feature_scaling_2.ipynb

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      "source": [
        "**PART 1**"
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              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 244,\n  \"fields\": [\n    {\n      \"column\": \"total_bill\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.902411954856856,\n        \"min\": 3.07,\n        \"max\": 50.81,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          22.12,\n          20.23,\n          14.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3836381890011822,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          3.35,\n          1.5,\n          6.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Sat\",\n          \"Fri\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Lunch\",\n          \"Dinner\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 1
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      ],
      "source": [
        "# Import necessary libraries\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Load a dataset (for example, using Seaborn's built-in dataset)\n",
        "df = sns.load_dataset('tips')\n",
        "\n",
        "# Display the first few rows of the dataset to understand its structure\n",
        "df.head()\n"
      ]
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        "# Plot boxplot for numerical columns\n",
        "plt.figure(figsize=(10, 6))     # Create a new figure with a specific size (10 inches wide by 6 inches tall)\n",
        "sns.boxplot(data=df)\n",
        "plt.title('Boxplot of Numerical Columns')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ],
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        {
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            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**STANDARDIZATION TECHNIQUE**"
      ],
      "metadata": {
        "id": "VePzkeKPQhfR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to replace outliers using standardization (z-score)\n",
        "def replace_outliers_std(df, threshold=3):\n",
        "    numerical_cols = df.select_dtypes(include=np.number).columns\n",
        "    scaler = StandardScaler()\n",
        "    df_scaled = pd.DataFrame(scaler.fit_transform(df[numerical_cols]), columns=numerical_cols)\n",
        "\n",
        "    # Identify outliers\n",
        "    for col in numerical_cols:\n",
        "        outliers = np.abs(df_scaled[col]) > threshold\n",
        "        df_scaled.loc[outliers, col] = np.sign(df_scaled.loc[outliers, col]) * threshold\n",
        "\n",
        "    # Inverse transform to original scale\n",
        "    df[numerical_cols] = scaler.inverse_transform(df_scaled)\n",
        "    return df\n"
      ],
      "metadata": {
        "id": "v2idmyLyME-I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace outliers in the dataframe\n",
        "df = replace_outliers_std(df)"
      ],
      "metadata": {
        "id": "SNHDwXNXMkqv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot boxplot after replacing outliers\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(data=df.select_dtypes(include=np.number))\n",
        "plt.title('Boxplot after Handling Outliers (Standardization)')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "AGm6yCanMohV",
        "outputId": "526cc28b-890c-4a7c-dc15-68c743b46d75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "KY96oQNLQaOx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Min-Max approach**"
      ],
      "metadata": {
        "id": "D4PNwlFIOMQM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "c_liIP5cNPPs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to replace outliers using Min-Max scaling\n",
        "def replace_outliers_minmax(data, threshold=1.5):\n",
        "    scaler = MinMaxScaler()\n",
        "    scaled_data = scaler.fit_transform(data)\n",
        "    mask = np.abs(scaled_data - 0.5) > threshold / 2\n",
        "    data[mask] = np.sign(scaled_data[mask] - 0.5) * threshold / 2 + 0.5\n",
        "    return data"
      ],
      "metadata": {
        "id": "t93Guau_PA-B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select numerical columns for scaling\n",
        "numerical_cols = df.select_dtypes(include=np.number).columns\n",
        "\n",
        "# Replace outliers in each numerical column\n",
        "df[numerical_cols] = replace_outliers_minmax(df[numerical_cols].values)\n"
      ],
      "metadata": {
        "id": "Pf9xVZwIOQ6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot boxplot after replacing outliers\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(data=df)\n",
        "plt.title('Boxplot after Handling Outliers (Min-Max Scaling)')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "Umy-C1lrPJF1",
        "outputId": "2336b25a-0487-46f3-a1cf-f0aa4420d6a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*PART 2 * : MaxAbS Scaler and Robust Scaler approach\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "8MFTQ-9rTs-I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MaxAbsScaler, RobustScaler"
      ],
      "metadata": {
        "id": "atmTiCk7TvfB"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=sns.load_dataset('tips')\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMzWWhRrUPp4",
        "outputId": "53f7d70b-1da4-4982-a5ac-14083a6a3924"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   total_bill   tip     sex smoker  day    time  size\n",
            "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
            "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
            "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
            "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
            "4       24.59  3.61  Female     No  Sun  Dinner     4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_features = df.select_dtypes(include=[np.number]).columns.tolist()\n",
        "print(numerical_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L14flMYjUZbD",
        "outputId": "60a5ba4a-9b82-4e40-f1ca-02bb85de8cec"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['total_bill', 'tip', 'size']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# apply maxabsScaler\n",
        "max_abs_scaler = MaxAbsScaler()\n",
        "df_max_abs_scaled = df.copy()\n",
        "df[numerical_features]=max_abs_scaler.fit_transform(df[numerical_features])\n"
      ],
      "metadata": {
        "id": "-eTIzssyU2Bp"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the MaxAbs scaled dataset\n",
        "print(\"\\nMaxAbs Scaled Data:\")\n",
        "print(df_max_abs_scaled.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FioJ2ygqVbte",
        "outputId": "97e8d7d4-32a6-45d1-b9b7-5ed857fb25b2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "MaxAbs Scaled Data:\n",
            "   total_bill   tip     sex smoker  day    time  size\n",
            "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
            "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
            "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
            "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
            "4       24.59  3.61  Female     No  Sun  Dinner     4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply RobustScaler\n",
        "robust_scaler = RobustScaler()\n",
        "df_robust_scaled = df.copy()\n",
        "df_robust_scaled[numerical_features] = robust_scaler.fit_transform(df[numerical_features])\n"
      ],
      "metadata": {
        "id": "QD2VDRxEVg1S"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nRobust Scaled Data:\")\n",
        "print(df_robust_scaled.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xebh2nXrVjZn",
        "outputId": "e706643e-7be3-4a9a-ff42-663711a164f2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Robust Scaled Data:\n",
            "   total_bill     tip     sex smoker  day    time  size\n",
            "0   -0.074675 -1.2096  Female     No  Sun  Dinner   0.0\n",
            "1   -0.691558 -0.7936    Male     No  Sun  Dinner   1.0\n",
            "2    0.298237  0.3840    Male     No  Sun  Dinner   1.0\n",
            "3    0.545918  0.2624    Male     No  Sun  Dinner   0.0\n",
            "4    0.630334  0.4544  Female     No  Sun  Dinner   2.0\n"
          ]
        }
      ]
    }
  ]
}