Simplified_Notebook/ML_Data_Preprocessing.ipynb

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      "cell_type": "markdown",
      "source": [
        "# **Data preprocessing**"
      ],
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      }
    },
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      "cell_type": "markdown",
      "source": [
        "Visit for more details: https://github.com/Ruban2205/Machine_learning_fundamentals"
      ],
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      "source": [
        "### **Data Preprocessing Concepts**\n",
        "\n",
        "- Data reading and Displaying\n",
        "\n",
        "- Null values Imputation (Mean, Median, Mode, Dropna, fillna) \n",
        "\n",
        "- Duplicate data dropping \n",
        "\n",
        "- OneHotEncoder \n",
        "\n",
        "- LabelEncoder\n",
        "\n",
        "- KNN Imputer\n",
        "\n",
        "- Z-Score\n",
        "\n",
        "- Binarizer \n",
        "\n",
        "- Min-Max Scalar\n",
        " \n",
        "- Standard Scalar\n",
        "\n",
        "- LDA, PCA\n",
        "\n",
        "- SelectPercentile, chi2, SelectKBest, VarianceThreshold\n",
        "\n",
        "- Correlation (Highest +ve and -ve) \n",
        "\n",
        "- Feature Selection \n",
        "\n",
        "- Plotting"
      ],
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      "source": [
        "# Data preprocessing libraries \n",
        "\n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, Binarizer\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.metrics import z_scores\n",
        "from sklearn.impute import KNNImputer\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.feature_selection import SelectKBest, chi2, mutual_info_classif, VarianceThreshold\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "n88NIb30Tjnx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1) Data reading and displaying**"
      ],
      "metadata": {
        "id": "JXShYwmNPfVX"
      }
    },
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              "   Price  Age  Mfg_Month  Mfg_Year     KM Fuel_Type  HP  Met_Color  Automatic  \\\n",
              "0  12950   23         10      2002  46986    Diesel  90          1          0   \n",
              "1  12950   23         10      2002  72937    Diesel  90          1          0   \n",
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              "4  17950   30          3      2002  38500    Diesel  90          0          0   \n",
              "\n",
              "     cc  Doors Gears  Quarterly_Tax  Weight  Mfr_Guarantee  BOVAG_Guarantee  \\\n",
              "0  2000    3.0  Five            210    1165            0.0                1   \n",
              "1  2000    3.0  Five            210    1165            0.0                1   \n",
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              "3  2000    3.0  Five            210    1165            1.0                1   \n",
              "4  2000    3.0  Five            210    1170            1.0                1   \n",
              "\n",
              "   Guarantee_Period  \n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv(\"Toyato.csv\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2) Null values Imputation (Mean, Median, Mode, Dropna, fillna)**"
      ],
      "metadata": {
        "id": "bH4qDUkiQeba"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the percentage of null data\n",
        "\n",
        "null_percent = (data.isna().sum() / len(data)) * 100\n",
        "null_percent[null_percent > 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LBk5S-knQVzk",
        "outputId": "e9edcca2-a549-4df4-dad8-ce667b7dbba0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Doors            12.0\n",
              "Mfr_Guarantee    46.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the mean of Each data\n",
        "\n",
        "data.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4o50uk3_UAkO",
        "outputId": "e2f750c6-e35f-4aa3-e06b-a72403709444"
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      "execution_count": null,
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          "text": [
            "<ipython-input-188-a49944042583>:3: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  data.mean()\n"
          ]
        },
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          "data": {
            "text/plain": [
              "Price               15100.100000\n",
              "Age                    26.280000\n",
              "Mfg_Month               6.720000\n",
              "Mfg_Year             2002.000000\n",
              "KM                  45948.100000\n",
              "HP                    113.060000\n",
              "Met_Color               0.600000\n",
              "Automatic               0.020000\n",
              "cc                   1694.000000\n",
              "Doors                   3.272727\n",
              "Quarterly_Tax         124.940000\n",
              "Weight               1150.800000\n",
              "Mfr_Guarantee           0.555556\n",
              "BOVAG_Guarantee         0.940000\n",
              "Guarantee_Period        4.260000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
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      "source": [
        "# Calculating the median for the data\n",
        "\n",
        "data.median()"
      ],
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        "colab": {
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        "id": "XPKvZ5QAXZ8o",
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      "execution_count": null,
      "outputs": [
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          "name": "stderr",
          "text": [
            "<ipython-input-189-45dfbade5d44>:3: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  data.median()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Price               14950.0\n",
              "Age                    27.0\n",
              "Mfg_Month               6.0\n",
              "Mfg_Year             2002.0\n",
              "KM                  41563.0\n",
              "HP                     97.0\n",
              "Met_Color               1.0\n",
              "Automatic               0.0\n",
              "cc                   1600.0\n",
              "Doors                   3.0\n",
              "Quarterly_Tax          85.0\n",
              "Weight               1120.0\n",
              "Mfr_Guarantee           1.0\n",
              "BOVAG_Guarantee         1.0\n",
              "Guarantee_Period        3.0\n",
              "dtype: float64"
            ]
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      "source": [
        "# Calculating the mode of the data\n",
        "\n",
        "data.mode()"
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          "height": 176
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              "     Price   Age  Mfg_Month  Mfg_Year       KM Fuel_Type  HP  Met_Color  \\\n",
              "0  12950.0  22.0       11.0    2002.0  72215.0    Petrol  90        1.0   \n",
              "1      NaN   NaN        NaN       NaN      NaN       NaN  97        NaN   \n",
              "\n",
              "   Automatic    cc  Doors Gears  Quarterly_Tax  Weight  Mfr_Guarantee  \\\n",
              "0        0.0  1400    3.0  Five           85.0  1100.0            1.0   \n",
              "1        NaN  2000    NaN   NaN            NaN     NaN            NaN   \n",
              "\n",
              "   BOVAG_Guarantee  Guarantee_Period  \n",
              "0              1.0               3.0  \n",
              "1              NaN               NaN  "
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              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      \n",
              "  <style>\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3b811b41-21f5-4154-95c1-d17a997a1a8d');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Syntax to calculate the Mean, Median and Mode for a dataset. \n",
        "# data['Doors'].mean().sum()\n",
        "# data['Doors'].median().sum()\n",
        "# data['Doors'].mode().sum()"
      ],
      "metadata": {
        "id": "tNyIiSWRXoBI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filling the null values for the column Doors\n",
        "\n",
        "data2 = data.copy()\n",
        "data2['Doors'].fillna(data2['Doors'].mean, inplace=True)\n",
        "data2.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_OkkgqOSisc",
        "outputId": "a0da1e66-7d70-4232-9456-36ce6d3b756a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Price                0\n",
              "Age                  0\n",
              "Mfg_Month            0\n",
              "Mfg_Year             0\n",
              "KM                   0\n",
              "Fuel_Type            0\n",
              "HP                   0\n",
              "Met_Color            0\n",
              "Automatic            0\n",
              "cc                   0\n",
              "Doors                0\n",
              "Gears                0\n",
              "Quarterly_Tax        0\n",
              "Weight               0\n",
              "Mfr_Guarantee       23\n",
              "BOVAG_Guarantee      0\n",
              "Guarantee_Period     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping the null values in Doors Column\n",
        "\n",
        "doors_drop = data.copy()\n",
        "doors_drop['Doors'].dropna(inplace=True)\n",
        "doors_drop.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CC1sHRX9ZQz2",
        "outputId": "4a905440-0f91-44c1-c229-7a9154112870"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Price               0\n",
              "Age                 0\n",
              "Mfg_Month           0\n",
              "Mfg_Year            0\n",
              "KM                  0\n",
              "Fuel_Type           0\n",
              "HP                  0\n",
              "Met_Color           0\n",
              "Automatic           0\n",
              "cc                  0\n",
              "Doors               0\n",
              "Gears               0\n",
              "Quarterly_Tax       0\n",
              "Weight              0\n",
              "Mfr_Guarantee       0\n",
              "BOVAG_Guarantee     0\n",
              "Guarantee_Period    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 274
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filling the null values for the column Mfr_Gurantee\n",
        "\n",
        "data3 = data.copy()\n",
        "data3['Mfr_Guarantee'].fillna(data3['Mfr_Guarantee'].mean, inplace=True)\n",
        "data3.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6gI-fGDVXd5",
        "outputId": "97047353-87a2-4632-806d-a1a6db2fd15c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Price               0\n",
              "Age                 0\n",
              "Mfg_Month           0\n",
              "Mfg_Year            0\n",
              "KM                  0\n",
              "Fuel_Type           0\n",
              "HP                  0\n",
              "Met_Color           0\n",
              "Automatic           0\n",
              "cc                  0\n",
              "Doors               6\n",
              "Gears               0\n",
              "Quarterly_Tax       0\n",
              "Weight              0\n",
              "Mfr_Guarantee       0\n",
              "BOVAG_Guarantee     0\n",
              "Guarantee_Period    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3) Duplicate Data Dropping**"
      ],
      "metadata": {
        "id": "7dPcOw40X9jG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# To Find the sum of duplicated data\n",
        "\n",
        "duplicated_data = data.duplicated().sum()\n",
        "print(duplicated_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cCgdy6DCWV5m",
        "outputId": "f91ddd5b-1310-42c1-9129-b61290f18948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CncoC-adZIQZ",
        "outputId": "8eed04d0-e3be-4e41-cf73-3a66c9de9e5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To Drop the duplicated data\n",
        "\n",
        "data4 = data2.copy()\n",
        "data4.drop_duplicates(inplace=True)\n",
        "\n",
        "data4.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzQUKMR2YfnD",
        "outputId": "d2916d3e-d2ac-4c4d-bbae-d516b322e520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4) One Hot Encoder**"
      ],
      "metadata": {
        "id": "o2dasJQPZS8q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "import numpy as np "
      ],
      "metadata": {
        "id": "L_tvAtDzY8tX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Taking the categorical values from the dataset \n",
        "\n",
        "categorical_data = data.select_dtypes(include=np.object_)\n",
        "categorical_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZvHdqVCfa0RW",
        "outputId": "e228ff68-232e-4c8d-9a2a-5b95372ecd02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Fuel_Type Gears\n",
              "0    Diesel  Five\n",
              "1    Diesel  Five\n",
              "2    Diesel  Five\n",
              "3    Diesel  Five\n",
              "4    Diesel  Five"
            ],
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              "      <th></th>\n",
              "      <th>Fuel_Type</th>\n",
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              "  </svg>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ad95a698-9e05-4579-be2a-a51080293013');\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",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = data4[['Fuel_Type']]"
      ],
      "metadata": {
        "id": "T6vOB6EudOiT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# One Hot encoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "encoder = OneHotEncoder()\n",
        "encoded_labels = encoder.fit_transform(labels)\n",
        "print(encoded_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJbTQ_wAbHsd",
        "outputId": "6c1e9631-a912-4890-e28d-c03c3179b91f"
      },
      "execution_count": null,
      "outputs": [
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            "  (38, 1)\t1.0\n",
            "  (39, 1)\t1.0\n",
            "  (40, 1)\t1.0\n",
            "  (41, 1)\t1.0\n",
            "  (42, 1)\t1.0\n",
            "  (43, 0)\t1.0\n",
            "  (44, 0)\t1.0\n",
            "  (45, 0)\t1.0\n",
            "  (46, 0)\t1.0\n",
            "  (47, 1)\t1.0\n",
            "  (48, 0)\t1.0\n",
            "  (49, 0)\t1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5) Label Encoder**"
      ],
      "metadata": {
        "id": "5_lt5z9wf5DU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Label Encoder \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "encoded_labels = encoder.fit_transform(labels)\n",
        "print(encoded_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJ-qNnrQeEeM",
        "outputId": "0929e384-1ad9-420e-9aa7-dfb843c72c2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 0 0 0 0 1 0 0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6) KNN Imputer**"
      ],
      "metadata": {
        "id": "K6tSeXTHfyOL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_data = data2.select_dtypes(include = np.number)\n",
        "numerical_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "J_1BQWOJkwpu",
        "outputId": "1d33feaf-124a-4124-ea30-22976661034e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Price  Age  Mfg_Month  Mfg_Year     KM  HP  Met_Color  Automatic    cc  \\\n",
              "0  12950   23         10      2002  46986  90          1          0  2000   \n",
              "1  12950   23         10      2002  72937  90          1          0  2000   \n",
              "2  12950   24          9      2002  41711  90          1          0  2000   \n",
              "3  13950   26          7      2002  48000  90          0          0  2000   \n",
              "4  17950   30          3      2002  38500  90          0          0  2000   \n",
              "\n",
              "   Quarterly_Tax  Weight  Mfr_Guarantee  BOVAG_Guarantee  Guarantee_Period  \n",
              "0            210    1165            0.0                1                 3  \n",
              "1            210    1165            0.0                1                 3  \n",
              "2            210    1165            1.0                1                 3  \n",
              "3            210    1165            1.0                1                 4  \n",
              "4            210    1170            1.0                1                 5  "
            ],
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              "      <td>38500</td>\n",
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              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>210</td>\n",
              "      <td>1170</td>\n",
              "      <td>1.0</td>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import KNNImputer\n",
        "\n",
        "KNN = KNNImputer(n_neighbors=2)\n",
        "\n",
        "KNN.fit_transform(numerical_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E81Yj45Bfh2J",
        "outputId": "b69c36f9-d03c-4868-fe77-5bd55c645b16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [1.36500e+04, 2.50000e+01, 8.00000e+00, 2.00200e+03, 2.84500e+04,\n",
              "        1.10000e+02, 1.00000e+00, 0.00000e+00, 1.60000e+03, 8.50000e+01,\n",
              "        1.12000e+03, 0.00000e+00, 1.00000e+00, 5.00000e+00],\n",
              "       [1.55000e+04, 2.70000e+01, 6.00000e+00, 2.00200e+03, 3.45450e+04,\n",
              "        1.10000e+02, 1.00000e+00, 0.00000e+00, 1.60000e+03, 8.50000e+01,\n",
              "        1.12000e+03, 1.00000e+00, 1.00000e+00, 3.00000e+00],\n",
              "       [1.62500e+04, 2.90000e+01, 4.00000e+00, 2.00200e+03, 4.14150e+04,\n",
              "        1.10000e+02, 1.00000e+00, 0.00000e+00, 1.60000e+03, 8.50000e+01,\n",
              "        1.12000e+03, 1.00000e+00, 1.00000e+00, 3.00000e+00],\n",
              "       [1.59500e+04, 2.80000e+01, 5.00000e+00, 2.00200e+03, 4.41420e+04,\n",
              "        1.10000e+02, 0.00000e+00, 0.00000e+00, 1.60000e+03, 8.50000e+01,\n",
              "        1.12000e+03, 1.00000e+00, 1.00000e+00, 3.00000e+00],\n",
              "       [1.74950e+04, 3.00000e+01, 3.00000e+00, 2.00200e+03, 1.10900e+04,\n",
              "        1.10000e+02, 1.00000e+00, 0.00000e+00, 1.60000e+03, 8.50000e+01,\n",
              "        1.12000e+03, 1.00000e+00, 1.00000e+00, 3.00000e+00],\n",
              "       [1.67500e+04, 2.90000e+01, 4.00000e+00, 2.00200e+03, 9.75000e+03,\n",
              "        9.70000e+01, 1.00000e+00, 0.00000e+00, 1.40000e+03, 1.90000e+01,\n",
              "        1.10000e+03, 1.00000e+00, 1.00000e+00, 1.20000e+01],\n",
              "       [1.27500e+04, 2.20000e+01, 1.10000e+01, 2.00200e+03, 3.51990e+04,\n",
              "        9.70000e+01, 1.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 1.00000e+00, 1.00000e+00, 3.00000e+00],\n",
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              "        9.70000e+01, 1.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
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              "        1.10000e+03, 0.00000e+00, 1.00000e+00, 5.00000e+00],\n",
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              "        9.70000e+01, 1.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 0.00000e+00, 0.00000e+00, 5.00000e+00],\n",
              "       [1.46000e+04, 2.60000e+01, 7.00000e+00, 2.00200e+03, 4.30000e+04,\n",
              "        9.70000e+01, 0.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 1.00000e+00, 1.00000e+00, 3.00000e+00],\n",
              "       [1.37500e+04, 2.50000e+01, 8.00000e+00, 2.00200e+03, 2.50000e+04,\n",
              "        9.70000e+01, 0.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 5.00000e-01, 1.00000e+00, 3.00000e+00],\n",
              "       [1.29500e+04, 2.30000e+01, 1.00000e+01, 2.00200e+03, 1.00000e+04,\n",
              "        9.70000e+01, 1.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 1.00000e+00, 1.00000e+00, 4.00000e+00],\n",
              "       [1.99500e+04, 3.20000e+01, 1.00000e+00, 2.00200e+03, 2.53290e+04,\n",
              "        9.70000e+01, 1.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 1.00000e+00, 1.00000e+00, 5.00000e+00],\n",
              "       [1.57500e+04, 2.70000e+01, 6.00000e+00, 2.00200e+03, 2.75000e+04,\n",
              "        9.70000e+01, 0.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 1.00000e+00, 1.00000e+00, 3.00000e+00],\n",
              "       [1.27500e+04, 2.20000e+01, 1.10000e+01, 2.00200e+03, 4.90590e+04,\n",
              "        9.70000e+01, 0.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 5.00000e-01, 1.00000e+00, 3.00000e+00],\n",
              "       [1.57500e+04, 2.70000e+01, 6.00000e+00, 2.00200e+03, 4.40680e+04,\n",
              "        9.70000e+01, 1.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 5.00000e-01, 1.00000e+00, 3.00000e+00],\n",
              "       [1.27500e+04, 2.20000e+01, 1.10000e+01, 2.00200e+03, 4.69610e+04,\n",
              "        9.70000e+01, 0.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.10000e+03, 5.00000e-01, 1.00000e+00, 4.00000e+00],\n",
              "       [1.58000e+04, 2.70000e+01, 6.00000e+00, 2.00200e+03, 1.10404e+05,\n",
              "        9.00000e+01, 0.00000e+00, 0.00000e+00, 2.00000e+03, 2.34000e+02,\n",
              "        1.25500e+03, 5.00000e-01, 1.00000e+00, 5.00000e+00],\n",
              "       [1.26000e+04, 2.20000e+01, 1.10000e+01, 2.00200e+03, 1.00250e+05,\n",
              "        9.00000e+01, 0.00000e+00, 0.00000e+00, 2.00000e+03, 2.34000e+02,\n",
              "        1.25500e+03, 5.00000e-01, 1.00000e+00, 3.00000e+00],\n",
              "       [1.29500e+04, 2.30000e+01, 1.00000e+01, 2.00200e+03, 8.40000e+04,\n",
              "        9.00000e+01, 0.00000e+00, 0.00000e+00, 2.00000e+03, 2.34000e+02,\n",
              "        1.27000e+03, 5.00000e-01, 1.00000e+00, 3.00000e+00],\n",
              "       [1.57500e+04, 2.70000e+01, 6.00000e+00, 2.00200e+03, 7.93750e+04,\n",
              "        9.00000e+01, 1.00000e+00, 0.00000e+00, 2.00000e+03, 2.34000e+02,\n",
              "        1.25500e+03, 5.00000e-01, 1.00000e+00, 4.00000e+00],\n",
              "       [1.19500e+04, 2.20000e+01, 1.10000e+01, 2.00200e+03, 7.50480e+04,\n",
              "        9.70000e+01, 1.00000e+00, 0.00000e+00, 1.40000e+03, 8.50000e+01,\n",
              "        1.11000e+03, 0.00000e+00, 1.00000e+00, 5.00000e+00],\n",
              "       [1.15000e+04, 2.20000e+01, 1.10000e+01, 2.00200e+03, 7.22150e+04,\n",
              "        9.00000e+01, 1.00000e+00, 0.00000e+00, 2.00000e+03, 2.34000e+02,\n",
              "        1.25500e+03, 0.00000e+00, 1.00000e+00, 3.00000e+00],\n",
              "       [1.15100e+04, 2.20000e+01, 1.10000e+01, 2.00200e+03, 7.22150e+04,\n",
              "        9.00000e+01, 1.00000e+00, 0.00000e+00, 2.00000e+03, 2.34000e+02,\n",
              "        1.25500e+03, 0.00000e+00, 1.00000e+00, 3.00000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7) Z-Score**"
      ],
      "metadata": {
        "id": "qqG9ryrVmd8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean = data.mean().sum()\n",
        "std = data.std().sum()\n",
        "\n",
        "z_scores = ((numerical_data - mean) / std)\n",
        "z_scores.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "6lR6N2SUlE68",
        "outputId": "57f93916-8187-49a6-bdbe-1962886cb54b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-204-5580ca48d83e>:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  mean = data.mean().sum()\n",
            "<ipython-input-204-5580ca48d83e>:2: FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  std = data.std().sum()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Price       Age  Mfg_Month  Mfg_Year        KM        HP  Met_Color  \\\n",
              "0 -1.980406 -2.461390  -2.461874 -2.387756 -0.714003 -2.458897  -2.462209   \n",
              "1 -1.980406 -2.461390  -2.461874 -2.387756  0.251575 -2.458897  -2.462209   \n",
              "2 -1.980406 -2.461353  -2.461911 -2.387756 -0.910274 -2.458897  -2.462209   \n",
              "3 -1.943198 -2.461278  -2.461985 -2.387756 -0.676275 -2.458897  -2.462246   \n",
              "4 -1.794367 -2.461130  -2.462134 -2.387756 -1.029748 -2.458897  -2.462246   \n",
              "\n",
              "   Automatic       cc  Quarterly_Tax    Weight  Mfr_Guarantee  \\\n",
              "0  -2.462246 -2.38783      -2.454432 -2.418899      -2.462246   \n",
              "1  -2.462246 -2.38783      -2.454432 -2.418899      -2.462246   \n",
              "2  -2.462246 -2.38783      -2.454432 -2.418899      -2.462209   \n",
              "3  -2.462246 -2.38783      -2.454432 -2.418899      -2.462209   \n",
              "4  -2.462246 -2.38783      -2.454432 -2.418713      -2.462209   \n",
              "\n",
              "   BOVAG_Guarantee  Guarantee_Period  \n",
              "0        -2.462209         -2.462134  \n",
              "1        -2.462209         -2.462134  \n",
              "2        -2.462209         -2.462134  \n",
              "3        -2.462209         -2.462097  \n",
              "4        -2.462209         -2.462060  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Age</th>\n",
              "      <th>Mfg_Month</th>\n",
              "      <th>Mfg_Year</th>\n",
              "      <th>KM</th>\n",
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              "      <th>0</th>\n",
              "      <td>-1.980406</td>\n",
              "      <td>-2.461390</td>\n",
              "      <td>-2.461874</td>\n",
              "      <td>-2.387756</td>\n",
              "      <td>-0.714003</td>\n",
              "      <td>-2.458897</td>\n",
              "      <td>-2.462209</td>\n",
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              "      <td>-2.462246</td>\n",
              "      <td>-2.462209</td>\n",
              "      <td>-2.462134</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.980406</td>\n",
              "      <td>-2.461390</td>\n",
              "      <td>-2.461874</td>\n",
              "      <td>-2.387756</td>\n",
              "      <td>0.251575</td>\n",
              "      <td>-2.458897</td>\n",
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              "      <td>-2.454432</td>\n",
              "      <td>-2.418899</td>\n",
              "      <td>-2.462246</td>\n",
              "      <td>-2.462209</td>\n",
              "      <td>-2.462134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.980406</td>\n",
              "      <td>-2.461353</td>\n",
              "      <td>-2.461911</td>\n",
              "      <td>-2.387756</td>\n",
              "      <td>-0.910274</td>\n",
              "      <td>-2.458897</td>\n",
              "      <td>-2.462209</td>\n",
              "      <td>-2.462246</td>\n",
              "      <td>-2.38783</td>\n",
              "      <td>-2.454432</td>\n",
              "      <td>-2.418899</td>\n",
              "      <td>-2.462209</td>\n",
              "      <td>-2.462209</td>\n",
              "      <td>-2.462134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.943198</td>\n",
              "      <td>-2.461278</td>\n",
              "      <td>-2.461985</td>\n",
              "      <td>-2.387756</td>\n",
              "      <td>-0.676275</td>\n",
              "      <td>-2.458897</td>\n",
              "      <td>-2.462246</td>\n",
              "      <td>-2.462246</td>\n",
              "      <td>-2.38783</td>\n",
              "      <td>-2.454432</td>\n",
              "      <td>-2.418899</td>\n",
              "      <td>-2.462209</td>\n",
              "      <td>-2.462209</td>\n",
              "      <td>-2.462097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.794367</td>\n",
              "      <td>-2.461130</td>\n",
              "      <td>-2.462134</td>\n",
              "      <td>-2.387756</td>\n",
              "      <td>-1.029748</td>\n",
              "      <td>-2.458897</td>\n",
              "      <td>-2.462246</td>\n",
              "      <td>-2.462246</td>\n",
              "      <td>-2.38783</td>\n",
              "      <td>-2.454432</td>\n",
              "      <td>-2.418713</td>\n",
              "      <td>-2.462209</td>\n",
              "      <td>-2.462209</td>\n",
              "      <td>-2.462060</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b8c0ac5-3842-415a-9650-f2eb38f1e7c1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4b8c0ac5-3842-415a-9650-f2eb38f1e7c1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing zscore value greater than 3\n",
        "\n",
        "mask = (z_scores > 3)\n",
        "mask.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k4I58Dk_oGI6",
        "outputId": "5768b3ce-bb4c-4818-a037-274424c0ee28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Price               0\n",
              "Age                 0\n",
              "Mfg_Month           0\n",
              "Mfg_Year            0\n",
              "KM                  0\n",
              "HP                  0\n",
              "Met_Color           0\n",
              "Automatic           0\n",
              "cc                  0\n",
              "Quarterly_Tax       0\n",
              "Weight              0\n",
              "Mfr_Guarantee       0\n",
              "BOVAG_Guarantee     0\n",
              "Guarantee_Period    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**8) Binarizer**"
      ],
      "metadata": {
        "id": "mPP2LNdGqYYL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# If error faced due to binarizer. run this to solve. \n",
        "\n",
        "Null1 = numerical_data.isna().sum()\n",
        "Null1[Null1 > 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kn0JUz4dq0Nj",
        "outputId": "d03ecf7f-cfd6-46c5-d69d-51d1f1885fdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mfr_Guarantee    23\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_data.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "XIHryVapBRxv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import Binarizer\n",
        "\n",
        "binarizer = Binarizer(threshold = 25)\n",
        "binarized = binarizer.fit_transform(numerical_data)\n",
        "binarized"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hi1mTy1YpBEK",
        "outputId": "1371bb7e-ab79-474a-ef68-3e49b62e9f47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 1., 1., 1., 0., 0., 1., 0., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 0., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**9) Min-Max Scalar**"
      ],
      "metadata": {
        "id": "02ymnz56ranX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Min-Max Scalar\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scalar = MinMaxScaler()\n",
        "scaled_features = scalar.fit_transform(numerical_data)\n",
        "\n",
        "scaled_features[:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hl_xH9B-qt6z",
        "outputId": "19e89edd-ddc6-48b1-c5df-87490ca04083"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.02162162, 0.1       , 0.9       , 0.        , 0.43878297,\n",
              "        0.17073171, 1.        , 0.        , 1.        , 1.        ,\n",
              "        0.55555556, 0.        , 1.        , 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standard Scalar \n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scalar = StandardScaler()\n",
        "\n",
        "scaled_featuers = scalar.fit_transform(numerical_data)\n",
        "scaled_features[:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eFcTahprwdII",
        "outputId": "0c87639d-74bd-4d76-d59e-eee1ab8e03f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.02162162, 0.1       , 0.9       , 0.        , 0.43878297,\n",
              "        0.17073171, 1.        , 0.        , 1.        , 1.        ,\n",
              "        0.55555556, 0.        , 1.        , 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**10) LDA, PCA**\n",
        "\n",
        "LDA - LinearDiscriminantAnalysis\n",
        "\n",
        "PCA"
      ],
      "metadata": {
        "id": "xU346_O4xRHd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data5 = data \n",
        "data5.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "Qz8G-QN_KiDC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data5.drop(['Fuel_Type','Gears'], axis=1)\n",
        "# x = x.select_dtypes(include=['int64', 'float64'])\n",
        "y = data5[['Fuel_Type']]"
      ],
      "metadata": {
        "id": "_C8oeY4yyusB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To find the null values \n",
        "# x = data.isna().sum()\n",
        "\n",
        "# To drop the null values. \n",
        "# x.dropna(inplace = True)\n",
        "\n",
        "x.shape\n",
        "y.shape"
      ],
      "metadata": {
        "id": "eI3uohHh1Npn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e68e93b6-9c1c-4e6a-fe03-f53c05a388dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LDA - Linear Discriminant Analysis\n",
        "\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "\n",
        "LDA = LinearDiscriminantAnalysis(n_components = 1)\n",
        "extracted_data = LDA.fit_transform(x, y)\n",
        "extracted_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMAKkQPgw821",
        "outputId": "a6c502eb-1a9a-489d-88c1-696d4ca90c3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-13.68873726],\n",
              "       [-13.6055062 ],\n",
              "       [-13.0235797 ],\n",
              "       [-14.22321604],\n",
              "       [-14.1732171 ],\n",
              "       [-13.47120531],\n",
              "       [-13.34911884],\n",
              "       [-13.36454881],\n",
              "       [  8.13021454],\n",
              "       [-13.16516464],\n",
              "       [  8.45754441],\n",
              "       [  7.9982076 ],\n",
              "       [  6.82218341],\n",
              "       [  7.24831255],\n",
              "       [  6.83205717],\n",
              "       [  6.82035496],\n",
              "       [  5.80394142],\n",
              "       [  4.68577679],\n",
              "       [  7.33854282],\n",
              "       [  5.42495362],\n",
              "       [  7.32774704],\n",
              "       [  8.23156037],\n",
              "       [  7.4741966 ],\n",
              "       [  9.26097117],\n",
              "       [  8.16864873],\n",
              "       [  6.0390807 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# PCA \n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components = 7)\n",
        "extracted_data = pca.fit_transform(x)\n",
        "extracted_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qRe79UUUzElO",
        "outputId": "9b283df8-3b48-4b11-b927-603672f47f18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.37450169e+03, -2.50362716e+03,  3.08237920e+02,\n",
              "         2.46786177e+00, -1.52522369e+01, -6.59185074e-02,\n",
              "        -3.10513065e-01],\n",
              "       [ 2.83245494e+04, -2.64844334e+03,  1.40479708e+02,\n",
              "         9.40099754e+00,  3.45201235e-01,  4.36042603e+00,\n",
              "         7.19105849e-01],\n",
              "       [-2.90030473e+03, -2.47418820e+03,  3.42336223e+02,\n",
              "         1.05262239e+00, -1.83922177e+01, -9.95363543e-01,\n",
              "        -3.84939291e-01],\n",
              "       [ 3.39400743e+03, -1.50931887e+03,  2.95867622e+02,\n",
              "         5.69815089e+00, -1.65506544e+01,  1.14396260e+00,\n",
              "         9.45219823e-01],\n",
              "       [-6.08346659e+03,  2.54356195e+03,  3.34670206e+02,\n",
              "         1.04490761e+01, -2.96089127e+01,  2.30982845e+00,\n",
              "         1.15326544e+00],\n",
              "       [ 1.64381569e+04,  6.46784307e+03,  1.65686178e+02,\n",
              "         2.85561547e+01, -2.41627757e+01,  1.09260463e+01,\n",
              "        -4.95521299e-01],\n",
              "       [ 5.00099272e+04, -7.69141885e+02, -1.01174756e+00,\n",
              "        -5.26968242e+01,  1.84368431e+01, -1.72337848e+01,\n",
              "        -5.63422238e-01],\n",
              "       [ 3.13042432e+04,  2.33522460e+03,  1.02583570e+02,\n",
              "        -4.87730379e+01,  1.24945106e+00, -1.70648188e+01,\n",
              "        -5.23307345e-01],\n",
              "       [-2.49008708e+04, -3.52343064e+02,  2.57314725e+02,\n",
              "        -4.89309043e+01,  7.76467137e+01,  2.99837360e+01,\n",
              "         1.30143049e+00],\n",
              "       [ 2.65248976e+04, -2.63921322e+03,  4.22258310e+01,\n",
              "         5.71273782e+01, -1.70092066e+01,  2.86797536e+00,\n",
              "         1.03694908e+00],\n",
              "       [-1.90506505e+04, -2.38663912e+03,  4.22655093e+00,\n",
              "         5.99736756e+01,  5.41959467e+01, -2.06470466e+01,\n",
              "        -2.11900802e+00],\n",
              "       [ 1.97662691e+04,  1.39687626e+03, -2.49607809e+02,\n",
              "         2.86028521e+01,  2.18154889e+01,  9.95535700e+00,\n",
              "         1.52746120e-01],\n",
              "       [ 2.30671478e+04,  1.37845571e+03, -2.70949059e+02,\n",
              "         2.94644403e+01,  2.38022233e+01,  1.04126804e+01,\n",
              "         2.26914753e+00],\n",
              "       [-6.86880523e+02,  1.51128031e+03, -1.05625883e+02,\n",
              "        -4.06613526e+01,  4.87836232e+00, -8.41064912e+00,\n",
              "        -2.56753873e-01],\n",
              "       [ 1.17510364e+04,  4.41672294e+02, -1.90091708e+02,\n",
              "         9.64696390e+00,  2.06262489e+01,  2.31921887e+00,\n",
              "        -6.66030746e-01],\n",
              "       [-1.23770782e+04,  5.76321114e+02, -3.41118120e+01,\n",
              "         3.20141446e+00,  6.12020588e+00, -1.80032106e+00,\n",
              "        -1.63531494e+00],\n",
              "       [-1.87783001e+04,  1.61203648e+03,  1.49127284e+00,\n",
              "         4.44779267e+00,  2.94800433e-01, -1.85908538e+00,\n",
              "         2.99823927e-02],\n",
              "       [-1.61596888e+04, -1.70255426e+03,  3.62619860e+00,\n",
              "        -4.67250856e+00,  8.38459218e+00, -5.14853013e+00,\n",
              "         2.61372732e-01],\n",
              "       [-3.18075271e+03,  8.24999821e+02, -9.52968209e+01,\n",
              "         6.54675444e+00,  1.10774399e+01,  9.19344575e-02,\n",
              "        -1.18750775e+00],\n",
              "       [-4.55515676e+02,  5.09791975e+02, -1.11179749e+02,\n",
              "         6.39039618e+00,  1.32891707e+01,  2.38641609e-01,\n",
              "        -1.15526178e+00],\n",
              "       [-3.34977389e+04,  2.23917623e+03,  9.35008313e+01,\n",
              "         2.15482220e+00, -9.61903045e+00, -3.65515205e+00,\n",
              "        -2.50872547e+00],\n",
              "       [-3.48431892e+04,  1.50050194e+03, -1.04155128e+02,\n",
              "         1.12540417e+00, -1.01337081e+00, -2.72171483e+01,\n",
              "         4.92530389e+00],\n",
              "       [-9.41716706e+03, -2.64137218e+03, -2.30319213e+02,\n",
              "        -2.05922035e+01, -3.02471140e+01,  6.36014596e+00,\n",
              "        -1.32363588e+00],\n",
              "       [-1.50893293e+04,  3.90264450e+02, -2.10982700e+02,\n",
              "        -1.31904247e+01, -3.95299699e+01,  8.75759159e+00,\n",
              "        -1.45352061e+00],\n",
              "       [-1.19174235e+04, -1.42742116e+03, -2.21093561e+02,\n",
              "        -1.77279909e+01, -3.40245983e+01,  7.12641487e+00,\n",
              "         9.06248098e-01],\n",
              "       [-3.61638001e+03, -2.67374376e+03, -2.67821647e+02,\n",
              "        -1.90615109e+01, -2.67526008e+01,  7.24385881e+00,\n",
              "         8.82690851e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**11) Feature Seletion - SelectPercentile, KBest, chi-squared, VarianceThreshold**"
      ],
      "metadata": {
        "id": "fi5Zl44R2V7-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using label encoder to convert categorical values into numerical values\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "encoded = le.fit_transform(y)\n",
        "print(encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VF2byu3z5Oju",
        "outputId": "02e7eecb-27ca-48b2-bdc4-e57a99cae5a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing the dataset for accuracy\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2)"
      ],
      "metadata": {
        "id": "S6xLvafA2Lib"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Chi Squared Test\n",
        "\n",
        "from sklearn.feature_selection import SelectKBest, chi2, VarianceThreshold, mutual_info_classif\n",
        "\n",
        "selector = SelectKBest(score_func=chi2, k=3)\n",
        "newda = selector.fit_transform(abs(x), encoded)\n",
        "support = selector.get_support()\n",
        "print(list(x.columns[support]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sieYu2G94oQm",
        "outputId": "37462172-1ba0-4350-9449-098023513f1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['KM', 'cc', 'Quarterly_Tax']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using Information\n",
        "\n",
        "info = SelectKBest(score_func = mutual_info_classif, k=3)\n",
        "newdata1 = info.fit_transform(abs(x), encoded)\n",
        "supports = info.get_support()\n",
        "print(list(x.columns[supports]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfOYRpVa6Ojw",
        "outputId": "dc3739c5-f111-4f3b-847b-74b91c371ea3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['HP', 'cc', 'Quarterly_Tax']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Variance Threshold\n",
        "\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "\n",
        "variance = VarianceThreshold(4)\n",
        "newdata = variance.fit_transform(x)\n",
        "print(f\"Old Shape = {x.shape} \\nNew Shape: {newdata.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UtH0YxyG7LaM",
        "outputId": "11bbc4da-dbfd-43fe-ed14-4c621ba8c2cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old Shape = (26, 15) \n",
            "New Shape: (26, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**12) Correlation (High positive, High Negative)**"
      ],
      "metadata": {
        "id": "cHNtwhxt7wXJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation \n",
        "\n",
        "corr = x.corr()\n",
        "corr\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "k6vNzZtm7pmv",
        "outputId": "cb92782b-e423-4712-dd47-eabc33ca323d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Price       Age  Mfg_Month  Mfg_Year        KM        HP  \\\n",
              "Price             1.000000  0.940183  -0.940183       NaN  0.051110  0.074260   \n",
              "Age               0.940183  1.000000  -1.000000       NaN  0.007842  0.191662   \n",
              "Mfg_Month        -0.940183 -1.000000   1.000000       NaN -0.007842 -0.191662   \n",
              "Mfg_Year               NaN       NaN        NaN       NaN       NaN       NaN   \n",
              "KM                0.051110  0.007842  -0.007842       NaN  1.000000 -0.417615   \n",
              "HP                0.074260  0.191662  -0.191662       NaN -0.417615  1.000000   \n",
              "Met_Color        -0.155809 -0.096767   0.096767       NaN  0.019692 -0.151382   \n",
              "Automatic         0.137090  0.143126  -0.143126       NaN -0.006770  0.068354   \n",
              "cc                0.081279  0.018142  -0.018142       NaN  0.556036 -0.228279   \n",
              "Doors                  NaN       NaN        NaN       NaN       NaN       NaN   \n",
              "Quarterly_Tax     0.029680 -0.082660   0.082660       NaN  0.622197 -0.435180   \n",
              "Weight            0.233013  0.207157  -0.207157       NaN  0.514409 -0.008790   \n",
              "Mfr_Guarantee     0.293034  0.393776  -0.393776       NaN -0.222342 -0.074232   \n",
              "BOVAG_Guarantee   0.340150  0.401475  -0.401475       NaN  0.159222 -0.008043   \n",
              "Guarantee_Period  0.133007  0.184860  -0.184860       NaN -0.406986  0.107736   \n",
              "\n",
              "                  Met_Color  Automatic        cc  Doors  Quarterly_Tax  \\\n",
              "Price             -0.155809   0.137090  0.081279    NaN       0.029680   \n",
              "Age               -0.096767   0.143126  0.018142    NaN      -0.082660   \n",
              "Mfg_Month          0.096767  -0.143126 -0.018142    NaN       0.082660   \n",
              "Mfg_Year                NaN        NaN       NaN    NaN            NaN   \n",
              "KM                 0.019692  -0.006770  0.556036    NaN       0.622197   \n",
              "HP                -0.151382   0.068354 -0.228279    NaN      -0.435180   \n",
              "Met_Color          1.000000  -0.300000 -0.320315    NaN      -0.172295   \n",
              "Automatic         -0.300000   1.000000 -0.091843    NaN      -0.073298   \n",
              "cc                -0.320315  -0.091843  1.000000    NaN       0.913107   \n",
              "Doors                   NaN        NaN       NaN    NaN            NaN   \n",
              "Quarterly_Tax     -0.172295  -0.073298  0.913107    NaN       1.000000   \n",
              "Weight            -0.101721   0.151255  0.759948    NaN       0.753349   \n",
              "Mfr_Guarantee      0.218596  -0.216025 -0.154838    NaN      -0.125007   \n",
              "BOVAG_Guarantee    0.120281   0.057735  0.260217    NaN       0.322959   \n",
              "Guarantee_Period   0.059586   0.105153 -0.316551    NaN      -0.352795   \n",
              "\n",
              "                    Weight  Mfr_Guarantee  BOVAG_Guarantee  Guarantee_Period  \n",
              "Price             0.233013       0.293034         0.340150          0.133007  \n",
              "Age               0.207157       0.393776         0.401475          0.184860  \n",
              "Mfg_Month        -0.207157      -0.393776        -0.401475         -0.184860  \n",
              "Mfg_Year               NaN            NaN              NaN               NaN  \n",
              "KM                0.514409      -0.222342         0.159222         -0.406986  \n",
              "HP               -0.008790      -0.074232        -0.008043          0.107736  \n",
              "Met_Color        -0.101721       0.218596         0.120281          0.059586  \n",
              "Automatic         0.151255      -0.216025         0.057735          0.105153  \n",
              "cc                0.759948      -0.154838         0.260217         -0.316551  \n",
              "Doors                  NaN            NaN              NaN               NaN  \n",
              "Quarterly_Tax     0.753349      -0.125007         0.322959         -0.352795  \n",
              "Weight            1.000000      -0.114648         0.362586         -0.121937  \n",
              "Mfr_Guarantee    -0.114648       1.000000         0.311805          0.103842  \n",
              "BOVAG_Guarantee   0.362586       0.311805         1.000000          0.006071  \n",
              "Guarantee_Period -0.121937       0.103842         0.006071          1.000000  "
            ],
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              "\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Age</th>\n",
              "      <th>Mfg_Month</th>\n",
              "      <th>Mfg_Year</th>\n",
              "      <th>KM</th>\n",
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              "      <th>Price</th>\n",
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              "      <th>Mfg_Month</th>\n",
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              "      <th>Mfg_Year</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KM</th>\n",
              "      <td>0.051110</td>\n",
              "      <td>0.007842</td>\n",
              "      <td>-0.007842</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.417615</td>\n",
              "      <td>0.019692</td>\n",
              "      <td>-0.006770</td>\n",
              "      <td>0.556036</td>\n",
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              "      <td>0.622197</td>\n",
              "      <td>0.514409</td>\n",
              "      <td>-0.222342</td>\n",
              "      <td>0.159222</td>\n",
              "      <td>-0.406986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HP</th>\n",
              "      <td>0.074260</td>\n",
              "      <td>0.191662</td>\n",
              "      <td>-0.191662</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.417615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.151382</td>\n",
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              "      <td>-0.008790</td>\n",
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              "      <td>-0.008043</td>\n",
              "      <td>0.107736</td>\n",
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              "    <tr>\n",
              "      <th>Met_Color</th>\n",
              "      <td>-0.155809</td>\n",
              "      <td>-0.096767</td>\n",
              "      <td>0.096767</td>\n",
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              "      <th>Automatic</th>\n",
              "      <td>0.137090</td>\n",
              "      <td>0.143126</td>\n",
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              "      <th>cc</th>\n",
              "      <td>0.081279</td>\n",
              "      <td>0.018142</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>-0.316551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doors</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>Quarterly_Tax</th>\n",
              "      <td>0.029680</td>\n",
              "      <td>-0.082660</td>\n",
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              "      <td>0.322959</td>\n",
              "      <td>-0.352795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>0.233013</td>\n",
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              "      <td>-0.207157</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>Mfr_Guarantee</th>\n",
              "      <td>0.293034</td>\n",
              "      <td>0.393776</td>\n",
              "      <td>-0.393776</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.311805</td>\n",
              "      <td>0.103842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOVAG_Guarantee</th>\n",
              "      <td>0.340150</td>\n",
              "      <td>0.401475</td>\n",
              "      <td>-0.401475</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.159222</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.006071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Guarantee_Period</th>\n",
              "      <td>0.133007</td>\n",
              "      <td>0.184860</td>\n",
              "      <td>-0.184860</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.006071</td>\n",
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              "    [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-0781d936-82ff-4ad4-a3dc-3ef4fc1ebc6f 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-0781d936-82ff-4ad4-a3dc-3ef4fc1ebc6f');\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": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Highest positive correlation \"Change the threshold as per you dataset correlation values\"\n",
        "correlated = []\n",
        "for i in range(corr.shape[0]):\n",
        "  for j in range(i+1, corr.shape[1]):\n",
        "    if abs(corr.iloc[i,j]) > 0.40:\n",
        "      correlated.append(corr.columns[i])\n",
        "print(list(set(correlated)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oIwUASfPAWwY",
        "outputId": "7d583adb-f64a-4ec8-dfb9-565e4ae606e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Mfg_Month', 'HP', 'KM', 'Quarterly_Tax', 'cc', 'Age', 'Price']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Highest Negative correlation \"Change the threshold as per you dataset correlation values\"\n",
        "\n",
        "correlated_row = []\n",
        "correlated_col = []\n",
        "for i in range(corr.shape[0]):\n",
        "    for j in range(i+1, corr.shape[1]):\n",
        "        if (corr.iloc[i,j]) < 0 and corr.iloc[i,j] <= -0.1:\n",
        "            correlated_row.append(corr.columns[i])\n",
        "            correlated_col.append(corr.columns[j])\n",
        "\n",
        "print(correlated_row)\n",
        "print(correlated_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YBW83iMAcPq",
        "outputId": "95d6352a-4abc-4ba1-ea97-219c83091629"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Price', 'Price', 'Age', 'Mfg_Month', 'Mfg_Month', 'Mfg_Month', 'Mfg_Month', 'Mfg_Month', 'Mfg_Month', 'KM', 'KM', 'KM', 'HP', 'HP', 'HP', 'Met_Color', 'Met_Color', 'Met_Color', 'Met_Color', 'Automatic', 'cc', 'cc', 'Quarterly_Tax', 'Quarterly_Tax', 'Weight', 'Weight']\n",
            "['Mfg_Month', 'Met_Color', 'Mfg_Month', 'HP', 'Automatic', 'Weight', 'Mfr_Guarantee', 'BOVAG_Guarantee', 'Guarantee_Period', 'HP', 'Mfr_Guarantee', 'Guarantee_Period', 'Met_Color', 'cc', 'Quarterly_Tax', 'Automatic', 'cc', 'Quarterly_Tax', 'Weight', 'Mfr_Guarantee', 'Mfr_Guarantee', 'Guarantee_Period', 'Mfr_Guarantee', 'Guarantee_Period', 'Mfr_Guarantee', 'Guarantee_Period']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**13) Plotting**\n"
      ],
      "metadata": {
        "id": "SWcjUTrgAgTW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Univariate plots**\n",
        "\n",
        "- bar plot -> matplotlib.pyplot -> 1 arg col.unique, previous arg.value_counts()\n",
        "- swarmplot -> sns 1 arg col\n",
        "- violinplot -> sns -> 1 arg col\n"
      ],
      "metadata": {
        "id": "e3NsB2xz4Q5c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns "
      ],
      "metadata": {
        "id": "CbYQhlk37-lb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2arg - unique() and value_counts() -> Categorical Data\n",
        "plt.bar(data['Fuel_Type'].unique(), data['Fuel_Type'].value_counts())\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "LqeQ-D02Adzn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "f33f8104-c808-4be2-be5b-b1f60cd52188"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1arg = Categorical Data\n",
        "sns.swarmplot(data['Fuel_Type'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "8nS5P2LxFgd7",
        "outputId": "9a1436ca-7101-402e-a51c-9f988ced634a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1arg -> Numerical column\n",
        "\n",
        "sns.violinplot(data['Age'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "iMiVL9zaFpWi",
        "outputId": "bd641b48-1105-4ac2-ead3-48447e34a931"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Bivariate Plots**\n",
        "\n",
        "- scatter plot -> matplotlib.pyplot -> 2 arg\n",
        "\n",
        "- catplot -> sns -> 1arg-colx, 2arg-coly, 3rd-arg-kind-box, 4th-arg-data=data\n",
        "\n",
        "- countplot -> sns -> 1arg-1col, 2arg-hue-1col, 3arg-data=data\n",
        "\n",
        "- crosstab -> pandas -> 1arg-data.col, 2arg-data.2col"
      ],
      "metadata": {
        "id": "zcqVRaqW4lM3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2arg -> Any type of columns\n",
        "\n",
        "plt.scatter(data['Gears'], data['Age'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "0nIpo0ac7ook",
        "outputId": "67737027-2ac9-4af9-f74a-9dcf21a905e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4arg -> X and Y Data, Kind, data=data\n",
        "\n",
        "sns.catplot(x = 'Age', y='Fuel_Type', kind='box', data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "XBjKdgftON3N",
        "outputId": "c1d63db4-f38d-49eb-8641-1eea1ff59457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0db15ed850>"
            ]
          },
          "metadata": {},
          "execution_count": 256
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3arg -> X and Y Data, data=data\n",
        "\n",
        "sns.countplot(x=\"Age\", hue=\"Fuel_Type\", data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "gVfIvM8tOJvQ",
        "outputId": "598c1fa1-91d7-4b36-c5df-1ee8df88443d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 257
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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r6scff9SECRN05MgRrV69Wo0bN9a6devUr18/HTx4UF27dtWiRYs0a9YsHT16VJLk5+fnOMZLL72kWbNmafbs2ZKkPXv2aPDgwXrmmWc0ZMgQ7dq1S3/5y19Uv359jRgxwow23eeC4vz8fK1du1a5ubmKjIwsdlxOTo6aNm2qgoICdezYUc8//3yxQUiS8vLylJeX53icnZ3t0roBAHB3hmEoISFBW7Zs0dChQxUXF6fU1FQ1btxYkvTkk09q8+bNiouL0/PPP6+AgIBiJxN69uypKVOmOB4PGzZMvXr10tNPPy1JuuWWW3TkyBHNnz/ftHBj+gXFBw8elJ+fn+x2ux599FGtW7dOrVu3LnJsRESEVqxYoQ0bNujNN99UQUGBunbtqu+//77Y48fGxiogIMCxhIaGVlYrAAC4lY0bN8rPz08+Pj7q37+/hgwZoj/96U/Kz8/XLbfcIj8/P8eyfft2nThxotRj3n777U6Pv/rqK3Xr1s1pXbdu3XTs2DHl5+e7tJ+yMn3mJiIiQvv371dWVpbeeecdDR8+XNu3by8y4ERGRjrN6nTt2lWtWrXSsmXLNG/evCKPHxMTo8mTJzseZ2dnE3AAADeEe+65R0uWLJG3t7caN26sGjVqaM2aNfL09NSePXvk6enpNP7Xp5+K4+vrW1nluozp4ebax9MkqVOnTvriiy/08ssva9myZaXu6+XlpQ4dOuj48ePFjrHb7bLb7S6rFwCA6sLX19fxHntNhw4dlJ+fr8zMTN11111F7uft7V3mWZdWrVpp586dTut27typW265pVB4qiqmn5b6rYKCAqdrZEqSn5+vgwcPKjg4uJKrAgDAGm655RYNGzZM0dHReu+995SSkqLdu3crNjZWH3zwgaSfPxWVk5OjhIQE/ec//9GFCxeKPd6UKVOUkJCgefPm6ZtvvtHrr7+uV155RU8++WRVtVSIqTM3MTEx6t+/v8LCwnT+/HmtWrVKiYmJ2rJliyQpOjpaISEhio2NlSTNnTtXXbp0UfPmzXXu3DnNnz9fJ0+e1OjRo81sAwBwg6nudwyOi4vTs88+qylTpuj06dNq0KCBunTpovvuu0/Sz5d9PProoxoyZIh+/PFHzZ492/Fx8N/q2LGj3n77bc2aNUvz5s1TcHCw5s6da9rFxJLJ4SYzM1PR0dFKS0tTQECA2rZtqy1btujee++VJKWmpsrD45fJpbNnz2rMmDFKT09X3bp11alTJ+3atavYC5ABALhRxcfHF7vNy8tLc+bM0Zw5c4ods2TJEi1ZssRp3XfffVfk2AcffFAPPvhgsccqbr/KYmq4Wb58eYnbr91Q6JqFCxdq4cKFlVgRAACo7tzumhsAAIDrQbgBAACWQrgBAKAUhmGYXcINwVU/Z8INAADFuHafFnf99murufZzvt7745h+Ez8AANxVjRo1VKtWLZ05c0ZeXl5On+CFaxUUFOjMmTOqVauWatS4vnhCuAEAoBg2m03BwcFKSUnRyZMnzS7H8jw8PBQWFiabzXZdxyHcAABQAm9vb7Vo0YJTU1XA29vbJbNjhBsAAErh4eEhHx8fs8tAGXHyEAAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWIqp4WbJkiVq27at/P395e/vr8jISG3atKnEfdauXauWLVvKx8dHbdq00YcfflhF1QIAgOrA1HDTpEkTvfDCC9qzZ4+Sk5PVs2dPDRw4UIcPHy5y/K5duzR06FCNGjVK+/btU1RUlKKionTo0KEqrhwAALgrm2EYhtlF/Fq9evU0f/58jRo1qtC2IUOGKDc3Vxs3bnSs69Kli9q3b6+lS5eW6fjZ2dkKCAhQVlaW/P39XVY3AKDqpc5t49Ljhc066NLjwXXK8/7tNtfc5Ofna/Xq1crNzVVkZGSRY5KSktS7d2+ndX379lVSUlKxx83Ly1N2drbTAgAArKuG2QUcPHhQkZGRunTpkvz8/LRu3Tq1bt26yLHp6ekKDAx0WhcYGKj09PRijx8bG6s5c+a4tGZ31mnqSpceb8/8aJceD4D7YNbDffBauJbpMzcRERHav3+/Pv/8c40bN07Dhw/XkSNHXHb8mJgYZWVlOZZTp0657NgAAMD9mD5z4+3trebNm0uSOnXqpC+++EIvv/yyli1bVmhsUFCQMjIynNZlZGQoKCio2OPb7XbZ7XbXFg0AANyW6TM3v1VQUKC8vLwit0VGRiohIcFp3datW4u9RgcAANx4TJ25iYmJUf/+/RUWFqbz589r1apVSkxM1JYtWyRJ0dHRCgkJUWxsrCRp4sSJ6t69uxYsWKABAwZo9erVSk5O1quvvmpmGwAAwI2YGm4yMzMVHR2ttLQ0BQQEqG3bttqyZYvuvfdeSVJqaqo8PH6ZXOratatWrVqlmTNn6qmnnlKLFi20fv163XbbbWa1AAAA3Iyp4Wb58uUlbk9MTCy0btCgQRo0aFAlVQQAAKo7t7vmBgAA4HoQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKWYGm5iY2N1xx13qHbt2mrUqJGioqJ09OjREveJj4+XzWZzWnx8fKqoYgAA4O5MDTfbt2/X+PHj9dlnn2nr1q26cuWK+vTpo9zc3BL38/f3V1pammM5efJkFVUMAADcXQ0zn3zz5s1Oj+Pj49WoUSPt2bNHd999d7H72Ww2BQUFVXZ5AACgGnKra26ysrIkSfXq1StxXE5Ojpo2barQ0FANHDhQhw8fLnZsXl6esrOznRYAAGBdbhNuCgoKNGnSJHXr1k233XZbseMiIiK0YsUKbdiwQW+++aYKCgrUtWtXff/990WOj42NVUBAgGMJDQ2trBYAAIAbcJtwM378eB06dEirV68ucVxkZKSio6PVvn17de/eXe+9954aNmyoZcuWFTk+JiZGWVlZjuXUqVOVUT4AAHATpl5zc82ECRO0ceNGffLJJ2rSpEm59vXy8lKHDh10/PjxIrfb7XbZ7XZXlAkAAKoBU2duDMPQhAkTtG7dOm3btk3h4eHlPkZ+fr4OHjyo4ODgSqgQAABUN6bO3IwfP16rVq3Shg0bVLt2baWnp0uSAgICVLNmTUlSdHS0QkJCFBsbK0maO3euunTpoubNm+vcuXOaP3++Tp48qdGjR5vWBwAAcB+mhpslS5ZIknr06OG0Pi4uTiNGjJAkpaamysPjlwmms2fPasyYMUpPT1fdunXVqVMn7dq1S61bt66qsgEAgBszNdwYhlHqmMTERKfHCxcu1MKFCyupIgAAUN25zaelAAAAXIFwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALKVC4aZnz546d+5cofXZ2dnq2bPn9dYEAABQYRUKN4mJibp8+XKh9ZcuXdKnn3563UUBAABUVLm+FfzAgQOO/z5y5IjS09Mdj/Pz87V582aFhIS4rjoAAIByKle4ad++vWw2m2w2W5Gnn2rWrKm///3vLisOAACgvMoVblJSUmQYhm666Sbt3r1bDRs2dGzz9vZWo0aN5Onp6fIiAQAAyqpc4aZp06aSpIKCgkopBgAA4HqVK9z82rFjx/Txxx8rMzOzUNiZNWvWdRcGAABQERUKN//85z81btw4NWjQQEFBQbLZbI5tNpuNcAMAAExToXDz7LPP6rnnntP06dNdXQ8AAMB1qdB9bs6ePatBgwa5uhYAAIDrVqFwM2jQIH300UeurgUAAOC6Vei0VPPmzfX000/rs88+U5s2beTl5eW0/fHHH3dJcQAAAOVVoXDz6quvys/PT9u3b9f27dudttlsNsINAAAwTYXCTUpKiqvrAAAAcIkKXXMDAADgrio0c/Pwww+XuH3FihUVKgYAAOB6VSjcnD171unxlStXdOjQIZ07d67IL9QEAACoKhUKN+vWrSu0rqCgQOPGjdPNN9983UUBAABUlMuuufHw8NDkyZO1cOFCVx0SAACg3Fx6QfGJEyd09epVVx4SAACgXCp0Wmry5MlOjw3DUFpamj744AMNHz7cJYUBAABURIXCzb59+5wee3h4qGHDhlqwYEGpn6QCAACoTBUKNx9//LFLnjw2Nlbvvfeevv76a9WsWVNdu3bViy++qIiIiBL3W7t2rZ5++ml99913atGihV588UX9/ve/d0lNAACgeruua27OnDmjHTt2aMeOHTpz5ky599++fbvGjx+vzz77TFu3btWVK1fUp08f5ebmFrvPrl27NHToUI0aNUr79u1TVFSUoqKidOjQoetpBQAAWESFZm5yc3P12GOPaeXKlSooKJAkeXp6Kjo6Wn//+99Vq1atMh1n8+bNTo/j4+PVqFEj7dmzR3fffXeR+7z88svq16+fpk6dKkmaN2+etm7dqldeeUVLly6tSDsAAMBCKjRzM3nyZG3fvl3/+te/dO7cOZ07d04bNmzQ9u3bNWXKlAoXk5WVJUmqV69esWOSkpLUu3dvp3V9+/ZVUlJSkePz8vKUnZ3ttAAAAOuq0MzNu+++q3feeUc9evRwrPv973+vmjVravDgwVqyZEm5j1lQUKBJkyapW7duuu2224odl56ersDAQKd1gYGBSk9PL3J8bGys5syZU+56rlfq3DYuPV7YrIMuPR6AiuPvG52mrnTp8dbVdunhbngVmrm5cOFCoYAhSY0aNdKFCxcqVMj48eN16NAhrV69ukL7FycmJkZZWVmO5dSpUy49PgAAcC8VCjeRkZGaPXu2Ll265Fh38eJFzZkzR5GRkeU+3oQJE7Rx40Z9/PHHatKkSYljg4KClJGR4bQuIyNDQUFBRY632+3y9/d3WgAAgHVV6LTUokWL1K9fPzVp0kTt2rWTJH355Zey2+366KOPynwcwzD02GOPad26dUpMTFR4eHip+0RGRiohIUGTJk1yrNu6dWuFQhUAALCeCoWbNm3a6NixY3rrrbf09ddfS5KGDh2qYcOGqWbNmmU+zvjx47Vq1Spt2LBBtWvXdlw3ExAQ4DhOdHS0QkJCFBsbK0maOHGiunfvrgULFmjAgAFavXq1kpOT9eqrr1akFQAAYDEVCjexsbEKDAzUmDFjnNavWLFCZ86c0fTp08t0nGsXHv/6wmRJiouL04gRIyRJqamp8vD45exZ165dtWrVKs2cOVNPPfWUWrRoofXr15d4ETIAALhxVCjcLFu2TKtWrSq0/tZbb9V//dd/lTncGIZR6pjExMRC6wYNGqRBgwaV6TkAAMCNpUIXFKenpys4OLjQ+oYNGyotLe26iwIAAKioCoWb0NBQ7dy5s9D6nTt3qnHjxtddFAAAQEVV6LTUmDFjNGnSJF25ckU9e/aUJCUkJGjatGnXdYdiAACA61WhcDN16lT9+OOP+stf/qLLly9Lknx8fDR9+nTFxMS4tEAAAIDyqFC4sdlsevHFF/X000/rq6++Us2aNdWiRQvZ7XZX1wcAAFAuFQo31/j5+emOO+5wVS0AAADXrUIXFAMAALgrwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUU8PNJ598ovvvv1+NGzeWzWbT+vXrSxyfmJgom81WaElPT6+aggEAgNszNdzk5uaqXbt2Wrx4cbn2O3r0qNLS0hxLo0aNKqlCAABQ3dQw88n79++v/v37l3u/Ro0aqU6dOq4vCAAAVHvV8pqb9u3bKzg4WPfee6927txZ4ti8vDxlZ2c7LQAAwLqqVbgJDg7W0qVL9e677+rdd99VaGioevToob179xa7T2xsrAICAhxLaGhoFVYMAACqmqmnpcorIiJCERERjsddu3bViRMntHDhQr3xxhtF7hMTE6PJkyc7HmdnZxNwAACwsGoVborSuXNn7dixo9jtdrtddru9CisCAABmqlanpYqyf/9+BQcHm10GAABwE6bO3OTk5Oj48eOOxykpKdq/f7/q1aunsLAwxcTE6PTp01q5cqUkadGiRQoPD9ett96qS5cu6bXXXtO2bdv00UcfmdUCAABwM6aGm+TkZN1zzz2Ox9eujRk+fLji4+OVlpam1NRUx/bLly9rypQpOn36tGrVqqW2bdvq3//+t9MxAADAjc3UcNOjRw8ZhlHs9vj4eKfH06ZN07Rp0yq5KgAAUJ1V+2tuAAAAfo1wAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALMXUcPPJJ5/o/vvvV+PGjWWz2bR+/fpS90lMTFTHjh1lt9vVvHlzxcfHV3qdAACg+jA13OTm5qpdu3ZavHhxmcanpKRowIABuueee7R//35NmjRJo0eP1pYtWyq5UgAAUF3UMPPJ+/fvr/79+5d5/NKlSxUeHq4FCxZIklq1aqUdO3Zo4cKF6tu3b2WVCQAAqpFqdc1NUlKSevfu7bSub9++SkpKKnafvLw8ZWdnOy0AAMC6TJ25Ka/09HQFBgY6rQsMDFR2drYuXryomjVrFtonNjZWc+bMqaoS4QKdpq506fHW1Z7v0uOFzTro0uOVVercNi49Xln7cOXrYdZr4frfKZceDjCNK/829syPdtmxrle1mrmpiJiYGGVlZTmWU6dOmV0SAACoRNVq5iYoKEgZGRlO6zIyMuTv71/krI0k2e122e32qigPAAC4gWo1cxMZGamEhASndVu3blVkZKRJFQEAAHdjarjJycnR/v37tX//fkk/f9R7//79Sk1NlfTzKaXo6F/O4T366KP69ttvNW3aNH399df6xz/+obfffltPPPGEGeUDAAA3ZGq4SU5OVocOHdShQwdJ0uTJk9WhQwfNmjVLkpSWluYIOpIUHh6uDz74QFu3blW7du20YMECvfbaa3wMHAAAOJh6zU2PHj1kGEax24u6+3CPHj20b9++SqwKAABUZ9XqmhsAAIDSEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAICluEW4Wbx4sZo1ayYfHx/deeed2r17d7Fj4+PjZbPZnBYfH58qrBYAALgz08PNmjVrNHnyZM2ePVt79+5Vu3bt1LdvX2VmZha7j7+/v9LS0hzLyZMnq7BiAADgzkwPN3/72980ZswYjRw5Uq1bt9bSpUtVq1YtrVixoth9bDabgoKCHEtgYGAVVgwAANyZqeHm8uXL2rNnj3r37u1Y5+Hhod69eyspKanY/XJyctS0aVOFhoZq4MCBOnz4cLFj8/LylJ2d7bQAAADrMjXc/Oc//1F+fn6hmZfAwEClp6cXuU9ERIRWrFihDRs26M0331RBQYG6du2q77//vsjxsbGxCggIcCyhoaEu7wMAALgP009LlVdkZKSio6PVvn17de/eXe+9954aNmyoZcuWFTk+JiZGWVlZjuXUqVNVXDEAAKhKNcx88gYNGsjT01MZGRlO6zMyMhQUFFSmY3h5ealDhw46fvx4kdvtdrvsdvt11woAAKoHU2duvL291alTJyUkJDjWFRQUKCEhQZGRkWU6Rn5+vg4ePKjg4ODKKhMAAFQjps7cSNLkyZM1fPhw3X777ercubMWLVqk3NxcjRw5UpIUHR2tkJAQxcbGSpLmzp2rLl26qHnz5jp37pzmz5+vkydPavTo0Wa2AQAA3ITp4WbIkCE6c+aMZs2apfT0dLVv316bN292XGScmpoqD49fJpjOnj2rMWPGKD09XXXr1lWnTp20a9cutW7d2qwWAACAGzE93EjShAkTNGHChCK3JSYmOj1euHChFi5cWAVVAQCA6qjafVoKAACgJIQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKW4RbhYvXqxmzZrJx8dHd955p3bv3l3i+LVr16ply5by8fFRmzZt9OGHH1ZRpQAAwN2ZHm7WrFmjyZMna/bs2dq7d6/atWunvn37KjMzs8jxu3bt0tChQzVq1Cjt27dPUVFRioqK0qFDh6q4cgAA4I5MDzd/+9vfNGbMGI0cOVKtW7fW0qVLVatWLa1YsaLI8S+//LL69eunqVOnqlWrVpo3b546duyoV155pYorBwAA7qiGmU9++fJl7dmzRzExMY51Hh4e6t27t5KSkorcJykpSZMnT3Za17dvX61fv77I8Xl5ecrLy3M8zsrKkiRlZ2dfZ/UlO38p36XHK2u9+XkXTXleV3J1D+e9zHktXM0Kv1NmvRb8ThXNrN8pV7LC34Vkjb+Nyn79rx3fMIzSBxsmOn36tCHJ2LVrl9P6qVOnGp07dy5yHy8vL2PVqlVO6xYvXmw0atSoyPGzZ882JLGwsLCwsLBYYDl16lSp+cLUmZuqEBMT4zTTU1BQoJ9++kn169eXzWarlOfMzs5WaGioTp06JX9//0p5jqpghT6s0INkjT6s0INEH+7ECj1I1uijKnowDEPnz59X48aNSx1rarhp0KCBPD09lZGR4bQ+IyNDQUFBRe4TFBRUrvF2u112u91pXZ06dSpedDn4+/tX21/UX7NCH1boQbJGH1boQaIPd2KFHiRr9FHZPQQEBJRpnKkXFHt7e6tTp05KSEhwrCsoKFBCQoIiIyOL3CcyMtJpvCRt3bq12PEAAODGYvppqcmTJ2v48OG6/fbb1blzZy1atEi5ubkaOXKkJCk6OlohISGKjY2VJE2cOFHdu3fXggULNGDAAK1evVrJycl69dVXzWwDAAC4CdPDzZAhQ3TmzBnNmjVL6enpat++vTZv3qzAwEBJUmpqqjw8fplg6tq1q1atWqWZM2fqqaeeUosWLbR+/XrddtttZrVQiN1u1+zZswudDqturNCHFXqQrNGHFXqQ6MOdWKEHyRp9uFsPNsMoy2eqAAAAqgfTb+IHAADgSoQbAABgKYQbAABgKYQbAABgKYSbCoqNjdUdd9yh2rVrq1GjRoqKitLRo0cd23/66Sc99thjioiIUM2aNRUWFqbHH3/c8d1W7qK0PiTpkUce0c0336yaNWuqYcOGGjhwoL7++muTKi5aWfq4xjAM9e/fXzabrdjvJDNDWXro0aOHbDab0/Loo4+aVHHRyvpaJCUlqWfPnvL19ZW/v7/uvvtuXbzo2u/rqajSevjuu+8KvQ7XlrVr15pYubOyvBbp6en67//+bwUFBcnX11cdO3bUu+++a1LFhZWlhxMnTuiPf/yjGjZsKH9/fw0ePLjQzV7NtmTJErVt29Zxk7vIyEht2rTJsf3SpUsaP3686tevLz8/Pz344INu14NUch/u9L5HuKmg7du3a/z48frss8+0detWXblyRX369FFubq4k6YcfftAPP/ygl156SYcOHVJ8fLw2b96sUaNGmVy5s9L6kKROnTopLi5OX331lbZs2SLDMNSnTx/l57v2i96uR1n6uGbRokWV9tUb16OsPYwZM0ZpaWmO5a9//atJFRetLH0kJSWpX79+6tOnj3bv3q0vvvhCEyZMcLrtg5lK6yE0NNTpNUhLS9OcOXPk5+en/v37m1z9L8ryWkRHR+vo0aN6//33dfDgQT3wwAMaPHiw9u3bZ2Llvyith9zcXPXp00c2m03btm3Tzp07dfnyZd1///0qKCgwufpfNGnSRC+88IL27Nmj5ORk9ezZUwMHDtThw4clSU888YT+9a9/ae3atdq+fbt++OEHPfDAAyZXXVhJfbjV+17pX2+JssjMzDQkGdu3by92zNtvv214e3sbV65cqcLKyqcsfXz55ZeGJOP48eNVWFn5FNfHvn37jJCQECMtLc2QZKxbt86cAsugqB66d+9uTJw40byiKqCoPu68805j5syZJlZVPmX5u2jfvr3x8MMPV2FV5VdUH76+vsbKlSudxtWrV8/45z//WdXllclve9iyZYvh4eFhZGVlOcacO3fOsNlsxtatW80qs0zq1q1rvPbaa8a5c+cMLy8vY+3atY5tX331lSHJSEpKMrHCsrnWR1HMet9zj38mWcC1abd69eqVOMbf3181aph+78RildZHbm6u4uLiFB4ertDQ0KosrVyK6uPChQv685//rMWLFxf7XWTupLjX4q233lKDBg102223KSYmRhcuXDCjvDL7bR+ZmZn6/PPP1ahRI3Xt2lWBgYHq3r27duzYYWaZJSrt72LPnj3av3+/283M/lZRfXTt2lVr1qzRTz/9pIKCAq1evVqXLl1Sjx49TKqyZL/tIS8vTzabzenmcT4+PvLw8HDb36n8/HytXr1aubm5ioyM1J49e3TlyhX17t3bMaZly5YKCwtTUlKSiZWW7Ld9FMW0970qjVIWlZ+fbwwYMMDo1q1bsWPOnDljhIWFGU899VQVVlY+JfWxePFiw9fX15BkREREuPWsTXF9jB071hg1apTjsdx45qa4HpYtW2Zs3rzZOHDggPHmm28aISEhxh//+EeTqixdUX0kJSUZkox69eoZK1asMPbu3WtMmjTJ8Pb2Nr755hsTqy1aWf6+x40bZ7Rq1aoKqyq/4vo4e/as0adPH0OSUaNGDcPf39/YsmWLSVWWrKgeMjMzDX9/f2PixIlGbm6ukZOTY0yYMMGQZIwdO9bEags7cOCA4evra3h6ehoBAQHGBx98YBiGYbz11luGt7d3ofF33HGHMW3atKous1TF9fFbZr7vEW5c4NFHHzWaNm1qnDp1qsjtWVlZRufOnY1+/foZly9fruLqyq6kPs6dO2d88803xvbt243777/f6Nixo3Hx4kUTqixdUX1s2LDBaN68uXH+/HnHOncON6X9Tl2TkJDg1qcIi+pj586dhiQjJibGaWybNm2MGTNmVHWJpSrttbhw4YIREBBgvPTSS1VcWfkU18eECROMzp07G//+97+N/fv3G88884wREBBgHDhwwKRKi1dcD1u2bDFuuukmw2azGZ6ensZDDz1kdOzY0Xj00UdNqrRoeXl5xrFjx4zk5GRjxowZRoMGDYzDhw9Xu3BTXB+/Zvb7HuHmOo0fP95o0qSJ8e233xa5PTs724iMjDR69erltmHAMErv49fy8vKMWrVqGatWraqCysqnuD4mTpzo+B/ftUWS4eHhYXTv3t2cYotRntciJyfHkGRs3ry5Ciorn+L6+Pbbbw1JxhtvvOG0fvDgwcaf//znqiyxVGV5LVauXGl4eXkZmZmZVVhZ+RTXx/Hjxw1JxqFDh5zW9+rVy3jkkUeqssRSleW1OHPmjHH27FnDMAwjMDDQ+Otf/1pF1VVMr169jLFjxzr+kXKt9mvCwsKMv/3tb+YUVw7X+rjGHd73uOamggzD0IQJE7Ru3Tpt27ZN4eHhhcZkZ2erT58+8vb21vvvvy8fHx8TKi1ZWfooah/DMJSXl1cFFZZNaX3MmDFDBw4c0P79+x2LJC1cuFBxcXEmVFxYRV6La30EBwdXcnVlV1ofzZo1U+PGjQt9nPebb75R06ZNq7LUYpXntVi+fLn+8Ic/qGHDhlVYYdmU1se167V++yk1T09Pt/mkUXleiwYNGqhOnTratm2bMjMz9Yc//KEKKy2/goIC5eXlqVOnTvLy8lJCQoJj29GjR5WamlrstSzu5Fofkhu975kSqSxg3LhxRkBAgJGYmGikpaU5lgsXLhiG8fOU3J133mm0adPGOH78uNOYq1evmlz9L0rr48SJE8bzzz9vJCcnGydPnjR27txp3H///Ua9evWMjIwMk6v/RWl9FEVudlqqtB6OHz9uzJ0710hOTjZSUlKMDRs2GDfddJNx9913m1y5s7K8FgsXLjT8/f2NtWvXGseOHTNmzpxp+Pj4uM3ptbL+Ph07dsyw2WzGpk2bTKq0ZKX1cfnyZaN58+bGXXfdZXz++efG8ePHjZdeesmw2WzFXkdR1cryWqxYscJISkoyjh8/brzxxhtGvXr1jMmTJ5tYdWEzZswwtm/fbqSkpBgHDhwwZsyYYdhsNuOjjz4yDOPnU25hYWHGtm3bjOTkZCMyMtKIjIw0uerCSurDnd73CDcVJKnIJS4uzjAMw/j444+LHZOSkmJq7b9WWh+nT582+vfvbzRq1Mjw8vIymjRpYvz5z382vv76a3ML/43S+ihuH3cKN6X1kJqaatx9991GvXr1DLvdbjRv3tyYOnWq00dg3UFZX4vY2FijSZMmRq1atYzIyEjj008/NafgIpS1h5iYGCM0NNTIz883p9BSlKWPb775xnjggQeMRo0aGbVq1TLatm1b6KPhZipLD9OnTzcCAwMNLy8vo0WLFsaCBQuMgoIC84ouwsMPP2w0bdrU8Pb2Nho2bGj06tXLEWwMwzAuXrxo/OUvfzHq1q1r1KpVy/jjH/9opKWlmVhx0Urqw53e92yGYRiumAECAABwB1xzAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwA6BaSEpKkqenpwYMGGB2KQDcHF+/AKBaGD16tPz8/LR8+XIdPXpUjRs3NrskAG6KmRsAbi8nJ0dr1qzRuHHjNGDAAMXHxzttf//999WiRQv5+Pjonnvu0euvvy6bzaZz5845xuzYsUN33XWXatasqdDQUD3++OPKzc2t2kYAVAnCDQC39/bbb6tly5aKiIjQQw89pBUrVujapHNKSor+9Kc/KSoqSl9++aUeeeQR/c///I/T/idOnFC/fv304IMP6sCBA1qzZo127NihCRMmmNEOgErGaSkAbq9bt24aPHiwJk6cqKtXryo4OFhr165Vjx49NGPGDH3wwQc6ePCgY/zMmTP13HPP6ezZs6pTp45Gjx4tT09PLVu2zDFmx44d6t69u3Jzc+Xj42NGWwAqCTM3ANza0aNHtXv3bg0dOlSSVKNGDQ0ZMkTLly93bL/jjjuc9uncubPT4y+//FLx8fHy8/NzLH379lVBQYFSUlKqphEAVaaG2QUAQEmWL1+uq1evOl1AbBiG7Ha7XnnllTIdIycnR4888ogef/zxQtvCwsJcVisA90C4AeC2rl69qpUrV2rBggXq06eP07aoqCj93//9nyIiIvThhx86bfviiy+cHnfs2FFHjhxR8+bNK71mAObjmhsAbmv9+vUaMmSIMjMzFRAQ4LRt+vTp2rZtm95++21FREToiSee0KhRo7R//35NmTJF33//vc6dO6eAgAAdOHBAXbp00cMPP6zRo0fL19dXR44c0datW8s8+wOg+uCaGwBua/ny5erdu3ehYCNJDz74oJKTk3X+/Hm98847eu+999S2bVstWbLE8Wkpu90uSWrbtq22b9+ub775RnfddZc6dOigWbNmca8cwKKYuQFgOc8995yWLl2qU6dOmV0KABNwzQ2Aau8f//iH7rjjDtWvX187d+7U/PnzuYcNcAMj3ACo9o4dO6Znn31WP/30k8LCwjRlyhTFxMSYXRYAk3BaCgAAWAoXFAMAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEv5f2KjQSZIJdtvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2args -> Any two columns of data\n",
        "\n",
        "pd.crosstab(data['Age'], data['Gears'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "1Qd23xKXPCVX",
        "outputId": "8f090ac4-ad1c-426e-ab2c-5a12463e4a19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gears  Five\n",
              "Age        \n",
              "22        2\n",
              "23        3\n",
              "24        2\n",
              "25        1\n",
              "26        2\n",
              "27        3\n",
              "28        3\n",
              "29        4\n",
              "30        5\n",
              "32        1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83944092-08e2-40f7-a2d8-75c8e791c43b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\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>Gears</th>\n",
              "      <th>Five</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83944092-08e2-40f7-a2d8-75c8e791c43b')\"\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-83944092-08e2-40f7-a2d8-75c8e791c43b 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-83944092-08e2-40f7-a2d8-75c8e791c43b');\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": 259
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Multivariate Plots**\n",
        "\n",
        "- pairplot -> sns -> 1arg-data, 2arg-hue"
      ],
      "metadata": {
        "id": "WbgF8DH97pOk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "id": "LK9omiUCSlUC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2args -> data, Hue(Only Categorical Columns are feasible)\n",
        "\n",
        "sns.pairplot(data.iloc[:, 1:4], hue=\"Age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "QyS-u-eQPmkU",
        "outputId": "c714b761-4084-42dc-bd00-9f3a02c50680"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f0d7c696fa0>"
            ]
          },
          "metadata": {},
          "execution_count": 268
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 567.625x500 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D6QoTSMyPwwv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}