Model_training_on_fake_data.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "from prophet import Prophet\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install yfinance prophet"
      ],
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.90-py2.py3-none-any.whl (29 kB)\n",
            "Requirement already satisfied: prophet in /usr/local/lib/python3.8/dist-packages (1.1.1)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 377 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.21.6)\n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.0.0->prophet) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.0.0->prophet) (0.11.0)\n",
            "Installing collected packages: requests, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "Successfully installed requests-2.28.1 yfinance-0.1.90\n"
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      "source": [
        "data = yf.download(tickers='BTC-USD', start='2020-01-01', end='2022-11-30', interval = '1d')"
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            "\r[*********************100%***********************]  1 of 1 completed\n"
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        "data.reset_index(inplace=True)\n",
        "data['Date'] = pd.to_datetime(data['Date'])\n",
        "data.head()"
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              "        Date         Open         High          Low        Close    Adj Close  \\\n",
              "0 2020-01-01  7194.892090  7254.330566  7174.944336  7200.174316  7200.174316   \n",
              "1 2020-01-02  7202.551270  7212.155273  6935.270020  6985.470215  6985.470215   \n",
              "2 2020-01-03  6984.428711  7413.715332  6914.996094  7344.884277  7344.884277   \n",
              "3 2020-01-04  7345.375488  7427.385742  7309.514160  7410.656738  7410.656738   \n",
              "4 2020-01-05  7410.451660  7544.497070  7400.535645  7411.317383  7411.317383   \n",
              "\n",
              "        Volume  \n",
              "0  18565664997  \n",
              "1  20802083465  \n",
              "2  28111481032  \n",
              "3  18444271275  \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        "k=df.shape[0]\n",
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        "sampl1=np.random.uniform(low=0,high=2,size=(k,))\n",
        "sampl3=np.random.uniform(low=5,high=10,size=(k,))\n",
        "sampl4=np.random.uniform(low=10,high=15,size=(k,))\n",
        "sampl5=np.random.uniform(low=15,high=25,size=(k,))\n",
        "sampl6=np.random.uniform(low=25,high=35,size=(k,))\n",
        "sampl7=np.random.uniform(low=35,high=45,size=(k,))\n",
        "sampl8=np.random.uniform(low=45,high=55,size=(k,))\n",
        "sampl9=np.random.uniform(low=55,high=70,size=(k,))\n",
        "sampl10=np.random.uniform(low=70,high=85,size=(k,))\n",
        "sampl11=np.random.uniform(low=85,high=100,size=(k,))"
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        "id": "ge4w1oqMtyt0"
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      "outputs": []
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        "df[\"85-100\"]=sampl11"
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      "outputs": []
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              "          ds            y       0-2       2-5      5-10      10-15      15-25  \\\n",
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              "\n",
              "   Simple_Linear_processing  \n",
              "0               3920.328400  \n",
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      "outputs": []
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              "      <td>1.558160</td>\n",
              "      <td>1.763703</td>\n",
              "      <td>1.892756</td>\n",
              "      <td>3920.328400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>6985.470215</td>\n",
              "      <td>1.007132</td>\n",
              "      <td>1.024255</td>\n",
              "      <td>1.085700</td>\n",
              "      <td>1.126438</td>\n",
              "      <td>1.231889</td>\n",
              "      <td>1.332680</td>\n",
              "      <td>1.355007</td>\n",
              "      <td>1.493486</td>\n",
              "      <td>1.675686</td>\n",
              "      <td>1.760822</td>\n",
              "      <td>1.858997</td>\n",
              "      <td>3839.327402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>7344.884277</td>\n",
              "      <td>1.003995</td>\n",
              "      <td>1.033209</td>\n",
              "      <td>1.081176</td>\n",
              "      <td>1.111136</td>\n",
              "      <td>1.151943</td>\n",
              "      <td>1.302965</td>\n",
              "      <td>1.445061</td>\n",
              "      <td>1.480906</td>\n",
              "      <td>1.573438</td>\n",
              "      <td>1.751965</td>\n",
              "      <td>1.880713</td>\n",
              "      <td>4003.065924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-04</td>\n",
              "      <td>7410.656738</td>\n",
              "      <td>1.000535</td>\n",
              "      <td>1.045953</td>\n",
              "      <td>1.079216</td>\n",
              "      <td>1.148830</td>\n",
              "      <td>1.151571</td>\n",
              "      <td>1.295483</td>\n",
              "      <td>1.418755</td>\n",
              "      <td>1.476941</td>\n",
              "      <td>1.551241</td>\n",
              "      <td>1.812745</td>\n",
              "      <td>1.996659</td>\n",
              "      <td>4062.749048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-05</td>\n",
              "      <td>7411.317383</td>\n",
              "      <td>1.003938</td>\n",
              "      <td>1.037847</td>\n",
              "      <td>1.079373</td>\n",
              "      <td>1.121353</td>\n",
              "      <td>1.218621</td>\n",
              "      <td>1.266195</td>\n",
              "      <td>1.439390</td>\n",
              "      <td>1.501612</td>\n",
              "      <td>1.556531</td>\n",
              "      <td>1.709309</td>\n",
              "      <td>1.988438</td>\n",
              "      <td>4037.430181</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = df[[\"0-2\",\"2-5\",\"5-10\",\"10-15\",\"15-25\",\"25-35\",\"35-45\",\"45-55\",\"55-70\",\"70-85\",\"85-100\"]].multiply(df[\"y\"], axis=\"index\")"
      ],
      "metadata": {
        "id": "mSA8BG8foHJr"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sepnOnGkodfn",
        "outputId": "8fa3b90b-6630-4c7d-9b16-d1056bab805a"
      },
      "execution_count": 112,
      "outputs": [
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              "           0-2          2-5         5-10        10-15        15-25  \\\n",
              "0  7330.320135  7411.453937  7772.548362  7985.884856  8881.467089   \n",
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              "2  7374.224686  7588.798803  7941.113347  8161.168724  8460.889926   \n",
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              "\n",
              "         25-35         35-45         45-55         55-70         70-85  \\\n",
              "0  9019.995564   9914.558115  10772.835729  11219.021474  12698.968726   \n",
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              "2  9570.127595  10613.806877  10877.080304  11556.721135  12867.977455   \n",
              "3  9600.376441  10513.907128  10945.103235  11495.714001  13433.634519   \n",
              "4  9384.173945  10667.774928  11128.921580  11535.947776  12668.231059   \n",
              "\n",
              "         85-100  \n",
              "0  13628.171221  \n",
              "1  12985.970236  \n",
              "2  13813.617216  \n",
              "3  14796.554659  \n",
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              "      <td>12698.968726</td>\n",
              "      <td>13628.171221</td>\n",
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              "      <th>1</th>\n",
              "      <td>7035.289793</td>\n",
              "      <td>7154.901719</td>\n",
              "      <td>7584.124627</td>\n",
              "      <td>7868.700958</td>\n",
              "      <td>8605.323696</td>\n",
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              "      <td>7941.113347</td>\n",
              "      <td>8161.168724</td>\n",
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              "      <td>12867.977455</td>\n",
              "      <td>13813.617216</td>\n",
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              "      <td>7691.816627</td>\n",
              "      <td>7999.573473</td>\n",
              "      <td>8310.704592</td>\n",
              "      <td>9031.584755</td>\n",
              "      <td>9384.173945</td>\n",
              "      <td>10667.774928</td>\n",
              "      <td>11128.921580</td>\n",
              "      <td>11535.947776</td>\n",
              "      <td>12668.231059</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-cdd17657-4285-4558-917b-6d167a9182cb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"2-5\"]=result[\"2-5\"]\n",
        "df[\"0-2\"]=result[\"0-2\"]\n",
        "df[\"10-15\"]=result[\"10-15\"]\n",
        "df[\"5-10\"]=result[\"5-10\"]\n",
        "df[\"15-25\"]=result[\"15-25\"]\n",
        "df[\"25-35\"]=result[\"25-35\"]\n",
        "df[\"35-45\"]=result[\"35-45\"]\n",
        "df[\"45-55\"]=result[\"45-55\"]\n",
        "df[\"55-70\"]=result[\"55-70\"]\n",
        "df[\"70-85\"]=result[\"70-85\"]\n",
        "df[\"85-100\"]=result[\"85-100\"]"
      ],
      "metadata": {
        "id": "fTQfsCTAosev"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df[df['ds']<='2022-1-31']\n",
        "df_test = df[df['ds']>'2022-1-31']"
      ],
      "metadata": {
        "id": "de6G1QKrpBk3"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p07tsCL2ZXjc",
        "outputId": "c6eee878-48e2-4e3b-b07f-97b8be6f3ecd"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(762, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXyGeLAlWwnY",
        "outputId": "94671673-bfa3-4c57-e242-a6af0594ae6e"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(302, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.drop(\"ds\",inplace=True,axis=1)\n",
        "df_test.drop(\"ds\",inplace=True,axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-C4nJ-zmzhIC",
        "outputId": "54ab4940-0208-4d9a-ba5f-f2449f951156"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:4906: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  return super().drop(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "AGrCVkXkzp2L",
        "outputId": "44cb9f56-191e-4e29-e1c4-1605cc566c0e"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "             y          0-2          2-5         5-10        10-15  \\\n",
              "0  7200.174316  7330.320135  7411.453937  7772.548362  7985.884856   \n",
              "1  6985.470215  7035.289793  7154.901719  7584.124627  7868.700958   \n",
              "2  7344.884277  7374.224686  7588.798803  7941.113347  8161.168724   \n",
              "3  7410.656738  7414.618879  7751.195414  7997.695835  8513.584412   \n",
              "4  7411.317383  7440.500511  7691.816627  7999.573473  8310.704592   \n",
              "\n",
              "         15-25        25-35         35-45         45-55         55-70  \\\n",
              "0  8881.467089  9019.995564   9914.558115  10772.835729  11219.021474   \n",
              "1  8605.323696  9309.395769   9465.361702  10432.699324  11705.454046   \n",
              "2  8460.889926  9570.127595  10613.806877  10877.080304  11556.721135   \n",
              "3  8533.898141  9600.376441  10513.907128  10945.103235  11495.714001   \n",
              "4  9031.584755  9384.173945  10667.774928  11128.921580  11535.947776   \n",
              "\n",
              "          70-85        85-100  \n",
              "0  12698.968726  13628.171221  \n",
              "1  12300.172345  12985.970236  \n",
              "2  12867.977455  13813.617216  \n",
              "3  13433.634519  14796.554659  \n",
              "4  12668.231059  14736.946858  "
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              "      <td>10772.835729</td>\n",
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              "      <td>12698.968726</td>\n",
              "      <td>13628.171221</td>\n",
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              "      <th>1</th>\n",
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              "      <td>7035.289793</td>\n",
              "      <td>7154.901719</td>\n",
              "      <td>7584.124627</td>\n",
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              "      <td>8605.323696</td>\n",
              "      <td>9309.395769</td>\n",
              "      <td>9465.361702</td>\n",
              "      <td>10432.699324</td>\n",
              "      <td>11705.454046</td>\n",
              "      <td>12300.172345</td>\n",
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              "      <td>7344.884277</td>\n",
              "      <td>7374.224686</td>\n",
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              "      <td>7941.113347</td>\n",
              "      <td>8161.168724</td>\n",
              "      <td>8460.889926</td>\n",
              "      <td>9570.127595</td>\n",
              "      <td>10613.806877</td>\n",
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              "      <td>11556.721135</td>\n",
              "      <td>12867.977455</td>\n",
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              "      <th>3</th>\n",
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              "      <td>7414.618879</td>\n",
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              "      <td>13433.634519</td>\n",
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              "      <td>7440.500511</td>\n",
              "      <td>7691.816627</td>\n",
              "      <td>7999.573473</td>\n",
              "      <td>8310.704592</td>\n",
              "      <td>9031.584755</td>\n",
              "      <td>9384.173945</td>\n",
              "      <td>10667.774928</td>\n",
              "      <td>11128.921580</td>\n",
              "      <td>11535.947776</td>\n",
              "      <td>12668.231059</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train=df_train.drop(\"y\", axis=1)\n",
        "y_train=df_train.y\n",
        "X_test=df_test.drop(\"y\",axis=1)\n",
        "y_test=df_test.y"
      ],
      "metadata": {
        "id": "lOhdQTmCzy1W"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_log_error, mean_absolute_error,mean_absolute_percentage_error\n",
        "\n",
        "def rmsle(y_test, y_preds):\n",
        "    return np.sqrt(mean_squared_log_error(y_test, y_preds))\n",
        "\n",
        "# Create function to evaluate our model\n",
        "def show_scores(model):\n",
        "    train_preds = model.predict(X_train)\n",
        "    test_preds = model.predict(X_test)\n",
        "    scores = {\"Training MAE\": mean_absolute_error(y_train, train_preds),\n",
        "              \"Testing MAE\": mean_absolute_error(y_test, test_preds),\n",
        "              \"Training RMSLE\": rmsle(y_train, train_preds),\n",
        "              \"Test RMSLE\": rmsle(y_test, test_preds),\n",
        "              \"Training R^2\": model.score(X_train, y_train),\n",
        "              \"Test R^2\": model.score(X_test, y_test),\n",
        "              \"Training MAPE\": mean_absolute_percentage_error(y_train, train_preds),\n",
        "              \"Testing MAPE\": mean_absolute_percentage_error(y_test, test_preds),}\n",
        "    return scores\n"
      ],
      "metadata": {
        "id": "PoYnf_uJ273M"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "# Different RandomForestClassifier hyperparameters\n",
        "rf_grid = {\"n_estimators\": np.arange(10, 100, 10),\n",
        "           \"max_depth\": [None, 3, 5, 10],\n",
        "           \"min_samples_split\": np.arange(2, 20, 2),\n",
        "           \"min_samples_leaf\": np.arange(1, 20, 2),\n",
        "           \"max_features\": [0.5, 1, \"sqrt\", \"auto\"],\n",
        "           \"max_samples\": [600]}\n",
        "\n",
        "rs_model = RandomizedSearchCV(RandomForestRegressor(),\n",
        "                              param_distributions=rf_grid,\n",
        "                              n_iter=100,\n",
        "                              cv=5,\n",
        "                              verbose=True)\n",
        "\n",
        "rs_model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQewCv903nWR",
        "outputId": "521f73c4-f175-4469-df90-6f0d6dcf20f1"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=100,\n",
              "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
              "                                        'max_features': [0.5, 1, 'sqrt',\n",
              "                                                         'auto'],\n",
              "                                        'max_samples': [600],\n",
              "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
              "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
              "                                        'n_estimators': array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
              "                   verbose=True)"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs_model.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdfgWnoVDAqE",
        "outputId": "0ad4f80f-a418-4e05-f2d0-75cc354fcfe3"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': 50,\n",
              " 'min_samples_split': 2,\n",
              " 'min_samples_leaf': 3,\n",
              " 'max_samples': 600,\n",
              " 'max_features': 'auto',\n",
              " 'max_depth': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ideal_model = RandomForestRegressor(n_estimators=rs_model.best_params_['n_estimators'],\n",
        "                                    min_samples_leaf=rs_model.best_params_['min_samples_leaf'],\n",
        "                                    min_samples_split=rs_model.best_params_['min_samples_split'],\n",
        "                                    max_features=rs_model.best_params_['max_features'],\n",
        "                                    n_jobs=-1,\n",
        "                                    max_samples=None)\n",
        "ideal_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xCLsJEMyDjkh",
        "outputId": "7d0af62a-1c63-4b5f-f81d-54843dfcdaca"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(min_samples_leaf=3, n_estimators=50, n_jobs=-1)"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_scores(ideal_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3_nncWnEidN",
        "outputId": "1cb8ad07-525c-4120-ddd5-796e8429df00"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Training MAE': 73.22242729960355,\n",
              " 'Testing MAE': 207.1229018327112,\n",
              " 'Training RMSLE': 0.0043751695509175285,\n",
              " 'Test RMSLE': 0.011335201786599271,\n",
              " 'Training R^2': 0.9999590411888026,\n",
              " 'Test R^2': 0.999216388743298,\n",
              " 'Training MAPE': 0.0026582517691461135,\n",
              " 'Testing MAPE': 0.008228447592920787}"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ideal_model.feature_importances_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IU1ppWqdEjIa",
        "outputId": "2ba2bf05-2550-4fe8-aa54-486b66660251"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.18702626, 0.18275184, 0.0431021 , 0.04165961, 0.12067766,\n",
              "       0.05815683, 0.00101422, 0.07033555, 0.18926116, 0.05294059,\n",
              "       0.05307418])"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Helper function for plotting feature importance\n",
        "def plot_features(columns, importances, n=20):\n",
        "    df = (pd.DataFrame({\"features\": columns,\n",
        "                        \"feature_importance\": importances})\n",
        "          .sort_values(\"feature_importance\", ascending=False)\n",
        "          .reset_index(drop=True))\n",
        "    \n",
        "    sns.barplot(x=\"feature_importance\",\n",
        "                y=\"features\",\n",
        "                data=df[:n],\n",
        "                orient=\"h\")"
      ],
      "metadata": {
        "id": "8xMSLFHGHMl0"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_features(X_train.columns, ideal_model.feature_importances_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "LsEDIRtCHSBn",
        "outputId": "52f7f517-52cb-4bdc-f4be-2607fae92cc1"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tmp=df"
      ],
      "metadata": {
        "id": "LrIFPwjJO_GD"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tmp[\"Simple_Linear_processing\"]=((df[\"0-2\"]/1.01)+(df[\"2-5\"]/1.035)+(df[\"5-10\"]/1.075)+(df[\"10-15\"]/1.125)+(df[\"15-25\"]/1.2)+(df[\"25-35\"]/1.3)+df[\"35-45\"]/1.4+df[\"45-55\"]/1.5+df[\"55-70\"]/1.625+df[\"70-85\"]/1.725+df[\"85-100\"]/1.925)/11"
      ],
      "metadata": {
        "id": "L6zJIZ_fHUwu"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tmp.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "6X3EbjfRKQa5",
        "outputId": "d90f038f-fc82-4f62-da57-6279730209b7"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ds            y          0-2          2-5         5-10        10-15  \\\n",
              "0 2020-01-01  7200.174316  7330.320135  7411.453937  7772.548362  7985.884856   \n",
              "1 2020-01-02  6985.470215  7035.289793  7154.901719  7584.124627  7868.700958   \n",
              "2 2020-01-03  7344.884277  7374.224686  7588.798803  7941.113347  8161.168724   \n",
              "3 2020-01-04  7410.656738  7414.618879  7751.195414  7997.695835  8513.584412   \n",
              "4 2020-01-05  7411.317383  7440.500511  7691.816627  7999.573473  8310.704592   \n",
              "\n",
              "         15-25        25-35         35-45         45-55         55-70  \\\n",
              "0  8881.467089  9019.995564   9914.558115  10772.835729  11219.021474   \n",
              "1  8605.323696  9309.395769   9465.361702  10432.699324  11705.454046   \n",
              "2  8460.889926  9570.127595  10613.806877  10877.080304  11556.721135   \n",
              "3  8533.898141  9600.376441  10513.907128  10945.103235  11495.714001   \n",
              "4  9031.584755  9384.173945  10667.774928  11128.921580  11535.947776   \n",
              "\n",
              "          70-85        85-100  Simple_Linear_processing  \n",
              "0  12698.968726  13628.171221               7154.191975  \n",
              "1  12300.172345  12985.970236               7005.101305  \n",
              "2  12867.977455  13813.617216               7297.029630  \n",
              "3  13433.634519  14796.554659               7426.291790  \n",
              "4  12668.231059  14736.946858               7409.985060  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53d763f7-3d37-4c4e-8e89-0f711980c39b\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>y</th>\n",
              "      <th>0-2</th>\n",
              "      <th>2-5</th>\n",
              "      <th>5-10</th>\n",
              "      <th>10-15</th>\n",
              "      <th>15-25</th>\n",
              "      <th>25-35</th>\n",
              "      <th>35-45</th>\n",
              "      <th>45-55</th>\n",
              "      <th>55-70</th>\n",
              "      <th>70-85</th>\n",
              "      <th>85-100</th>\n",
              "      <th>Simple_Linear_processing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>7200.174316</td>\n",
              "      <td>7330.320135</td>\n",
              "      <td>7411.453937</td>\n",
              "      <td>7772.548362</td>\n",
              "      <td>7985.884856</td>\n",
              "      <td>8881.467089</td>\n",
              "      <td>9019.995564</td>\n",
              "      <td>9914.558115</td>\n",
              "      <td>10772.835729</td>\n",
              "      <td>11219.021474</td>\n",
              "      <td>12698.968726</td>\n",
              "      <td>13628.171221</td>\n",
              "      <td>7154.191975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>6985.470215</td>\n",
              "      <td>7035.289793</td>\n",
              "      <td>7154.901719</td>\n",
              "      <td>7584.124627</td>\n",
              "      <td>7868.700958</td>\n",
              "      <td>8605.323696</td>\n",
              "      <td>9309.395769</td>\n",
              "      <td>9465.361702</td>\n",
              "      <td>10432.699324</td>\n",
              "      <td>11705.454046</td>\n",
              "      <td>12300.172345</td>\n",
              "      <td>12985.970236</td>\n",
              "      <td>7005.101305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>7344.884277</td>\n",
              "      <td>7374.224686</td>\n",
              "      <td>7588.798803</td>\n",
              "      <td>7941.113347</td>\n",
              "      <td>8161.168724</td>\n",
              "      <td>8460.889926</td>\n",
              "      <td>9570.127595</td>\n",
              "      <td>10613.806877</td>\n",
              "      <td>10877.080304</td>\n",
              "      <td>11556.721135</td>\n",
              "      <td>12867.977455</td>\n",
              "      <td>13813.617216</td>\n",
              "      <td>7297.029630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-04</td>\n",
              "      <td>7410.656738</td>\n",
              "      <td>7414.618879</td>\n",
              "      <td>7751.195414</td>\n",
              "      <td>7997.695835</td>\n",
              "      <td>8513.584412</td>\n",
              "      <td>8533.898141</td>\n",
              "      <td>9600.376441</td>\n",
              "      <td>10513.907128</td>\n",
              "      <td>10945.103235</td>\n",
              "      <td>11495.714001</td>\n",
              "      <td>13433.634519</td>\n",
              "      <td>14796.554659</td>\n",
              "      <td>7426.291790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-05</td>\n",
              "      <td>7411.317383</td>\n",
              "      <td>7440.500511</td>\n",
              "      <td>7691.816627</td>\n",
              "      <td>7999.573473</td>\n",
              "      <td>8310.704592</td>\n",
              "      <td>9031.584755</td>\n",
              "      <td>9384.173945</td>\n",
              "      <td>10667.774928</td>\n",
              "      <td>11128.921580</td>\n",
              "      <td>11535.947776</td>\n",
              "      <td>12668.231059</td>\n",
              "      <td>14736.946858</td>\n",
              "      <td>7409.985060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53d763f7-3d37-4c4e-8e89-0f711980c39b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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    {
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        "mean_absolute_percentage_error(df_tmp[\"Simple_Linear_processing\"],df_tmp[\"y\"])"
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        "outputId": "a9523bd5-64a9-433e-da16-050ba9e53149"
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      "execution_count": 131,
      "outputs": [
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          "output_type": "execute_result",
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              "0.0050898340980518484"
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          "metadata": {},
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      "execution_count": null,
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            "text/plain": [
              "(2525, 6)"
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          "execution_count": 215
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              "          0-2         2-5        5-10       10-15       15-25       25-35\n",
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              "          ds            y          0-2          2-5         5-10        10-15  \\\n",
              "0 2020-01-01  7200.174316  7330.320135  7411.453937  7772.548362  7985.884856   \n",
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              "4 2020-01-05  7411.317383  7440.500511  7691.816627  7999.573473  8310.704592   \n",
              "\n",
              "         15-25        25-35         35-45         45-55         55-70  \\\n",
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              "4  9031.584755  9384.173945  10667.774928  11128.921580  11535.947776   \n",
              "\n",
              "          70-85        85-100  Simple_Linear_processing  model_preds  \n",
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              "      <td>9914.558115</td>\n",
              "      <td>10772.835729</td>\n",
              "      <td>11219.021474</td>\n",
              "      <td>12698.968726</td>\n",
              "      <td>13628.171221</td>\n",
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              "      <td>2020-01-02</td>\n",
              "      <td>6985.470215</td>\n",
              "      <td>7035.289793</td>\n",
              "      <td>7154.901719</td>\n",
              "      <td>7584.124627</td>\n",
              "      <td>7868.700958</td>\n",
              "      <td>8605.323696</td>\n",
              "      <td>9309.395769</td>\n",
              "      <td>9465.361702</td>\n",
              "      <td>10432.699324</td>\n",
              "      <td>11705.454046</td>\n",
              "      <td>12300.172345</td>\n",
              "      <td>12985.970236</td>\n",
              "      <td>7005.101305</td>\n",
              "      <td>6988.198872</td>\n",
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              "      <th>2</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>7344.884277</td>\n",
              "      <td>7374.224686</td>\n",
              "      <td>7588.798803</td>\n",
              "      <td>7941.113347</td>\n",
              "      <td>8161.168724</td>\n",
              "      <td>8460.889926</td>\n",
              "      <td>9570.127595</td>\n",
              "      <td>10613.806877</td>\n",
              "      <td>10877.080304</td>\n",
              "      <td>11556.721135</td>\n",
              "      <td>12867.977455</td>\n",
              "      <td>13813.617216</td>\n",
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              "      <th>3</th>\n",
              "      <td>2020-01-04</td>\n",
              "      <td>7410.656738</td>\n",
              "      <td>7414.618879</td>\n",
              "      <td>7751.195414</td>\n",
              "      <td>7997.695835</td>\n",
              "      <td>8513.584412</td>\n",
              "      <td>8533.898141</td>\n",
              "      <td>9600.376441</td>\n",
              "      <td>10513.907128</td>\n",
              "      <td>10945.103235</td>\n",
              "      <td>11495.714001</td>\n",
              "      <td>13433.634519</td>\n",
              "      <td>14796.554659</td>\n",
              "      <td>7426.291790</td>\n",
              "      <td>7403.166741</td>\n",
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              "      <td>2020-01-05</td>\n",
              "      <td>7411.317383</td>\n",
              "      <td>7440.500511</td>\n",
              "      <td>7691.816627</td>\n",
              "      <td>7999.573473</td>\n",
              "      <td>8310.704592</td>\n",
              "      <td>9031.584755</td>\n",
              "      <td>9384.173945</td>\n",
              "      <td>10667.774928</td>\n",
              "      <td>11128.921580</td>\n",
              "      <td>11535.947776</td>\n",
              "      <td>12668.231059</td>\n",
              "      <td>14736.946858</td>\n",
              "      <td>7409.985060</td>\n",
              "      <td>7382.114496</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-176bdc48-f044-4548-97ee-b3f71a10881c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-176bdc48-f044-4548-97ee-b3f71a10881c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-176bdc48-f044-4548-97ee-b3f71a10881c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_percentage_error(df[\"model_preds\"],df[\"y\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ae_lUH_QLdJq",
        "outputId": "a83fcbcf-97f7-4f2d-f5bc-4810add77c5e"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0042457729784521675"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_scores(ideal_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8jPWnscRLlh5",
        "outputId": "4b7d2a90-48f0-4806-bf4a-72b8de5fa446"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Training MAE': 73.2224272996036,\n",
              " 'Testing MAE': 207.1229018327112,\n",
              " 'Training RMSLE': 0.0043751695509175285,\n",
              " 'Test RMSLE': 0.011335201786599271,\n",
              " 'Training R^2': 0.9999590411888026,\n",
              " 'Test R^2': 0.999216388743298,\n",
              " 'Training MAPE': 0.0026582517691461135,\n",
              " 'Testing MAPE': 0.008228447592920787}"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df_tmp[\"ds\"],df_tmp[\"y\"],'r',df_tmp[\"ds\"],df_tmp[\"model_preds\"],'--g');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "yw7f4SwBL4HM",
        "outputId": "afd281c1-23bb-4b81-c651-fb0d96a37745"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, a = plt.subplots(1, 3)\n",
        "f.set_figwidth(30)\n",
        "f.set_figheight(7.5)\n",
        "a[0].plot(df_tmp[\"ds\"],df_tmp[\"y\"],'r')\n",
        "a[0].set_title(\"original\")\n",
        "a[1].plot(df_tmp[\"ds\"],df_tmp[\"model_preds\"],'--g')\n",
        "a[1].set_title(\"Ideal_Model\")\n",
        "a[2].plot(df_tmp[\"ds\"],df_tmp[\"Simple_Linear_processing\"],'--y')\n",
        "a[2].set_title(\"Linear_Processing\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "qLN3VJvyWUaC",
        "outputId": "874066cb-9424-42c0-96c4-83df783a8d14"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Linear_Processing')"
            ]
          },
          "metadata": {},
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x540 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QHmWKUeYeFG6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}