Standardization_Z_score.ipynb

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        "id": "AewiE6ootMJi"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLBuf9Ygte-T",
        "outputId": "0bf20b7b-9a7d-42a0-c259-795c599db22c"
      },
      "execution_count": null,
      "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",
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/100-days-of-machine-learning/day24-standardization/Social_Network_Ads.csv')"
      ],
      "metadata": {
        "id": "N-vxRXn4t2eY"
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      "execution_count": null,
      "outputs": []
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        "df.sample(5)"
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      "outputs": [
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              "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
              "231  15694453    Male   39            42000          0\n",
              "131  15801247    Male   33            31000          0\n",
              "257  15794493    Male   40            57000          0\n",
              "138  15610403  Female   28            59000          0\n",
              "365  15807525  Female   59            29000          1"
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        "df=df.iloc[:,2:]"
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          "data": {
            "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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              "                                                     [key], {});\n",
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              "\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample"
      ],
      "metadata": {
        "id": "Dmz1c0eG8X1f",
        "outputId": "8beb3747-8618-4b13-8e5a-bb13182a910a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.sample of      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\n",
              "..   ...              ...        ...\n",
              "395   46            41000          1\n",
              "396   51            23000          1\n",
              "397   50            20000          1\n",
              "398   36            33000          0\n",
              "399   49            36000          1\n",
              "\n",
              "[400 rows x 3 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "pIsuF3cvuj2M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# x_train,x_test,y_train,y_test = train_test_split(df.drop(\"Purchased\",axis=1),df[\"Purchased\"],test_size=0.3,random_state=0)\n",
        "x=df.iloc[:,0:2]\n",
        "y=df.iloc[:,-1]\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=0)"
      ],
      "metadata": {
        "id": "jJAO36akuyHM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape, x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JsO7Jjoovckz",
        "outputId": "1ac1a57d-27b8-402c-82f4-022577f0345b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((280, 2), (120, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "\n",
        "# fit the scaler to train set , it will learn the parameters\n",
        "\n",
        "scaler.fit(x_train)\n",
        "\n",
        "# transforms train and test sets\n",
        "\n",
        "x_train_scaled = scaler.transform(x_train)\n",
        "x_test_scaled = scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "aewYcLRZvkqs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.mean_  #provides age and salary mean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TXf5pxH2vtgg",
        "outputId": "c46e8d7a-26ca-4d23-89fe-3acccd04ce39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.78642857e+01, 6.98071429e+04])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "qPKZ1JupwWfK",
        "outputId": "6b98bee2-3e9e-42d7-9c01-039230f377a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  EstimatedSalary\n",
              "92    26            15000\n",
              "223   60           102000\n",
              "234   38           112000\n",
              "232   40           107000\n",
              "377   42            53000\n",
              "..   ...              ...\n",
              "323   48            30000\n",
              "192   29            43000\n",
              "117   36            52000\n",
              "47    27            54000\n",
              "172   26           118000\n",
              "\n",
              "[280 rows x 2 columns]"
            ],
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>26</td>\n",
              "      <td>15000</td>\n",
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              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>60</td>\n",
              "      <td>102000</td>\n",
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              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>38</td>\n",
              "      <td>112000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>40</td>\n",
              "      <td>107000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>377</th>\n",
              "      <td>42</td>\n",
              "      <td>53000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>323</th>\n",
              "      <td>48</td>\n",
              "      <td>30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>29</td>\n",
              "      <td>43000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>36</td>\n",
              "      <td>52000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>27</td>\n",
              "      <td>54000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>26</td>\n",
              "      <td>118000</td>\n",
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              "<p>280 rows × 2 columns</p>\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_scaled  # now in numpy array form , we should convert into DataFrame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqrjs0bSww-1",
        "outputId": "141d0321-7539-4b15-c37b-645893527010"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [-0.18277423, -0.45712749],\n",
              "       [-1.7514113 , -0.97767033],\n",
              "       [ 1.7780221 ,  0.98882482],\n",
              "       [ 0.20938504, -0.37037036],\n",
              "       [ 0.40546467,  1.104501  ],\n",
              "       [-1.7514113 , -1.35361793],\n",
              "       [ 0.20938504, -0.13901799],\n",
              "       [ 0.89566375, -1.44037507],\n",
              "       [-1.94749093,  0.46828198],\n",
              "       [-0.28081405,  0.26584866],\n",
              "       [ 1.87606192, -1.06442747],\n",
              "       [-0.37885386,  0.06341534],\n",
              "       [ 1.09174339, -0.89091319],\n",
              "       [-1.06513258, -1.12226556],\n",
              "       [-1.84945111,  0.00557724],\n",
              "       [ 0.11134522,  0.26584866],\n",
              "       [-1.1631724 ,  0.32368675],\n",
              "       [-1.26121221,  0.29476771],\n",
              "       [-0.96709276,  0.43936294],\n",
              "       [ 1.67998229, -0.89091319],\n",
              "       [ 1.1897832 ,  0.52612008],\n",
              "       [ 1.09174339,  0.52612008],\n",
              "       [ 1.38586284,  2.31910094],\n",
              "       [-0.28081405, -0.13901799],\n",
              "       [ 0.40546467, -0.45712749],\n",
              "       [-0.37885386, -0.775237  ],\n",
              "       [-0.08473441, -0.51496559],\n",
              "       [ 0.99370357, -1.1511846 ],\n",
              "       [-0.86905295, -0.775237  ],\n",
              "       [-0.18277423, -0.51496559],\n",
              "       [-1.06513258, -0.45712749],\n",
              "       [-1.1631724 ,  1.39369146]])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_scaled = pd.DataFrame(x_train_scaled, columns=x_train.columns)\n",
        "x_test_scaled = pd.DataFrame(x_test_scaled, columns=x_test.columns)"
      ],
      "metadata": {
        "id": "T2BU3Yzlw0Hw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "Wce5wMPAxPZc",
        "outputId": "0d86b148-9dbe-41d5-f384-893cda0c7b91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Age  EstimatedSalary\n",
              "0   -1.163172        -1.584970\n",
              "1    2.170181         0.930987\n",
              "2    0.013305         1.220177\n",
              "3    0.209385         1.075582\n",
              "4    0.405465        -0.486047\n",
              "..        ...              ...\n",
              "275  0.993704        -1.151185\n",
              "276 -0.869053        -0.775237\n",
              "277 -0.182774        -0.514966\n",
              "278 -1.065133        -0.457127\n",
              "279 -1.163172         1.393691\n",
              "\n",
              "[280 rows x 2 columns]"
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              "      <th>279</th>\n",
              "      <td>-1.163172</td>\n",
              "      <td>1.393691</td>\n",
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              "<p>280 rows × 2 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "source": [
        "np.round(x_train.describe(),1) #Original Dataset"
      ],
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          "base_uri": "https://localhost:8080/",
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        },
        "id": "pn4-BEtRxuzV",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Age  EstimatedSalary\n",
              "count  280.0            280.0\n",
              "mean    37.9          69807.1\n",
              "std     10.2          34641.2\n",
              "min     18.0          15000.0\n",
              "25%     30.0          43000.0\n",
              "50%     37.0          70500.0\n",
              "75%     46.0          88000.0\n",
              "max     60.0         150000.0"
            ],
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              "      <th>EstimatedSalary</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>280.0</td>\n",
              "      <td>280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>37.9</td>\n",
              "      <td>69807.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.2</td>\n",
              "      <td>34641.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.0</td>\n",
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              "      <td>37.0</td>\n",
              "      <td>70500.0</td>\n",
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              "      <th>75%</th>\n",
              "      <td>46.0</td>\n",
              "      <td>88000.0</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>60.0</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-3441d26f-d15c-47fc-91f9-3b3aa044c2f3');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "np.round(x_train_scaled.describe(),1) #Scaled Dataset  herre we see Mean=0 Std_Dev=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ZYAhQO8xyQTc",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Age  EstimatedSalary\n",
              "count  280.0            280.0\n",
              "mean     0.0              0.0\n",
              "std      1.0              1.0\n",
              "min     -1.9             -1.6\n",
              "25%     -0.8             -0.8\n",
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              "75%      0.8              0.5\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>std</th>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.9</td>\n",
              "      <td>-1.6</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.8</td>\n",
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              "      <th>50%</th>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.5</td>\n",
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              "      <th>max</th>\n",
              "      <td>2.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1cce7c6-c318-4755-b75a-08d47c1b9f74')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a1cce7c6-c318-4755-b75a-08d47c1b9f74 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a1cce7c6-c318-4755-b75a-08d47c1b9f74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# WHY SCALING IS IMPORTANT"
      ],
      "metadata": {
        "id": "rfQDOz5ByZcW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "wm3gGgVz6hNy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LogisticRegression()\n",
        "lr_scaled = LogisticRegression()"
      ],
      "metadata": {
        "id": "yDUCc7VJ69le"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(x_train,y_train)\n",
        "lr_scaled.fit(x_train_scaled,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PrZ5isdg7DI0",
        "outputId": "331b4ec2-dc03-4816-c7c5-39f7a93f7dc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=lr.predict(x_test)\n",
        "y_pred_scaled = lr_scaled.predict(x_test_scaled)"
      ],
      "metadata": {
        "id": "tM-oh3jd7RNh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "2vO7nVLd7doC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Actual : \",accuracy_score(y_test,y_pred))\n",
        "print(\"Scaled : \",accuracy_score(y_test,y_pred_scaled))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cg7fotPl7nB5",
        "outputId": "c4151556-aa36-4893-9334-256d8cec7561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual :  0.6583333333333333\n",
            "Scaled :  0.8666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CCwXpIsu8MA7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}