Pipe_Analysis_3.ipynb

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  "metadata": {
    "colab": {
      "name": "Copia de Pipe Analysis 2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNnpIkN7XWHz+XlWBe8KThs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pabloderen/BIMinferer/blob/master/Pipe_Analysis_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeBe9Qe18MzP",
        "colab_type": "text"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHjuBxluIVMr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "66f3ebb5-0a24-473d-8066-2c438cfc9112"
      },
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "import urllib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "import numpy as np\n",
        "\n",
        "import joblib"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tho_BSd-8ZPH",
        "colab_type": "text"
      },
      "source": [
        "# Download the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUK3RdE1uwiK",
        "colab_type": "text"
      },
      "source": [
        "We download from de API in Flasgger (http://3.14.88.102:5000/apidocs/) the Pipe list item and it´s parameters\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8evbTkGaURM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "jsonfile = urllib.request.urlopen(\"http://3.14.88.102:5000/api/1/elements/pipes/?limit=6500\").read().decode()\n",
        "j=json.loads(jsonfile)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZAw2F-Mu2KX",
        "colab_type": "text"
      },
      "source": [
        "Then we make a Data Frame with the json file"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfjKOj7w8g1h",
        "colab_type": "text"
      },
      "source": [
        "# Create the DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDuejJ1WaoY_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "6ff5d433-0590-4259-c7b2-27115fc3ea3f"
      },
      "source": [
        "df= pd.json_normalize(j)\n",
        "df.head(3)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>projectId</th>\n",
              "      <th>Category</th>\n",
              "      <th>modelId</th>\n",
              "      <th>_id.$oid</th>\n",
              "      <th>properties_Dimensions_Length</th>\n",
              "      <th>properties_Dimensions_Size</th>\n",
              "      <th>properties_Mechanical_Material</th>\n",
              "      <th>properties_Mechanical_System Classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee8651cb573f2fc2216a82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee8651cb573f2fc2216a83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee8651cb573f2fc2216a84</td>\n",
              "      <td>0.398 ft</td>\n",
              "      <td>2 1/2\"</td>\n",
              "      <td>Copper</td>\n",
              "      <td>Sanitary</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                projectId  ... properties_Mechanical_System Classification\n",
              "0  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                         NaN\n",
              "1  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                         NaN\n",
              "2  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                    Sanitary\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRqkmUdp9AgZ",
        "colab_type": "text"
      },
      "source": [
        "# Material Edition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhPq5G4TvKTg",
        "colab_type": "text"
      },
      "source": [
        "we can see how many different materials we have, so then we will \n",
        "group by PVC, Copper and Steel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgAbL4g_bzz-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "1ed0cf77-c2f2-4053-a4e8-ad3df75eeee6"
      },
      "source": [
        "df[\"properties_Mechanical_Material\"].unique()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'Copper', 'Steel, Carbon', '<By Category>',\n",
              "       'Polyvinyl Chloride - Rigid', 'CP_PI - Copper - ASTM B88 H.D.',\n",
              "       'CP_PI - Steel - ASTM A53-B', 'CP_PI - PVC', 'Cast Iron',\n",
              "       'Black Steel', 'Carbon Steel', 'Ductile Iron',\n",
              "       'Polyvinyl Chloride, Rigid', 'Carbon Steel - ASTM A53 B'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgBVVzDvvX9G",
        "colab_type": "text"
      },
      "source": [
        "To make such a grouping, we define a function that search in the \"properties_Mechanical_Material\" and change each row that has another different name for the same material"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTHogQgzb2l2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def search(data):\n",
        "   \n",
        "    if \"A53\" in data.upper():\n",
        "        return \"Carbon Steel - ASTM A53 B\"\n",
        "    if \"CATEGORY\" in data.upper():\n",
        "        return \"NaN\"\n",
        "    if \"POLYVINYL\" in data.upper():\n",
        "        return \"PVC\"\n",
        "    if \"PVC\" in data.upper():\n",
        "        return \"PVC\"\n",
        "    if \"NAN\" in data.upper():\n",
        "        return \"NaN\"\n",
        "    else:\n",
        "        return data"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybOsaokEvXE9",
        "colab_type": "text"
      },
      "source": [
        "For practical purpuses, we will make a copy of the DataFrame, \n",
        "then apply the function in each row of the \"properties_Mechanical_Material\" column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plRdbEgthiWL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2=df\n",
        "df2[\"properties_Mechanical_Material\"]=df2[\"properties_Mechanical_Material\"].apply(lambda x: search(str(x)))\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOwwSG9NvlL6",
        "colab_type": "text"
      },
      "source": [
        "here is the amount of each material kind"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iT4kvHHviEZA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "4287fa45-edc1-4fe0-d876-8b9fb27550d7"
      },
      "source": [
        "\n",
        "df2[\"properties_Mechanical_Material\"].value_counts()\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PVC                               1745\n",
              "Carbon Steel                      1737\n",
              "Carbon Steel - ASTM A53 B         1322\n",
              "Copper                            1102\n",
              "CP_PI - Copper - ASTM B88 H.D.     424\n",
              "Steel, Carbon                      121\n",
              "NaN                                 41\n",
              "Black Steel                          4\n",
              "Cast Iron                            2\n",
              "Ductile Iron                         2\n",
              "Name: properties_Mechanical_Material, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWwVmgDth6Z0",
        "colab_type": "text"
      },
      "source": [
        "Here we can see a graphic of the different amount of each materials:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kFswPbNH843V",
        "colab_type": "text"
      },
      "source": [
        "# Graphics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPtZ6q2UZydL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "eaccfcea-5264-4385-8727-257aeac03e73"
      },
      "source": [
        "\n",
        "plt.figure(figsize=(20,4))\n",
        "ax=sns.countplot(y=\"properties_Mechanical_Material\",data=df2,order=df2[\"properties_Mechanical_Material\"].value_counts().index)\n",
        "ax.set( ylabel=\"Material\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Material')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYFuxyS8i3ww",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "e3f4d17d-9314-4135-bbdf-38643ce202eb"
      },
      "source": [
        "df2[\"properties_Mechanical_System Classification\"].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hydronic Supply        1668\n",
              "Hydronic Return        1523\n",
              "Sanitary               1499\n",
              "Domestic Cold Water     730\n",
              "Vent                    417\n",
              "Domestic Hot Water      321\n",
              "Other                   187\n",
              "Fire Protection Dry     116\n",
              "Fire Protection Wet       2\n",
              "Name: properties_Mechanical_System Classification, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "785lV-VNamPz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l064bxxYqmhp",
        "colab_type": "text"
      },
      "source": [
        "Here we have an graphic of the system"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4IMTSsYlD3z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "2e89d648-085d-44ac-b887-bf80bba6a7cb"
      },
      "source": [
        "plt.figure(figsize=(20,4))\n",
        "ax=sns.countplot(y=\"properties_Mechanical_System Classification\",data=df2,order=df2[\"properties_Mechanical_System Classification\"].value_counts().index)\n",
        "ax.set( ylabel=\"System Classification\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'System Classification')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WaVPOze3952n",
        "colab_type": "text"
      },
      "source": [
        "# Length Edition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3BhD3XMvrpr",
        "colab_type": "text"
      },
      "source": [
        "We drop the rows with properties_Dimensions_Length NaN values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M75QW1_8neCa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df3=df2.dropna(axis=0,subset=[\"properties_Dimensions_Length\"])\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcNUU0BYvwqm",
        "colab_type": "text"
      },
      "source": [
        "Her we start to convert the properties_Dimensions_Length from string to float \n",
        "(we use x.replace(\" ft\",\"\") and x.replace(\"  ft\",\"\"), notice the spaces in each case)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNgt0y4mlQti",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "0383628f-ac33-40ba-84f7-e6813fafcd8b"
      },
      "source": [
        "df3[\"properties_Dimensions_Length\"]= df3[\"properties_Dimensions_Length\"].apply(lambda x: x.replace(\" ft\",\"\"))\n",
        "df3[\"properties_Dimensions_Length\"]= df3[\"properties_Dimensions_Length\"].apply(lambda x: x.replace(\"  ft\",\"\"))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHkyOp41o4ai",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "974b83ad-75cd-4f0c-ba8c-be2ee3a6b984"
      },
      "source": [
        "df3[\"properties_Dimensions_Length\"]= df3[\"properties_Dimensions_Length\"].apply(lambda x: float(x))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDqTjUIQ-TO0",
        "colab_type": "text"
      },
      "source": [
        "# Size Edition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDSSSckRv-Nv",
        "colab_type": "text"
      },
      "source": [
        "we are analazing pipes, so we know that the dimensions Sizes are the diameter sizes. Then we can see that because a Size has the diameter simbol, creates another dimension. So we will take of the diameter simbol of all the column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGjqjizjo7Xx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "d46935b3-4d8b-4ebb-976d-e2f51c8d9a6c"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"].value_counts().head(5)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2\"     643\n",
              "4\"ø    612\n",
              "6\"ø    549\n",
              "4\"     522\n",
              "3\"     436\n",
              "Name: properties_Dimensions_Size, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPl-4Ya6qSIj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "646cfa87-99a4-42d3-fec5-09701f896549"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"]= df3[\"properties_Dimensions_Size\"].apply(lambda x: x.replace(\"ø\",\"\"))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2na5JNpnwHjp",
        "colab_type": "text"
      },
      "source": [
        "Here we eliminate any leading and trailing whitespaces"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5oBxJ0zxsBcc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "368535ba-c616-445e-b1a1-c0cf0cd0e6bb"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"]= df3[\"properties_Dimensions_Size\"].apply(lambda x: x.strip())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cU7zWrMusOSS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "6e5a87eb-206d-49f8-ca27-a3739f7189cc"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"].value_counts()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4\"        1152\n",
              "2\"         843\n",
              "3/4\"       745\n",
              "6\"         719\n",
              "3\"         704\n",
              "1\"         598\n",
              "2 1/2\"     340\n",
              "1 1/2\"     310\n",
              "1 1/4\"     309\n",
              "1/2\"       241\n",
              "16\"        146\n",
              "14\"         96\n",
              "8\"          88\n",
              "12\"         64\n",
              "10\"         60\n",
              "5\"          22\n",
              "30\"         22\n",
              "36\"          2\n",
              "1 3/8\"       2\n",
              "Name: properties_Dimensions_Size, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m17CZx9CpzrU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "2d0fbb43-9fb3-40c4-d747-0c6bf7e61c37"
      },
      "source": [
        "plt.figure(figsize=(20,4))\n",
        "ax=sns.countplot(x=\"properties_Dimensions_Size\",data=df3,order=df3[\"properties_Dimensions_Size\"].value_counts().index)\n",
        "ax.set( xlabel= \"Dimensions Size\")"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 0, 'Dimensions Size')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dn1gNV3CYFBx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "8ff9ac08-9e97-47c6-8d09-1a1e39e89f25"
      },
      "source": [
        "df3[\"Size_F\"]= df3[\"properties_Dimensions_Size\"].apply(lambda x: x.replace('\"',\"\"))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyB3roHBXmKm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convert_to_float(frac_str):\n",
        "    try:\n",
        "        return float(frac_str)\n",
        "    except ValueError:\n",
        "        try:\n",
        "            num, denom = frac_str.split('/')\n",
        "        except ValueError:\n",
        "            return None\n",
        "        try:\n",
        "            leading, num = num.split(' ')\n",
        "        except ValueError:\n",
        "            return float(num) / float(denom)        \n",
        "        if float(leading) < 0:\n",
        "            sign_mult = -1\n",
        "        else:\n",
        "            sign_mult = 1\n",
        "        return float(leading) + sign_mult * (float(num) / float(denom))"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-VuBHv0X1N2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "e1beba9a-acd9-4cb2-fb88-86f53f391fb1"
      },
      "source": [
        "df3[\"Size_F\"]= df3[\"Size_F\"].apply(lambda x: convert_to_float(x))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9tVJ3bzYVO-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "8839c0b3-8933-45b8-c9ca-f7af2be542eb"
      },
      "source": [
        "df_3d=df3[[\"Size_F\",\"properties_Dimensions_Length\",\"properties_Mechanical_System Classification\",\"properties_Mechanical_Material\"]]\n",
        "df_3d"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Size_F</th>\n",
              "      <th>properties_Dimensions_Length</th>\n",
              "      <th>properties_Mechanical_System Classification</th>\n",
              "      <th>properties_Mechanical_Material</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.5</td>\n",
              "      <td>0.398</td>\n",
              "      <td>Sanitary</td>\n",
              "      <td>Copper</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.5</td>\n",
              "      <td>2.536</td>\n",
              "      <td>Sanitary</td>\n",
              "      <td>Steel, Carbon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.5</td>\n",
              "      <td>0.031</td>\n",
              "      <td>Sanitary</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.5</td>\n",
              "      <td>0.031</td>\n",
              "      <td>Sanitary</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.5</td>\n",
              "      <td>0.736</td>\n",
              "      <td>Other</td>\n",
              "      <td>Steel, Carbon</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>6495</th>\n",
              "      <td>3.0</td>\n",
              "      <td>4.218</td>\n",
              "      <td>Hydronic Return</td>\n",
              "      <td>Carbon Steel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6496</th>\n",
              "      <td>3.0</td>\n",
              "      <td>4.218</td>\n",
              "      <td>Hydronic Return</td>\n",
              "      <td>Carbon Steel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6497</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.078</td>\n",
              "      <td>Hydronic Return</td>\n",
              "      <td>Carbon Steel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6498</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.078</td>\n",
              "      <td>Hydronic Return</td>\n",
              "      <td>Carbon Steel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6499</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.960</td>\n",
              "      <td>Hydronic Return</td>\n",
              "      <td>Carbon Steel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6463 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Size_F  ...  properties_Mechanical_Material\n",
              "2        2.5  ...                          Copper\n",
              "4        2.5  ...                   Steel, Carbon\n",
              "5        2.5  ...                             NaN\n",
              "6        2.5  ...                             NaN\n",
              "7        2.5  ...                   Steel, Carbon\n",
              "...      ...  ...                             ...\n",
              "6495     3.0  ...                    Carbon Steel\n",
              "6496     3.0  ...                    Carbon Steel\n",
              "6497     3.0  ...                    Carbon Steel\n",
              "6498     3.0  ...                    Carbon Steel\n",
              "6499     6.0  ...                    Carbon Steel\n",
              "\n",
              "[6463 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdaOoBGkasMI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2[\"properties_Mechanical_System Classification\"].value_counts().index\n",
        "Class_1={}\n",
        "j=1\n",
        "for i in df2[\"properties_Mechanical_System Classification\"].value_counts().index:\n",
        "  Class_1[i]=j\n",
        "  j=j+1"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJaffvUpbiL_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "55f7b56c-54d0-4607-ac3c-5b063380dee7"
      },
      "source": [
        "Class_1"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Domestic Cold Water': 4,\n",
              " 'Domestic Hot Water': 6,\n",
              " 'Fire Protection Dry': 8,\n",
              " 'Fire Protection Wet': 9,\n",
              " 'Hydronic Return': 2,\n",
              " 'Hydronic Supply': 1,\n",
              " 'Other': 7,\n",
              " 'Sanitary': 3,\n",
              " 'Vent': 5}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1kLzoBlb1fv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "2966f173-deb1-48ef-a4dd-3c843cfc071a"
      },
      "source": [
        "df_3d[\"Classification\"]= df_3d[\"properties_Mechanical_System Classification\"].apply(lambda x: Class_1[x])"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tr_aE7yLkruf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "dc7fed79-75b3-4084-f57a-90ba0955ad53"
      },
      "source": [
        "dicc\n",
        "df_3d[\"Mate_n\"]= df_3d[\"properties_Mechanical_Material\"].apply(lambda x: dicc[x])"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPcjU791gtfF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "b2aa4f7e-2561-48d8-95d1-4cc024cd5243"
      },
      "source": [
        "df_3d[\"Size_F\"].value_counts()"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.000     1152\n",
              "2.000      843\n",
              "0.750      745\n",
              "6.000      719\n",
              "3.000      704\n",
              "1.000      598\n",
              "2.500      340\n",
              "1.500      310\n",
              "1.250      309\n",
              "0.500      241\n",
              "16.000     146\n",
              "14.000      96\n",
              "8.000       88\n",
              "12.000      64\n",
              "10.000      60\n",
              "30.000      22\n",
              "5.000       22\n",
              "1.375        2\n",
              "36.000       2\n",
              "Name: Size_F, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTpx7YHJZrvW",
        "colab_type": "text"
      },
      "source": [
        "# 3DGraphic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S83ozeDZZyb8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 862
        },
        "outputId": "dbdbc542-a5c6-4432-c80d-1bba18b80af0"
      },
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "group=(1,2,3,4,5,6,7,8,9)\n",
        "fig = plt.figure(figsize=(15,15))\n",
        "ax = fig.add_subplot(projection='3d')\n",
        "cb=ax.scatter(df_3d.Size_F, df_3d.properties_Dimensions_Length, df_3d.Classification,c=df_3d.Mate_n, linewidth=0.1,label=group)\n",
        "ax.set_xlabel('Size [inches]')\n",
        "ax.set_ylabel('Lenght [ft]')\n",
        "ax.set_zlabel('Classification')\n"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Classification')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZi9uQtymMqk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "dee518e8-7cd6-45ed-fc3c-2da18633ca21"
      },
      "source": [
        "\n",
        "df2[\"properties_Mechanical_Material\"].value_counts()\n"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PVC                               1745\n",
              "Carbon Steel                      1737\n",
              "Carbon Steel - ASTM A53 B         1322\n",
              "Copper                            1102\n",
              "CP_PI - Copper - ASTM B88 H.D.     424\n",
              "Steel, Carbon                      121\n",
              "NaN                                 41\n",
              "Black Steel                          4\n",
              "Cast Iron                            2\n",
              "Ductile Iron                         2\n",
              "Name: properties_Mechanical_Material, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujOqPXMD-hTc",
        "colab_type": "text"
      },
      "source": [
        "# Enlist Materials"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INS87A1Dr6VT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "g1=(df_3d[\"Size_F\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RC1L-PFYwRVk",
        "colab_type": "text"
      },
      "source": [
        "The \"\"properties_Mechanical_Material\" are 10 different types. Our target of inference will be the differents kinds of materials"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3k5FOAuKzDk-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        },
        "outputId": "d5036c2b-060c-4bff-ceb7-33650cf3b3ed"
      },
      "source": [
        " lista = df3[\"properties_Mechanical_Material\"].unique()\n",
        "lista"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Copper', 'Steel, Carbon', 'NaN', 'PVC',\n",
              "       'CP_PI - Copper - ASTM B88 H.D.', 'Carbon Steel - ASTM A53 B',\n",
              "       'Cast Iron', 'Black Steel', 'Carbon Steel', 'Ductile Iron'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_J6RFunyEKN",
        "colab_type": "text"
      },
      "source": [
        "Set a number designator to all properties_Mechanical_System Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6Kv2FqT4lPJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dicc={}\n",
        "i=0\n",
        "for i in range(0,10):\n",
        "  dicc[lista[i]]=i"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QiZ6DhR8yJjX",
        "colab_type": "text"
      },
      "source": [
        "we create a column with this designation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrHPPEAY7HLW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "a42fc407-1ee0-4edd-9fae-47c5572fd41d"
      },
      "source": [
        "df3[\"Material_Number\"]=df3[\"properties_Mechanical_Material\"].apply(lambda x: dicc[x])"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I6s_CtQc-24D",
        "colab_type": "text"
      },
      "source": [
        "# Dummies Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t7saSN21yTX-",
        "colab_type": "text"
      },
      "source": [
        "Then we get the dummies properties_Dimensions_Size and properties Mechanical System Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AmyPgZCRAvew",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "4b44a8bb-bb52-49d1-c074-00f03ccc8201"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"].nunique()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ycVwcKjCgvR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classification= pd.get_dummies(df3[\"properties_Mechanical_System Classification\"])\n"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqO2WjXXC68e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Dimensions_Size= pd.get_dummies(df3[\"properties_Dimensions_Size\"])\n"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDwJ9-vADJn-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df4 = pd.concat([df3,classification,Dimensions_Size],axis=1)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Km2r9PUNyahz",
        "colab_type": "text"
      },
      "source": [
        "We eliminate some columns that we will not use "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZk1FdqEDb71",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "droplist=[\"Category\",\"properties_Mechanical_System Classification\",\"properties_Mechanical_Material\"\n",
        ",\"properties_Dimensions_Size\", \"projectId\", \"modelId\",\"_id.$oid\",\"Size_F\"]\n",
        "df5=df4.drop(droplist, axis=1)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stq6cB-oDdfN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "outputId": "7fa9e8d5-9ab3-4002-e96c-767ce9b1b085"
      },
      "source": [
        "df5.head(3).columns\n"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['properties_Dimensions_Length', 'Material_Number',\n",
              "       'Domestic Cold Water', 'Domestic Hot Water', 'Fire Protection Dry',\n",
              "       'Fire Protection Wet', 'Hydronic Return', 'Hydronic Supply', 'Other',\n",
              "       'Sanitary', 'Vent', '1 1/2\"', '1 1/4\"', '1 3/8\"', '1\"', '1/2\"', '10\"',\n",
              "       '12\"', '14\"', '16\"', '2 1/2\"', '2\"', '3\"', '3/4\"', '30\"', '36\"', '4\"',\n",
              "       '5\"', '6\"', '8\"'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nI1P23i8_QeH",
        "colab_type": "text"
      },
      "source": [
        "# Train Test Split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W96izbzx0jOk",
        "colab_type": "text"
      },
      "source": [
        "Here we define the size of our test size. We will use a 30% of the download data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5QHgzA_E6uo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df5.drop('Material_Number',axis=1)\n",
        "y = df5['Material_Number']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=101)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u20TPA6H_hFo",
        "colab_type": "text"
      },
      "source": [
        "# Model Fit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xInldBn0zXE",
        "colab_type": "text"
      },
      "source": [
        " Here we train our Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByAQB0N0GTun",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "outputId": "6c46d06f-3eca-47c4-c810-296d5d17e443"
      },
      "source": [
        "rfc = RandomForestClassifier(n_estimators=200)\n",
        "rfc.fit(X_train,y_train)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADnCoczu_5sZ",
        "colab_type": "text"
      },
      "source": [
        "# Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YN89tZcuGqGP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "f95957e2-0a97-4887-9aa2-58f5644bf56e"
      },
      "source": [
        "\n",
        "predictions = rfc.predict(X_test)\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(classification_report(y_test,predictions))\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.83      0.88       115\n",
            "           1       1.00      1.00      1.00        18\n",
            "           3       0.99      1.00      1.00       175\n",
            "           4       0.71      0.89      0.79        38\n",
            "           5       0.97      0.97      0.97       130\n",
            "           8       0.94      0.96      0.95       171\n",
            "\n",
            "    accuracy                           0.95       647\n",
            "   macro avg       0.93      0.94      0.93       647\n",
            "weighted avg       0.95      0.95      0.95       647\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1s3ak8f7AA_5",
        "colab_type": "text"
      },
      "source": [
        "# Dispersion Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jlEJIXkl-6mr",
        "colab_type": "text"
      },
      "source": [
        "Here we compare the Mean Absolute error of the inferences made with and without consider the lenghts of the pipes.\n",
        "\n",
        "-With Pipe Lengths MAE= 0.23\n",
        "\n",
        "-Without Pipe Lengths MAE= 0.69\n",
        "\n",
        "So we note that the lengths give us more dispersion, and we will take off from the inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRD3rOERGxMO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "2d505809-cec6-4d31-d1a1-a4054d4c30ec"
      },
      "source": [
        "score = mean_absolute_error(y_test,predictions)\n",
        "print('MAE:', score)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE: 0.25193199381761977\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQ5L0J7SBW7q",
        "colab_type": "text"
      },
      "source": [
        "# Sección nueva"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbQCE_M6G8yH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "f6802aae-5e1a-4460-cc8a-5a015e45d6cf"
      },
      "source": [
        "dicc"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Black Steel': 7,\n",
              " 'CP_PI - Copper - ASTM B88 H.D.': 4,\n",
              " 'Carbon Steel': 8,\n",
              " 'Carbon Steel - ASTM A53 B': 5,\n",
              " 'Cast Iron': 6,\n",
              " 'Copper': 0,\n",
              " 'Ductile Iron': 9,\n",
              " 'NaN': 2,\n",
              " 'PVC': 3,\n",
              " 'Steel, Carbon': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWXHDM-5A9CL",
        "colab_type": "text"
      },
      "source": [
        "# Download the Data (second part)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kh3hXCHy1Hc3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "jsonfile2= urllib.request.urlopen(\"http://3.14.88.102:5000/api/1/elements/pipes/?limit=6500\").read().decode()\n",
        "j2=json.loads(jsonfile2)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixwG52fJAro0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "c3c4cd56-2b63-43a3-c9a5-fbeb45e1534d"
      },
      "source": [
        "dataf= pd.json_normalize(j2)\n",
        "dataf.head(3)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>projectId</th>\n",
              "      <th>Category</th>\n",
              "      <th>modelId</th>\n",
              "      <th>_id.$oid</th>\n",
              "      <th>properties_Dimensions_Length</th>\n",
              "      <th>properties_Dimensions_Size</th>\n",
              "      <th>properties_Mechanical_Material</th>\n",
              "      <th>properties_Mechanical_System Classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee8651cb573f2fc2216a82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee8651cb573f2fc2216a83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee8651cb573f2fc2216a84</td>\n",
              "      <td>0.398 ft</td>\n",
              "      <td>2 1/2\"</td>\n",
              "      <td>Copper</td>\n",
              "      <td>Sanitary</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                projectId  ... properties_Mechanical_System Classification\n",
              "0  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                         NaN\n",
              "1  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                         NaN\n",
              "2  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                    Sanitary\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6xIdn7aBZjF",
        "colab_type": "text"
      },
      "source": [
        "# Data to Infer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPjeCVg8CdM8",
        "colab_type": "text"
      },
      "source": [
        "For a given Item, we infer what material it could be. In this case we toke the 100th elemnt"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZV4-99kBWSr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "outputId": "47dc5f72-6bab-4abf-b362-06bf769afcce"
      },
      "source": [
        "eli=dataf.iloc[55].to_frame().T\n",
        "#First individualizate the elemnt\n",
        "eli\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>projectId</th>\n",
              "      <th>Category</th>\n",
              "      <th>modelId</th>\n",
              "      <th>_id.$oid</th>\n",
              "      <th>properties_Dimensions_Length</th>\n",
              "      <th>properties_Dimensions_Size</th>\n",
              "      <th>properties_Mechanical_Material</th>\n",
              "      <th>properties_Mechanical_System Classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee866fcb573f2fc2216e51</td>\n",
              "      <td>0.050 ft</td>\n",
              "      <td>2\"</td>\n",
              "      <td>Polyvinyl Chloride - Rigid</td>\n",
              "      <td>Sanitary</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 projectId  ... properties_Mechanical_System Classification\n",
              "55  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                    Sanitary\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8dCDF9NEFRc",
        "colab_type": "text"
      },
      "source": [
        "here we make a dictionary with the size, classification and Length elements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuTCS_ceD_sn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "b6f513c3-c8be-45aa-8544-332a6bbc1ba3"
      },
      "source": [
        "\n",
        "def search_title(var):\n",
        "  i=0\n",
        "  values={}\n",
        "\n",
        "  Ele=var.iloc[0].index.nunique()\n",
        "  for i in range(0,Ele):\n",
        "    value=var.iloc[0].index[i].upper()\n",
        "    if \"SIZE\" in value:\n",
        "        values[\"Size\"]=var.iloc[0][i]\n",
        "\n",
        "    if \"CLASSIFICATION\" in value:\n",
        "        values[\"Classification\"]=var.iloc[0][i]\n",
        "\n",
        "    if \"LENGTH\" in value:\n",
        "        values[\"Length\"]=var.iloc[0][i]\n",
        "  return values\n",
        "\n",
        "item=search_title(eli)\n",
        "item\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Classification': 'Sanitary', 'Length': '0.050 ft', 'Size': '2\"'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o_bjDiSTVuh7",
        "colab_type": "text"
      },
      "source": [
        "Edit the Size format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZSlohL8UMsG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ac394284-0b06-4ebe-fff0-3f359c6b9dce"
      },
      "source": [
        "item[\"Size\"]=item[\"Size\"].replace(\"ø\",\"\")\n",
        "item[\"Size\"]=item[\"Size\"].strip()\n",
        "item[\"Size\"]"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'2\"'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLijGJU_8pLS",
        "colab_type": "text"
      },
      "source": [
        "Edit the Lenght format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHBwp1wJ6a0J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ec2e6313-3cf6-4036-bb46-3968b1f2a313"
      },
      "source": [
        "item[\"Length\"]=item[\"Length\"].replace(\" ft\",\"\")\n",
        "item[\"Length\"]=item[\"Length\"].replace(\"  ft\",\"\")\n",
        "item[\"Length\"]\n",
        "item[\"Length\"]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'0.050'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sw-nEZKZZfPs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pro=df5.columns.to_frame(index=True).T\n",
        "pro.drop(labels=0,axis=0,inplace=True)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OI5EqKD2sU3d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#this list is just for make the new_row dictionary \n",
        "items=[item[\"Classification\"],item[\"Size\"]]\n",
        "\n",
        "new_row={}\n",
        "\n",
        "for i in pro.columns:\n",
        "  if i == \"properties_Dimensions_Length\":\n",
        "    new_row[i]=float(item[\"Length\"])\n",
        "  elif i in items:\n",
        "    new_row[i]=1\n",
        "  else:\n",
        "      new_row[i]=0\n"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bH5ZQZmS_nce",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "69c17009-3b69-4923-deca-8f2e85cdeff9"
      },
      "source": [
        "new_row"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'1 1/2\"': 0,\n",
              " '1 1/4\"': 0,\n",
              " '1 3/8\"': 0,\n",
              " '1\"': 0,\n",
              " '1/2\"': 0,\n",
              " '10\"': 0,\n",
              " '12\"': 0,\n",
              " '14\"': 0,\n",
              " '16\"': 0,\n",
              " '2 1/2\"': 0,\n",
              " '2\"': 1,\n",
              " '3\"': 0,\n",
              " '3/4\"': 0,\n",
              " '30\"': 0,\n",
              " '36\"': 0,\n",
              " '4\"': 0,\n",
              " '5\"': 0,\n",
              " '6\"': 0,\n",
              " '8\"': 0,\n",
              " 'Domestic Cold Water': 0,\n",
              " 'Domestic Hot Water': 0,\n",
              " 'Fire Protection Dry': 0,\n",
              " 'Fire Protection Wet': 0,\n",
              " 'Hydronic Return': 0,\n",
              " 'Hydronic Supply': 0,\n",
              " 'Material_Number': 0,\n",
              " 'Other': 0,\n",
              " 'Sanitary': 1,\n",
              " 'Vent': 0,\n",
              " 'properties_Dimensions_Length': 0.05}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0IgwGeO3FZu2",
        "colab_type": "text"
      },
      "source": [
        "This is the one row DataFrame that we will use to test the model to predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYHjcmGawYak",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "k=pro.append(new_row, ignore_index=True)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ROrmJx1ysZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_infer = k.drop('Material_Number',axis=1)\n",
        "y_infer = pro['Material_Number']"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DI_endT3IFy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "be55fedf-4e8c-498d-c1ff-e6ac95f6587e"
      },
      "source": [
        "predictions = rfc.predict(X_infer)\n",
        "int(predictions)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mh7zv5MZIvWW",
        "colab_type": "text"
      },
      "source": [
        "Recalling the dictionary we made, we change key for value and vice versa"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ga23NBMl9D_X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "50b16a2f-5e48-4ce4-ac01-de2d91a4520a"
      },
      "source": [
        "dicc"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Black Steel': 7,\n",
              " 'CP_PI - Copper - ASTM B88 H.D.': 4,\n",
              " 'Carbon Steel': 8,\n",
              " 'Carbon Steel - ASTM A53 B': 5,\n",
              " 'Cast Iron': 6,\n",
              " 'Copper': 0,\n",
              " 'Ductile Iron': 9,\n",
              " 'NaN': 2,\n",
              " 'PVC': 3,\n",
              " 'Steel, Carbon': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRuupVaoFv6D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "e72fcb36-1d10-4d66-90ae-88ab3165905f"
      },
      "source": [
        "dicc2={}\n",
        "\n",
        "for i in dicc.keys():\n",
        "  dicc2[dicc[i]]=i\n",
        "dicc2\n"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'Copper',\n",
              " 1: 'Steel, Carbon',\n",
              " 2: 'NaN',\n",
              " 3: 'PVC',\n",
              " 4: 'CP_PI - Copper - ASTM B88 H.D.',\n",
              " 5: 'Carbon Steel - ASTM A53 B',\n",
              " 6: 'Cast Iron',\n",
              " 7: 'Black Steel',\n",
              " 8: 'Carbon Steel',\n",
              " 9: 'Ductile Iron'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pw9o7gD3JVmK",
        "colab_type": "text"
      },
      "source": [
        "# Result of a determined prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B78dl8huG0oP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ccd2e024-9eda-4b58-9323-814492d3a526"
      },
      "source": [
        "dicc2[int(predictions)]"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'PVC'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFshfM50Jyv3",
        "colab_type": "text"
      },
      "source": [
        "recalling, eli is the element of that we want to infer wich material it is"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRP9heC9ITuR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1d2ba238-54a1-406f-a643-565dfe8a9e40"
      },
      "source": [
        "eli.iloc[0][6]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Polyvinyl Chloride - Rigid'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TP04N7YxPrFC",
        "colab_type": "text"
      },
      "source": [
        "# Prediction Model Download"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLi7s1DEJsOj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import dill"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6L5ajc40p1dG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wd = \"Users\\bdere\\Desktop\\computer\\haha\""
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7x8Iq9a1qql2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump(rfc, open(wd + \"PipeAnalysis.sav\",\"wb\"))"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2fvtcLsrIlF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open(wd + \"PipeAnalysis.sav\",\"wb\") as f:\n",
        "    dill.dump(rfc,f)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRUj5RQmrRDN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = dill.load(open(wd + \"PipeAnalysis.sav\",\"rb\"))"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiM3UEMIrVh9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "66211ba4-2b96-45d0-bde9-5c17cc23fe55"
      },
      "source": [
        "predic=model.predict(X_infer)\n",
        "int(predic)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSRpejTbrgRt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 54,
      "outputs": []
    }
  ]
}