SKLearn_Logistic_Regression.ipynb

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      "name": "SKLearn_Logistic_Regression.ipynb",
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      "cell_type": "code",
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        "id": "lGl3AG97iF4c",
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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "df = pd.read_csv('Social_Network_Ads.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Jc1S50VihNk",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d43d5db1-5eef-4c70-af43-3d22e692ea03"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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",
              "      <th>1</th>\n",
              "      <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",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15804002</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\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": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
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        "id": "YcYcFAuoipub",
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          "height": 221
        },
        "outputId": "6276aa99-68b0-43f4-8418-2a58e5b92135"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 5 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",
            " 4   Purchased        400 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 15.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
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    {
      "cell_type": "code",
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        "id": "tLx6cDrQi29x",
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        },
        "outputId": "6a289e23-bd12-4d64-d0df-e1fcc348c890"
      },
      "source": [
        "data = df.drop(columns=['User ID'])\n",
        "data = pd.get_dummies(data)\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>46</td>\n",
              "      <td>41000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>51</td>\n",
              "      <td>23000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>50</td>\n",
              "      <td>20000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>36</td>\n",
              "      <td>33000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>49</td>\n",
              "      <td>36000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Age  EstimatedSalary  Purchased  Gender_Female  Gender_Male\n",
              "0     19            19000          0              0            1\n",
              "1     35            20000          0              0            1\n",
              "2     26            43000          0              1            0\n",
              "3     27            57000          0              1            0\n",
              "4     19            76000          0              0            1\n",
              "..   ...              ...        ...            ...          ...\n",
              "395   46            41000          1              1            0\n",
              "396   51            23000          1              0            1\n",
              "397   50            20000          1              1            0\n",
              "398   36            33000          0              0            1\n",
              "399   49            36000          1              1            0\n",
              "\n",
              "[400 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SBBHxgYjDFA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = ['Age' , 'EstimatedSalary' , 'Gender_Female' , 'Gender_Male']\n",
        "X = data[predictions]\n",
        "y = data['Purchased'] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "labcuAwajMgg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyivlDNAjR5r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "a3a5183e-2bbd-423a-d44a-30a643be0726"
      },
      "source": [
        "from sklearn import linear_model\n",
        "model = linear_model.LogisticRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4YSay6x8jWCn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e7c06e38-83a1-455c-dc67-d28bf6dd33d5"
      },
      "source": [
        "model.score(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.63"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    }
  ]
}