0.23536758],\n",
              "       [-0.95622623, -1.07771665],\n",
              "       [-0.66387681, -0.03204194],\n",
              "       [ 0.8953201 ,  1.27505145],\n",
              "       [ 1.18766952, -1.42627489],\n",
              "       [ 0.11572165,  0.78126061],\n",
              "       [ 0.21317145, -0.1191815 ],\n",
              "       [-0.27407758, -1.22294925],\n",
              "       [-0.27407758,  0.63602801],\n",
              "       [-1.54092507, -1.48436793],\n",
              "       [-1.34602545,  0.57793497],\n",
              "       [ 0.01827184, -0.5548793 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "Classifier = DecisionTreeClassifier(criterion=\"entropy\", random_state=0)\n",
        "Classifier.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hjqV55PdF_lu",
        "outputId": "3adca311-c73b-4d4b-df8a-7ef8f8111b69"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Classifier.predict(sc.transform([[30,87000]]))"
      ],
      "metadata": {
        "id": "gXrE70ghRdOE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f720ef4-2142-4a8d-93ba-0743168d4636"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = Classifier.predict(x_test)\n",
        "Ftest = np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1)\n",
        "Ftest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVmpGtitCQlH",
        "outputId": "db136b39-aa8b-492e-90e2-32d30f4d4246"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [1, 1],\n",
              "       [1, 1],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [1, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 1],\n",
              "       [1, 1],\n",
              "       [1, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 1],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [0, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [1, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 0],\n",
              "       [1, 1],\n",
              "       [1, 1],\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [0, 1],\n",
              "       [1, 1],\n",
              "       [0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6va4zDWKfGD",
        "outputId": "4d8ac7d4-cfba-4d06-bc02-90aced9d2036"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[49,  6],\n",
              "       [ 4, 21]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acs = accuracy_score(y_test, y_pred)\n",
        "acs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i094J3k0LE3Y",
        "outputId": "a4bd56ba-1323-43d2-e9f2-e6c8a956bc73"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.875"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ]
}