SVC_SocialNetworkAdsClassification.ipynb

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  "metadata": {
    "colab": {
      "name": "Social_Network_Ads_Using_SVC.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3cFmUtMFCED"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ixb2L7KFNj0"
      },
      "source": [
        "data = pd.read_csv(\"https://raw.githubusercontent.com/DependerKumarSoni/Social-Network-Ads-Using-SVC/main/Social_Network_Ads.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MJaCo83lFVXL",
        "outputId": "60ed783d-b1e9-4cf6-9ed5-565268206d6c"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15624510</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>15810944</td>\n",
              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15668575</td>\n",
              "      <td>Female</td>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15603246</td>\n",
              "      <td>Female</td>\n",
              "      <td>27</td>\n",
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              "      <td>Male</td>\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"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "HtO4HrOkFZNX",
        "outputId": "41ea0a8e-6ea4-498b-843a-2fdb8164fb6f"
      },
      "source": [
        "print(len(data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "400\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
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        "id": "rnG6MmPDFiz7",
        "outputId": "d7d626f2-9d35-4bc7-f317-655d1bdc04c4"
      },
      "source": [
        "data.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User ID            0\n",
              "Gender             0\n",
              "Age                0\n",
              "EstimatedSalary    0\n",
              "Purchased          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "colab": {
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        "outputId": "e60a6309-453e-4eaa-fc9b-ceb97f85fede"
      },
      "source": [
        "data.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User ID   Gender  Age  EstimatedSalary  Purchased\n",
              "15815236  Female  45   131000           1            1\n",
              "15660200  Female  37   71000            0            1\n",
              "15649668  Male    41   79000            0            1\n",
              "15651983  Male    34   112000           1            1\n",
              "15652400  Female  38   50000            0            1\n",
              "                                                    ..\n",
              "15733883  Male    47   25000            1            1\n",
              "15733964  Female  38   50000            0            1\n",
              "15733973  Female  47   113000           1            1\n",
              "15734161  Female  37   93000            1            1\n",
              "15566689  Female  35   57000            0            1\n",
              "Length: 400, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "YhqyCFHLFsMl"
      },
      "source": [
        "X = data.drop('Purchased', axis=1)\n",
        "Y = data['Purchased']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "agbLCoyUJFNN",
        "outputId": "504a311a-0294-4063-ba4e-4e9e4f38b77f"
      },
      "source": [
        "X.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 4 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   User ID          400 non-null    int64 \n",
            " 1   Gender           400 non-null    object\n",
            " 2   Age              400 non-null    int64 \n",
            " 3   EstimatedSalary  400 non-null    int64 \n",
            "dtypes: int64(3), object(1)\n",
            "memory usage: 12.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdF20II8KX3v"
      },
      "source": [
        "X=pd.get_dummies(X, drop_first=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8zmHAj7KZEo",
        "outputId": "006c9fcd-a0e7-4a2b-f10f-2fca2629ea18"
      },
      "source": [
        "X.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 4 columns):\n",
            " #   Column           Non-Null Count  Dtype\n",
            "---  ------           --------------  -----\n",
            " 0   User ID          400 non-null    int64\n",
            " 1   Age              400 non-null    int64\n",
            " 2   EstimatedSalary  400 non-null    int64\n",
            " 3   Gender_Male      400 non-null    uint8\n",
            "dtypes: int64(3), uint8(1)\n",
            "memory usage: 9.9 KB\n"
          ]
        }
      ]
    },
    {
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "72e03f35-ad0f-4aaf-dda4-fa06bfb0cbce"
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      "source": [
        "X.head()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\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>User ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15624510</td>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15810944</td>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15668575</td>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15603246</td>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15804002</td>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "    User ID  Age  EstimatedSalary  Gender_Male\n",
              "0  15624510   19            19000            1\n",
              "1  15810944   35            20000            1\n",
              "2  15668575   26            43000            0\n",
              "3  15603246   27            57000            0\n",
              "4  15804002   19            76000            1"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8L61G71K9_G",
        "outputId": "3cd7ed4a-a7ca-43c1-c14b-8661afd5ef56"
      },
      "source": [
        "Y.head()"
      ],
      "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",
              "Name: Purchased, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hb7CAetDLBhH"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6TZc0qiO_L8"
      },
      "source": [
        "ss = StandardScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irGaT9jKPBdL"
      },
      "source": [
        "X_s = ss.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHcT0gTyPGQu"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFBzQ_taPK-y"
      },
      "source": [
        "x_train, x_test, y_train , y_test = train_test_split(X_s, Y, test_size=0.2, random_state=12)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCqbu48yE8Jf",
        "outputId": "7a3d8341-cd73-4d08-9980-d03cfbb0a62d"
      },
      "source": [
        "print(len(x_train), len(y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "320 320\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGxTiuuhPbbF"
      },
      "source": [
        "from sklearn.svm import SVC"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajtrh-prPp-J"
      },
      "source": [
        "clf = SVC(kernel='linear')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylYEIdHJPvlR",
        "outputId": "9dd181aa-a93d-4b4e-8811-183ca0d73c9c"
      },
      "source": [
        "clf.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9wPAaabPoTC"
      },
      "source": [
        "y_pred = clf.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dp1D7zYVP5LJ"
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjXhCmT0P8el",
        "outputId": "4bc539d2-d762-4737-f7e0-d9bab583cdf6"
      },
      "source": [
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8375"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    }
  ]
}