ML_2_LinkedIn.ipynb

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      "name": "ML_2-LinkedIn.ipynb",
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    {
      "cell_type": "markdown",
      "source": [
        "# Introduction  \n",
        "Lets have an example of Story telling with data and using multiple ML models to predict various parameters giving full flexibilty to the business."
      ],
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        "id": "b0rmNMVJaVmP"
      }
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      "metadata": {
        "id": "NcMop5O-A16T"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.get_dataset_names()"
      ],
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        },
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            "text/plain": [
              "['anagrams',\n",
              " 'anscombe',\n",
              " 'attention',\n",
              " 'brain_networks',\n",
              " 'car_crashes',\n",
              " 'diamonds',\n",
              " 'dots',\n",
              " 'exercise',\n",
              " 'flights',\n",
              " 'fmri',\n",
              " 'gammas',\n",
              " 'geyser',\n",
              " 'iris',\n",
              " 'mpg',\n",
              " 'penguins',\n",
              " 'planets',\n",
              " 'taxis',\n",
              " 'tips',\n",
              " 'titanic']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading the dataset"
      ],
      "metadata": {
        "id": "laNvs-PyFmva"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tips = sns.load_dataset(\"tips\")"
      ],
      "metadata": {
        "id": "NrTjwr6hBeGd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tips"
      ],
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          "height": 424
        },
        "id": "nYRADRkjByou",
        "outputId": "695d1f06-34ae-4b15-d5b7-fa55d998302c"
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              "     total_bill   tip     sex smoker   day    time  size\n",
              "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
              "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
              "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
              "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
              "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
              "..          ...   ...     ...    ...   ...     ...   ...\n",
              "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
              "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
              "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
              "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
              "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
              "\n",
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              "pandas.core.frame.DataFrame"
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            "text/plain": [
              "       total_bill         tip        size\n",
              "count  244.000000  244.000000  244.000000\n",
              "mean    19.785943    2.998279    2.569672\n",
              "std      8.902412    1.383638    0.951100\n",
              "min      3.070000    1.000000    1.000000\n",
              "25%     13.347500    2.000000    2.000000\n",
              "50%     17.795000    2.900000    2.000000\n",
              "75%     24.127500    3.562500    3.000000\n",
              "max     50.810000   10.000000    6.000000"
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              "      <th>min</th>\n",
              "      <td>3.070000</td>\n",
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              "      <th>25%</th>\n",
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              "          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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Visualisation"
      ],
      "metadata": {
        "id": "Om3rYz97afim"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Here we do a scatterplot and find the relatinship to be approximately linear between tip and total bill\n",
        "sns.scatterplot(x='total_bill', y='tip', data=tips)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Nm2-sqnVB-f5",
        "outputId": "d8bd4fb0-4244-4ed2-aa54-a84fb2f4365b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6aa7dbbb10>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To gain more insight we catagorize tip vs total bill based on gender of customer\n",
        "sns.scatterplot(x='total_bill', y='tip', data=tips, hue='sex')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "7hWM0Px-CNk4",
        "outputId": "5db9a2c7-1998-4831-fc91-125b814860d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6aa4cfb610>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now we plot a bar plot to compare day wise earnings\n",
        "sns.barplot(x='day', y='tip', data=tips)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "WJKLCm_5CYOj",
        "outputId": "28562ab1-98a9-4b0e-c5a2-f0dd7eb4d698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6aa47d8210>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The following grps are variations of Histograms which give distribitions of data\n",
        "sns.distplot(tips['tip'], kde=True, bins=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "zXfwAe8WCjq_",
        "outputId": "04422626-a938-4b66-a1fb-8e3841924670"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6aa47171d0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(tips['tip'], kde=True, bins=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "jtTMI-fYDweI",
        "outputId": "f75c198d-f854-4784-d315-1860e0ba287d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f6aa46c79d0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(tips['tip'], kde=True, bins=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "FWmXF7rlDY5w",
        "outputId": "b3c86657-aa9b-4eff-9a90-c108383eb32b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a9fd61b50>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pair plot is helpful to visualise variance and graph of parameters against each other\n",
        "sns.pairplot(data=tips)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "HoqifKZ3E105",
        "outputId": "a17807c3-d0f1-4807-ad86-02a150759f04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f6a9fd61190>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### One Hot Encoding to fix dataset"
      ],
      "metadata": {
        "id": "61U8v1lZarEX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before applying any ML algorithm we need to perform one hot encoding to convert groups and their names into numericals"
      ],
      "metadata": {
        "id": "IshXjTSUM-kW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are going to keep the name of all sub groups so that it is easy for us to enter values in future."
      ],
      "metadata": {
        "id": "DiE9pTC3Pngf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "days = pd.get_dummies(tips['day'], drop_first=False)\n",
        "days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2i572cawNOG-",
        "outputId": "61ae0445-55f7-473c-bcc6-56df3110fa77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Thur  Fri  Sat  Sun\n",
              "0       0    0    0    1\n",
              "1       0    0    0    1\n",
              "2       0    0    0    1\n",
              "3       0    0    0    1\n",
              "4       0    0    0    1\n",
              "..    ...  ...  ...  ...\n",
              "239     0    0    1    0\n",
              "240     0    0    1    0\n",
              "241     0    0    1    0\n",
              "242     0    0    1    0\n",
              "243     1    0    0    0\n",
              "\n",
              "[244 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-feff21a4-d187-4dfc-a930-a7eb411a00ab\">\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></th>\n",
              "      <th>Thur</th>\n",
              "      <th>Fri</th>\n",
              "      <th>Sat</th>\n",
              "      <th>Sun</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>244 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-feff21a4-d187-4dfc-a930-a7eb411a00ab')\"\n",
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    {
      "cell_type": "code",
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        "time = pd.get_dummies(tips['time'], drop_first=False)\n",
        "time"
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      "execution_count": null,
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              "     Lunch  Dinner\n",
              "0        0       1\n",
              "1        0       1\n",
              "2        0       1\n",
              "3        0       1\n",
              "4        0       1\n",
              "..     ...     ...\n",
              "239      0       1\n",
              "240      0       1\n",
              "241      0       1\n",
              "242      0       1\n",
              "243      0       1\n",
              "\n",
              "[244 rows x 2 columns]"
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              "<p>244 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "smoke = pd.get_dummies(tips['smoker'], drop_first=False)\n",
        "smoke"
      ],
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        "colab": {
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Yes  No\n",
              "0      0   1\n",
              "1      0   1\n",
              "2      0   1\n",
              "3      0   1\n",
              "4      0   1\n",
              "..   ...  ..\n",
              "239    0   1\n",
              "240    1   0\n",
              "241    1   0\n",
              "242    0   1\n",
              "243    0   1\n",
              "\n",
              "[244 rows x 2 columns]"
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              "      <th>...</th>\n",
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              "      <th>239</th>\n",
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              "      <th>243</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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              "<p>244 rows × 2 columns</p>\n",
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              "\n",
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              "          const element = document.querySelector('#df-e1520dd5-2ed7-4252-9f83-b439a9314eaa');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sex = pd.get_dummies(tips['sex'], drop_first=False)\n",
        "sex"
      ],
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        "colab": {
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          "height": 424
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Male  Female\n",
              "0       0       1\n",
              "1       1       0\n",
              "2       1       0\n",
              "3       1       0\n",
              "4       0       1\n",
              "..    ...     ...\n",
              "239     1       0\n",
              "240     0       1\n",
              "241     1       0\n",
              "242     1       0\n",
              "243     0       1\n",
              "\n",
              "[244 rows x 2 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Male</th>\n",
              "      <th>Female</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now remove the old columns with strings"
      ],
      "metadata": {
        "id": "KRuycy8tTnPu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_tips=tips.drop(['sex','day','time','smoker'], axis=1, inplace=False)\n",
        "new_tips"
      ],
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        "colab": {
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          "height": 424
        },
        "id": "1q-yxTY-Tweu",
        "outputId": "dbb023f0-46ef-47c9-e4ea-b6d5add19dcf"
      },
      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "     total_bill   tip  size\n",
              "0         16.99  1.01     2\n",
              "1         10.34  1.66     3\n",
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              "3         23.68  3.31     2\n",
              "4         24.59  3.61     4\n",
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              "241       22.67  2.00     2\n",
              "242       17.82  1.75     2\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we concate the above columns to make data ready to feed it to a ML algorithm"
      ],
      "metadata": {
        "id": "RQo7b2NQP8i-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_tips = pd.concat([new_tips, sex, smoke, days, time], axis=1)\n",
        "new_tips"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "VqiPVpNTO3m2",
        "outputId": "e3c8022c-a50d-4b4a-bd7e-2f02a2ca831b"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     total_bill   tip  size  Male  Female  Yes  No  Thur  Fri  Sat  Sun  \\\n",
              "0         16.99  1.01     2     0       1    0   1     0    0    0    1   \n",
              "1         10.34  1.66     3     1       0    0   1     0    0    0    1   \n",
              "2         21.01  3.50     3     1       0    0   1     0    0    0    1   \n",
              "3         23.68  3.31     2     1       0    0   1     0    0    0    1   \n",
              "4         24.59  3.61     4     0       1    0   1     0    0    0    1   \n",
              "..          ...   ...   ...   ...     ...  ...  ..   ...  ...  ...  ...   \n",
              "239       29.03  5.92     3     1       0    0   1     0    0    1    0   \n",
              "240       27.18  2.00     2     0       1    1   0     0    0    1    0   \n",
              "241       22.67  2.00     2     1       0    1   0     0    0    1    0   \n",
              "242       17.82  1.75     2     1       0    0   1     0    0    1    0   \n",
              "243       18.78  3.00     2     0       1    0   1     1    0    0    0   \n",
              "\n",
              "     Lunch  Dinner  \n",
              "0        0       1  \n",
              "1        0       1  \n",
              "2        0       1  \n",
              "3        0       1  \n",
              "4        0       1  \n",
              "..     ...     ...  \n",
              "239      0       1  \n",
              "240      0       1  \n",
              "241      0       1  \n",
              "242      0       1  \n",
              "243      0       1  \n",
              "\n",
              "[244 rows x 13 columns]"
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>29.03</td>\n",
              "      <td>5.92</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>27.18</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>22.67</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>17.82</td>\n",
              "      <td>1.75</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>18.78</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>244 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a351613-01a1-4b0a-9ba2-f9a3d23601bd')\"\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-0a351613-01a1-4b0a-9ba2-f9a3d23601bd 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-0a351613-01a1-4b0a-9ba2-f9a3d23601bd');\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": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Vizualising again with pair plot"
      ],
      "metadata": {
        "id": "mDtYhAUPUqcN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data=new_tips)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qJ8ibGMUUqqF",
        "outputId": "8400f13b-0513-4b0a-a77c-1eea32f92564"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f6a9fb3ad10>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2340x2340 with 182 Axes>"
            ],
            "image/png": 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WRS9RYxduqeUtis/g6KQOfOfnWTXhwHd+rqHRyQX9vrly1utYgFfIWwQN/QH85kftmytvB2KTuuuJo1nx7nriqAZi1Frkz+tzbPoDBM0b9QfD8Ul9cFuHvvjUcX3h0DH93feP64PbOnQ2Tt4if16PK86Vt6+ccY/1yhnuoUf+vB6XfqNaS18LL5jsawt1nQrFxY/aV+hJPqclnXYc50cX/v8hTU/6yeI4zr2O42xzHGdbc3Oz0QMMioU2j/l+gLfWRRWNZKdLNBJSS23Ug6MvTuQtgmap5WwhJttIhZlwQ41duKWWtyg+IxMp15pwfiK1oN83V856HQvwCnmLoKE/gN/8qH1z5W08mXaNN55MLzgeSo/X59j0BwiaN+oPystCuvtQ9sWXuw8dVaSs0JdQECRejyvOlbcnz8ZdY506yyQf5M/rcek3qrX0tfCCyb62UNepUFz8qH0F7VAdx+mX9EvLstZd+NJOST8r4CEF1kKbx3w/wDsbq3Xgpq2Zn51Z+rGzsdqDoweA2Qq1uk0hJtxQY4Glqy4aca0JddFIoGMBXiFvAZQi07Vv9fJq13gdyzlfQP5MnmPTHyCIxpNTOS6+TBXoiBBEJscVq8vDrrGqysOex0LxMj0uTV8LL5jsa9mFAV7wo/YthWnofyTpq5ZlPS9pq6S/LPDxBIZtOzo+NKanf3FGVeVhrW6szPp+Ps1jrg/wkCU9/YszOj40Jtt2FApZ2tXTpm/t3a6v7XmbvrV3O3u7AvBVa11Uqxsr9ZF3Xa6P7pj+Z3Vjpe+r2xRiwk0oZOm6Da16cM/V+p+/faUe3HO1rtvQSo0FloDWugrtf/farJqw/91r1VpXEehYgFda6yq0b2d3Vt7u29lN3mLeLj6/nTkPLYZYKE6ma9+apmp97gPZ5yif+8BWrWniYgjyZ/LCM30tgijXOFRrHassI38mxxU5F4MXQiFL165r0VdueZu+8Jtv1ldveZuuXdfi27g0fS28YLavZRcGLJ4ftW9BU3oty7pS0jWSHEn/5jjOTxZ6AI7jPCtp20IfX6pm9gCcWSIsGgnpzt0b9flDR3VyeCLv5nFm8s76vds1OJpQc01UrwyPaddd38/83gM3bc1M6OlqrlFXc42hZwmglHU0VOmPdnTrkw8fyapzHQ1Vvsa9tC621EbV2Vjt64Qb23b07RcHsmr6xbUXQOFcVleptvqo9ryjS7YjhSyprT6qy+oq3/jBSzgW4JWVy6q0sqEyK29XNlRq5TJ/P69RXNzOb/3qhUzGQvEyXfts25Ht2FnxbMfO3JQF5GPmwvOl9c+PC8/0tQiilfWV+si7unXrN18fh7rjxo1aWU/eIn8mxxU7lleru7Umq9Z2t9awIgrmJZ229cgLr80ag9+9ZYXCYe/XiaCvhRdM9rUmY6F4+VH75j3Jx7KsWyV9QNI/XfjSly3L+rrjOHcu6AiwIG57AH7y4SN6cM/VmkhNzat5vHjyzvGhMX30H346a2/B9Xu3M7kHgFGnzo1nTi6k1+vclR0Nvtcj05Mac+3rSu0FCu/FgZg+/tDzWcuyRiMhrd5TpS2rGgIbC/DKqXPj+uy3X9b1m1fIsqQpW/rst19Wz2X1fIYhbyZ7IfoueMF07evtG9GfuvQIHcvpEZA/kxee6WsRRC8OxDITfKTpHuHWbx7RutYa8hbzYmpcMRSytGNdq7qaaozdqIji09s34joG393iT+2jr4UXTPa1hbgpHMXHj9q3kJV8fkvSFsdxEpJkWdZfSXpWEpN8DMq1B+BEakpXdzV5/nsHRxMMeAIwqpTqUSk9VyBo+kbc35/9IwltWRXcWIBXBmIJnRye0D1PHsv6Op9hmA+TvRB9F7xguvbRI8Arpi48k7MIIvIWQcTuC1gs07WPWguvmKx/1Foslh+1byFrrb0m6eKN5iokvbqw8Fgov/YAZG9BAEtFKdWjUnquQNC011e6vj/b6r1/f5qMBXiFzzB4wWQekbPwguk8okdA0JCzCCLyFkApMl37qLUASpEftW8hk3xGJPValvX3lmV9WdIRSecty7rbsqy7F3wkmJeZPQBnEsKrPQD9+r0AMF+lVI9K6bkCQdPTXqc7d2/Men/euXujetrrAx0L8AqfYfCCyTwiZ+EF03lEj4CgIWcRROQtgFJkuvZRawGUIj9q30K26/rnC//M+O6Co2PB/NoDkL0FASwVpVSPSum5AkETDoe0e8sKdbfUqH8kobb6qHra6xUOL2Su/NKJBXiFzzB4wWQekbPwguk8okdA0JCzCCLyFkApMl37qLUASpEftW/ek3wcx7lvwdHgKb/2AFzI77VtRyeG4xqIJdRaxyApAG+U4l6njlPoIygufD7BC+FwSFtWNRjZG9xkLBSvQtU+PsOwGCb7vlLsMeEfU7UvFLJUG41oPDml2miEnhZLHjmLICJvAZQi07WPWgugFHld+/Ke5GNZ1kHHcW6yLOsFSbOGMBzH2byoIzGsFC76zfc5LvQ1sW1Hh14e0POnR2Q7UpklbVpZrx3rWovuNQVgVinUamn6eX7/2KBGJ6YUn0xrOJ7UL8/Ftf3yFl+fb7G/voV6XVF80mlbvX0j6htJqL2+Uj3tdb7dYWQyFoqT6dpHrYVXqLUIkkLU2h++MqT0lHQ2nlJqylZ/bFxXr2mm1mJeTJ0D2rajw6fOaGrK0tDopCTpTHxC2zqayFksWdRaeMXkeBt9LRaLvhZBZbLWFvt1FPjPj9o3n5V89l3494uS/vSir1uSPrOg6AVi244e7+3X/oPPKpGyM3un7+ppK5o35Xyf42Jek1Nn4zo6MKZ7v3c889h9O7t1eXONOpu4MxLAwpRCrZ7xy3Nx9Y9M6rZHejPP9fYbevTLc3GtbvSnjpbC6/vLc3G9dn5Stz/6+ut623v8fV1RfNJpWw8/96o++fCRTB7duXujdm9Z4fnAmclYKF6ma18hPsNQfKi1CBrTte/0+bh+eTYxK97Khrg6llNrkR+T54CvjsR1YmhCt16Us3fc0KP2+rhWNZCzWJpOn4/r9NnErLw9Ta3FPJistfS18ILpMQT6WnjBZK0theso8J8ftS/vST6O4/Rd+M/LHcc5efH3LMtav6DoBXJiOJ55M0pSImVr/8FntX7v9iW1XLfbzEBJbzhb0LYdvfDqedfnuGLP1RpPTs167Ctn3F+TlXuuVvyin3eLPxCb1F1PHM167F1PHNXmlfXqWL6w2Yy5ZkUuZrYkMy2BYClkrTZdLwZjr18gkKaf622P9OqBD1/l2wXSE8NxffrxF3XLNV2yLjy1Tz/+ota31fr6+pq8w2hgZFL/81+PZT3H//mvx/Smpi1ceEbeel8byQyYSdPvz08+fETdzTXa0tEQ2FgoXqZr32BsUl8/fEqfef8WTUymVVUR1n0/OK6upmpqLfLW25ej/rXUaMsqj2utwVgoXqZr38UTiqTXzxfu//BVXAxB3k4Mx/XwT0/pf/3OW3QuntLy6oi+8sNXfDkH7Ds/mZkoIU3n7K0XcpZJPliq+kcmddCltnc2VVNrkTeT45n0tfDCQCzHGEKzP2MI9LXwgsm+1mQsFC8/at98tuv6Q0n/r6Quy7Kev+hbtZL+bUHRC2Qglsi8iDMSKVuDo4kl84bMNTOwPGzpo//w05yzBWce91J/zPU5PvHSoO5+4tisx548G3f9+Zf6R/Xxb7ygaCSkL/zmm5VMO7OOqTIScn3sq+cm9Hhv/7xnM+Z67tdtaNW3XxxY0GxJZloCwdN/3r1W94/4W6sLUS+Gx5Ouz/XseNKXeJI0HJ/UB7d16O5DRzPPc++Obg3HJ317fU3fYTQ6mXJ9jmPJlOexULxeG3GvRX2xhLYEOBaKl+nal5xK631XdujjDz33+l1/1/coOTXlSzwUp/4c5+gDsclAx0LxMl37hkYnXfP2zBh5i/yNTaa0c0O7/uCBZ7LuHh2b9L5HODPmnrPD5CyWsImUe22fSKULfWgIkP4c5/UDMe/HMwdj7rV2cJRai/zFEu5jCKMJf8YQ6GvhBZN9rclYKF5+1L75XFH7B0nvkfTIhX/P/PMWx3F+e8FHUACtdVFFI9lPPRoJqaU2WqAjmi3XjO/nT4/M+tqJ4fisx9mOXJ/j1IX8ufSx1eVh15+vLA9nfv750yOux7S8usL1sYOjk7OObzHPvbfPPX4+vz/X75zvsQEwJxK2XGtLpMzfiXmFqBfLKiOuz7UuGvEtpiUrc/ImTT/Puw8dlSX/Xt9cdxj19o34Eq82GnF9jjUV/r2uKD6NNeWu78/lVeWBjoXiZbr2RUJh3f5Y9p0otz/Wq0iozJd4KE4NVe71b1mV93lrMhaKl+na11zrPu7SVF3hSzwUp5m7RS+9e/TSwWYvNNW452wjOYslrDLiXtsrw3nfJw2oIhzKMZ7p/c1t9VXu44n1lfS1yF9dhfsYQq1PYwj0tfCCyb7WZCwULz9qX96dheM4I47jnHAc5zccxzl50T9nFxy9QDobq3Xgpq2ZF3NmlYaZ7aiWglyrDdmOZn1tcDQx63HfeOa09u7oznqOe3d0659+ctr1sa11Fdq3M/vn9+3s1ulz45mftx25HlNqamrW6/nH167VV390atbxLea59+WYBZ/P759r9SYAS9O5iZRrHTs/4e8M6ULUi4lU2vW5Tqb9WwXhbNx95vDZuH+rB+Wq4/0j/ry2uWZHD3FnCOZhJEctGvHhjiaTsVC8TNe+gRzxuHsU8xFLuNe/mA/1z2QsFC/TtS9n3nL3KOZhINeKDz6sZHZuvDDn88Bi5HyP0NdiHkYSSdf6N5rwfrwtV609N06tRf7O5BgjPhOnr8XSZbKvNRkLxcuP2leS09BDIUu7etq0fu92DY4m1FIbVWdj9ZLatmlmtaGLC0c0EtKlh3jpCkQzj+sbSeiBH57ULdd0qSwkvaO7WR/7+rPqu+hC6sWP7Vhere7WGu15R5dsRwpZUnV5mf7mX49nfr7MkusxLa+u0JUdy7Viz9V64qVBTdnS3//ghPpGEgtaISnXc2+vr3T9ej6/P9fvXEqrNwHI1lhVrv96+FRmP2DHkR48fEqf+8BWX+MWol40Vkf14OGfzXqun//1N/sWs7XOvaa21vl310SuOt5W789r23JhdvSsv2UNd4Ygf/WVET3oUov++/u930DLZCwUL9O1r7UuRzwfP09QfBoqy13rnx99n8lYKF6ma1991L1H+Cw9AuahLUfe+nEO2FBFX4vgoa+FF3KN8d3twxgftRZeyDWG0OzTGAJ9Lbxgsq81GQvFy4/a5/0agQERClnqaq7R1V1N6mquWVITfKTcqw1tXlk/5wpEFz+ubyShLz51XOvb6rR15TJ9YteGnI8NhSztWNeq3VtX6JrLG3XjlhW6rKFS58aTmZ/ftLI+5wpIoZClTSuWaX1bnb741PHMBJ/5rJBk246OD41pOD6pT79v86w4Pe11C16BKQirNwHItumyen3kXd364lPH9YVDx/TFp47rI+/q1ubL6n2NW4h6cUVbnfbuXJv1XPfuXKsr2v17rj3tdbpz98as53nn7o3qKaKYm9rrdccN2fHuuGGjNvn4HFF8NrbV6iPvvKQWvbNbG9tqAx0Lxct07etpq3GN19NW40s8FKdNl9Xro5f0fR/1qe8zGQvFy3Tt62mrdT036qFHwDyY7BHWt1W79rXr2xiHw/zMjBc//YszOj40JvvSZe49dEVbtet75AryFvPQ016nP9qRXf/+aEe3L2NfjCHACxvbTI8h0Ndi8Uz2tVxjgBf8qH0luZJPEORabUiSvvUGKxCVh62sFXnKw1YiA6d8AAAgAElEQVReqxfNTHzqap4elFrTVD0rlqScv2MmxhX7tmsgNql4Mq3Vy/Of4PN4b7/2H3xWiZSt1Y2Vuvd3tilSZqm17vU4C12BKQirNwHIVl5ept2bL1NXU7UGYgm11kW1+bJ6lZeX+Ro3FLJ03YZWPbjnavWNJNReX6me9jrf60V52SW1u8zfeOFwSLu3rFB3S436RxJqq4+qp71e4bB/839Nx4xGw3rPxjZ1NlVpIDap1roKbWqrUzRK+4P8lUfCaq0r172/8xadG0+poSoix3FUHvE+j0zGQvEyXfsqK8q1aVWN7v+9qzQwOv15XVsZUmVFuS/xUJzC4ZCWVUWyeqFlVRFfegSTsVC8TNe+8khYrbXTPcLZeErLq+kRMH/RaFg3bGrXmot7hPZ6X3qEmooKvaWzNus90lhTppoK7nhG/i4dL565CWtXT5svYzTRSLk6m6K67/euyowfW5ataIS+FvkLh0O6YdNl6mysVn8sofa6qDZd5s/YV7Qios6mqL78obdqaGxSzTUVKgs5ilZEPI+F4lVeXqbO5uk8OjM2qaaaCpWVOb6NwdPXwgsm+1qTsVC8/Kh9ZOASdumkmxluX5txYjiuj/7DT2ctG/atvdszj8v1WC/iS9LP+kbnffJ1YjieeYwknRye0J4HDmeO+42OaTHPB8DSVV5epm2dy43GtG1H335xwNggkiT19o3oTx96flbt7lhepS2rGnyJKU0PPGxZ1aAtq3wLUdCYtu3oyWNnjP4tUXx6+0b0B1/5yaz354N7rvb8/WkyFoqX6dp36mxc331pWHc9cTQTb9/OblWGI+psou9Gfk4Mx/VH/5j7PDaosVC8TNe+lwZi+lnf6Kx4jTUV2rhimefxULyi0bDeuqbR9zinzsb17d4zs3L2320M0x8gb5eOFydStvYffFbrffrM7u0b0c1fOsz5GBbFth39y8uDRs7HTp2N65mTI7NqbUttJbUWeTsxHNfNX5xd+/w6P6KvhVdM9bWmY6E4+VH7uFWtyAzEElkfxtL0CdDgaMJI/FwnXyeG43M+rtDHDQAzFlrHFqNvxL0G9o9QAxejEH9LFB+T709qAbxguvYNxCYzJ6gz8e564qgGYpO+xENxMnk+yLknvGC69p0bT7nGOzee8iUesFj0B/CC6c9szsfgBZPnY9RaeMF0raWvBVCK/Kh9rORTZFrroopGQrNm3bbURhf8O23b0YnheGa7nLm2uZqrIZhr1q8fxw0AC7HQOrYY7fWVWt1Yqes3r5B1obw++tyraqunBi5GIf6WKD4m35/UAnjBdO2LJ9Ou8caTac9joXiZPB/k3BNeMF37Eqkp13iJ1JQv8YDFoj+AF0x/ZnM+Bi+YPB+j1sILrXVR19rnV62lrwVQivyofUzyKTKdjdU6cNPWWctBdjZWL+j3zXfv44WefHl93ACwUIW48LOhtVYfeVe3bv3mkUwNvOPGjdrQWudbzFLARTx4weT7k1oAL5iufasbqlzjrWqo8iUeipPJ80HOPeEF07Wvs7HaNR55i6WK/gBeMP2ZzfkYvGDyfGz1cvf+oGM5/QHy19FQpT/a0a1PPvx67btz90Z10NcCgGf8qH1s11VkQiFLu3ra9Pi+7Xpwz9X669+6Uutaaxf8++a7vOTMyVc0Mp1a+Z58zRz3t/Zu19f2vE3f2rvdl31qAeCNLLSOLcbpkYnMIJI0XWtv/eYRnR6Z8C1mKSjE3xLFx+T7k1oAL5iufWVllvbt7M6Kt29nt8Jl9PHIn8nzQc494QXTtS+SI16EWosliv4AXjD9mc35GLxg8nxsTZN7rDVNjHshf6fOjWcm+EjTte+TDx/RqXPjvsSjrwVQivyofazkY8h8trxa7ONt29GpsxM6fPKsbEe6/dFefWLXhgWdBM13ecmZk6/1e7drcDShltr8n2soZKmruYYtVABkJJNTev61EfXHEmqvi2rTZfUqLy/zNeZi6thC5aq1AzG2lVqMUMjSdRta9eCeq9U3klB7fVQ97fVcxMO8mHx/UgvgBdO1r28kofufPqlbrumSZUmOI93/9EltXbVMnU3kLfJn245GEymdH0+pMhKWbTu+5a3JWChOpmsftRZBQ87Ca47jfwzOx+CFUMjSteta9JVb3qb+WEJtdVFtvsyf87FQyNKO7mY98OGr1B+bVFtdhTYx7oV5Ml376BEAlCI/ah+TfAyY75ZXi3m8bTv630f69IlvPJ/52b07uvXpx1/U+rbaeX8oL2R5SSbrAPBCMjmlh59/bdYyybs3X2Zkoo/JOlZVHnattVU+P89iZ9uOvv3iwII/fwFJqjb4/jQZC8XLdO2rKg/r3HhS9zx5LPO1aCSkqgh5i/yl07Yefu7VWUvE796yQuGwtwsQm4yF4mW69lVXuMerpkfAElWd6z1CzmIeFjumPl/VFe7nY9RazEc6beuRF14z0msmk1N65EhfQcZOUTxMj0vT1wIoRX7UPib5GJBry6v1e7fPuoDstmLPfB5/YjiemeAz87N3HzqqW67pyrn6zlyxTe99DAAznn9tRPc8eTQzs1WS7nnyqLqaqrWtc3lhD85jyakp7d3RrbsPHc2aoJmast/4wQGz2JXt5uPEcFxfeuoX+sz7t2hiMq2qirC+9NQvFjTpFaVrIp3Wvp3duuuJ19+f+3Z2K5GeCnQsFC/TtS+WSOqPr12r/+9ffp7J2z++dq1GJ1Oex0Lx6u0b0ecPZfd9nz90VN0tNdqyqiGwsVC8TNe+ZHrKtUdIFuH5AvyVTtvq7Ru5sNpfpXra63yZ4DiRTuuT/36DBkcnZTtSmSU111bQ12Je5jMm7oVU2nbN29SUgWWEUDR6+0Zctz7yo9d8/rURHfzxyaxzv/t+cLwox07hH9Pj0vS18IqpvtZ0LBQnP2ofk3wMyHfLq1x3JzTXlue9ZVauWGUhzbn6zlx3RpjetgYAJOnseFIf3NYx6wTj3Hiy0IfmucbqCj14+FTWUn0PHj6lXRvbCn1onjJ9F97IRFLvu7JDH3/ouUy8267vUWyi+HII/jkXT7kupdnlwxLCJmOheJmufVWRsCojIe15R5dsRwpZUmUkpMoIp5rI33B80rXvOxufDHQsFC/Tta8/NunaI3Qsr/IlHoqTyZXMkukphUMh3fu945lYf/GeHqXSXMBD/vIdU/dKynbP27TN5DTkr2/EPW/7RxLassrbWGOTKddzvzg3XGAeTI9L09fCC6wGjKDxo/Yx8mpAri2vmmuiOj40llnNwHHkenfC3928TV/4zTfr9Llx3feDk+obSeTcMitXrG2rl8+5+s4b3RnB9lsATGuojGQuvkivr0z2wIevKvCRea+zsVqf2LWh6FdNM30X3pQt3f5Yb1a82x/r1VdueZvnsVC8LltW6bqUZnt9RaBjoXiZrn2RMkt/+X9emnX+cXDP1b7EQ3Gqi5a79n1+5K3JWChepmtfe33UtUdoq8t9MxdwKZOrS0TDYf3Foz/JivUXj/YW5fk8/JNrnHuuG1kXIxwq01882jsrb+kRMB/t9ZWuedtW733eVpWHdftjP5l17nc/tRbzYHpcmr4WXjDZ15qMheLlR+1bEpN8LMsqk3RY0quO41xf6ONZzFYic2159aWnfqGbf6VLE8m0OpZXaWB0XP/5n47o19/aoVUN0zO1fn97l77xzOkLS35F9d4rV+rVcxN6bWRCjdXl+k/v6NKXfvCKPrFrgzoaqrImCV0c69OPv6jrN69QWUh6S0eDru5cLtt29MKr512XEzN1Z4TJbVoABFsybauhqlzvvXJlZiuFbzxzWkkDd/4lEmm90Dei/tik2uoqtKm9XtGofx+ZoZCla9e16Cu3vE39sYTa66LadFl90dVH03fhnRmbdI13Zow79ZG/tU01+urvb9PUVEgDo9P9S1nI1trm2kDHQvEyXfvGElOu8cYmueMZ+UumbL19zXJ96Jo1OhdPaXl1RF9+6hVf+j6TsVC8TNe+DS01+uot2zRlX9QjWLY2tHIzFvLXN5JwrX9+rC5xZizpGms4zqqqyF9nY7W+8Jtv1vOnRzLbZ21aWe/bheczY5OuecsYAuZjbVO1PvO+zTo2NJbJ2zc112itDyv0DueotWeptZiHUMjSOy9v1P0fvkoDsUm11lVoY1utb+PS9LXwgsm+1mQsFC8/at+SmOQjaZ+kFyXVFfpAFrOVyFyPvXZdi+KT6eylE9/To49d261XRyb1Jxd9fe+Obj1+pE+7NrZnLSG+b2e3yizpcx/Yqs2X1evbLw64xrpuQ6tSU7Y+8Y3nM9/73Ae2ynZs/elDr3/t4uXETNwZYXqbFgDBVlsZ1s1vXz1rj8oaHyfbSNMTfB55oU+3PvL68ot33LBRN2xq922iTzpt65EXXiv6JR9N34XXWleRIx6roiB//WNj+sXghG59pPeimtCjxuqIOqPLAhsLxct07ausCLnGqywvns8v+K+5Lqxdm9r1Bw88k6l/t9/Qo6Za73svk7FQvEzXvr7Rcf1iaHaPsKwqojdVlvsSE8VnTVOla/3rbKr0Plaze6zVjd7HQnFLpp2s7bMO3LTVt1hdOd4jXT68R1C8Xh4aU38skZW3+9+9Vi8PjWlrh7crPnQsd8/ZVQ3kLPI3MZHSY0cGZo2Fv2djmyorI57Ho6+FF4z2tQZjoXj5UfsKPvJqWdZKSf9e0t8V+lik3FuJnBiOL+qxLw7E9OffzF7O6/ZHe7WsuiJzAXvm63cfOqrff8ebZi0hftcTR9W+rErfOzqkH544mzPWqXPjmQk+M9/72Nef1dHBsVnLifX2jUhSZgWgaGQ6JfxYkm8xry2A0jN5oe5dWgf9vsv6hb6RzEnNTNxbHzmiFy7USz/kWvKx18eYhWDisyaLI+3b2Z0Vb9/ObjGtFPMxGJvKNN/STE3o1WDM+zv1TcZCETNc+9K24xovbTs+RUQxOjc+pdsuqX+3PdKr8+Pe1z+TsVC8TNe+4TH3HmF4jLxF/kYm3OvfyIT3eRTLEWs0Qc4if6bHks/nyNvzPrxHULxiiZQOfOfnWXl04Ds/VyyR8jxWPOmes/EkOYv8vdAfcx8L74/5Eo++Fl4w2deajIXi5UftWwq3qv0PSR+XlHMfAsuy9kjaI0kdHR2+HsxithKZ67Hnx1Ou3zsbd/966MK/L/36RDKtKVs6fPJszliO4/7YS8eaEik7s5xYKGRpV0+b1u/drsHRhFpqvd9Ky/Q2LYVmMm8BLyy1nO2PuW830h/zd5nkXHEHfIzbN+JeH4ttyUc/PmvmyttXzyd0/9Mndcs1XbIsyXGk+58+qVXLq/TmRT0TlJKB0Rw1YXRhNWGunPU6FkqTH7VvzrwdmcwZD8jXgMf915w5W4BeD8XHj9pHjwC/UWsRNF6PJb/RuBd5Cy8kUrZrHl36tXxRa+E3k/2BRF8Lb9DXImj8qH0FXcnHsqzrJQ06jvPMXD/nOM69juNscxxnW3Nzs6/HNLOVyMXy3Upkrse211e6fq+xutz166uWV7l+vaGqXP/0k9OyHbl+v7kmqqryMu3debk+uuNytddHM9+79BpqNBJSW/3rzysUstTVXKOru5rU1Vzj+RZai3ltg8hk3gJeWGo5O7PdyMWikZBa6/zdaqmtAHFzfUZcXKOLhdefNXPlbXt9VOfGk7rnyWP6wqFjuufJYzo3nlRbXfG9rvCP17VorpwtVN1DcfGj9r1R3paHX6/lliWVhy22RsS8UGsRNH7UPvIWfqPWImi8Hkt+o3GvnHlLX4t5WNNU7ZpHa5oWtoo1tRZ+87r2LbjWkreYB/paBI0feVTo7br+H0k3WJZ1QtLXJO2wLOsrhTygxWwlMtdjN7TW6lM3bsz63qdu3Ki0PTVriefPfWCretrrZv2u297To88fOqq+kYQefe5Vffp9m2fFemV4TB+894e6+4lj+rvvH9fvXL1aqxsr9bkPbFV3S03Wz9+5e6N62us9ed1s29HxoTE9/YszOj40JttliWrj27QACLSVDRW644bsunnHDRu1ssHf5mlTe71r3E0e1Us365prXD8j1jXnXOQOeehpq3P9W25sqyvwkSFIOnLUog4fapHJWChepmvfZQ0V+sg7u/XFp47rC4emz0E+8s5urSBvMQ8b2qpd83ZDm/fniiZjoXiZrn0dDRWu5wv0CJiPTTl6hE0+9Aj0tfCC6bHknHm7nLxF/lY3VOmOSz6z77hxo1Y3eL/SKX0tvLAiR5+5wqfaR18LL5jsa03GQvHyo/YVdLsux3H+s6T/LEmWZb1T0p84jvPbhTymxWwlkuuxkvQvLw/qwR+f1Gfev0WJZFqrlldpWWVY/+FvnlZDVXlmieeQJa1urFQ4HMr8roFYQqkpR3/+zRd0cnhC0UhIn9i1QddtaNWmFfWZWCFL2nXX97P2c7v70FE9uOdqbVqxTLbtqGN5lfpHEmqrj6qnvV7h8OLnedm2o8d7+zN7JM+ccO3qact63UxsCQageEwkpcMnhvSlD71Vw2OTaqyp0MM/OaW3rG7wNW55eZk6m6P68ofeqjNjk2qqqVBZmaPy8jLfYh4bHtNEMq097+iS7Ux/Fkwk0zo2PKaNK5b5FrfYvTaa0D3fPZq1jcI93z2qbZ0N6ooW3zaR8MfQWFrLq8uyakJ8MqmhsbTaPH57moyF4mW69g3FUrr1kSOX7Cl9RF/7j1eLjzDkazieVnNtOKv+JVIpDcfTqq0MbiwUL9O1rz+WUnpqKut8IT01pf5Yih4BeausjOg9G9vU2VSlgdikWusqtKmtTpWVEc9j9cdSrn0tOYv5MD2WPJ50XPN2PDn7ZlYglxcHYrrnyUvOx548qnWtNdqyytsxzeNnJlzHTte11WjLKlaxRn4mk45rnznpU+2jr4UXTPa1JmOhePlR+wo6yWepmtlKZCF7+7o99vjQWGYCzOGTP5U0c+fBFiVStvpGErrnyWOZn+9pr9PGFcuyfpdtO/ryh66adULT1VyjzsZqnRiO66X+Udf93CZSUwqFLIVClrasatCWVQt8YXI4MRzPPL+ZmPsPPqv1e7fPeg0X89oCKC2DowkdfKZPB5/py/r6e9/SoTe1+FdDTgzH9adff0HXb14hy5JeHhjTo8+9qi9/6Crfate58ZT+8v+8lFXDo5GQ/vbmbb7EKxUDsYRODk9kfcZK07nF5xDydW48qY/+43Oz3p/3/s5bAh0Lxct07XttZML1HOS1kQltlb8Tc1E8BmKT+sOvPjur/t33e1eps8nbvDUZC8XLdO2LJVK6/bEX6RGwaJWVEV21ptH3OLFEir4WnjA5ltwXS7jm7Rd/d5u6WlhpGfnpG3E/H+sfSXh+XaZvxH3sdOeGds9joXj1xRKufaZftY++Fl4x1deajoXi5EftWzKTfBzH+a6k7xb4MHwxEEtk/mjt9VH94a92aXVTtSbTtu7/8Fv1s9dGNZZM6xvPnNa58aQaqsp1fGhMnY3Vsm1HvX0jGogl1FhdobKQJceRTp2Nq29kesLPK8Nj+ug//FS/v71L0UhoVoIsdJ/ihTy/GYmUbfQiqm07OjEc10AsodY6VggCikFrXVTXXdGk37p6jc7FU1peHdFXfviK7zVtOD6pj123Vssqy3X2Qty1rdU6G5/0raYlUlOudTSRnPIlXqloqY1q2+p63fwrXZqYTKuqIqz7fnBczTXczYT8TaSm1FBVrvdeuVLWhdbiG8+cnvWeDVosFC/Tta+ppkKrGyszk2Ml6dHnXlVTDUttI3/xZFpvX7NcH7rm9b7vy0+9ovFkOtCxULxM176J1JRr3tIjYL5MjZ+Rs/CKyTHf0YR7jzCWYGwG+Wuvr3S9RtNW5/35WHt9pWs/4kcsFC/TtY8eAV4x2SNwDRqL5UftWzKTfILsjd7crXVRRSMhNVSV6493Xq4pWbr1m0f0wW0duvvQ0cwWV/t2dqutPqo7//fP9PPBMX3hN9+s8+MpffLhI5mf2bujWw8ePqU973iTRhMp/eP/PaVff2uHGqrK9b2XB3Xgpq16qT8m25lu6Pa/e51v+xTPaKmNujaupi6i5rtdGIBgaasp1/WbV+qZk+dkO1LZGen6zSvVVlPua9y6aJmOD9n6+EPPZGrK7Tf0qDbq33ZdbXXudbS1ngukixEtl25+e6eODY5O55A1/f9Rf1MIRWblsqhufvtq3fVEds922TLv358mY6F4ma59zTVl+ti71+rYUDwT72PvXqvmGv8+N1F83tRUpd1Xrsjq+3ZfuUJdTVWBjoXiZbr2rWmqdM3bzkYu4iF/JsfP1jRVatemdv3BA9nn1eQs5sO2HR16eUDPnx7J1NpNK+u1Y12rL2O+b2p2r7Vrmslb5G9ZVVi339Cj2x7pzap/y6q939ZleU1YH3v3Oh0bGruoH1mn5bVsIYP8deX6zG7yp/bRI8ALJnsE0/0IipMftY9JPvPgNplH0hueIHc2VuvATVt1+mxcbcsqteeBZ3TLNV2ZCT7S9IoNdz1xVH9y3Vo9/2pMkvT86RHd+73jWT9z96Hp/Vw/9djPtOcdXfrgtg597cendPPbV8txlHUcf379FVq9vFLS9JZhfs0wLAtJ+3Z2z7ogVRbyLMSc5rNdGIDgeGlwTOfHk1lfOz+e1EuDY7py9XLf4p4fn9Jff/dYZu9sSfrr7x7TZ963xbeYybTtWkdTafZ9X4yBkaRePZ/IfJbOvK4rR5JawR7PyNNE0tYTL/brM+/fkrUqyltXe78Vh8lYKF6ma9+5uK3z46msr50fT+lc3NaaZu/joTidjac0EJvMytv9716rs/GUVjcFNxaKl+naN5awNZrIXm1qNJHW2CR3PCN/JsfPzo9P6euHT83qa9c0bfA0DorbqbNxHR0Ym9XXXt5c48sWm6MJW6+59NGrGpgIjPwNjk6qsnx6+42ZFcLPTyQ1NDqp1Y3e5u2ZWFKxRHY/EkukdCaW1CqGEZCn8xNTmUlp0nR/cNsjvbr/w1f5E48eAR4w2SOY7kdQnPyofUzyyVOuu12uaK99wxPkUMjSdRta9fBzr6lvZHprK8uS69Ys5ydeH7SxHfefmXms7Sgz6WdlQ5X+9KHnso7jU4/9TA/9p7f7fpdO30hC9z99MnNB3HGk+58+qTd3LDNS4JbCdmEAvJdITymenJrVPCXS/i6TfH4iNWultb07unV+IvXGD16gkUTKtY6ub2PP98UYnUxnJk5Jr0+o/V/s8Yx5GEum9b4rO/TxC31WNBLSbdf3aMyHbV1MxkLxMl37ElM5Pq+n2NYA+RtLTunAd36elbcHvvNz/e3N2wIdC8XLdO1LTk1P8rk0XnKKST7IX67xs4GY9+Nn8Rx9bZy+FvMwEJt07Wuv7GjwZ5IPYwjwQE1FmeKT2SuE3/aeHlVXeL/aX9LO0R/Y9AfI30BsMkd/MOlLPHoEeMFkj2C6H0Fx8qP2GVprJfhy3e2S6wNwcDSR9bVT58b1yYdfUFV5WNHI9Ms+8+8Z0UhIzkWLNpRZuX9m5t+JlK2ykDSeTLseR3xyyvW4TwzHF/5iXKK1Lqpz40nd8+QxfeHQMd3z5DGdG0+qpdbM8noz26FdLBoJGYsPwB9TtlybJ7/HsRuqIrNWWrv70FE1VPq31O3q5dWudbRjub/bLRa7RGrK9bNxMslgB/JXFQnr9sey72i6/bFeVUW8nytvMhaKl+nal+vzmnFlzEeu89mJSe8nTJiMheJluvalphzXeOkpVv5E/i4ek5wRjYRUVe79hefqcve+tpq+FvMQz/GZPe7ThWDGEOCF2MSUbn/0kvr3aK9GJ7zvNZNp9/4gycrgmIfWugrX/qC11p+t4+kR4IVYIuX6mX3p6mZBi4Xi5UftY5JPnnLd7TKeTLt+AFZGyvT0L87o+NCYbNvRcHxSt1zTpVfPTeie37xSP/zFkPbu6M6a8PNf/8Mm/ej4kCRpdWOlNq+s163XX5H1M3t3dOux51/V3h3d+qefnFY0EtLmFfWqq4y4Hkeuk6FLJyEtxsx2ZBcf54Gbtma2M/NboeMD8MdE0n1wZSLp7wWY0RxN2+ikf03bmib3OramiTq2GJfVV7p+NrYt8+ckFcVpaMx9QvfQmPd3NJmMheJluvZN5Lz4woQJ5K826n7huSbq/YVnk7FQvEzXvrFJ93hjk9zxjPwlp6b0X35tg/buvFwf3XG59u28XP/l1zYo5cOdNEOj7n3tmTh9LfK3enm162e2XzdEtda6X+huriv3JR6K0xmD5/VjiVw3ftMfIH+ttRHdcUNP1rj0HTf0qLXOn0k39AjwQn2Oa+LLfLhR22QsFC8/ah9TI/M0s1rMxX+AmZOKAzdtzdoO687dG7X3az/VyeEJrW6s1F+9d7NePZ/QF596fdnET924UfWVYX3xd7fpp6fOayJl6+4nfq79716nW99TpeF4Sj89dU5V5WXat7NbrXVRVV+4s+bGrSv0wA9P6tx4Uvt2dqtvJKGvH/6l9u7oztpe5sBNWzMnQ5ce93xWubFtRyeG4xqIJdRaF1VnY3XWVl+hkKVdPW1av3e7BkcTaqmd/TN+KnR8AP5oqS13rV/Ntf4Orsw0bZfGrfexaaOO+ePyxmrdceNG3frNI5nPxjtu3Khuj/dAR3FruTDQO7sWeT9hwmQsFC/Tta+pxj1vG6u5GIL8tdZGtW9nd+ZO5JmtBlrrvF+d1WQsFC/Tta8lR7ymGnoE5K+ltkLHh+JZ27rcfkOPL73mzKoAs/pachbzsHp5le7cvVGffPhI1rj76uVVvsSrqwy59tH1Ue6TRv5ynde3+DGGQK2FF6yQVjdFdd/vXZUZl5Y1JVn+3ARBjwAvtNVX6I4benTrI72vf2bf0KPWeu/zyGQsFC8/ah+TfPI0s1rMxZN5ZlZZWNNUnbkwWxkpy0zwaa+P6oPbOvT08eHMCbQ0PTPrz795RPf+zjbdcqdbh4UAACAASURBVN/hrD/oJ77xvP7mt9+iP/zK63u27t3Rrbue+Llu3LpC113Rqt6+mN73lpVyHOn+p6cn+9xyTZce+OFJ3XJNlzavqFN3a21mJZtLj/u//YfNWllf6fo8L53Q09FQpW+/OJB5/OrGSn3qxk2KlFlZE35CIUtdzTWe7+Gdr0LHB+C9cCjkOpgTCfk7uFIZKXO98FMZ8ffubtt2NJpI6fx4SpWRsGzbYZLPIvUOjOrgj0/qM+/foolkWlXlYd33g+PqaqrWts7lhT48BERdZZnuuGGjbn3kooHeGzaqvtL7mmAyFoqX6drnONL+d6/Vge/8PJO3+9+91vM4KG6J9JQaqyP67Pu3KJ5Mq7o8rPFkSom096uimIyF4mW69lWVu58bVZdz4Rn5Ox9P6bZHspeIv+2RXj34H6/W6kZvY0XDZbr9hp5MvJkJRX6fV6O4nDo3rs8fOqpbrumSZU3X3s8fOqorOxp8GQMeHpvSPU9mx7vnyaP67Pu3qMvzaChWleXTN3j/+UWTxT5140ZV+vCZHQlZrheeI2WMJyJ/Z0aTOjowrsHRSdmOdGxwTM21FQpbYc/7A0mqLi9zfY/UVNAjIH/nxlK657vHsj+zv3tM61rfrA6Ph75MxkLx8qP2McknT2+0ykJXc406G6v14xNn9Z4tKyRJ0XBIdx86qt/f3uW6BNP58aTr13966lzWCffdF05m1rbWajSR1t1PHJt1fJYl9Y1Mrxb0rb3bs050rl3Xor/5rSv101+e15Qt/Y8nXtaUY2v3lhUKh19vLm3b0eO9/VkTgj79vs068J2XlUjZmUlLex44nDXRaVdPGxeiAXhuIj2l8cm09ryjS7YjhSxpfDKtCZ8vwAzEJnX/0yezmrb7nz6p9W216vEpZjpt6+HnXp01aH9pncb8nB1Pasf6Nn38oeeyJs6eHU8W+tAQIGMJW5I962Lw9NeDGwvFy3Tt64sl9OV/O5H1ufnlfzuhFcvcbyoA3AyNTurceFq3PvKzTN7+8bVrdWbUh60RDcZC8TJd+xJpW5EyK+vcKFJmKZGmR0D++mIJ13HI/lhCWzyOdSaeUDJtZ+VsMs1WHJifgVhCJ4cndM+T2WPhg6MJfyb5xJOu8YbjjCEgf8mUo8n0VFb9m0xPKZlyPI81NDapyUtq7WSaLb8xP2nH1mginbXS376d3Uo7/vSZibSt8nB2X1sepq/F/PTl6BH86GtNxkLx8qP2MclnHuZaLca2HR16eUDPnx6RJJVZ0tq2Wn30XZers7FaX/rdbUrZjl45M6b7fjC9+s6yqoj27bxcT740qO1rW2RZ04+74rJ6/dm/W6f0lCPbkS5vqVZZKKTaijLVRsOuyzk5jjKTbmZW8Jnx4kBMf/jVn2Q95vOHjqqzsVqpKVtV5WElp6ZUXhbKTPCRpk/0P/GN53XLNV2658ljeu+VKzPbgc18f//BZ7X+kklFc5lZKWg4PqnyspDGk1NZKwK90dZgAErHRHJKTx0b1G9dvUbn4iktr47oKz98RZ1N/uy9PqO6IqwVyyq0rq1WE5NpVV34/6py/z4ye/tGsu5Ok6brdHdLjbasavAtbrHX3GWVER16qX96NYsLf8v7fnBc2zo3FPrQECDxZFp95+K6+vJWOaOOWuoq9PTR82rLsSpiUGKheJmufU015dq0okZXrWnIfF4fHTiv5WzXhXmojIT1nZ/1zcrbrau8z1uTsVC8TNe+RMrWvx0d1O4rO3RmdFLNtRX655+cUsuVHb7EQ3Fqr6/U6sZKXb95Rea889HnXlVbvffbFVZGInrh9GuzcnZt62rPY6F4tdZFdd0VTbPGhVpq/dlic1lVRNtW1+vmX+nK6hH83L4dxSeemtL3fj4wK29XN3o/nllVHtbJMzFd27NCQxduTP9O76u6vIXdDpC/VNrR1358Kmtc+ms/PqVNK+p9iUdfCy+Y7GtNxkLx8qP2McnHI6fOxnV0YCwz23V1Y6U6Gqv1hSePZc1+rS4v0x/+6vQCn5/4xgsqD1v6T796uW5/9PUlFf/kunWqipTpL//lpay7bx88fEq/9bbV+sz7N+vjDz2ftZzT+rYaXXdFi8aTUzoxHM+6UNs3kn2nzsyKPL/9xR9l/X7bcVzv6Cm7sIiEZcn1+/nePTGzUtCnH39RH9zWkZkwNDM56boNrVlbg7FSEFDaKiLSzg3t+oMHXt++8PYbelQR8bceLK8u003bOrJWQLjjhh4tr/ZvydDz48lZdXHvjm6d93HFGbfV24qt5qanpvS+K7P/lrdd36P0FNtxIH/Lq8vU3lCj3/3y/72kJnjfRpuMheJluva114d17YbLsj6v77hhoy5bRt4if2k77Z63djrQsVC8TNe+SNjRts4mffjvf5zVI5SHvV8VAMWru6lKH3ln96ytYbt9uJEmXGa75my4jLv0kb/2mgrXWtteU+FLvPIyuY4HVdDWYh4qwjnGM33Io+oKS2vblulDl4whVFcUx7gezJiybddx6Snbn89s+lp4wWRfazIWipcftY89QDwyEJvUXU+8vsrN9ZtX6L/88wtZq97c9cRRnYkndSaeVCyRVt9IQtdvXpGZ4DPzc5/99ss6E09mfe3uQ0d1/eYV+uqPTioSCum/v3+LPv8bb9a+nd168McndfzMuD547w/1G3/7I/3a3d/X4739su3pxGivr1Q08vqf2m1FnrsPHdXqxuqsn5OmVwfatnp55utu38/37okTw3HtP/isrt+8wnVFoN6+kVkrCe0/+KxODMfz/CsAKCaWynTbI9n18bZHemX5/NF1bnwqs5f1TNxbH+nVuXH/JoZUlodd63Klj6sHzdTkYq654bIy3f5Y9t/y9sd6FS5jj2fkbzwp15ownvR+8MFkLBQv07VvIDaVGeiYiXfrI0c0EGNCJfIXKQu75u3/z969x8dV3ffe/+65aaSRZizr7otsC8tAJNlADOHkACV2aN0eME5CyGn7SnPrQ0+fgGnJaXjaUqiB9jy51H1Cwjkpza3kNKeQ8ITgNKVNIAlJSXLqBHwLBRNjObYl2ZbtkTTSSJqZff4QGjzWHnkk7b1nz9bn/XrNCyzNzG/NzE+/vfbaa9YKB+3vC7kZC/7ldu3L5QKWfYRsjmE9lO7nAyOWefvzgRHbY2XJWdhgf/+QZc7u7x9yJF4mZ1jmbSbHhAnMRcByPFOm/fUvPWk9hpCetD0UfMztcWn6tbCDm/1aN2PBv5yofVRNm6QmMgWr3BRb9SZnSjlT+T3WZrvf+T+riwb1no3t+sPHX9Qd/+sF/dHX9sg0pc2XturuJ/YWvVDb1RbXg9u68xN0ggHrmMfOjmr7ps6CCT07b71Mb+1o0Le2X6vr1zXqY+9aP+P3528PVszA63t/F3vN5684NP3zE8Ppkp4fgL8MDI1b14QhZ/eVPlEs7rBzcU8OW8c85eAe2tM1+fyYfqq5xd5X9ibHXBSrRQMO1CI3Y8G/3K595C3s4Gb/qxx9PfiP27WvXOdG8JeifQQH6h/9A9ih3yO1lrzFXBQ9ZlNr4VEnXOwfSOQt7OFmv9bNWPAvJ2ofX1VboFzOnJpMY0p3bl6rx3cfVV9y6gJpNBwo+MCi4YCmd0DJnvM5zna/c3/WXl+ju766Z8aM2k/esqHohdqOplqFQgFt27Bcnc216k+m1Vhbld9W7NznH05n9c29x/TYbVdrbDKr5rpoftuvjqZadTTV6oqcqZ7lCZ14fY/Xc7cFu5CWeLRggtD58dsSUcufO7XPMgBva4lXWdeEuDPLMk8rVota487VoobaiGXM+pqIYzGna7Kfa25TnXUONcWczSH4S7Fa1OJALXIzFvzL7drXXOx4XUfeonRu5hE5Czu4nUflOjeCvyRqQpZ5lKi2f3i4aL+WWos5aHX5/Ii8hR3cPGaTs7BDc7ExBJf7tYx9YS7c7Ne6GQv+5UTtYyWfBcjlTD19oF8f+NL/1k+PnJUp6d6b3qT1y+PateeYHrj5jdVzouGA7tzcqcZYRBc1xfTNvcckSbv2HNP9W7sK7nffTV1qjEVmPHZ0Mms5mScYMC64jVYoFNCGlfX6te42Xd5er523Xlbw/Ns3deqbe4/p7i2Xqmf5El3d0aiOptoZE3imJ/wU+/1sVjfEtPPWy7RrzzHLFYO62hIz2jWXlYIA+EvOzOq+G8+rjzd2yTSd2Q94WldrXPdvLazf92/tVndr3LGYrXVVuu+mmceCNgdPbqZrsp9rbnoyM+N4s31Tp9KZTJlbhkqyrjVmWRPWtdr/t+JmLPiX27WvIRaccd7zwM3daqhla0SULmiYlv2+oGH/doVuxoJ/uV37AkXyNkDeYg6WVIctzzuX1IRtjxU0ctpx3njnjq1dCgacPZ+Hv/S0JSzPj3raEo7Ea6wNzhinv39rlxrr6NeidG72NQNFam2AWos56Gqttay1Xa21jsSLBGWZtxFKLebAzX6tm7HgX07UPqaZLcDhwZQ+9vRL+q2rVumvv/OK0pM5RcMB/bd39GhdS61MU3r0g1fpdGpCtVUhVYeDioQC2rFrv25cv1yGIZmm9PjuI3r0g1dpOD2phliVTo6M695vHNCHrunI3+fRH/Xq47est5zl1dlcq523Xqa7Hn8x34bZLtQGAoa2dLXqku3XamAorZpIUJPZnLZ0t85pZZ65ysdtrdPp1Lgeu+1qjU5k1RJ/Y0Wg6XbNZ6UgAP4SMIJ64mdH9PFbNmhsIqPqSEiPPn9IH91yqaNxjw+n9fju3hlxN66uV0fUmZOb9oZa9Z4e1Sdv2aDUREaxSEh10aDaG5yJJ2lR1NxIKKjHdh8pOJ4+tvuIrli1vtxNQwU5M5JRNKyCv8+cmdWZkYyWVFduLPiX27Xv7GhWuVxOt13XoZwpBQwpl8vp7GjWkXjwp/pYVE/87N9n9L8+fstlFR0L/uV27QsYActzoz/+jTc5Eg/+NJTO6rPff7Wgj/DZ77+qj79rg+2xcmZAz7zUp79575t1dnRSS2rC+vsfv6aOxrW2x4J/RaMhbe1p05rGGg0MjaslXqWetoSiUWcuaZwczuqV/rP60geu0snhtJrqovrOgWNa3RBTR5MjIeFD4VDI8ph9741dtscqVmvXUGsxBwMjk/rOS8dn5NHG1fXqqLb/C6g507DM27XNnbbHgn+52a91Mxb8y4naxySfWUxvxTUwlC6YiDJtYCitd795ZX6CjzS1ss4ff32f/vGOa3VR88yLs8/++4B29ya1uzdZ8PPBkQn9WlerAgFDPzk0qDOjE3r4u6/mfx8NB1QTDhadzBMKGvq7D1yl0YmM2pfGtKZx9gu1gYCRnwRU7PU54dytv+bze8x0/Q1b1Ddwqujv21oa9b1vP+1iiwB7mDK1+dJWffRre/I1787NnZKc/bbqwFD69Tr9woyfO1WbAgFD13Y26/BgytUJN36vuZPZrP7LdWu145sH8jl0341dymT5RhNKdzw5po8+sX/GJOvPv2+j1tj8t+NmLPiX27UvPZnTfbt+PiNvP/c7Gx2JB39a3RDTB6+5qOQvrlRKLPiX27Uvk8tZnhtlc/RrUbrh9KQmMm+cTxuGNJExNZSetD1WzjR1eXuDfu/LPy3I2ZzJ6lOYm2g0pCvXNLgSayg9qZVL6/T8L04pZ0qvnhjRyqV1SjrwNwL/ymSLHbPtr3/Faq1JrcUcDKbGddnKwjzavqlTp1PjjowZZ3I5y7ylX4u5cLNf62Ys+JcTtY9JPkVMb8V17sDfZ37rcq1pqNWJ4bRa41HVhINa1RCz3ELrFydGFAxI7UunLtJmMjkd6EtqdCKrz/zW5Xrk+7/Q3mNDkqYGgrI5U/uOnVXP8iVqiVfpzs2d+tQzBws+6IbaiC5vry9YdaG9vkb/8tLAjAHKNY2zD1Bavb6dt16mLa9PNKoEF5qEtVj0DZzS5bd/uujvX/jMHS62BrBPcjSjR3/UO2NVs7UOX+iuiVjvsVrj0pqhnIfbpyYc1gtHjukL779Sp0bG1VRbpa//7IjWtawud9NQQVLjGcu+Xmrc/m/quxkL/uV27UtNWOftKHmLOWqsDemL779SJ1/P22DAuU6Rm7HgT27XvrOjk5bnRhcxCRhz0BCL6Pd/pUOnUhPKmVLQkH7/VzrUGIvYHis5Vp7zefjP9Jh6XzKttkS1utriCoUCjsRqiEWUHJtQQ22dUuMZxaIhpSczanDgbwT+NZiasK5/Fl8IX6ihsWL9Ayavo3SRQKBgNWBpajXgt17kzARL+rWwQ0Msoj98+1rVRML5Y/Yfvn2tI/1aN2PBv5yofUzyKeLwYCo/AUaS6msiOjgwotu/8oLqa6ZOitOZnHI50/JicM409U/7+9XZUqvrLmrSU/uO654n97/xbdqbuqSf9OqVEyO6c3OnfnlmVK+cGNaxs2n96qUt6myp1Z2bO7W0JqJYVUiNtRGtWFIzY9WFQydHCtqZnszprsdf1CXbr511lu35r6/Ux3mFHyYpeQUrAcGrGmrDlquaLY05u9dpzszpvhu7ZqyA4OS3YHI5U8++PKC9R5P5wdaeFQlturiFmrYgOb15daM++KV/y3+WO7Z2SQbfDEHplsYiln29egf2XXYzFvzM3doXj1pPjq2rdmdyLPzhl6dH9NqpMd331IGCvG2pG9GqxrqKjQX/crv2LamxPjdaUsOwHkpXFQ4oNZHVI88dKvhSYSRs/4SJpTHrnK13+Hwe/pLJ5PTknmMFY+oPbuvWtg3LHZnoYximJrPSPU/uKRjDNwwmA6N09UWO2U6c1zfVVVnGaqqzf4sl+NfZsQm9Z2O7Hnr2jUUHtm/q1FB6wpF48Wrrv5F4Nf1alK6mKqCsaei/fq3wmF1dZX//wM1Y8C8nah8ZWMTAULpgsOadV6zIr6zzzitW6FRqQju//YpMSXdu7lT09RPi6RNkI2DoU88c1N6jSe09nsyfjEhTE2p27Dqgj/zaxbrtug7VhIP6h387omxOuuvxF3XkzKiu72zWsiXVum/XAd3+v17Q+7/0b/qXlwaUO29Zx/PbOf38J4bTc3p9pT7OK4pNUjo8mCpzyyrP9EpAxW6zTQACHGUalvVVcngLK8PQZ5+b2mP19k1r9aFrOvTZ516VYTgX98jplA4OjOiR5w7pM8++qr957pAODozoyGlq2kJkTSN/AU96fVuFpw4om2PiFEo3lJ7U9k2FtWj7pk6NjGcqOhb8y+3aVxUKWh6vq4JM8kHp+ocmLPO2f8j+gWU3Y8G/3K59ISNgGS9kMKyH0p1JTebHNqWp+vepZw7qTMr+rQaKjZcyVQJzcaBv5pj6PU/u14G+pCPx0pOmduw6MGMMPz1J5qJ0QcN6PDPowLhiLmdda9n1CHMRDYfyE3ykqdr30LMHVRVyZtJNJGDdr40E6NeidGdSGctj9pmU/WOobsaCfzlR+5gaWURLPFrwrSzDmPrDbUtE1V5frV+eHZvaPmEiqyd+enTG8kq/eVW70pM55UypP2k9oWZobFLZnPSF51/Th69fq9OjE/rdazt0OjUuSbr7ib0XXGnn/HZKU4nRXBed0+sr9XFeMdskpUpYiQjAhR1Ppi2Xr1tRX6M3Oxh3dCKr3sGxghm10z93ysDQuOVg6xXt9VrdSE2br4GhcctjxcDQeJlahEoUOm/ZYtOcWrb4/pu7KzoW/Mvt2lfseN2+tMaRePCnE8PWeXti2P68dTMW/Mvt2ndslnOjKxyJCD8aTltvMzfswITygeS4Zc6upH+AOegrMqben0xrw0r74w2OTFjGG0wxERilm62PcLlLsZweO4W/DKasz48GU86cHxXr165cSr8WpXPzmE3/AHZwovYxyaeI1Q0x7bz1svxqMUFDWtVQrfdsbNfx5JiChvKzrayWVxrPTC3ZFTCk5niV5YSapbGIDEO6+bLlOjs6of/3n15WNBxQZ3OtTKmkSSznt3N626rVDbPvuzrfx3lFpU9SAnBhLXHrJWdb4s4uOdtc5359SY1bD7aOTjAbfCFaihx/W1i2GHNQVxXSf76yPT8Rb3qWfV2V/d1oN2PBv9yufc3FloivpdaidK3F8taBfp+bseBfbte+cp0bwV+K9RGaHcjbYjnbzLkY5qAtUW2Zs60JZ8Zniv6NkLeYg6WxiGX9WxqL2B6L/gHs0FxnXfvc7tdSazEXrQn3xr7cjAX/cqL2sf5ZEYGAoS1drfrW9mv1D7e9Re+4fLkeuLlHDz17UN/99xPqXpbQX76jR93L4vqLd3TP2Fbhm3uP6c7NnXpTW1yNtUHt2NpVcJ8dW7t07MyoPveDQ6oOB/XF53slTV3UvfuJvYoEA1rVUK0Pv22tbt80dVvVUD3jIvP57fzW9mu1patVgcDsyz/O93FeMT1J6dz3tJImKQG4sJZ4UA/cXFhfH7i5Wy1xZ7f/CAaku25YVxD3rhvWKejgEbPx9ZOpc0XDATU4MACwmDTXBXX/1sIcun9rt5odziH4S3UkoItba/XIe9+s/+89l+mR975ZF7fWqiZif1FwMxb8y+3at6Q6qI+/q0fbN0+ds9y5ea0+/q4eLamh1qJ03a1xy35fT2u8omPBv9yufQ0x63Ojhhi1FqVrikcs86g5Yf95Z2Otdc421ZKzKF1XW1wPbivMowe3daurLeFIvLZ667+RZfWMzaB0sYj1taBYxP76R/8AduhuTejjt6wv7Nfesl49DtXa+pqgZW2vZwwBc9C6pMqy/rXVOzDJx8VY8C8nah9fC55FIGCoo6k2v3JOXzKt+pqItnS36Y5/eCH/Des/fPs63fX2Ti1bUqOV9dU6lRrXH/3qJfrlmVH9t396Sffd2KWv7j6ij9+yQWMTGVVHQnr0+UP60DUX6a/evUEP/uNL6kum83HTkzlNZHO6Y1Nnft/h6Q+7vX5qWdtcztThwZQGhtJqiUe1uiE2522qzn99lWR6ktIl26/VieG0muum3oNKmaQE4MKGxqSqcEC3XdehnCkFjKl/D405G/fkyLiWREP65C0blBrPKBYNaTQ9qVMj445tnTWeyeoP375Of/2dVwqOLRNZNtFeiGw2qEDAnPosJzKKRUJKZzLKZjlpROkGhsd1cnhC9z11IP/3uWNrl4IBQ5e0VW4s+Jfbte/0aEYnRyb0yHOH8nn7R792sZpHWY0OpQuHg4qe1++LhgMKh+3PWzdjwb/crn1jk1nLc6OxSc4XULrjZ8b1me8eLFgi/jPfPaj2pRu0st7ec93BVFaRkFGQs5GQocFUVmuabA0FHwsEDC2pCRfk0ZKasGPjv4NDGeu8HcpoxRJHQsKHJnMZVUeCBXlUHQlqMpe1PdaZUetae2bU/ljwr0gkqKBhFPRr/+rdGxRxYGKaJI1OZBQJBc7L24BGJ8hblG7g7ITGM9mCPBrPZDVwdsL2Y7abseBfTtQ+JvnMQUs8qndvXKGHnj2YX5YrPZnTX3/nFX3omg7t/NoePXbb1frwV14oWLbrxaNntbs3qd29LxQ839UXDUma2u7rXNFwQJFgID/BZzrOPU/u1xXt9VrdENPTB/pnbLVVSSvx2KGSJykBuLDRiaw++rW9M5ZB/LsPXOVo3OpwUIOjk7p3188Ltst5k4MXfhpiVfrK/y7cj/Mr/7tXN7ypxbGYi0HfUFr3PHlgRg597nc26qKWujK2DJWkJhzSfU/9rKBPdt9TB/TlD9pfi9yMBf9yu/ZlsqY+8c8vF+TtJ/75ZX3udzbaHgv+daAvqT+y6Pe1L63RhpX1FRsL/uV27RudkOW50aP0ETAHoxMZ9Q6OFSwRP/1zu01mTd39xL4ZOfvF919peyz41+HBlG4/b5w9Gg7oW9uvdWQ8eCyTtczbL32AvEXpsrmAa8dsai3scHgwpY98dU9Bv/YjX92jS9vijtTa8YxBvxYLls5k9eA/vjQjjz7/PvvPx9yMBf9yovaVdZKPYRgrJT0qqUWSKekR0zQ/Vc42zWZ1Q0zrmusKPgBp6qBnGFP/7Uum879vS0T1zitWaGV9je7cvFaP7z6aX7EnGg4oYEhf3X1Ud27u1KeeOVgwYSc1nrWMc2J46vHTE3ymf/6xp1/S8iVRjU5k8yv7XGjCj9VqQItpkhAAbzsxPF6kDo47Gjc1ns3X5OmYn3rmoK5od+6iz+qGmO7ecumMyZtsQbgww+mMZQ4Nj7O6BErnZi0qV92Dv7hd+0bGreONUGsxB+eeR0+bPr/esLJyY8G/3K59A0PWfYSBIfoIKN3SWJU2rkrod97aobHxjGqqQvq75w9paY39WxENpiYsc/Z0aqLII4CZBoasj9knhtOOXHgeHLHO28ER8halO1HkmO3EeT21FnZwu9bSr4UdRiesr6GPjtu/IpSbseBfTtS+cq/kk5H0EdM0f2YYRp2knxqG8W3TNH/uRvDzJ7m019foyJnRgn+/NpjSa6dSioYDqouGtGxJVNFwYMZMK9Oc+m9jbUSP/d5blBzLaGhssmC7rfu3dilRE1HvqRG1N8R0aVud/uNFjRqdyOix2/6DJrNZNdZWKZM1dTw5ZhmnuS6qwdR4frUHSTpw9KzefWW7nvn3E8qZ0q49x3TXDRfrP3W3KRQKWL7e5rqoXhscyX8bYvqC8q9e2qLe06PqPZ1SLBJSS7xK7UuZ/APAfW2JKv3qmxr121ev0ZnUpJbGwvqfP35NrXFn9zodnchoXXOtfve6i/IDn3/73C8c+XbjtEDA0NvWNurRD16lgaFxtcSr1NMad7z2+n2yZ2vCehC7pY79clG65jrrPGp2II/cjAX/crv2JapDluct8Wi5TzVRSZrrqnTrm9u07Yp2nRoeV1Ndlb7+syNqqnWm1roVC/7ldu1rjVvnbYvD50bwF8Mw9b63rtHBE8PKmVLQkN731jUyAhd+7Fy1JchZLFxLPKpVDdW6cf3y/Dj4rj3H1FwXdSRea5F4LXFn4sGfWovUv1YHTtpQRQAAIABJREFUzsdaivVrGUPAHDTXRS3H4Jtqnal9xfoITo/5w1+WxiKWx+ylMfsnr7sZC/7lRO0r68iraZp9kvpe//9hwzBekrRckuOTfHI5s2DLq1UN1frzm7p04PiQ0pmcfvyLk/qtt6zWn3x9X8F2LWsaa/TAzd36s2+8MXln+6ZOPbb7iO7c3Kne06Mam8hqOJ2ZsRLEvU8d0G3Xdag6HFQoYGj/sWF95KuFqzacHZvU7V95QfU1kRkr/HzsXevVXl+jfceS+vwP39gfc8fWLn3s6ZfUOziWb8/Ob7+s+pqIrlnbqEDAmPF6p19PfU0k/03Gjz39kjLZnD76xN6C+3S21GrTxS2+uvALYO7cnhCSqA7q7Ze26fe+/NOCyZJLapzbNkuaOhn/zbes0ke/ticf976buhwdjEynM9q1v1/3PnXuxNBube1pU9ShCwVWxwW/bf1YXxPUrRvbCz7L+7d2aWnM2RyCvyRqgrp146rz8qhbCQdqkZux4F9u176VDWHdv7V7xjGsvTHsSDz4U31NQBtXN+mDX/q3gjxaGrP/yrObseBfbte+5njQMm9b4vQRMDdHz4zpkecOFYz7rWqosT1OPBrUxtWN5+VslxLV5CxK115fozs2dRZ8ifbBbd1qr7c/ZyWpNhrQ7W/rLBj3f+DmbtVF6SOgdHVF6l/cgfrXWGvdP2iqpdaidLGIaTkGH4uYjsRbGrP+G2kgbzEHsSrrY3bMgWO2m7HgX07UPs9koGEYqyVdLuknbsQ7PJjKX9hsS0T1no3t+v2//5k++S+v6HM/OKQPXnNRfoKP9MZ2LT/vG9aK+qg+ccsGffLd6/XJWzYoEJBuXL9cj/6oV3/69f06MTyuFfU1lssu5UzlJ+5MT/CZ/t1dj7+ovUeT+aXCH/1Rr267rkMfe2ePPnRNh5YvierImVHd/cTegsfd99QB3bh+ef7fDz17UDeuX67dvad1eDA14/We+3reecWKfPtuXL88P8Hn3PvsPZrMPw+AxWl6QshvPPQD/ebf/kS/8dAP9PSBfuVyznT2Jel0Kqt7nzowY7Lk6ZSzyyAOjWW1Y1dh3B27Dmg47VzcfX3J/AWC6Zj3PrVf+/qSjsW0Oi7c9fiLvqr3A0MZyxwaGGILGZTubCpr+fd51oFa5GYs+Jfbte/4mawe/t5BfeiaDt2+aa0+dE2HHv7eQR0/Q96idCeHrevfyWH788jNWPAvt2vfQNI6bweS5C1Klxyb+YXETz1zUMkx+/sIZ4qcz5+hX4s56D09mp/gI03l0T1P7lfv6VFH4p0dzeYv4E3H+7Nv7NfZUfIWpUuOWtc/J/KIfi3scOT0hGXOHjntzLZvp4at/0ZOkbeYg2SRY3bSgVrrZiz4lxO1zxNrqBuGUSvpCUl/YJrmkMXvb5N0myS1t7fbEvPcfSbfecUKPfRs4Unuv/cPFZ2kc2J4QgdPjChgSA898+qM586ZU9u9FNvWKz2ZU6rI/u3nXi/vS6b10DOv6vZNa/X5Hx7Su65YXnR/TMMo/HcwIGVzyu+bWcrjggHN8pqd2X/Tz5zIW8BJs+Xsa6esJ4RcfMe1uqjZmdpwYti9PazPdXLEOu6pEefi9pdhL2K391t2ymx5W64cgr8UzaN51oR55ayD9Qf+40Ttmy1vB4bG1Ts4poe/++qMnwOlGiiStwPzzNtZc9bmWFicnKh95C2cNpK2HoscSc9vkg85C6f1nk5Z5tGR06l5jUVdaKz2dGrCMt7plDMXuuFP9GtRadzMWSfiYXGy+5g9W97SP4AdnKh9ZZ/kYxhGWFMTfP7eNM3/3+o+pmk+IukRSdq4caMty0a0xKP5STiGMXNyS86U5SSdgCHV14T1xE+P6i/f0V30PieHx7V9U2d+8tD0Nlpf/nGvouGA6msj2r55bX5SzxM/PaozoxM6f4eU6efbeetlWt0Qy//MavLQuf++pDWujz39kt51xfIZr/f8557+/ytXLS16H6f2OvYzJ/IWcNJsOWv3wEopWuJVRfZCd3Z/3raEdb1sdXAP9tZ4lWVMJ19rseNCpdX72fK2pQzvK/ynsc563+WGee67PFvO2h0Li5MTte9CtbYcx2v4S9G8rZtfHs2rfzDPWFicnKh986q15C3moKkuYln/Gmvt79fSP4AdYpGQZc7WROZ3SeNCY7V1Uet4dQ5tow5/srv+0T+A0+weQ7hQraWPADvYfcyeLW/pH8AOTtS+smagYRiGpM9Lesk0zZ1uxl7dENPOWy/TXY+/KGnmxJlde47pz2/q0p+/vmXL9D7Vy5ZEdTY1rjOjE9r14jH9xbYe/emT+wruUxcN6eHv/kKRkKH//ttXKDWe0cETI/ryj3t1ZnRCf/Lrl2hobHLGHtidLbUFbYmGA/rLd/ToivYlal8aUyBgFLR7+j4PbuvWp589mH/sn934Jn3hh7/Q3VsuzU8Msnrczlsv05va6vTWixrUXBdVe33NjPtMt2v6eQAsTnYPrJSivT6kD1/fmV92dnpf6fZ6Zw9dPW0J3X9zt+49Z4/V+2/uVs+yhLMxt3bPeK09bc7FLHZc8FO9b00ELd/X1gR7PKN0zXVh/d/Xr9V9T73RJ9yxtUst8XBFx4J/uV37VtQH9eHr1+aXnJ3eU3pFPbUWpWuuC+n+rV0z8qg5bn+/z81Y8C+3a19LPGQZryVB3qJ0rUuCeuDm7vx2A9FwQA/c3K1lDuRte32wyPk8/QOUriVepTs3d+a3mZseq3bqQnBjbcgyXmMttRalW9cas6x/61rtH29rqrXujzTVUWtRunWtMcsxBCdydjqeW38j8K+GIsfspQ4cs92MBf9yovaVOwP/o6T3StpnGMaLr//sT0zT/JbTgQMBQ1u6WnXJ9mt1OjWuzuZa3f3E3vwb+5+vbNdXftKrP/31S9VYV6XqSFB1VSFVRQydSWX0iVs2KGBIQeX06Aeu0qnUuOqqwoqEDAUChnZs7VJjbURBw1A8GlRbolqXtsZVFw2pJhLUb33uJwXb3nzqmYP6xzuu1ZrGmL61/VqdGE6ruS6q1Q1Tk3us2j19n/b6Gl3RXq+BobRqIkFNZnP6+C2XFTzW6nHTv1/d+MYqHFu6WnXxHdfqyOmUaiIhtcSr8hOMACxebg+sSNLh05OW+0p/6QNXqnWJY2EVCBhqqgvrkfe+WWdGJ1VfE1bWzDlaB6PRkLb2tGlNY40GhsbVEq9ST1tCUQdng892XPCL3lMTury9Vo9+4CoNDKfVEo+qviagI4MTal9a7tahUpxOZfOTbqSpWnTfUwf0Pz/0FnU0VW4s+Jfbte/I6YzlntJf+sCVWubg8Rp+E9SK+ip98f1X6tTIuBprq2SaWUlOXKBwMxb8yu3ad2okaxnv73/3LVrVYH88+NMvBzN6ue+MvvSBq3RyOK2muqi+c+CYVtRXa7nNeXv4dKYs5/Pwl/alMXW21Oq26zqUM6WAIXW21Kp9qTMXgofSWbUvrS6I1760WkPprCPx4E+9p8Ys69/FrVdryUp7V88+WaR/8OgHr9IaxhBQojMjGV3cEn1jDKEuqqpQTmdGMlpSbX+8VwdGLf9GOpreoo2rK2uFeZTPyHhOTXWRgmN2U11EqfHchR/s4VjwLydqX1kn+Zim+UNJZbuaGAgY6miqVUdTra7ImepZntCJ4bQMGfqDx6bmHA2PZ/QX//RS/qL2fTd16bPff1W9g2P5VXM+968v6S0dTQXLK33hfVfp5YHhGSskvPWiRv3ktUHLbW9OjqR1UXNtvk2ltHvahR5T7HFW97moudax7Xcwd9ffsEV9A6eK/v7Y8eO63MX2YHFye2BFKt9epwf6kvr9//nCjFWLHrvtam1YWe9Y3Gg0pCvXuDtCX8pxoZLV10S0u3dIO85Zle++m7rUsyxe7qahgvQl05a1qH8oXdGx4F9u177BEevj9eAIe5OjdGdGJ3R2NKucmdXoeFaDxoQCkmoi9ueRm7HgX27XvhND1n2EE8P0EVC61HhGn/vXX+pz//rLgp9ffVGz7bHKdT4PfwkEDF3f2aym2ir1JdNqS1Srqy3u2JeTzo5m9DffP6Tfve4ijY1nVFMV0t98/xe6Y1OnI/HgTyeGxq2P2UPjtsc6NWIda3DE/ljwr+PJMf3J1/flt5B5eWBEu/Yc01++o0drHBgzPjls3a89OUK/FqVLjk7q0ed7C47Zf/vcL7R9s/3HbDdjwb+cqH3lXsnHE3I5U6+dSqn3dEqxSEjV4aDOjE7oQ9d06KFnD6q+JqJ3XrFCVaGpPfbuevs6HTyZ0hM/PapHnvuFbrvuIj3wzZ/nB9G3b+pUX3JUdz3+Yv6xhiG93D+kS1vr1BKPWm57Ux0O6ke/OKWWuL2rKeRypg4PpjQwlLb9ueG8voFTuvz2Txf9fe/d21xsDRarQMDQpotb1NFY69qqL0211vsBN9U6uz9v0YvsybQ2rHQ0NGw2PJ7JX+SWpj7HHbsO6Ivvv7LMLUMlaUsU2ZvcgT3u3YwF/3K79jXWWedto8PHa/iLIUN9Q2l94p9fzp9X/9GvXaxlDnx11M1Y8C+3a19jkXOjxhi1FqWLV4ct8ygetX9rWHIWdsjlTP3zzwf0ka++8SXav3r3Zfr17lZHxqPqY2HduGGZPvq1Pfl4d92wTktibJ+M0i2psa61S2rsz6NiY6eci2EuJrM5ffCtazQ4OqGcKQUN6YNvXaPJrDOrlDQUyduGGvIWpVsai+jXe9oKjtl3bu7U0ppIRceCfzlR+wJ2NKxS5XKmXjs5oh++elI/7T2tl/qG9NEn9ujFX57Rjq1dCgamvgn73qtX6fM/PKSd335F//Wre3TsbFq79hzTe69epXe/eWV+go80NYj+0LMHFQoGCh77mWdf1d88d0g/PXJW7fU12nnrZYqGp97+aDigB7d1a/s/vKDf/Nuf6Dce+oGePtCvXM605TU+faBfv/HQD2x/blSGo0d/qYvXbyx6O3b8eLmbiAoxverL1R2N6miqdXyyYDAo3XdTV0GtvO+mLoUc3kmhLVGdjzktGg6oNcFyoZWm2LdHz4zy7VGULhoKWNai6rD93Wg3Y8G/3K59ASOnHVsL83bH1i4FAixbjNKNTWbyk26kqZz9xD+/rLHJTEXHgn+5XfuCAetzoyC7zGEOJjJZ/fGWS7R981rdvmmt7ty8Vn+85RJN5hzYisjI6f6t3QU5e//Wbhn0DzAHh06O5Cf4SFPH7I989UUdOjniSLxcztTOb79SEG/nt19hHB1zkjWzln2ErGl/rQ0U6R8EGELAHCSiYY1OZvXIc29cxxydzCrhwCTgKdZ9BNFHwByMZ3L61DMHC47Zn3rmoCZy9ueRm7HgZ/bXvkW7kk8uZ+rZlwd0cGAk/8cZDQf0h29fp8//62v67ava9eZVU9uyPPTswRmTeKZX+dmxtctyEH1oLKN3b1wx47H3PLlPV7Qv0ZauVl2y/VqdGE6rJhzUjw8N6qYNyyVJT/z0qO56/EVdsv3aBW+hcngwld8ybLoNdj03KkPWFCsBoSKNjuf02e+/qg9d0yHDkExT+uz3X9VfbOtxNG5XW1yfuGW9Dp4YyX97YW1zrbraEo7Ghf2aWF0CNjg7lnGtFrkZC/7leu0zA/rv3yvM2//+vVf1yVs2OBMPvjSczlqeV4+k7b8Y4mYs+JjLtW90ojznRvCXmkhI49mcHnnuUMEqJdVh+4eHA2ZAD3/vYEHOPvy9g/or+geYg9cGU5bH7NcGU1rbUmd7vORYpug4P1CqkBG07CM4Uf/G6B/ABqmJrOUEhp7f2ehIPEPWfQTGEDAXw2nrY/awA8dsN2PBv5yofYt2ks/hwZT2Hk3mT2ylqT/Kv/7OK/rQNR0aGs/qpb4hrWmMWf7xGsbUf5uLDKK3xqs0lK4pumd6R1OtOppqtbohpidfPKad33ljotH2TZ368o978/dbiIFZ9m1nkg8ALxsZz6h3cEwPf/fVGT93UiBgKGAECgY+/+rdl7HNYQWayE59e+q+pw7kP8sdW7s0meUiHkrnZi0qV92Dv7hd+06lJizzdjDFqmkoXV00ZHleXRu1f8jCzVjwL7drH30E2GFsMmu5Ssnn32f/RbyTI9Z/IyfpH2AOouGg5TE7GnZmGTP6CLCDm/WP/gHsMDZp/SWIsUmHxhCK/I2cGqGPgNLFq62P2XXV9h+z3YwF/3Ki9i3aDBwYSitnyvLgFQxI1eGgApra13T75rXKmVMr7PQl04qGAzLNqT/iw6dS2r6pM79iTzQc0M5bL9Ob2hIaHs9Y/uE3172x5cvhwZT+5Ov7ZqwUdNt1HQX3m6+WePSCbQAW4vobtqhv4NSs92lradT3vv20Sy2CX9RFQ1rVUK0b1y+X8fr8ml17jqnO4cGVw4Mpy+WgL21jBbRKEwkG9dXdR/TxWzZobCKj6khIjz5/SB/dcmm5m4YK4mYtKlfdg7+4XfvqqqzztraKvEXpxjPZGefV2zd1ajxj/8Cym7HgX27Xvni1dbw4fQTMwci49beQU+P2179a+gewQX1NWHdu7ixYhf/OzZ2qr3FmC5l0JkMfAQtW9LzegfrHGALssLohZnkNcXVDzJF45C3sMDZpfcxOOzA5zc1Y8C8nat+irZot8aiChiwPXpe2xmXK1Eg6o999dHfBH+1ju4/oPRvb9djuI7rrhnV65AevKRIy9Mh7NyocNNQSj2p1Q0yBgKG3rG7Qx961Xnc/sbdgAtC5B8diK+2sa6mz5SC6uiGmnbdelt+yy6oNc5HLmTo8mNLAULrgtcK/jh79pS5eX/xbXceOH9eNf/nErM/xwmfusLtZWASW1IT1B29fp9dOpfLbZv3B29cpUe3UfsBTWAHNPyYzOe3uTWp37wszfg6UKlHtXi1yMxb8y+3aV1MV0H/5lbXaseuNlYPuu6lLNZGAI/HgT8uX1Ogzzx6cmpw2nlFNVUh/9/wh3fCmloqOBf9yu/YtLXJuVB+jj4DSJarDluOgTkwWS1SHrPu1XMDDHFzSEtdrp1K67boO5UwpYEgr6qt1SUvckXjV4ZAe232kYBuFx3Yf0cfetd6RePCnpjrrY3Zjnf3H7Noq61rLhErMRTho6K4b1uVX+5vezjMcdOa6X2Ot9d9IQy39WpSu6DH7nfYfs92MBf9yovYt2qP96oaYelYkZnwb4L6buvQ/vveqrr+kecZWXg89e1Bf+sCVGp/M6tO/ebkmM6Y6m2vVvjSmNY0zJ7uEQgHdtH6ZepYndGI4rea6mZNiiq20c2lr3JbJM4GAoS1drbpk+7VF21CqXM7U0wf6Z0wY2tLVykQ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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data=new_tips)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "3b9fZyXEVMae",
        "outputId": "de5673d1-2a07-47c2-9c6c-b65216f9778b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a9b923490>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ML Algorithm Selection route map  \n",
        "Need Dimension Reduction (NO) --> Is Data Labelled (YES) --> Predicting Numeric value? (YES) --> Speed or Accuracy (SPEED)  \n",
        "  \n",
        "We are considering speed we have a small dataset and its ok for results to be approximate."
      ],
      "metadata": {
        "id": "tutnu8UQSQ5_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data segmentaion to Test and Train"
      ],
      "metadata": {
        "id": "NM1Av8ZPVmft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "_ctC9OakSRWm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(new_tips.drop('tip', axis=1), new_tips['tip'], test_size=0.25, random_state=101)"
      ],
      "metadata": {
        "id": "kjmvGcDvVqi9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     total_bill  size  Male  Female  Yes  No  Thur  Fri  Sat  Sun  Lunch  \\\n",
              "15        21.58     2     1       0    0   1     0    0    0    1      0   \n",
              "98        21.01     2     1       0    1   0     0    1    0    0      0   \n",
              "217       11.59     2     1       0    1   0     0    0    1    0      0   \n",
              "50        12.54     2     1       0    0   1     0    0    0    1      0   \n",
              "2         21.01     3     1       0    0   1     0    0    0    1      0   \n",
              "..          ...   ...   ...     ...  ...  ..   ...  ...  ...  ...    ...   \n",
              "63        18.29     4     1       0    1   0     0    0    1    0      0   \n",
              "70        12.02     2     1       0    0   1     0    0    1    0      0   \n",
              "81        16.66     2     1       0    0   1     1    0    0    0      1   \n",
              "11        35.26     4     0       1    0   1     0    0    0    1      0   \n",
              "95        40.17     4     1       0    1   0     0    1    0    0      0   \n",
              "\n",
              "     Dinner  \n",
              "15        1  \n",
              "98        1  \n",
              "217       1  \n",
              "50        1  \n",
              "2         1  \n",
              "..      ...  \n",
              "63        1  \n",
              "70        1  \n",
              "81        0  \n",
              "11        1  \n",
              "95        1  \n",
              "\n",
              "[183 rows x 12 columns]"
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              "      <th>81</th>\n",
              "      <td>16.66</td>\n",
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              "      <td>1</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YD2zI8A-WOr9",
        "outputId": "bc078734-e32c-4395-d317-c4d68430b3d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15     3.92\n",
              "98     3.00\n",
              "217    1.50\n",
              "50     2.50\n",
              "2      3.50\n",
              "       ... \n",
              "63     3.76\n",
              "70     1.97\n",
              "81     3.40\n",
              "11     5.00\n",
              "95     4.73\n",
              "Name: tip, Length: 183, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Linear Regression for \"Tip\" prediction"
      ],
      "metadata": {
        "id": "Fjp3GP88a4GF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Machine Learning to predict the amount of tip based on customer details for the order"
      ],
      "metadata": {
        "id": "NirtEwarE7TA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets try to apply some ML algorithm to predict the Tip for given parameters "
      ],
      "metadata": {
        "id": "-nM4V4lHL_cn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression, LogisticRegression"
      ],
      "metadata": {
        "id": "VvfHIdfbFLBx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_lin_reg = LinearRegression().fit(x_train,y_train)"
      ],
      "metadata": {
        "id": "jjNyA51hWenx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_lin_reg.score(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmFdTevIWsxn",
        "outputId": "b28b0186-bf94-4513-e1e4-73b51bce12c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5225897232462324"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = model_lin_reg.predict(x_test)\n",
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGWUHgLrW0Hb",
        "outputId": "f49169d3-8832-4e5b-daa9-ef25d0c44d73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.2439024 , 2.78499868, 2.235506  , 3.84561198, 3.79050231,\n",
              "       2.0421289 , 6.37006824, 2.39627188, 1.39935313, 1.90177619,\n",
              "       3.15741547, 2.7027649 , 1.91873185, 2.18750534, 2.27002501,\n",
              "       2.53641449, 2.41351344, 3.25133374, 2.27480067, 2.90838302,\n",
              "       2.50144344, 3.87257603, 4.15846145, 2.31111734, 4.11755709,\n",
              "       1.9679479 , 2.91788982, 3.69348189, 5.05553619, 3.56934082,\n",
              "       5.61071853, 4.0387159 , 2.48658026, 6.65238868, 3.10339678,\n",
              "       2.43905861, 3.19300177, 2.34351689, 3.62430755, 3.9163279 ,\n",
              "       2.842352  , 5.06066491, 4.3883719 , 5.86366089, 3.43403968,\n",
              "       2.08608427, 3.38918317, 4.56047677, 1.85157136, 2.81592443,\n",
              "       3.48064027, 3.13577642, 2.75803693, 4.05180325, 4.87009124,\n",
              "       2.61165522, 2.32974042, 2.66750796, 2.94756978, 3.2790334 ,\n",
              "       4.260655  ])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Metrics for evaluation"
      ],
      "metadata": {
        "id": "nHzChevdXQ6g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "U6WgEjAtW5SP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.mean_squared_error(y_test, prediction))\n",
        "print(metrics.mean_absolute_error(y_test, prediction))\n",
        "print(np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgw2UTRfXXF7",
        "outputId": "a06c8c71-b041-41c8-e6dc-607c73264399"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.456385271636951\n",
            "0.8927042298137938\n",
            "1.2068078851403612\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coef = pd.DataFrame(model_lin_reg.coef_, new_tips.drop('tip', axis=1).columns)\n",
        "coef"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "FKG06-RHX5cz",
        "outputId": "f9c9ec70-555f-4d86-d853-0a328fa5751a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   0\n",
              "total_bill  0.105973\n",
              "size        0.173799\n",
              "Male       -0.019831\n",
              "Female      0.019831\n",
              "Yes        -0.110714\n",
              "No          0.110714\n",
              "Thur       -0.084923\n",
              "Fri         0.126528\n",
              "Sat         0.003359\n",
              "Sun        -0.044964\n",
              "Lunch       0.082361\n",
              "Dinner     -0.082361"
            ],
            "text/html": [
              "\n",
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              "      <th>Female</th>\n",
              "      <td>0.019831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>-0.110714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>0.110714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <td>-0.084923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fri</th>\n",
              "      <td>0.126528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <td>0.003359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <td>-0.044964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lunch</th>\n",
              "      <td>0.082361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dinner</th>\n",
              "      <td>-0.082361</td>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### MultiClass Classification for Customer Gender prediction"
      ],
      "metadata": {
        "id": "XvFYvcxObQPd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train1, x_test1, y_train1, y_test1 = train_test_split(new_tips.drop(['Male','Female'], axis=1), new_tips[['Male','Female']], test_size=0.25, random_state=101)"
      ],
      "metadata": {
        "id": "L-6yvKNJYTeP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "gUubTKyLRTGu",
        "outputId": "794b2e8b-b098-4960-e6da-dc00ab178926"
      },
      "execution_count": null,
      "outputs": [
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              "     total_bill   tip  size  Yes  No  Thur  Fri  Sat  Sun  Lunch  Dinner\n",
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              "..          ...   ...   ...  ...  ..   ...  ...  ...  ...    ...     ...\n",
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              "81        16.66  3.40     2    0   1     1    0    0    0      1       0\n",
              "11        35.26  5.00     4    0   1     0    0    0    1      0       1\n",
              "95        40.17  4.73     4    1   0     0    1    0    0      0       1\n",
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          },
          "metadata": {},
          "execution_count": 35
        }
      ]
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    {
      "cell_type": "code",
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        "y_train1"
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        "id": "n5D0YkF4RXqV",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Male  Female\n",
              "15      1       0\n",
              "98      1       0\n",
              "217     1       0\n",
              "50      1       0\n",
              "2       1       0\n",
              "..    ...     ...\n",
              "63      1       0\n",
              "70      1       0\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>183 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75cc1a97-339d-4aaa-9f8e-9ad63f632700')\"\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-75cc1a97-339d-4aaa-9f8e-9ad63f632700 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-75cc1a97-339d-4aaa-9f8e-9ad63f632700');\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": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Two column classification using Linear Regression.  \n",
        "As we can see that the two column arrays are predicted in real numbers which are similar to probability of prediction respective classes. "
      ],
      "metadata": {
        "id": "UqDDM7vDXaEN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_lin_reg_2 = LinearRegression().fit(x_train1,y_train1)"
      ],
      "metadata": {
        "id": "o67H1rBuR9xl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction1 = model_lin_reg_2.predict(x_test1)\n",
        "prediction1"
      ],
      "metadata": {
        "id": "CQuxTUxdR_w_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d092acff-1169-4025-cf4e-de54072db2e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.71160241, 0.28839759],\n",
              "       [0.73660761, 0.26339239],\n",
              "       [0.62273409, 0.37726591],\n",
              "       [0.79591808, 0.20408192],\n",
              "       [0.76944628, 0.23055372],\n",
              "       [0.72607985, 0.27392015],\n",
              "       [0.85707799, 0.14292201],\n",
              "       [0.40226429, 0.59773571],\n",
              "       [0.56501771, 0.43498229],\n",
              "       [0.47215111, 0.52784889],\n",
              "       [0.74128768, 0.25871232],\n",
              "       [0.72357972, 0.27642028],\n",
              "       [0.47517805, 0.52482195],\n",
              "       [0.3946545 , 0.6053455 ],\n",
              "       [0.64379122, 0.35620878],\n",
              "       [0.72842114, 0.27157886],\n",
              "       [0.61392453, 0.38607547],\n",
              "       [0.68103548, 0.31896452],\n",
              "       [0.48484587, 0.51515413],\n",
              "       [0.76232968, 0.23767032],\n",
              "       [0.73477301, 0.26522699],\n",
              "       [0.50853194, 0.49146806],\n",
              "       [0.7231664 , 0.2768336 ],\n",
              "       [0.70679227, 0.29320773],\n",
              "       [0.68612206, 0.31387794],\n",
              "       [0.72541397, 0.27458603],\n",
              "       [0.72233242, 0.27766758],\n",
              "       [0.49226614, 0.50773386],\n",
              "       [0.59622528, 0.40377472],\n",
              "       [0.55011626, 0.44988374],\n",
              "       [0.75886422, 0.24113578],\n",
              "       [0.73142466, 0.26857534],\n",
              "       [0.72478742, 0.27521258],\n",
              "       [0.93033542, 0.06966458],\n",
              "       [0.62390828, 0.37609172],\n",
              "       [0.49050525, 0.50949475],\n",
              "       [0.80058316, 0.19941684],\n",
              "       [0.71053816, 0.28946184],\n",
              "       [0.7854908 , 0.2145092 ],\n",
              "       [0.57519503, 0.42480497],\n",
              "       [0.53441643, 0.46558357],\n",
              "       [0.81839222, 0.18160778],\n",
              "       [0.8006399 , 0.1993601 ],\n",
              "       [0.65021195, 0.34978805],\n",
              "       [0.66948417, 0.33051583],\n",
              "       [0.64491318, 0.35508682],\n",
              "       [0.51485508, 0.48514492],\n",
              "       [0.81559319, 0.18440681],\n",
              "       [0.38210122, 0.61789878],\n",
              "       [0.40856412, 0.59143588],\n",
              "       [0.81032829, 0.18967171],\n",
              "       [0.79997648, 0.20002352],\n",
              "       [0.49648945, 0.50351055],\n",
              "       [0.79936079, 0.20063921],\n",
              "       [0.63993935, 0.36006065],\n",
              "       [0.73860424, 0.26139576],\n",
              "       [0.70466228, 0.29533772],\n",
              "       [0.49746064, 0.50253936],\n",
              "       [0.77925495, 0.22074505],\n",
              "       [0.45765404, 0.54234596],\n",
              "       [0.74847993, 0.25152007]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.multioutput import MultiOutputClassifier \n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "8fJJBISMU92o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_multi_class = MultiOutputClassifier(KNeighborsClassifier()).fit(x_train1,y_train1)"
      ],
      "metadata": {
        "id": "HoP0TQAsRaIm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction2 = model_multi_class.predict(x_test1)\n",
        "prediction2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qEQ7awoMRaPc",
        "outputId": "c4dceef2-8e5a-4aa1-9a55-1345768a99b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [0, 1],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [0, 1],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [0, 1],\n",
              "       [1, 0],\n",
              "       [0, 1],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [0, 1],\n",
              "       [0, 1],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [0, 1],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [0, 1],\n",
              "       [0, 1],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [0, 1],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Metrics for evaluation"
      ],
      "metadata": {
        "id": "8lafL8MVbjk0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "J4FGHDFbbmiM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf = confusion_matrix(y_test1.values.argmax(axis=1), prediction2.argmax(axis=1))\n",
        "cf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDiE8q30mpSC",
        "outputId": "5d6d56b5-35bb-435e-c1b7-a989abac043c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[33,  7],\n",
              "       [18,  3]])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Confusion Matrix plotting"
      ],
      "metadata": {
        "id": "_3vJ5LHApBqe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [\"{0:.2%}\".format(value) for value in cf.flatten()/np.sum(cf)]\n",
        "\n",
        "labels = np.asarray(labels).reshape(2,2)\n",
        "\n",
        "ax = sns.heatmap(cf, annot=labels, fmt='', cmap='Blues')\n",
        "\n",
        "ax.set_title(\"Confusion Matrix\")\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "kA2cZcelpAna",
        "outputId": "988118c4-5359-4e6b-f41b-aa2dcad8fd30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test1.values.argmax(axis=1), prediction2.argmax(axis=1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZDOg0A2Jmpa3",
        "outputId": "d0640852-047a-4cd7-841b-aa3c6716aa3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.65      0.82      0.73        40\n",
            "           1       0.30      0.14      0.19        21\n",
            "\n",
            "    accuracy                           0.59        61\n",
            "   macro avg       0.47      0.48      0.46        61\n",
            "weighted avg       0.53      0.59      0.54        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_report=classification_report(y_test1.values.argmax(axis=1), prediction2.argmax(axis=1), output_dict=True)\n",
        "sns.heatmap(pd.DataFrame(clf_report).iloc[:-1, :].T, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "urMTE8swuIEs",
        "outputId": "2a9a996b-bb93-46ba-ebfc-697089104952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a98ab0750>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion  \n",
        "As we see that after trying various algorithms we are able to achieve 60% accuracy and cannot push it further.  \n",
        "Now, since that dataset is small this is as expected."
      ],
      "metadata": {
        "id": "1IY1S1mdH5OQ"
      }
    }
  ]
}