Earthquake_Prediction_Challenge.ipynb

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  "metadata": {
    "colab": {
      "name": "Earthquake_Prediction_Challenge.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ucalyptus/Earthquake_/blob/master/Earthquake_Prediction_Challenge.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "lZekXH4mfjG-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "424a5e7f-2d87-4451-c66b-e9b4462c5119"
      },
      "cell_type": "code",
      "source": [
        "!pip install kaggle\n",
        "!pip install numpy==1.15.0\n",
        "!pip install catboost"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.2)\n",
            "Requirement already satisfied: urllib3<1.23.0,>=1.15 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.22)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.11.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2018.11.29)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.5.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.18.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.0.1)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: Unidecode>=0.04.16 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.0.23)\n",
            "Requirement already satisfied: numpy==1.15.0 in /usr/local/lib/python3.6/dist-packages (1.15.0)\n",
            "Requirement already satisfied: catboost in /usr/local/lib/python3.6/dist-packages (0.12.2)\n",
            "Requirement already satisfied: pandas>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from catboost) (0.22.0)\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: enum34 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.1.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.1->catboost) (2.5.3)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.1->catboost) (2018.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "U3dZEGpcgYBJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from catboost import CatBoostRegressor, Pool\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import NuSVR, SVR\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ntDlfO7pjcwN",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "55f72385-1752-4bc4-acce-2504406309e0"
      },
      "cell_type": "code",
      "source": [
        "##IMPORT DATASET FROM KAGGLE\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n",
        "!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle && chmod 600 ~/.kaggle/kaggle.json\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d37ac293-0404-47b3-80f1-1d91a1dc02d7\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-d37ac293-0404-47b3-80f1-1d91a1dc02d7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "s2g7T1unkI9J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "d6d10e88-c3c9-42ec-ee64-c2051733584c"
      },
      "cell_type": "code",
      "source": [
        "!kaggle competitions list"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ref                                            deadline             category            reward  teamCount  userHasEntered  \n",
            "---------------------------------------------  -------------------  ---------------  ---------  ---------  --------------  \n",
            "digit-recognizer                               2030-01-01 00:00:00  Getting Started  Knowledge       2628            True  \n",
            "titanic                                        2030-01-01 00:00:00  Getting Started  Knowledge       9918            True  \n",
            "house-prices-advanced-regression-techniques    2030-01-01 00:00:00  Getting Started  Knowledge       4139            True  \n",
            "imagenet-object-localization-challenge         2029-12-31 07:00:00  Research         Knowledge         33           False  \n",
            "competitive-data-science-predict-future-sales  2019-12-31 23:59:00  Playground           Kudos       2289           False  \n",
            "two-sigma-financial-news                       2019-07-15 23:59:00  Featured          $100,000       2897           False  \n",
            "LANL-Earthquake-Prediction                     2019-06-03 23:59:00  Research           $50,000        999            True  \n",
            "tmdb-box-office-prediction                     2019-05-30 23:59:00  Playground       Knowledge         41           False  \n",
            "dont-overfit-ii                                2019-05-07 23:59:00  Playground            Swag        120           False  \n",
            "gendered-pronoun-resolution                    2019-04-22 23:59:00  Research           $25,000        103           False  \n",
            "histopathologic-cancer-detection               2019-03-30 23:59:00  Playground       Knowledge        569           False  \n",
            "petfinder-adoption-prediction                  2019-03-28 23:59:00  Featured           $25,000       1016           False  \n",
            "vsb-power-line-fault-detection                 2019-03-21 23:59:00  Featured           $25,000        822           False  \n",
            "microsoft-malware-prediction                   2019-03-13 23:59:00  Research           $25,000       1547           False  \n",
            "humpback-whale-identification                  2019-02-28 23:59:00  Featured           $25,000       1770           False  \n",
            "elo-merchant-category-recommendation           2019-02-26 23:59:00  Featured           $50,000       3592           False  \n",
            "quora-insincere-questions-classification       2019-02-26 23:59:00  Featured           $25,000       4037           False  \n",
            "ga-customer-revenue-prediction                 2019-02-15 23:59:00  Featured           $45,000       1104           False  \n",
            "reducing-commercial-aviation-fatalities        2019-02-12 23:59:00  Playground            Swag        157           False  \n",
            "pubg-finish-placement-prediction               2019-01-30 23:59:00  Playground            Swag       1534           False  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bFY1OgAWlLRp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "f470d002-dcd2-4564-fab1-adecb73b1677"
      },
      "cell_type": "code",
      "source": [
        "!kaggle competitions download -c LANL-Earthquake-Prediction"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading sample_submission.csv to /content\n",
            "\r  0% 0.00/33.3k [00:00<?, ?B/s]\n",
            "100% 33.3k/33.3k [00:00<00:00, 13.1MB/s]\n",
            "Downloading test.zip to /content\n",
            " 99% 240M/242M [00:01<00:00, 168MB/s]\n",
            "100% 242M/242M [00:01<00:00, 156MB/s]\n",
            "Downloading train.csv.zip to /content\n",
            " 99% 2.01G/2.03G [00:14<00:00, 171MB/s]\n",
            "100% 2.03G/2.03G [00:14<00:00, 151MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Hdk-VpdelaVH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "14bdfdd4-eb63-41af-a524-e5b1bf59f815"
      },
      "cell_type": "code",
      "source": [
        "!ls "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data  sample_submission.csv  test.zip  train.csv.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VqdRB-SOltRo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "894471cb-d9bf-4ef8-e571-f50ff695e843"
      },
      "cell_type": "code",
      "source": [
        "!unzip train.csv.zip\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  train.csv.zip\n",
            "replace train.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: train.csv               \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "23wcAdwnnJW_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e19f13d7-214d-483b-e64d-a323f2c3d592"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data  sample_submission.csv  test.zip  train.csv  train.csv.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dsf8ddk6l1xs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train = pd.read_csv('train.csv',nrows = 6000000, dtype={'acoustic_data':np.int16,'time_to_failure':np.float64})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "e48v9yL9nE8g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "0e6bc3b9-4d89-4d70-a2b7-39b3fc1b9bf1"
      },
      "cell_type": "code",
      "source": [
        "train.head(10)"
      ],
      "execution_count": 17,
      "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>acoustic_data</th>\n",
              "      <th>time_to_failure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-5</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acoustic_data  time_to_failure\n",
              "0             12           1.4691\n",
              "1              6           1.4691\n",
              "2              8           1.4691\n",
              "3              5           1.4691\n",
              "4              8           1.4691\n",
              "5              8           1.4691\n",
              "6              9           1.4691\n",
              "7              7           1.4691\n",
              "8             -5           1.4691\n",
              "9              3           1.4691"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "ojmuu3Yyn7oY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_ad_sample_df = train['acoustic_data'].values[::100]\n",
        "train_ttf_sample_df = train['time_to_failure'].values[::100]\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oKZFpcEGofkg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "f2c3cc5e-f4ae-45eb-87d8-2d56db2bfb55"
      },
      "cell_type": "code",
      "source": [
        "fig, ax1 = plt.subplots(figsize=(12,8))\n",
        "plt.title(\"acoustic data + ttf\")\n",
        "plt.plot(train_ad_sample_df,color='r')\n",
        "ax1.set_ylabel('acoustic data',color='r')\n",
        "plt.legend(['acoustic data'],loc=(0.01,0.95))\n",
        "ax2 = ax1.twinx()\n",
        "plt.plot(train_ttf_sample_df,color='b')\n",
        "ax1.set_ylabel('time to failure',color='b')\n",
        "plt.legend(['time to failure'],loc=(0.01,0.9))\n",
        "plt.grid(True)\n",
        "\n",
        "\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IiKjYuW1YCwBQ/X6Cwftj4N490/DVISEZiIqSQ6uV4c4dOS5dsv5CSXNeXgJlyhgREJAd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\ngwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "PjRoTK0gqQVn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#This is a supervised learning , we need to find co relation betwwen ad and ttf, btw there are only two features.. ideally we need more features"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Bor7N9qvtbTe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def generatefeatures(X):\n",
        "  strain = []\n",
        "  strain.append(X.mean())\n",
        "  strain.append(X.std())\n",
        "  strain.append(X.min())\n",
        "  strain.append(X.max())\n",
        "  strain.append(X.kurtosis())\n",
        "  strain.append(X.skew())\n",
        "  strain.append(np.quantile(X,0.01))\n",
        "  return pd.Series(strain)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GWQgPZ3SutdN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "##kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution.\n",
        "##Data sets with high kurtosis tend to have heavy tails,or outliers.Data sets with low kurtosis tend to have light tails , or lack of outliers.\n",
        "\n",
        "\n",
        "##Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative,or undefined.\n",
        "##positive skewness is going to right, left negative and so on.\n",
        "\n",
        "\n",
        "##quantiles are cut points dividing the range of a probdist into continuous intervals with equal probabilities, or dividing the observations in a sample in the same way."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X3SPtUeByYQK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train = pd.read_csv('train.csv',iterator=True,chunksize=150_000, dtype={'acoustic_data':np.int16,'time_to_failure':np.float64})\n",
        "\n",
        "X_train = pd.DataFrame()\n",
        "y_train = pd.Series()\n",
        "for df in train:\n",
        "  ch = generatefeatures(df['acoustic_data'])\n",
        "  X_train = X_train.append(ch,ignore_index=True)\n",
        "  y_train = y_train.append(pd.Series(df['time_to_failure'].values[-1]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UdKK0z1O0tHQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "86cb43ef-bebc-41b0-c04f-03405a535bd6"
      },
      "cell_type": "code",
      "source": [
        "X_train.describe()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4195.000000</td>\n",
              "      <td>4195.000000</td>\n",
              "      <td>4195.000000</td>\n",
              "      <td>4195.000000</td>\n",
              "      <td>4195.000000</td>\n",
              "      <td>4195.000000</td>\n",
              "      <td>4195.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.519475</td>\n",
              "      <td>6.547788</td>\n",
              "      <td>-149.190942</td>\n",
              "      <td>163.522288</td>\n",
              "      <td>68.297997</td>\n",
              "      <td>0.125830</td>\n",
              "      <td>-11.224603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.256049</td>\n",
              "      <td>8.503939</td>\n",
              "      <td>265.087984</td>\n",
              "      <td>272.930331</td>\n",
              "      <td>70.532565</td>\n",
              "      <td>0.477901</td>\n",
              "      <td>14.106852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.596313</td>\n",
              "      <td>2.802720</td>\n",
              "      <td>-5515.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.648602</td>\n",
              "      <td>-4.091826</td>\n",
              "      <td>-336.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.349497</td>\n",
              "      <td>4.478637</td>\n",
              "      <td>-154.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>28.090227</td>\n",
              "      <td>-0.040779</td>\n",
              "      <td>-14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.522147</td>\n",
              "      <td>5.618798</td>\n",
              "      <td>-111.000000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>45.816625</td>\n",
              "      <td>0.085620</td>\n",
              "      <td>-10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.693350</td>\n",
              "      <td>6.880904</td>\n",
              "      <td>-79.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>78.664202</td>\n",
              "      <td>0.253930</td>\n",
              "      <td>-6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.391993</td>\n",
              "      <td>153.703569</td>\n",
              "      <td>-15.000000</td>\n",
              "      <td>5444.000000</td>\n",
              "      <td>631.158927</td>\n",
              "      <td>4.219429</td>\n",
              "      <td>-2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 0            1            2            3            4  \\\n",
              "count  4195.000000  4195.000000  4195.000000  4195.000000  4195.000000   \n",
              "mean      4.519475     6.547788  -149.190942   163.522288    68.297997   \n",
              "std       0.256049     8.503939   265.087984   272.930331    70.532565   \n",
              "min       3.596313     2.802720 -5515.000000    23.000000     0.648602   \n",
              "25%       4.349497     4.478637  -154.000000    92.000000    28.090227   \n",
              "50%       4.522147     5.618798  -111.000000   123.000000    45.816625   \n",
              "75%       4.693350     6.880904   -79.000000   170.000000    78.664202   \n",
              "max       5.391993   153.703569   -15.000000  5444.000000   631.158927   \n",
              "\n",
              "                 5            6  \n",
              "count  4195.000000  4195.000000  \n",
              "mean      0.125830   -11.224603  \n",
              "std       0.477901    14.106852  \n",
              "min      -4.091826  -336.000000  \n",
              "25%      -0.040779   -14.000000  \n",
              "50%       0.085620   -10.000000  \n",
              "75%       0.253930    -6.000000  \n",
              "max       4.219429    -2.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "VdR4YWo-00hx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "4e5de5de-733e-426a-848a-61b5079d9867"
      },
      "cell_type": "code",
      "source": [
        "X_train.head(10)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.884113</td>\n",
              "      <td>5.101106</td>\n",
              "      <td>-98.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>33.662481</td>\n",
              "      <td>-0.024061</td>\n",
              "      <td>-8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.725767</td>\n",
              "      <td>6.588824</td>\n",
              "      <td>-154.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>98.758517</td>\n",
              "      <td>0.390561</td>\n",
              "      <td>-11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.906393</td>\n",
              "      <td>6.967397</td>\n",
              "      <td>-106.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>33.555211</td>\n",
              "      <td>0.217391</td>\n",
              "      <td>-15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.902240</td>\n",
              "      <td>6.922305</td>\n",
              "      <td>-199.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>116.548172</td>\n",
              "      <td>0.757278</td>\n",
              "      <td>-12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.908720</td>\n",
              "      <td>7.301110</td>\n",
              "      <td>-126.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>52.977905</td>\n",
              "      <td>0.064531</td>\n",
              "      <td>-15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.913513</td>\n",
              "      <td>5.434111</td>\n",
              "      <td>-144.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>50.215147</td>\n",
              "      <td>-0.100697</td>\n",
              "      <td>-10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.855660</td>\n",
              "      <td>5.687823</td>\n",
              "      <td>-78.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>23.173004</td>\n",
              "      <td>0.208810</td>\n",
              "      <td>-12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4.505427</td>\n",
              "      <td>5.854512</td>\n",
              "      <td>-134.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>52.388738</td>\n",
              "      <td>-0.176333</td>\n",
              "      <td>-11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.717833</td>\n",
              "      <td>7.789643</td>\n",
              "      <td>-156.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>65.360261</td>\n",
              "      <td>-0.160166</td>\n",
              "      <td>-16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.730960</td>\n",
              "      <td>6.890459</td>\n",
              "      <td>-126.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>53.760207</td>\n",
              "      <td>0.150779</td>\n",
              "      <td>-14.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1      2      3           4         5     6\n",
              "0  4.884113  5.101106  -98.0  104.0   33.662481 -0.024061  -8.0\n",
              "1  4.725767  6.588824 -154.0  181.0   98.758517  0.390561 -11.0\n",
              "2  4.906393  6.967397 -106.0  140.0   33.555211  0.217391 -15.0\n",
              "3  4.902240  6.922305 -199.0  197.0  116.548172  0.757278 -12.0\n",
              "4  4.908720  7.301110 -126.0  145.0   52.977905  0.064531 -15.0\n",
              "5  4.913513  5.434111 -144.0  142.0   50.215147 -0.100697 -10.0\n",
              "6  4.855660  5.687823  -78.0  120.0   23.173004  0.208810 -12.0\n",
              "7  4.505427  5.854512 -134.0  139.0   52.388738 -0.176333 -11.0\n",
              "8  4.717833  7.789643 -156.0  168.0   65.360261 -0.160166 -16.0\n",
              "9  4.730960  6.890459 -126.0  152.0   53.760207  0.150779 -14.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "6ZtTmjbw1weR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "19f387f0-9c2d-46f3-d601-05a810d66ab8"
      },
      "cell_type": "code",
      "source": [
        "#Catboost explained simply:\n",
        "\n",
        "\"\"\"\n",
        "1. We first model data with simple models and analyze data for errors.\n",
        "2. These errors signify data points that are difficult to fit by a simple model\n",
        "3. Then for later models, we particularly focus on those hard to fit data to get them right.\n",
        "4. In the end, we combine all the predictors by giving some weights to each predictor.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\"\"\""
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n1. We first model data with simple models and analyze data for errors.\\n2. These errors signify data points that are difficult to fit by a simple model\\n3. Then for later models, we particularly focus on those hard to fit data to get them right.\\n4. In the end, we combine all the predictors by giving some weights to each predictor.\\n\\n\\n\\n\\n\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "Rem6Y0LX40KP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e339165c-2278-4936-8114-781d489e703d"
      },
      "cell_type": "code",
      "source": [
        "train_pool = Pool(X_train,y_train)\n",
        "m = CatBoostRegressor(iterations=10000,loss_function='MAE',boosting_type='Ordered')\n",
        "m.fit(X_train,y_train,silent=True)\n",
        "m.best_score_\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learn': {'MAE': 1.856830702463856}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "dEjXUUvn5VAm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "##we will use SVM for regression\n",
        "##kernel will be gaussian aka radial basis\n",
        "##k(x1,x2) = exp(-gamma*(x1-x2)^2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QEXa8Gpn7THf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "###using GridSearch for hype-optimization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NmjQ_Bqr7dXK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a5405680-441b-4e34-a2f8-86c034c70ad9"
      },
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train_scaled = scaler.transform(X_train)\n",
        "\n",
        "parameters = [{'gamma': [0.001,0.005,0.01,0.02,0.05,0.1],\n",
        "              'C':[0.1,0.2,0.25,0.5,1,1.5,2]}]\n",
        "\n",
        "reg1 = GridSearchCV(SVR(kernel='rbf',tol=0.01),parameters,cv=5)\n",
        "reg1.fit(X_train_scaled,y_train.values.flatten())\n",
        "y_pred1 = reg1.predict(X_train_scaled)\n",
        "\n",
        "print(\"Best CV Score: {:.4f}\".format(reg1.best_score_))\n",
        "print(reg1.best_params_)\n",
        "\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best CV Score: 0.3662\n",
            "{'C': 2, 'gamma': 0.1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8Qvms9tF9mZo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}