Multi_Layer_Perceptron.ipynb

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    "colab": {
      "name": "Multi Layer Perceptron.ipynb",
      "provenance": [],
      "mount_file_id": "1lDQYSSMgR2RPePwK5Mk-YmJa1TToGiLN",
      "authorship_tag": "ABX9TyMCAK3dFibOI/+1NFa8URj3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Tanjib-Rafi/Social-Network-Ads/blob/main/Multi_Layer_Perceptron.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vV_FbHqV8h1X",
        "outputId": "4b32c1fb-eb97-424d-ae26-3a18db08162a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx5uuuG9B0v1"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZHViGuDCCvh"
      },
      "source": [
        "dataset = pd.read_csv('/content/drive/MyDrive/dataset KNN/Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, [1,2,3]].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "QmSO5S3fD9D1",
        "outputId": "195bdb81-7e64-4eb4-8779-65a021aa5da1"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>User ID</th>\n",
              "      <th>Gender</th>\n",
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              "      <th>EstimatedSalary</th>\n",
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              "      <td>36000</td>\n",
              "      <td>1</td>\n",
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              "<p>400 rows × 5 columns</p>\n",
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            "text/plain": [
              "      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]"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fceVkNM_GcEB"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "X[:,0] = le.fit_transform(X[:,0])"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VT4LU96PG0-p",
        "outputId": "e1dba54e-b1a6-4e2a-cae2-9f100567246c"
      },
      "source": [
        "X"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 19, 19000],\n",
              "       [1, 35, 20000],\n",
              "       [0, 26, 43000],\n",
              "       ...,\n",
              "       [0, 50, 20000],\n",
              "       [1, 36, 33000],\n",
              "       [0, 49, 36000]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_t5xsX1fIi3i"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gynmcMIr5PcB",
        "outputId": "25f6ba59-bde4-4cb9-c128-a918c62d52eb"
      },
      "source": [
        "X_train"
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      "execution_count": 100,
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              "       [1, 49, 88000],\n",
              "       [1, 52, 150000],\n",
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              "       [1, 34, 43000],\n",
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              "       [0, 27, 54000],\n",
              "       [0, 26, 118000]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ocPfSDfM53MW"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
        "sc = MinMaxScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtlmWMFgBKtQ",
        "outputId": "bbd794e3-c455-4c53-cd3d-993542bfb820"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [1.        , 0.19047619, 0.00740741],\n",
              "       [1.        , 0.35714286, 0.11851852],\n",
              "       [0.        , 0.54761905, 0.42222222],\n",
              "       [1.        , 0.42857143, 0.13333333],\n",
              "       [0.        , 0.88095238, 0.81481481],\n",
              "       [0.        , 0.71428571, 0.85925926],\n",
              "       [0.        , 0.54761905, 0.41481481],\n",
              "       [0.        , 0.28571429, 0.34814815],\n",
              "       [1.        , 0.45238095, 0.42222222],\n",
              "       [0.        , 0.54761905, 0.35555556],\n",
              "       [0.        , 0.95238095, 0.23703704],\n",
              "       [0.        , 0.28571429, 0.74814815],\n",
              "       [1.        , 0.04761905, 0.25185185],\n",
              "       [1.        , 0.45238095, 0.43703704],\n",
              "       [1.        , 0.54761905, 0.32592593],\n",
              "       [1.        , 0.73809524, 0.54814815],\n",
              "       [1.        , 0.23809524, 0.47407407],\n",
              "       [0.        , 0.83333333, 0.4962963 ],\n",
              "       [1.        , 0.52380952, 0.31111111],\n",
              "       [1.        , 1.        , 0.14074074],\n",
              "       [1.        , 0.4047619 , 0.68888889],\n",
              "       [1.        , 0.07142857, 0.42222222],\n",
              "       [1.        , 0.47619048, 0.41481481],\n",
              "       [1.        , 0.5       , 0.67407407],\n",
              "       [0.        , 0.45238095, 0.31111111],\n",
              "       [0.        , 0.19047619, 0.42222222],\n",
              "       [0.        , 0.4047619 , 0.05925926],\n",
              "       [0.        , 0.85714286, 0.68888889],\n",
              "       [1.        , 0.28571429, 0.01481481],\n",
              "       [1.        , 0.5       , 0.88148148],\n",
              "       [1.        , 0.26190476, 0.20740741],\n",
              "       [1.        , 0.35714286, 0.20740741],\n",
              "       [1.        , 0.4047619 , 0.17037037],\n",
              "       [1.        , 0.54761905, 0.22222222],\n",
              "       [0.        , 0.54761905, 0.42222222],\n",
              "       [0.        , 0.5       , 0.88148148],\n",
              "       [0.        , 0.21428571, 0.9037037 ],\n",
              "       [0.        , 0.07142857, 0.00740741],\n",
              "       [1.        , 0.19047619, 0.12592593],\n",
              "       [1.        , 0.30952381, 0.37777778],\n",
              "       [0.        , 0.5       , 0.42962963],\n",
              "       [1.        , 0.54761905, 0.47407407],\n",
              "       [0.        , 0.69047619, 0.25925926],\n",
              "       [0.        , 0.54761905, 0.11111111],\n",
              "       [0.        , 0.45238095, 0.57777778],\n",
              "       [0.        , 1.        , 0.22962963],\n",
              "       [1.        , 0.16666667, 0.05185185],\n",
              "       [0.        , 0.23809524, 0.16296296],\n",
              "       [0.        , 0.47619048, 0.2962963 ],\n",
              "       [0.        , 0.42857143, 0.28888889],\n",
              "       [0.        , 0.04761905, 0.15555556],\n",
              "       [0.        , 0.9047619 , 0.65925926],\n",
              "       [1.        , 0.52380952, 0.31111111],\n",
              "       [0.        , 0.57142857, 0.68888889],\n",
              "       [0.        , 0.04761905, 0.05925926],\n",
              "       [1.        , 0.52380952, 0.37037037],\n",
              "       [1.        , 0.69047619, 0.03703704],\n",
              "       [0.        , 0.        , 0.52592593],\n",
              "       [1.        , 0.4047619 , 0.47407407],\n",
              "       [0.        , 0.92857143, 0.13333333],\n",
              "       [0.        , 0.38095238, 0.42222222],\n",
              "       [0.        , 0.73809524, 0.17777778],\n",
              "       [0.        , 0.21428571, 0.11851852],\n",
              "       [1.        , 0.02380952, 0.40740741],\n",
              "       [0.        , 0.5       , 0.47407407],\n",
              "       [1.        , 0.19047619, 0.48888889],\n",
              "       [1.        , 0.16666667, 0.48148148],\n",
              "       [0.        , 0.23809524, 0.51851852],\n",
              "       [1.        , 0.88095238, 0.17777778],\n",
              "       [0.        , 0.76190476, 0.54074074],\n",
              "       [1.        , 0.73809524, 0.54074074],\n",
              "       [1.        , 0.80952381, 1.        ],\n",
              "       [0.        , 0.4047619 , 0.37037037],\n",
              "       [1.        , 0.57142857, 0.28888889],\n",
              "       [1.        , 0.38095238, 0.20740741],\n",
              "       [1.        , 0.45238095, 0.27407407],\n",
              "       [0.        , 0.71428571, 0.11111111],\n",
              "       [1.        , 0.26190476, 0.20740741],\n",
              "       [1.        , 0.42857143, 0.27407407],\n",
              "       [0.        , 0.21428571, 0.28888889],\n",
              "       [0.        , 0.19047619, 0.76296296]])"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUt49_iLBX_0",
        "outputId": "9c0a9596-c619-477c-e2c6-748faf9c4339"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 103,
      "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, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "clf = MLPClassifier(solver='sgd', alpha=0.0001,\n",
        " hidden_layer_sizes=(15,), random_state=1)\n",
        "\n",
        "y_pred = clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmGUC3080hSU",
        "outputId": "1c5c12bd-4d4e-4015-81d6-fdf787b4c9a1"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_z8q4J9VBbQ3"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "ac = accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRluiPuDBeYY",
        "outputId": "0eeabec3-cb1a-4813-cd78-c9af12711698"
      },
      "source": [
        "print(cm)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[58  0]\n",
            " [13  9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AV5PXlzfBgh1",
        "outputId": "840f6cad-e9b5-4c3c-a188-f8f0aefcd145"
      },
      "source": [
        "print(ac)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8375\n"
          ]
        }
      ]
    }
  ]
}