Random Forest with Bigquery.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/TirendazAcademy/Hands-on-Data-Science-with-GCP/blob/main/Random%20Forest%20with%20Bigquery.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest with Bigquery"
      ],
      "metadata": {
        "id": "UqlrIq6eXXtM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hi guys 😀 This notebook shows how to build a random forest model and predict the values. Let's get started."
      ],
      "metadata": {
        "id": "SVm_zm8vZgO6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Authentication"
      ],
      "metadata": {
        "id": "Is-GG3ZYXckt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "print('Authenticated')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "RcvdhtTFwenQ",
        "outputId": "1881646f-d28c-4a46-deba-77ac960661d2"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Authenticated\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating dataset"
      ],
      "metadata": {
        "id": "4AovTpR4XmAy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery --project tirendaz-data-engineering df\n",
        "SELECT limit_balance, education_level, age, default_payment_next_month\n",
        "FROM `bigquery-public-data.ml_datasets.credit_card_default`"
      ],
      "metadata": {
        "id": "KUee8A0_wjH0"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "outputId": "b97d0ed3-ca51-43df-8195-26daf25655cd"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   limit_balance education_level   age default_payment_next_month\n",
              "0        50000.0               3  50.0                          0\n",
              "1       200000.0               3  45.0                          0\n",
              "2       130000.0               1  45.0                          0\n",
              "3       230000.0               2  41.0                          1\n",
              "4        50000.0               2  42.0                          0"
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    {
      "cell_type": "markdown",
      "source": [
        "## Creating input and output sets"
      ],
      "metadata": {
        "id": "0nLkSOj6Xt8O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target_column = \"default_payment_next_month\"\n",
        "labels = df[target_column]\n",
        "features = df.drop(target_column, axis = 1)"
      ],
      "metadata": {
        "id": "2-LNVlHFx6eM"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting sets into train and test"
      ],
      "metadata": {
        "id": "kSQGMzhCX2SU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = \\\n",
        "  train_test_split(features, labels, test_size=0.3)"
      ],
      "metadata": {
        "id": "dODOXG2i0PoM"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Building the random forest model"
      ],
      "metadata": {
        "id": "XQyoQFbLX-78"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "random_forest_classifier = RandomForestClassifier(n_estimators=100)\n",
        "random_forest_classifier.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bByOW1eR0uC0",
        "outputId": "d7156b2c-f997-43c4-f95c-999c66bbd277"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Making prediction"
      ],
      "metadata": {
        "id": "RmXapHwFYEjT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "y_pred=random_forest_classifier.predict(X_test) \n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9tUgUrfl1Du5",
        "outputId": "7ef62e89-689b-48cc-bb2e-9b067e75c8d2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7191011235955056\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving the model"
      ],
      "metadata": {
        "id": "-8j9l8J7YLwg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "model_name = \"cc_default_rf_model.sav\"\n",
        "joblib.dump(random_forest_classifier, model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jGTRNvLU1MXW",
        "outputId": "d90dd844-c169-43b0-d4e9-1ef18eab1224"
      },
      "execution_count": 8,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['cc_default_rf_model.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the model"
      ],
      "metadata": {
        "id": "-Hb7TxJeYOks"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = joblib.load(model_name)"
      ],
      "metadata": {
        "id": "SsecGQeC1Sr3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predicting some values"
      ],
      "metadata": {
        "id": "dXI13VIOYRey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery --project tirendaz-data-engineering df_predict\n",
        "SELECT limit_balance, education_level, age, \n",
        "FROM `bigquery-public-data.ml_datasets.credit_card_default`\n",
        "LIMIT 10;"
      ],
      "metadata": {
        "id": "e2y-Y9JLC6vD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's take a look at the test set"
      ],
      "metadata": {
        "id": "8aH7wee9YWnT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_predict.head()"
      ],
      "metadata": {
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          "height": 206
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        "id": "xpBqnW0VDIRy",
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      },
      "execution_count": 11,
      "outputs": [
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          "data": {
            "text/plain": [
              "   limit_balance education_level   age\n",
              "0        50000.0               3  50.0\n",
              "1       200000.0               3  45.0\n",
              "2       130000.0               1  45.0\n",
              "3       230000.0               2  41.0\n",
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              "          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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's predict the test set with the random forest model"
      ],
      "metadata": {
        "id": "p5YlBFW8YeSr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prediction=loaded_model.predict(df_predict)\n",
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_iWoRpFIDKbq",
        "outputId": "5c6562ee-3c42-4763-d55f-102dc4be14fe"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '0', '0', '1', '0', '0', '1', '0', '1', '0'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's get an example and predict it with the model"
      ],
      "metadata": {
        "id": "P8jjBa-XYzdu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "loaded_model = joblib.load(model_name)\n",
        "limit_balance = 1000\n",
        "education_level = 1\n",
        "age = 25\n",
        "feature_json = json.dumps([[limit_balance, education_level, age]])\n",
        "feature_list = json.loads(feature_json)\n",
        "feature_df = pd.DataFrame(feature_list)\n",
        "prediction=loaded_model.predict(feature_df)\n",
        "print(\"Online Prediction :\")\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Quv3Z8IBDTU6",
        "outputId": "37be08b5-7864-4b69-cfa5-b60aeb953240"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Online Prediction :\n",
            "['0']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Thanks for reading. Follow me on [Twitter](https://twitter.com/TirendazAcademy) | [Instagram](https://www.instagram.com/tirendazacademy) | [YouTube](https://www.youtube.com/channel/UCFU9Go20p01kC64w-tmFORw) | [Tiktok](https://www.tiktok.com/@tirendazacademy) | [Medium](https://tirendazacademy.medium.com) | [Reddit](https://www.reddit.com/user/TirendazAcademy) "
      ],
      "metadata": {
        "id": "u58j0VPaY93_"
      }
    }
  ]
}