practice5.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error,r2_score\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report"
      ]
    },
    {
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      "source": [
        "df = pd.read_csv(\"/content/Social_Network_Ads.csv\")\n",
        "df.head()"
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              "    User ID  Gender   Age  EstimatedSalary  Purchased\n",
              "0  15624510    Male  19.0          19000.0          0\n",
              "1  15810944    Male  35.0          20000.0          0\n",
              "2  15668575  Female  26.0          43000.0          0\n",
              "3  15603246  Female  27.0          57000.0          0\n",
              "4  15804002    Male  19.0          76000.0          0"
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              "      <th>User ID</th>\n",
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              "      <th>EstimatedSalary</th>\n",
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      "source": [
        "x = df[['Age','EstimatedSalary']]\n",
        "x = StandardScaler().fit(x).transform(x)\n",
        "x"
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              "       [ 2.03872775, -0.81465453],\n",
              "       [-0.25358736, -0.34481683],\n",
              "       [ 0.89257019, -0.78528968],\n",
              "       [ 2.13424088,  1.123426  ],\n",
              "       [ 1.08359645, -0.13926283],\n",
              "       [ 0.22397829,  0.2424803 ],\n",
              "       [ 0.79705706,  0.77104772],\n",
              "       [ 2.03872775,  2.15119598],\n",
              "       [ 0.31949142,  0.30121002],\n",
              "       [-0.25358736,  0.62422344],\n",
              "       [-0.0625611 ,  2.18056084],\n",
              "       [ 2.13424088,  0.94723686],\n",
              "       [-0.25358736, -0.28608712],\n",
              "       [-0.0625611 , -0.49164111],\n",
              "       [-0.15807423,  1.65199342],\n",
              "       [ 1.75218836,  1.85754742],\n",
              "       [ 0.22397829,  0.06629116],\n",
              "       [ 0.41500455,  0.30121002],\n",
              "       [-0.25358736,  2.26865541],\n",
              "       [ 0.12846516, -0.81465453],\n",
              "       [ 0.22397829,  1.09406114],\n",
              "       [ 1.08359645,  0.47739916],\n",
              "       [ 0.03295203,  1.24088543],\n",
              "       [ 0.79705706,  0.27184516],\n",
              "       [ 0.22397829, -0.37418169],\n",
              "       [-0.0625611 ,  0.30121002],\n",
              "       [ 0.79705706,  0.35993973],\n",
              "       [ 1.46564897,  2.15119598],\n",
              "       [ 0.41500455,  2.32738512],\n",
              "       [ 0.03295203, -0.31545197],\n",
              "       [ 1.17910958,  0.53612887],\n",
              "       [ 1.75218836,  1.00596657],\n",
              "       [ 0.31949142,  0.06629116],\n",
              "       [ 1.27462271,  2.23929055],\n",
              "       [-0.25358736, -0.57973568],\n",
              "       [ 1.84770149,  1.53453399],\n",
              "       [ 0.31949142, -0.52100597],\n",
              "       [-0.25358736,  0.80041258],\n",
              "       [ 0.60603081, -0.9027491 ],\n",
              "       [-0.0625611 , -0.52100597],\n",
              "       [ 0.98808332,  1.88691227],\n",
              "       [-0.0625611 ,  2.23929055],\n",
              "       [ 1.17910958, -0.75592482],\n",
              "       [ 1.37013584,  0.59485858],\n",
              "       [ 0.31949142,  0.06629116],\n",
              "       [ 0.22397829, -0.37418169],\n",
              "       [ 1.94321462,  0.74168287],\n",
              "       [ 0.70154394,  1.7988177 ],\n",
              "       [-0.25358736,  0.21311545],\n",
              "       [-0.15807423,  2.18056084],\n",
              "       [ 1.65667523,  1.62262856],\n",
              "       [-0.25358736,  0.06629116],\n",
              "       [ 0.98808332,  0.59485858],\n",
              "       [ 0.41500455,  1.123426  ],\n",
              "       [ 0.22397829,  0.15438573],\n",
              "       [-0.0625611 ,  0.12502088],\n",
              "       [ 0.89257019,  2.18056084],\n",
              "       [ 0.22397829, -0.25672226],\n",
              "       [ 0.51051768,  1.85754742],\n",
              "       [ 2.03872775,  0.18375059],\n",
              "       [ 2.13424088, -0.81465453],\n",
              "       [ 0.12846516,  1.06469629],\n",
              "       [ 1.84770149, -1.28449224],\n",
              "       [ 1.84770149,  0.12502088],\n",
              "       [ 0.03295203,  0.03692631],\n",
              "       [ 1.08359645,  0.53612887],\n",
              "       [ 1.37013584, -0.93211396],\n",
              "       [ 1.17910958, -0.99084367],\n",
              "       [ 2.03872775,  0.53612887],\n",
              "       [-0.25358736, -0.25672226],\n",
              "       [-0.0625611 ,  0.00756145],\n",
              "       [ 1.37013584, -1.43131652],\n",
              "       [ 0.98808332,  2.09246627],\n",
              "       [-0.0625611 ,  0.68295315],\n",
              "       [-0.0625611 , -0.2273574 ],\n",
              "       [ 0.98808332,  2.0043717 ],\n",
              "       [ 0.31949142,  0.27184516],\n",
              "       [-0.0625611 ,  0.2424803 ],\n",
              "       [ 0.12846516,  1.88691227],\n",
              "       [ 1.08359645,  0.56549373],\n",
              "       [ 1.65667523, -0.9027491 ],\n",
              "       [-0.0625611 ,  0.21311545],\n",
              "       [-0.25358736, -0.37418169],\n",
              "       [-0.15807423, -0.19799255],\n",
              "       [ 0.41500455,  0.09565602],\n",
              "       [ 0.51051768,  1.24088543],\n",
              "       [ 0.70154394,  0.27184516],\n",
              "       [ 0.79705706,  1.38770971],\n",
              "       [ 1.94321462, -0.93211396],\n",
              "       [ 0.98808332,  0.12502088],\n",
              "       [-0.0625611 ,  1.97500684],\n",
              "       [-0.0625611 ,  0.27184516],\n",
              "       [ 0.22397829, -0.28608712],\n",
              "       [ 0.41500455, -0.46227625],\n",
              "       [ 1.27462271,  1.88691227],\n",
              "       [ 0.89257019,  1.27025028],\n",
              "       [-0.15807423,  1.62262856],\n",
              "       [ 0.03295203, -0.57973568],\n",
              "       [ 0.41500455,  0.00756145],\n",
              "       [ 0.12846516,  0.77104772],\n",
              "       [ 0.03295203, -0.57973568],\n",
              "       [ 1.08359645,  2.09246627],\n",
              "       [ 0.12846516,  0.27184516],\n",
              "       [ 0.12846516,  0.15438573],\n",
              "       [ 1.5611621 ,  1.00596657],\n",
              "       [-0.25358736, -0.4329114 ],\n",
              "       [ 0.70154394, -1.1083031 ],\n",
              "       [-0.15807423, -0.28608712],\n",
              "       [ 1.37013584,  2.0043717 ],\n",
              "       [ 1.46564897,  0.35993973],\n",
              "       [ 0.31949142, -0.52100597],\n",
              "       [ 0.98808332, -1.16703281],\n",
              "       [ 0.98808332,  1.7988177 ],\n",
              "       [ 0.31949142, -0.28608712],\n",
              "       [ 0.31949142,  0.06629116],\n",
              "       [ 0.41500455,  0.15438573],\n",
              "       [-0.15807423,  1.41707457],\n",
              "       [ 0.89257019,  1.09406114],\n",
              "       [ 0.03295203, -0.55037082],\n",
              "       [ 0.98808332,  1.44643942],\n",
              "       [ 0.41500455, -0.13926283],\n",
              "       [ 0.22397829, -0.13926283],\n",
              "       [ 1.84770149, -0.28608712],\n",
              "       [-0.15807423, -0.46227625],\n",
              "       [ 1.94321462,  2.18056084],\n",
              "       [-0.25358736,  0.27184516],\n",
              "       [ 0.03295203, -0.4329114 ],\n",
              "       [ 0.12846516,  1.53453399],\n",
              "       [ 1.46564897,  1.00596657],\n",
              "       [-0.25358736,  0.15438573],\n",
              "       [ 0.03295203, -0.13926283],\n",
              "       [ 0.89257019, -0.55037082],\n",
              "       [ 0.89257019,  1.03533143],\n",
              "       [ 0.31949142, -0.19799255],\n",
              "       [ 1.46564897,  0.06629116],\n",
              "       [ 1.5611621 ,  1.123426  ],\n",
              "       [ 0.12846516,  0.21311545],\n",
              "       [ 0.03295203, -0.25672226],\n",
              "       [ 0.03295203,  1.27025028],\n",
              "       [-0.0625611 ,  0.15438573],\n",
              "       [ 0.41500455,  0.59485858],\n",
              "       [-0.0625611 , -0.37418169],\n",
              "       [-0.15807423,  0.85914229],\n",
              "       [ 2.13424088, -1.04957339],\n",
              "       [ 1.5611621 ,  0.00756145],\n",
              "       [ 0.31949142,  0.06629116],\n",
              "       [ 0.22397829,  0.03692631],\n",
              "       [ 0.41500455, -0.46227625],\n",
              "       [ 0.51051768,  1.74008799],\n",
              "       [ 1.46564897, -1.04957339],\n",
              "       [ 0.89257019, -0.57973568],\n",
              "       [ 0.41500455,  0.27184516],\n",
              "       [ 0.41500455,  1.00596657],\n",
              "       [ 2.03872775, -1.19639767],\n",
              "       [ 1.94321462, -0.66783025],\n",
              "       [ 0.79705706,  0.53612887],\n",
              "       [ 0.03295203,  0.03692631],\n",
              "       [ 1.5611621 , -1.28449224],\n",
              "       [ 2.13424088, -0.69719511],\n",
              "       [ 2.13424088,  0.38930459],\n",
              "       [ 0.12846516,  0.09565602],\n",
              "       [ 2.03872775,  1.76945285],\n",
              "       [-0.0625611 ,  0.30121002],\n",
              "       [ 0.79705706, -1.1083031 ],\n",
              "       [ 0.79705706,  0.12502088],\n",
              "       [ 0.41500455, -0.49164111],\n",
              "       [ 0.31949142,  0.50676401],\n",
              "       [ 1.94321462, -1.37258681],\n",
              "       [ 0.41500455, -0.16862769],\n",
              "       [ 0.98808332, -1.07893824],\n",
              "       [ 0.60603081,  2.03373655],\n",
              "       [ 1.08359645, -1.22576253],\n",
              "       [ 1.84770149, -1.07893824],\n",
              "       [ 1.75218836, -0.28608712],\n",
              "       [ 1.08359645, -0.9027491 ],\n",
              "       [ 0.12846516,  0.03692631],\n",
              "       [ 0.89257019, -1.04957339],\n",
              "       [ 0.98808332, -1.02020853],\n",
              "       [ 0.98808332, -1.07893824],\n",
              "       [ 0.89257019, -1.37258681],\n",
              "       [ 0.70154394, -0.72655996],\n",
              "       [ 2.13424088, -0.81465453],\n",
              "       [ 0.12846516, -0.31545197],\n",
              "       [ 0.79705706, -0.84401939],\n",
              "       [ 1.27462271, -1.37258681],\n",
              "       [ 1.17910958, -1.46068138],\n",
              "       [-0.15807423, -1.07893824],\n",
              "       [ 1.08359645, -0.99084367]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['Purchased']\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lR6W4jJp8yuG",
        "outputId": "262965e2-cf32-4f8c-ab48-b8b7fb91463f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0\n",
              "      ..\n",
              "395    1\n",
              "396    1\n",
              "397    1\n",
              "398    0\n",
              "399    1\n",
              "Name: Purchased, Length: 400, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,Y_train,Y_test = train_test_split(x,y, test_size =0.20, random_state = 0)\n",
        "print(\"X_train\", X_train.shape)\n",
        "print(\"Y_train\", Y_train.shape)\n",
        "print(\"X_test\", X_test.shape)\n",
        "print('Y_test', Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h5lEAPm784Mb",
        "outputId": "972807b7-9088-473a-a764-4c3e339dc508"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train (320, 2)\n",
            "Y_train (320,)\n",
            "X_test (80, 2)\n",
            "Y_test (80,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = LogisticRegression()\n",
        "reg.fit(X_train,Y_train)\n",
        "Y_predict = reg.predict(X_test)\n",
        "Y_predict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bz0C1p2V9GQH",
        "outputId": "30fc4724-bef9-49be-c74f-448c505b1aa0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
              "       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf = confusion_matrix(Y_test,Y_predict)\n",
        "sns.heatmap(cf,annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "GnNiRW9kAF02",
        "outputId": "9e87c9d4-f6f7-416d-da64-aa2793744ef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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CtruYw2Fn+HeROU6GAUBidLQAkBhBCwCJEbRdYvt02z+2vdL2RVXXg+rZvsn2etvPV10L0iJou8D2GEnXSjpD0kxJH7c9s9qqUAM3Szq96iKQHkHbHUdKWhkRqyLibUm3SZpbcU2oWEQ8Imlj1XUgPYK2O6ZL+umQ96uLfQB2AwQtACRG0HbHGkkHDHk/o9gHYDdA0HbHk5IOtX2Q7b0knS9pccU1AegSgrYLImJA0gWSHpD0kqRFEfFCtVWharYXSnpc0m/ZXm17ftU1IQ0uwQWAxOhoASAxghYAEiNoASAxghYAEiNoASAxghYAEiNoASCx/wMQffwXysRgzwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clas=classification_report(Y_test,Y_predict)\n",
        "print(clas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-XWN-jtkSXK9",
        "outputId": "0948ff3f-c3cf-4a03-d439-ea65c22bc433"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.98      0.95        58\n",
            "           1       0.94      0.77      0.85        22\n",
            "\n",
            "    accuracy                           0.93        80\n",
            "   macro avg       0.93      0.88      0.90        80\n",
            "weighted avg       0.93      0.93      0.92        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy\",accuracy_score(Y_test,Y_predict)*100 )\n",
        "print(\"precision\",cf[1,1]/cf[0,1]+cf[1,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRnf2ES0Bpq_",
        "outputId": "bb592a2f-e1ae-4f48-bfb2-fa4e47e1cbca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 92.5\n",
            "precision 34.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(Y_test, Y_predict)\n",
        "print(mse)\n",
        "\n",
        "r2 = r2_score(Y_test, Y_predict)\n",
        "print(r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qpAWN9oMaxy-",
        "outputId": "7d9a3eba-5801-4ec8-e758-1a9796e6e8f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.075\n",
            "0.6238244514106583\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "C2Ae-b3Ca7_p"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}