Prediction number of sunspots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # linear algebra\n",
    "import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mlp\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler # OneHE is used for convert 'str' data to numerical\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline  # multi preproseccing\n",
    "from sklearn.base import BaseEstimator, TransformerMixin  #use to construct eg. AttributesAdder \n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Date In Fraction Of Year</th>\n",
       "      <th>Number of Sunspots</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Observations</th>\n",
       "      <th>Indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1818.001</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1818.004</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1818.007</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1818.010</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1818.012</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1818.015</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1818.018</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1818.021</td>\n",
       "      <td>65</td>\n",
       "      <td>10.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1818.023</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1818.026</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0  Year  Month  Day  Date In Fraction Of Year  Number of Sunspots  \\\n",
       "0           0  1818      1    1                  1818.001                  -1   \n",
       "1           1  1818      1    2                  1818.004                  -1   \n",
       "2           2  1818      1    3                  1818.007                  -1   \n",
       "3           3  1818      1    4                  1818.010                  -1   \n",
       "4           4  1818      1    5                  1818.012                  -1   \n",
       "5           5  1818      1    6                  1818.015                  -1   \n",
       "6           6  1818      1    7                  1818.018                  -1   \n",
       "7           7  1818      1    8                  1818.021                  65   \n",
       "8           8  1818      1    9                  1818.023                  -1   \n",
       "9           9  1818      1   10                  1818.026                  -1   \n",
       "\n",
       "   Standard Deviation  Observations  Indicator  \n",
       "0                -1.0             0          1  \n",
       "1                -1.0             0          1  \n",
       "2                -1.0             0          1  \n",
       "3                -1.0             0          1  \n",
       "4                -1.0             0          1  \n",
       "5                -1.0             0          1  \n",
       "6                -1.0             0          1  \n",
       "7                10.2             1          1  \n",
       "8                -1.0             0          1  \n",
       "9                -1.0             0          1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('/home/mohammadreza/Kaggle/sunspot/sunspot_data.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Unnamed: 0'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73718, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73718 entries, 0 to 73717\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Year                      73718 non-null  int64  \n",
      " 1   Month                     73718 non-null  int64  \n",
      " 2   Day                       73718 non-null  int64  \n",
      " 3   Date In Fraction Of Year  73718 non-null  float64\n",
      " 4   Number of Sunspots        73718 non-null  int64  \n",
      " 5   Standard Deviation        73718 non-null  float64\n",
      " 6   Observations              73718 non-null  int64  \n",
      " 7   Indicator                 73718 non-null  int64  \n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 4.5 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Date In Fraction Of Year</th>\n",
       "      <th>Number of Sunspots</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Observations</th>\n",
       "      <th>Indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>73718.000000</td>\n",
       "      <td>73718.000000</td>\n",
       "      <td>73718.000000</td>\n",
       "      <td>73718.000000</td>\n",
       "      <td>73718.000000</td>\n",
       "      <td>73718.000000</td>\n",
       "      <td>73718.000000</td>\n",
       "      <td>73718.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1918.416750</td>\n",
       "      <td>6.518896</td>\n",
       "      <td>15.729347</td>\n",
       "      <td>1918.916406</td>\n",
       "      <td>79.248732</td>\n",
       "      <td>6.924587</td>\n",
       "      <td>4.429678</td>\n",
       "      <td>0.998331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.264401</td>\n",
       "      <td>3.447114</td>\n",
       "      <td>8.800032</td>\n",
       "      <td>58.264520</td>\n",
       "      <td>77.470942</td>\n",
       "      <td>4.778793</td>\n",
       "      <td>7.884112</td>\n",
       "      <td>0.040814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1818.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1818.001000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1868.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1868.458500</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1918.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1918.917500</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1969.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1969.373250</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2019.832000</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>77.700000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Year         Month           Day  Date In Fraction Of Year  \\\n",
       "count  73718.000000  73718.000000  73718.000000              73718.000000   \n",
       "mean    1918.416750      6.518896     15.729347               1918.916406   \n",
       "std       58.264401      3.447114      8.800032                 58.264520   \n",
       "min     1818.000000      1.000000      1.000000               1818.001000   \n",
       "25%     1868.000000      4.000000      8.000000               1868.458500   \n",
       "50%     1918.000000      7.000000     16.000000               1918.917500   \n",
       "75%     1969.000000     10.000000     23.000000               1969.373250   \n",
       "max     2019.000000     12.000000     31.000000               2019.832000   \n",
       "\n",
       "       Number of Sunspots  Standard Deviation  Observations     Indicator  \n",
       "count        73718.000000        73718.000000  73718.000000  73718.000000  \n",
       "mean            79.248732            6.924587      4.429678      0.998331  \n",
       "std             77.470942            4.778793      7.884112      0.040814  \n",
       "min             -1.000000           -1.000000      0.000000      0.000000  \n",
       "25%             15.000000            3.000000      1.000000      1.000000  \n",
       "50%             58.000000            6.600000      1.000000      1.000000  \n",
       "75%            125.000000           10.000000      1.000000      1.000000  \n",
       "max            528.000000           77.700000     60.000000      1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Number of Sunspots          1.000000\n",
       "Standard Deviation          0.837165\n",
       "Date In Fraction Of Year    0.128850\n",
       "Year                        0.128837\n",
       "Indicator                   0.041408\n",
       "Month                       0.002993\n",
       "Day                        -0.002208\n",
       "Observations               -0.028468\n",
       "Name: Number of Sunspots, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_mx=df.corr()\n",
    "cor_mx['Number of Sunspots'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2018=df[df['Year']==2018]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of sunspot in each day of Jan 2018')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(50,30))\n",
    "sns.set(font_scale = 2.5)\n",
    "sns.barplot(data=df_2018, y='Month', x='Number of Sunspots', hue='Day', orient='h')\n",
    "plt.title('Number of sunspot in each day of Jan 2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Date In Fraction Of Year</th>\n",
       "      <th>Number of Sunspots</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Observations</th>\n",
       "      <th>Indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73049</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018.001</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73050</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2018.004</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73051</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2018.007</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73052</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2018.010</td>\n",
       "      <td>13</td>\n",
       "      <td>0.6</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73053</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018.012</td>\n",
       "      <td>11</td>\n",
       "      <td>0.9</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Month  Day  Date In Fraction Of Year  Number of Sunspots  \\\n",
       "73049  2018      1    1                  2018.001                   0   \n",
       "73050  2018      1    2                  2018.004                   0   \n",
       "73051  2018      1    3                  2018.007                   0   \n",
       "73052  2018      1    4                  2018.010                  13   \n",
       "73053  2018      1    5                  2018.012                  11   \n",
       "\n",
       "       Standard Deviation  Observations  Indicator  \n",
       "73049                 0.0            23          1  \n",
       "73050                 0.0            37          1  \n",
       "73051                 0.0            33          1  \n",
       "73052                 0.6            19          1  \n",
       "73053                 0.9            16          1  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of sunspot in each day of Jan 2018')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set(font_scale=0.8)\n",
    "sns.barplot(data=df_2018, y=df_2018['Day'][df_2018['Month']==1], x='Number of Sunspots', orient='h', color='blue')\n",
    "plt.title('Number of sunspot in each day of Jan 2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of sunspot in each day of Feb 2018')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set(font_scale=0.8)\n",
    "sns.barplot(data=df_2018, y=df_2018['Day'][df_2018['Month']==2], x='Number of Sunspots', orient='h', color='red')\n",
    "plt.title('Number of sunspot in each day of Feb 2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of sunspot in each day of Jun 2018')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set(font_scale=0.8)\n",
    "sns.barplot(data=df_2018, y=df_2018['Day'][df_2018['Month']==6], x='Number of Sunspots', orient='h', color='green')\n",
    "plt.title('Number of sunspot in each day of Jun 2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                        0\n",
       "Month                       0\n",
       "Day                         0\n",
       "Date In Fraction Of Year    0\n",
       "Number of Sunspots          0\n",
       "Standard Deviation          0\n",
       "Observations                0\n",
       "Indicator                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Month','Day','Observations', 'Indicator'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>Year</th>\n",
       "      <th>Date In Fraction Of Year</th>\n",
       "      <th>Number of Sunspots</th>\n",
       "      <th>Standard Deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1818</td>\n",
       "      <td>1818.001</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1818</td>\n",
       "      <td>1818.004</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1818</td>\n",
       "      <td>1818.007</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1818</td>\n",
       "      <td>1818.010</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1818</td>\n",
       "      <td>1818.012</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73713</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019.821</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73714</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019.823</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73715</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019.826</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73716</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019.829</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73717</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019.832</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73718 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Date In Fraction Of Year  Number of Sunspots  Standard Deviation\n",
       "0      1818                  1818.001                  -1                -1.0\n",
       "1      1818                  1818.004                  -1                -1.0\n",
       "2      1818                  1818.007                  -1                -1.0\n",
       "3      1818                  1818.010                  -1                -1.0\n",
       "4      1818                  1818.012                  -1                -1.0\n",
       "...     ...                       ...                 ...                 ...\n",
       "73713  2019                  2019.821                   0                 0.0\n",
       "73714  2019                  2019.823                   0                 0.0\n",
       "73715  2019                  2019.826                   0                 0.0\n",
       "73716  2019                  2019.829                   0                 0.0\n",
       "73717  2019                  2019.832                   0                 0.0\n",
       "\n",
       "[73718 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>Year</th>\n",
       "      <th>Date In Fraction Of Year</th>\n",
       "      <th>Number of Sunspots</th>\n",
       "      <th>Standard Deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25834</th>\n",
       "      <td>1888</td>\n",
       "      <td>1888.731</td>\n",
       "      <td>18</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30595</th>\n",
       "      <td>1901</td>\n",
       "      <td>1901.768</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67547</th>\n",
       "      <td>2002</td>\n",
       "      <td>2002.938</td>\n",
       "      <td>179</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36359</th>\n",
       "      <td>1917</td>\n",
       "      <td>1917.549</td>\n",
       "      <td>112</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40427</th>\n",
       "      <td>1928</td>\n",
       "      <td>1928.687</td>\n",
       "      <td>172</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37194</th>\n",
       "      <td>1919</td>\n",
       "      <td>1919.837</td>\n",
       "      <td>160</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6265</th>\n",
       "      <td>1835</td>\n",
       "      <td>1835.155</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54886</th>\n",
       "      <td>1968</td>\n",
       "      <td>1968.275</td>\n",
       "      <td>147</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>1820</td>\n",
       "      <td>1820.357</td>\n",
       "      <td>60</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>1861</td>\n",
       "      <td>1861.245</td>\n",
       "      <td>287</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58974 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Date In Fraction Of Year  Number of Sunspots  Standard Deviation\n",
       "25834  1888                  1888.731                  18                 4.8\n",
       "30595  1901                  1901.768                  12                 3.0\n",
       "67547  2002                  2002.938                 179                 9.4\n",
       "36359  1917                  1917.549                 112                 9.4\n",
       "40427  1928                  1928.687                 172                11.7\n",
       "...     ...                       ...                 ...                 ...\n",
       "37194  1919                  1919.837                 160                11.3\n",
       "6265   1835                  1835.155                  -1                -1.0\n",
       "54886  1968                  1968.275                 147                 8.1\n",
       "860    1820                  1820.357                  60                 9.8\n",
       "15795  1861                  1861.245                 287                20.9\n",
       "\n",
       "[58974 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test=train_test_split(df, test_size=0.2, random_state=42)\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train.drop('Number of Sunspots', axis=1, inplace=False)\n",
    "y=train['Number of Sunspots'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=StandardScaler()\n",
    "X_scaled=scale.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg=LinearRegression()\n",
    "\n",
    "lin_reg.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_scores=cross_val_score(lin_reg, X_scaled, y, scoring='neg_mean_absolute_error', cv=10)\n",
    "lin_mae=-lin_scores\n",
    "lin_mae\n",
    "\n",
    "def cost_fun(scores):\n",
    "    print('Scores:', scores,\n",
    "         'Mean:', scores.mean(),\n",
    "         'Standard Deviation:', scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [27.21077816 27.45575339 27.32588867 26.37522104 27.25226668 27.06564574\n",
      " 26.96801563 27.20004121 27.33302689 27.09969291] Mean: 27.128633032259888 Standard Deviation: 0.28514191247161785\n"
     ]
    }
   ],
   "source": [
    "cost_fun(lin_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [27.21080862 27.45477018 27.32599388 26.37514283 27.2522472  27.06496493\n",
      " 26.96503474 27.19815392 27.33302731 27.09913429] Mean: 27.12792778934184 Standard Deviation: 0.28520009193005713\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge, Lasso\n",
    "\n",
    "ridge=Ridge()\n",
    "ridge.fit(X_scaled, y)\n",
    "ridge_scores=cross_val_score(ridge, X_scaled, y, cv=10, scoring='neg_mean_absolute_error')\n",
    "ridge_mae=-ridge_scores\n",
    "\n",
    "cost_fun(ridge_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=Ridge(),\n",
       "             param_grid={'alpha': array([1.00000000e-05, 1.29154967e-04, 1.66810054e-03, 2.15443469e-02,\n",
       "       2.78255940e-01, 3.59381366e+00, 4.64158883e+01, 5.99484250e+02,\n",
       "       7.74263683e+03, 1.00000000e+05])},\n",
       "             return_train_score=True, scoring='neg_mean_absolute_error')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid=({\n",
    "    'alpha': np.logspace(-5,5,10)\n",
    "})\n",
    "\n",
    "ridge_search=GridSearchCV(ridge, param_grid, cv=10, \n",
    "                         scoring='neg_mean_absolute_error',\n",
    "                         return_train_score=True)\n",
    "ridge_search.fit(X_scaled,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 46.41588833612782}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [27.2115913  27.45371537 27.32808149 26.37486348 27.25122693 27.06551609\n",
      " 26.9619704  27.19500424 27.33316753 27.09840589] Mean: 27.127354271289228 Standard Deviation: 0.28538309239540377\n"
     ]
    }
   ],
   "source": [
    "ridge_model=ridge_search.best_estimator_\n",
    "ridge_model.fit(X_scaled,y)\n",
    "ridge_model_scores=cross_val_score(ridge_model, X_scaled, y, cv=10, scoring='neg_mean_absolute_error')\n",
    "ridge_model_mae=-ridge_model_scores\n",
    "\n",
    "cost_fun(ridge_model_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [27.91416468 27.5608659  26.98687024 26.69229755 26.53855414 27.7214721\n",
      " 26.76461212 26.67189337 27.54876291 27.26448036] Mean: 27.166397336062357 Standard Deviation: 0.4728473700446538\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "sgd_reg=SGDRegressor(max_iter=5000, tol=1e-8, penalty=None, eta0=0.1)\n",
    "sgd_reg.fit(X_scaled, y)\n",
    "\n",
    "sgd_scores=cross_val_score(sgd_reg, X_scaled, y, cv=10, scoring='neg_mean_absolute_error')\n",
    "sgd_mae=-sgd_scores\n",
    "\n",
    "cost_fun(sgd_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [4.43557138 4.31095287 4.33231604 4.         4.33542479 4.52806512\n",
      " 4.41716127 4.52636934 4.47295235 4.12277429] Mean: 4.348158744441498 Standard Deviation: 0.16306782807847914\n"
     ]
    }
   ],
   "source": [
    "tree_reg=DecisionTreeRegressor()\n",
    "tree_reg.fit(X_scaled, y)\n",
    "tree_scores=cross_val_score(tree_reg, X_scaled, y, cv=10, scoring='neg_mean_absolute_error')\n",
    "tree_mae=-tree_scores\n",
    "\n",
    "cost_fun(tree_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [3.64629196 3.66801289 3.64672092 3.38847067 3.63100051 3.82487197\n",
      " 3.58696625 3.88670341 3.72873834 3.37928269] Mean: 3.6387059607183403 Standard Deviation: 0.154240571033558\n"
     ]
    }
   ],
   "source": [
    "random_forest=RandomForestRegressor()\n",
    "random_forest.fit(X_scaled, y)\n",
    "\n",
    "forest_score=cross_val_score(random_forest, X_scaled, y, cv=10, scoring='neg_mean_absolute_error')\n",
    "forest_mae=-forest_score\n",
    "\n",
    "cost_fun(forest_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Model with polynomial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "poly=PolynomialFeatures(degree=3)\n",
    "X_poly=poly.fit_transform(X_scaled)\n",
    "\n",
    "lin_reg.fit(X_poly, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [17.87041059 18.18188424 17.69383484 16.96129449 17.98096439 17.69330643\n",
      " 17.73144602 17.76776697 17.76489854 17.44317705] Mean: 17.708898356696302 Standard Deviation: 0.30998727083565675\n"
     ]
    }
   ],
   "source": [
    "lin_scores=cross_val_score(lin_reg, X_poly, y, scoring='neg_mean_absolute_error', cv=10)\n",
    "lin_mae=-lin_scores\n",
    "\n",
    "cost_fun(lin_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [4.53526619 4.42641573 4.652255   4.05866395 4.20128879 4.59708326\n",
      " 4.45294217 4.93488214 4.71934882 4.1989147 ] Mean: 4.477706077651659 Standard Deviation: 0.25463777720551956\n"
     ]
    }
   ],
   "source": [
    "tree_reg=DecisionTreeRegressor()\n",
    "tree_reg.fit(X_poly, y)\n",
    "\n",
    "tree_scores=cross_val_score(tree_reg, X_poly, y, cv=10, scoring='neg_mean_absolute_error')\n",
    "tree_mae=-tree_scores\n",
    "\n",
    "cost_fun(tree_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [3.77202798 3.68067147 3.8763671  3.84079695 3.64333814] Mean: 3.762640327903994 Standard Deviation: 0.08953465361918901\n"
     ]
    }
   ],
   "source": [
    "random_forest=RandomForestRegressor()\n",
    "random_forest.fit(X_poly, y)\n",
    "\n",
    "forest_score=cross_val_score(random_forest, X_poly, y, cv=5, scoring='neg_mean_absolute_error')\n",
    "forest_mae=-forest_score\n",
    "\n",
    "cost_fun(forest_mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It ( Polynomial) does not make sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "             param_grid=[{'max_features': [2, 3], 'n_estimators': [10, 20, 40]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3],\n",
       "                          'n_estimators': [20, 40]}],\n",
       "             return_train_score=True, scoring='neg_mean_absolute_error')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "param_grid = [\n",
    "    # Try 20 (4*5) combination of hyperparameters\n",
    "    {'n_estimators': [10, 20, 40], 'max_features': [2, 3]},\n",
    "    # Try bootstrap as False with 12 combination\n",
    "    {'bootstrap': [False], 'n_estimators': [20, 40], 'max_features': [2, 3]},\n",
    "]\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "# Train across 5 fold, that is a total of (16 + 12)*5 rounds of training\n",
    "forest_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring=\"neg_mean_absolute_error\",\n",
    "                           return_train_score=True)\n",
    "forest_search.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 3, 'n_estimators': 40}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [1.93250075 1.90019633 1.95849688 1.95146927 1.89465435] Mean: 1.9274635151517394 Standard Deviation: 0.02601791958881354\n"
     ]
    }
   ],
   "source": [
    "forest_model=forest_search.best_estimator_\n",
    "\n",
    "forest_model.fit(X_scaled, y)\n",
    "\n",
    "forest_model_score=cross_val_score(forest_model, X_scaled, y, cv=5, scoring='neg_mean_absolute_error')\n",
    "forest_model_rmse=np.sqrt(-forest_model_score)\n",
    "\n",
    "cost_fun(forest_model_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's go through our `test_set` for final `MAE`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "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>Year</th>\n",
       "      <th>Date In Fraction Of Year</th>\n",
       "      <th>Number of Sunspots</th>\n",
       "      <th>Standard Deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56571</th>\n",
       "      <td>1972</td>\n",
       "      <td>1972.887</td>\n",
       "      <td>82</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66678</th>\n",
       "      <td>2000</td>\n",
       "      <td>2000.559</td>\n",
       "      <td>286</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36998</th>\n",
       "      <td>1919</td>\n",
       "      <td>1919.300</td>\n",
       "      <td>27</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16814</th>\n",
       "      <td>1864</td>\n",
       "      <td>1864.037</td>\n",
       "      <td>87</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48277</th>\n",
       "      <td>1950</td>\n",
       "      <td>1950.179</td>\n",
       "      <td>261</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61615</th>\n",
       "      <td>1986</td>\n",
       "      <td>1986.697</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41229</th>\n",
       "      <td>1930</td>\n",
       "      <td>1930.884</td>\n",
       "      <td>72</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43935</th>\n",
       "      <td>1938</td>\n",
       "      <td>1938.292</td>\n",
       "      <td>178</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31396</th>\n",
       "      <td>1903</td>\n",
       "      <td>1903.963</td>\n",
       "      <td>92</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49329</th>\n",
       "      <td>1953</td>\n",
       "      <td>1953.059</td>\n",
       "      <td>26</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14744 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Date In Fraction Of Year  Number of Sunspots  Standard Deviation\n",
       "56571  1972                  1972.887                  82                 6.0\n",
       "66678  2000                  2000.559                 286                15.9\n",
       "36998  1919                  1919.300                  27                 4.6\n",
       "16814  1864                  1864.037                  87                11.3\n",
       "48277  1950                  1950.179                 261                10.8\n",
       "...     ...                       ...                 ...                 ...\n",
       "61615  1986                  1986.697                   0                 2.0\n",
       "41229  1930                  1930.884                  72                 7.6\n",
       "43935  1938                  1938.292                 178                11.9\n",
       "31396  1903                  1903.963                  92                 8.5\n",
       "49329  1953                  1953.059                  26                 3.5\n",
       "\n",
       "[14744 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8667815908315593"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test=test.drop('Number of Sunspots', axis=1, inplace=False)\n",
    "y_test=test['Number of Sunspots'].copy()\n",
    "\n",
    "X_test_scaled=scale.transform(X_test)\n",
    "final_prediction=forest_model.predict(X_test_scaled)\n",
    "final_mae=mean_absolute_error(final_prediction, y_test)\n",
    "final_mae=np.sqrt(final_mae)\n",
    "final_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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