Concrete_compressive_strength_estimation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimate Concrete Compressive Strength with ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Cement (component 1)(kg in a m^3 mixture)</th>\n",
       "      <th>Blast Furnace Slag (component 2)(kg in a m^3 mixture)</th>\n",
       "      <th>Fly Ash (component 3)(kg in a m^3 mixture)</th>\n",
       "      <th>Water  (component 4)(kg in a m^3 mixture)</th>\n",
       "      <th>Superplasticizer (component 5)(kg in a m^3 mixture)</th>\n",
       "      <th>Coarse Aggregate  (component 6)(kg in a m^3 mixture)</th>\n",
       "      <th>Fine Aggregate (component 7)(kg in a m^3 mixture)</th>\n",
       "      <th>Age (day)</th>\n",
       "      <th>Concrete compressive strength(MPa, megapascals)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>270</td>\n",
       "      <td>40.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>365</td>\n",
       "      <td>41.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.6</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.4</td>\n",
       "      <td>825.5</td>\n",
       "      <td>360</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>276.4</td>\n",
       "      <td>116.0</td>\n",
       "      <td>90.3</td>\n",
       "      <td>179.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>870.1</td>\n",
       "      <td>768.3</td>\n",
       "      <td>28</td>\n",
       "      <td>44.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>322.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.6</td>\n",
       "      <td>196.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>817.9</td>\n",
       "      <td>813.4</td>\n",
       "      <td>28</td>\n",
       "      <td>31.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>148.5</td>\n",
       "      <td>139.4</td>\n",
       "      <td>108.6</td>\n",
       "      <td>192.7</td>\n",
       "      <td>6.1</td>\n",
       "      <td>892.4</td>\n",
       "      <td>780.0</td>\n",
       "      <td>28</td>\n",
       "      <td>23.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>159.1</td>\n",
       "      <td>186.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>175.6</td>\n",
       "      <td>11.3</td>\n",
       "      <td>989.6</td>\n",
       "      <td>788.9</td>\n",
       "      <td>28</td>\n",
       "      <td>32.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>260.9</td>\n",
       "      <td>100.5</td>\n",
       "      <td>78.3</td>\n",
       "      <td>200.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>864.5</td>\n",
       "      <td>761.5</td>\n",
       "      <td>28</td>\n",
       "      <td>32.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1030 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cement (component 1)(kg in a m^3 mixture)  \\\n",
       "0                                         540.0   \n",
       "1                                         540.0   \n",
       "2                                         332.5   \n",
       "3                                         332.5   \n",
       "4                                         198.6   \n",
       "...                                         ...   \n",
       "1025                                      276.4   \n",
       "1026                                      322.2   \n",
       "1027                                      148.5   \n",
       "1028                                      159.1   \n",
       "1029                                      260.9   \n",
       "\n",
       "      Blast Furnace Slag (component 2)(kg in a m^3 mixture)  \\\n",
       "0                                                   0.0       \n",
       "1                                                   0.0       \n",
       "2                                                 142.5       \n",
       "3                                                 142.5       \n",
       "4                                                 132.4       \n",
       "...                                                 ...       \n",
       "1025                                              116.0       \n",
       "1026                                                0.0       \n",
       "1027                                              139.4       \n",
       "1028                                              186.7       \n",
       "1029                                              100.5       \n",
       "\n",
       "      Fly Ash (component 3)(kg in a m^3 mixture)  \\\n",
       "0                                            0.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            0.0   \n",
       "...                                          ...   \n",
       "1025                                        90.3   \n",
       "1026                                       115.6   \n",
       "1027                                       108.6   \n",
       "1028                                         0.0   \n",
       "1029                                        78.3   \n",
       "\n",
       "      Water  (component 4)(kg in a m^3 mixture)  \\\n",
       "0                                         162.0   \n",
       "1                                         162.0   \n",
       "2                                         228.0   \n",
       "3                                         228.0   \n",
       "4                                         192.0   \n",
       "...                                         ...   \n",
       "1025                                      179.6   \n",
       "1026                                      196.0   \n",
       "1027                                      192.7   \n",
       "1028                                      175.6   \n",
       "1029                                      200.6   \n",
       "\n",
       "      Superplasticizer (component 5)(kg in a m^3 mixture)  \\\n",
       "0                                                   2.5     \n",
       "1                                                   2.5     \n",
       "2                                                   0.0     \n",
       "3                                                   0.0     \n",
       "4                                                   0.0     \n",
       "...                                                 ...     \n",
       "1025                                                8.9     \n",
       "1026                                               10.4     \n",
       "1027                                                6.1     \n",
       "1028                                               11.3     \n",
       "1029                                                8.6     \n",
       "\n",
       "      Coarse Aggregate  (component 6)(kg in a m^3 mixture)  \\\n",
       "0                                                1040.0      \n",
       "1                                                1055.0      \n",
       "2                                                 932.0      \n",
       "3                                                 932.0      \n",
       "4                                                 978.4      \n",
       "...                                                 ...      \n",
       "1025                                              870.1      \n",
       "1026                                              817.9      \n",
       "1027                                              892.4      \n",
       "1028                                              989.6      \n",
       "1029                                              864.5      \n",
       "\n",
       "      Fine Aggregate (component 7)(kg in a m^3 mixture)  Age (day)  \\\n",
       "0                                                 676.0         28   \n",
       "1                                                 676.0         28   \n",
       "2                                                 594.0        270   \n",
       "3                                                 594.0        365   \n",
       "4                                                 825.5        360   \n",
       "...                                                 ...        ...   \n",
       "1025                                              768.3         28   \n",
       "1026                                              813.4         28   \n",
       "1027                                              780.0         28   \n",
       "1028                                              788.9         28   \n",
       "1029                                              761.5         28   \n",
       "\n",
       "      Concrete compressive strength(MPa, megapascals)   \n",
       "0                                                79.99  \n",
       "1                                                61.89  \n",
       "2                                                40.27  \n",
       "3                                                41.05  \n",
       "4                                                44.30  \n",
       "...                                                ...  \n",
       "1025                                             44.28  \n",
       "1026                                             31.18  \n",
       "1027                                             23.70  \n",
       "1028                                             32.77  \n",
       "1029                                             32.40  \n",
       "\n",
       "[1030 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Concrete_Data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['Cement','BF','FA','W','SP','CA','FAgg','Age','Compr_Str']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Cement</th>\n",
       "      <th>BF</th>\n",
       "      <th>FA</th>\n",
       "      <th>W</th>\n",
       "      <th>SP</th>\n",
       "      <th>CA</th>\n",
       "      <th>FAgg</th>\n",
       "      <th>Age</th>\n",
       "      <th>Compr_Str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>281.167864</td>\n",
       "      <td>73.895825</td>\n",
       "      <td>54.188350</td>\n",
       "      <td>181.567282</td>\n",
       "      <td>6.204660</td>\n",
       "      <td>972.918932</td>\n",
       "      <td>773.580485</td>\n",
       "      <td>45.662136</td>\n",
       "      <td>35.817961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.506364</td>\n",
       "      <td>86.279342</td>\n",
       "      <td>63.997004</td>\n",
       "      <td>21.354219</td>\n",
       "      <td>5.973841</td>\n",
       "      <td>77.753954</td>\n",
       "      <td>80.175980</td>\n",
       "      <td>63.169912</td>\n",
       "      <td>16.705742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>192.375000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>164.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>932.000000</td>\n",
       "      <td>730.950000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>23.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>272.900000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>968.000000</td>\n",
       "      <td>779.500000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>34.445000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>142.950000</td>\n",
       "      <td>118.300000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>1029.400000</td>\n",
       "      <td>824.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>46.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>540.000000</td>\n",
       "      <td>359.400000</td>\n",
       "      <td>200.100000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>32.200000</td>\n",
       "      <td>1145.000000</td>\n",
       "      <td>992.600000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>82.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cement           BF           FA            W           SP  \\\n",
       "count  1030.000000  1030.000000  1030.000000  1030.000000  1030.000000   \n",
       "mean    281.167864    73.895825    54.188350   181.567282     6.204660   \n",
       "std     104.506364    86.279342    63.997004    21.354219     5.973841   \n",
       "min     102.000000     0.000000     0.000000   121.800000     0.000000   \n",
       "25%     192.375000     0.000000     0.000000   164.900000     0.000000   \n",
       "50%     272.900000    22.000000     0.000000   185.000000     6.400000   \n",
       "75%     350.000000   142.950000   118.300000   192.000000    10.200000   \n",
       "max     540.000000   359.400000   200.100000   247.000000    32.200000   \n",
       "\n",
       "                CA         FAgg          Age    Compr_Str  \n",
       "count  1030.000000  1030.000000  1030.000000  1030.000000  \n",
       "mean    972.918932   773.580485    45.662136    35.817961  \n",
       "std      77.753954    80.175980    63.169912    16.705742  \n",
       "min     801.000000   594.000000     1.000000     2.330000  \n",
       "25%     932.000000   730.950000     7.000000    23.710000  \n",
       "50%     968.000000   779.500000    28.000000    34.445000  \n",
       "75%    1029.400000   824.000000    56.000000    46.135000  \n",
       "max    1145.000000   992.600000   365.000000    82.600000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The data doesn't have any missing values\n",
    "# The mean quantity of Cement is 281\n",
    "# The mean quantity of Blast Furnance Slag is 73.89\n",
    "# Water quantity the mean is 181.56\n",
    "# Superplastizer mean is 6.2\n",
    "# Coarse aggregate 972.92\n",
    "# Fine agg 773.57"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = df.columns\n",
    "plt.figure(figsize=(15,15))\n",
    "for i in range(len(col)):\n",
    "    plt.subplot(4,3,i+1)\n",
    "    sns.histplot(x=df[col[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Concrete Compressive Strength and the Cement tend to have a normal distribution\n",
    "# The age, Fine Aggregate, Coarse Aggregate, Superplasticizer, Water,Fly Ash and Blast Furnance Slag distributions have many peak values. It could be a sign of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "df.drop(['Compr_Str'],axis=1).boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we concluded that some values are considered as outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['W/C']=df['W']/df['Cement']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Compressive Strength (MPA)')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['W/C'],df['Compr_Str'])\n",
    "plt.xlabel('W/C ratio')\n",
    "plt.ylabel('Compressive Strength (MPA)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the more the ratio Water/Cement increasing, we are loosing compressive strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f36208bd9a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Cement'],df['Compr_Str'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f36208d1310>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['SP'],df['Compr_Str'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(abs(df.drop_duplicates().corr()),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = df.drop_duplicates().copy()\n",
    "#df_ = df_[df_['Age']<30]\n",
    "#df_ = df_[df_['Age']>=7]\n",
    "X = df_.drop(['Compr_Str','W/C'],axis=1)\n",
    "y = df_['Compr_Str']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, RobustScaler,PolynomialFeatures,FunctionTransformer,StandardScaler\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.linear_model import SGDRegressor,Lasso\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def log_func(x):\n",
    "#    y = np.log(abs(x)+1)\n",
    "#    return y\n",
    "from log import log_func\n",
    "log_transformer = FunctionTransformer(func=log_func)\n",
    "col_for_log = ['Age','BF','SP','FA','Cement'];\n",
    "col_transformer=make_column_transformer((make_pipeline(log_transformer,RobustScaler(unit_variance=True)),col_for_log),\n",
    "                                        (RobustScaler(unit_variance=True),['W','CA','FAgg']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_selector = SelectFromModel(SGDRegressor(alpha=0.1,penalty='l1'),threshold='mean')\n",
    "preprocessor = make_pipeline(col_transformer)#,feature_selector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,y,test_size=0.225,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error,median_absolute_error,mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model,X_train=X_train,Y_train=Y_train,scoring=['r2']):\n",
    "    for s in scoring:\n",
    "        cv_score = cross_val_score(model,X_train,Y_train,scoring=s,cv=5)\n",
    "        print(s+' mean_cv_score: ',cv_score.mean())\n",
    "        #print(s+' std_cv_score: ',cv_score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(model,X_train=X_train,Y_train=Y_train,scoring='r2'):\n",
    "    N,train_score,val_score = learning_curve(model,X_train,Y_train,train_sizes=np.arange(0.2,1+0.2,0.2),scoring=scoring)\n",
    "    plt.plot(N,train_score.mean(axis=1),c='orange',label='train_score',scaley='autoscale_view')\n",
    "    plt.plot(N,val_score.mean(axis=1),c='blue',label='val_score',scaley='autoscale_view')\n",
    "    plt.title('Learning curves')\n",
    "    plt.xlabel('Train size')\n",
    "    plt.ylabel(scoring)\n",
    "    print('Train Score: ',train_score.mean(axis=1)[-1])\n",
    "    print('Val Score: ', val_score.mean(axis=1)[-1])\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(model,grid,X_train=X_train,Y_train=Y_train,scoring='r2'):\n",
    "    gs = GridSearchCV(model,param_grid=grid,scoring=scoring,cv=5,n_jobs=-1)\n",
    "    gs.fit(X_train,Y_train)\n",
    "    print('Best Score: ',gs.best_score_)\n",
    "    print('Best Parameters',gs.best_params_)\n",
    "    print('Retraining the model:\\n ...')\n",
    "    model = gs.best_estimator_\n",
    "    return model\n",
    "\n",
    "def error_ci(model,X_test,Y_test,err='se'):\n",
    "    confidence=0.95\n",
    "    if err=='ae':\n",
    "        error = abs(model.predict(X_test)-Y_test)\n",
    "        ci = stats.t.interval(confidence,len(error)-1,loc=np.mean(error))\n",
    "    elif err=='se':\n",
    "        error = (model.predict(X_test)-Y_test)**2\n",
    "        ci = np.sqrt(stats.t.interval(confidence,len(error)-1,loc=np.mean(error)))\n",
    "    else:\n",
    "        error = (model.predict(X_test)-Y_test)**2\n",
    "        ci = np.sqrt(stats.t.interval(confidence,len(error)-1,loc=np.mean(error)))\n",
    "    return ci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training an SGDRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "isf = make_pipeline(preprocessor,StandardScaler(),MinMaxScaler(),IsolationForest(random_state=33,contamination=0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_outliers=isf.fit_predict(X_train,Y_train)==1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train[non_outliers]\n",
    "Y_train = Y_train[non_outliers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('pipeline',\n",
       "                 Pipeline(steps=[('columntransformer',\n",
       "                                  ColumnTransformer(transformers=[('pipeline',\n",
       "                                                                   Pipeline(steps=[('functiontransformer',\n",
       "                                                                                    FunctionTransformer(func=<function log_func at 0x7f3620364a60>)),\n",
       "                                                                                   ('robustscaler',\n",
       "                                                                                    RobustScaler(unit_variance=True))]),\n",
       "                                                                   ['Age', 'BF',\n",
       "                                                                    'SP', 'FA',\n",
       "                                                                    'Cement']),\n",
       "                                                                  ('robustscaler',\n",
       "                                                                   RobustScaler(unit_variance=True),\n",
       "                                                                   ['W', 'CA',\n",
       "                                                                    'FAgg'])]))])),\n",
       "                ('sgdregressor',\n",
       "                 SGDRegressor(average=True, max_iter=2000, random_state=33,\n",
       "                              warm_start=True))])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "sgd = make_pipeline(preprocessor,SGDRegressor(average=True,warm_start=True,max_iter=2000,random_state=33))\n",
    "sgd.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score:  0.7912398908917354\n",
      "Val Score:  0.7837833898525458\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAxm0lEQVR4nO3deXxU1fnH8c9DCIGwEwIii6CigsiiEbQq4g4qi0tb3KtWSout0v6sSxdpXau1VX5aLf1ptbVC3cUVREVckaAomyiySFgkrAKyJXl+f5wbGMIkJJDJJJPv+/W6r8zce+6d54xynznn3nuOuTsiIiIl1Ul2ACIiUj0pQYiISFxKECIiEpcShIiIxKUEISIicSlBiIhIXEoQIhVgZieY2bxkxyFSFUzPQUhNYWaLgB+7+6RkxyJSG6gFIRLDzNKSHcO+SoU6SPWgBCE1npnVMbMbzOwrM1ttZk+aWYuY7U+Z2QozW29mU8zs8Jhtj5rZg2b2ipltAk4ys0Vm9j9m9lm0z3/NrH5Uvp+Z5cXsX2rZaPuvzWy5mS0zsx+bmZvZwaXUo4WZ/TMqu9bMno/W/8jM3i1Rdsdx4tThxqi+aTHlzzGzz/b0fZlZfTN7PFq/zsymmVnrffjPIzWYEoSkgl8AQ4ATgf2BtcADMdtfBToDrYCPgf+U2P9C4DagMVB8Iv4B0B/oBHQHflTG58cta2b9gV8CpwIHR/GV5d9AJnB4FOtf91C+tDr8GdgEnFxi+xPR67K+r8uApkB7IAsYDmyuQBySQpQgJBX8BPiNu+e5+1ZgFHC+mdUFcPdH3H1DzLYeZtY0Zv8X3P09dy9y9y3RutHuvszd1wAvAj3L+PzSyv4A+Ke7z3b374A/lHYAM2sDDACGu/tad9/u7m9X4DsoWYexwAXRsRsDZ0broOzvazshMRzs7oXuPt3dv61AHJJClCAkFRwAPBd1iawD5gKFQGszSzOzO6PulG+BRdE+LWP2XxLnmCtiXn8HNCrj80sru3+JY8f7nGLtgTXuvraMMmUpeewngHPNLAM4F/jY3RdH20r9vgitmAnAuKir6y4zS9/LmKSGU4KQVLAEGODuzWKW+u6+lNC1MpjQzdMU6BjtYzH7J+pWvuVAu5j37csouwRoYWbN4mzbROh6AsDM9otTZpc6uPscYDGhVRLbvVT8WXG/r6jl8gd37wp8DzgbuLSMuCWFKUFITZMeXUgtXuoCDwG3mdkBAGaWbWaDo/KNga3AasJJ9vYqjPVJ4HIz62JmmcDvSyvo7ssJ10r+ZmbNzSzdzPpGmz8FDjezntEF8FHl/PwnCNcb+gJPxawv9fsys5PM7IjoAve3hC6nwnJ+nqQYJQipaV4hXDQtXkYB9wHjgYlmtgH4EOgTlf8X4Zf0UmBOtK1KuPurwGjgLWA+8EG0aWspu1xCOCF/DqwEro2O8wXwR2AS8CU7L6TvyVigH/Cmu6+KWV/W97Uf8DQhOcwF3gYeL+fnSYrRg3IiVcTMugCzgAx3L0h2PCJ7ohaESAJFzx/UM7PmwJ+AF5UcpKZQghBJrJ8A+cBXhL78nyY3HJHyUxeTiIjEpRaEiIjEVTfZAVSmli1beseOHZMdhohIjTF9+vRV7p4db1tKJYiOHTuSm5ub7DBERGoMM1tc2jZ1MYmISFxKECIiEpcShIiIxJVS1yBEpObZvn07eXl5bNmyZc+FZa/Vr1+fdu3akZ5e/sF5lSBEJKny8vJo3LgxHTt2xMz2vINUmLuzevVq8vLy6NSpU7n3UxeTiCTVli1byMrKUnJIIDMjKyurwq00JQgRSTolh8Tbm+9YCQJg5i2Q9yIUbU92JCIi1YauQWzfCF/eD1tWQkYWdBgKnS6GrD6gXzUiUoupBZHeCIbkwYkvwX6nwYKHYeKx8GJn+GwUfPtlsiMUkQRat24df/vb3yq835lnnsm6desqP6BqRAkCoE46tD0LjhsL534Dx/wTGnaEWX+Elw6BCcfAvPthS36yIxWRSlZagigsLHum1VdeeYVmzZolKKry21Oc+0JdTCWlN4EDfxSW75bC4rGw8HGY/nP4+Fpo0x86XgztBkHdzD0cTEQqZPq1sHZG5R6zeU846t5SN99www189dVX9OzZk/T0dBo1akSbNm2YMWMGc+bMYciQISxZsoQtW7ZwzTXXMGzYMGDn2G8bN25kwIABHH/88bz//vu0bduWF154gQYNGsT9vNGjR/PQQw9Rt25dunbtyrhx49i4cSM///nPyc3Nxcy4+eabOe+88xg7diy333477s5ZZ53Fn/70JwAaNWrEL3/5SyZMmMA999zDokWLGD16NNu2baNPnz787W9/Iy0tbZ+/OrUgypLZFrr8D5w5A878LLxe9ym8fwE82xo++BGsmARFmtNdpKa68847Oeigg5gxYwZ33303H330Ebfddhtz5swB4JFHHmH69Onk5uYyevRoVq9evdsxvvzyS0aMGMHs2bNp1qwZzzzzTJmf98knn/DZZ5/x0EMPAXDLLbfQtGlTZs6cyWeffcbJJ5/MsmXLuP7663nzzTeZMWMG06ZN4/nnnwdg06ZNdOvWjalTp5KVlcV///tf3nvvPWbMmEFaWhr/+c9/KuW7UQuivJodAT3vhB63w8opsOhx+PopWPgYNGgDB1wYLm4366GL2yJ7q4xf+lWld+/euzxMNnr0aJ577jkAlixZwpdffklWVtYu+3Tq1ImePXsCcNRRR7Fo0aJSj9+9e3cuuugihgwZwpAhQwCYNGkS48aN21GmefPmTJkyhX79+pGdHUbivuiii5gyZQpDhgwhLS2N8847D4A33niD6dOnc/TRRwOwefNmWrVqtU/fQTEliIqyOtC6X1hy7oelL4Vk8cVo+PweaHp46ILqeBE0bJ/saEWkgho2bLjj9eTJk5k0aRIffPABmZmZ9OvXL+7DZhkZGTtep6WlsXnz5lKP//LLLzNlyhTGjx/PLbfcwuzZs3H33Z5TKGu2z/r16+/oQnJ3LrvsMu64445y17G81MW0L9LqQ4fzoe/zcM5yOPpBqNcMPr0RXugAk06Crx6GbeuSHKiIlKZx48Zs2LAh7rb169fTvHlzMjMz+fzzz/nwww/36bOKiopYsmQJJ510EnfddRfr1q1j48aNnH766dx///07yq1du5Y+ffrw9ttvs2rVKgoLCxk7diwnnnjibsc85ZRTePrpp1m5ciUAa9asYfHiUqd4qBAliMqSkQWdh8Np78Kgr6D7LbB5GUz9MTy7H7zzfch7AQq3JTtSEYmRlZXFcccdR7du3bjuuut22da/f38KCgro3r07v/vd7zjmmGP26bMKCwu5+OKLOeKII+jVqxcjR46kWbNm/Pa3v2Xt2rV069aNHj168NZbb9GmTRvuuOMOTjrpJHr06MGRRx7J4MGDdztm165dufXWWzn99NPp3r07p512GsuXL9+nOItZWc2YmiYnJ8er1Yxy7rBmeuiCWjw2PIxXrwV0+EG4XtHye7peIbXe3Llz6dKlS7LDqBXifddmNt3dc+KV1zWIRDKDrJyw9PozrHg93DK78DGY/xA07BSuVXS8CJoeluxoRUR2oQRRVerUhf0HhGX7Bsh7PiSLObfD7FuhRU64uH3AUGjQOtnRisg+GjFiBO+9994u66655houv/zyJEVUcUoQyZDeGDpdEpbNy2HxuJAsPr4WPvlVGPKj48XQfgjUbbino4lINfTAAw8kO4R9povUydagDRw2EgZMh7NmQ9frYf0c+ODi8DDe+5fAsglQVJDsSEWkllGCqE6adoUet8HghXDqlHBtYulLMLk/PN8Opo+ENR+Hi98iIgmW0ARhZv3NbJ6ZzTezG+Jsb2pmL5rZp2Y228wuj9n2iJmtNLNZiYyxWrI60OoE6P13OHcFnPBMuOPpy7/Ba0fBy4fD7Nth46JkRyoiKSxhCcLM0oAHgAFAV+ACM+taotgIYI679wD6AfeYWb1o26NA/0TFV2OkZUD7c6Hvs+FhvN5/h4yW8OlvYHwneL0vzB8D29YmO1IRSTGJbEH0Bua7+wJ33waMA0o+5eFAYwvPmDcC1gAFAO4+JXovxTJawMHD4LQpMGhh6I7amg8f/SQ8jDflXFjyLBRuTXakIimtUaNGyQ6hSiQyQbQFlsS8z4vWxbof6AIsA2YC17h7UUU+xMyGmVmumeXm59ei+RoadYTDb4Kz5kD/6dB5BKx6H945LySLqcPCoIIV+zpFpJopKEjeDSqJvM013iPCJa+ungHMAE4GDgJeN7N33P3b8n6Iu48BxkB4knrvQq3BzKDFkWHpdRd882a4ZXbxE/DVPyCzQ7jY3enicBFcpBq79lqYMaNyj9mzJ9x7b9llrr/+eg444AB+9rOfATBq1CjMjClTprB27Vq2b9/OrbfeGneoi5KWL1/OD3/4Q7799lsKCgp48MEHOeGEE3jttde46aabKCwspGXLlrzxxhusWbOGK664ggULFpCZmcmYMWPo3r07o0aNYtmyZSxatIiWLVty3333MXz4cL7++msA7r33Xo477rh9/Gb2LJEJIg+IHc60HaGlEOty4E4P433MN7OFwGHARwmMK3XVqQttTg9LwYNh7KeFj8Pcu2DOHdC8VzTS7AXh9loRAWDo0KFce+21OxLEk08+yWuvvcbIkSNp0qQJq1at4phjjmHQoEG7jbpa0hNPPMEZZ5zBb37zGwoLC/nuu+/Iz8/nqquuYsqUKXTq1Ik1a0Lv+c0330yvXr14/vnnefPNN7n00kuZEWXI6dOn8+6779KgQQMuvPBCRo4cyfHHH8/XX3/NGWecwdy5cxP6nUBiE8Q0oLOZdQKWAkOBC0uU+Ro4BXjHzFoDhwILEhhT7VG3IXS8MCybv4Gv/xuSxSe/ghnXQetToofxzgkP7olUA3v6pZ8ovXr1YuXKlSxbtoz8/HyaN29OmzZtGDlyJFOmTKFOnTosXbqUb775hv3226/MYx199NFcccUVbN++nSFDhtCzZ08mT55M3759d8wz0aJFCwDefffdHZMLnXzyyaxevZr169cDMGjQoB2z0k2aNGnHBEYA3377LRs2bKBx48T+201YgnD3AjO7GpgApAGPuPtsMxsebX8IuAV41MxmErqkrnf3VQBmNpZwZ1NLM8sDbnb3hxMVb0pr0BoO/UVYvp0XEsWix+HDy2DacGg3JCSLNqeHVohILXT++efz9NNPs2LFCoYOHcp//vMf8vPzmT59Ounp6XTs2DHuXBAl9e3blylTpvDyyy9zySWXcN1119GsWbO4LY94g6UWl4udl6KoqIgPPvig1GlMEyWhz0G4+yvufoi7H+Tut0XrHoqSA+6+zN1Pd/cj3L2buz8es+8F7t7G3dPdvZ2SQyVpcij0uAUGLQhDk3e6DJa/Bm+fBc/tD7nXwOppehhPap2hQ4cybtw4nn76ac4//3zWr19Pq1atSE9P56233ir3HAuLFy+mVatWXHXVVVx55ZV8/PHHHHvssbz99tssXLgQYEcXU9++fXdMDzp58mRatmxJkyZNdjtmyfkiZlT2hZpS6OdibWUG2ceF5aj7YPmroWUx/+9hdrzGh4RWRaeLoNGByY5WJOEOP/xwNmzYQNu2bWnTpg0XXXQRAwcOJCcnh549e3LYYeUbcXny5MncfffdpKen06hRI/71r3+RnZ3NmDFjOPfccykqKqJVq1a8/vrrjBo1issvv5zu3buTmZnJY489FveYo0ePZsSIEXTv3p2CggL69u27Yz7rRNJ8ELKrbetgyTMhWaycHNa1/F64C6rDD8LESCKVSPNBVJ2KzgehsZhkV/WawUFXwqlvweDF0PNO2L4epv0sPF/x9mD4+iko2p7sSEUkwdTFJKVr2CGMLtvl17Dus3Bhe9ETsHQ8NOwYHtTrdBmk1dvjoURSzcyZM7nkkkt2WZeRkcHUqVOTFFHlU4KQPTOD5j3C0uNOWPYKzLoFPhoGs26Fw2+EAy8P40aJ7AV33+PzBdXNEUccUWUXiyvD3lxOUBeTVEydNGg3EM6YCv1ehQb7w7SfwosHwxcPQOGebwMUiVW/fn1Wr169VycwKR93Z/Xq1dSvX79C++kitewbd1gxCWb9AfLfCwmj6/Vw0FVQt2rv2Zaaafv27eTl5ZXrGQPZe/Xr16ddu3akp6fvsr6si9RKEFI53OGbt2DWH2Hl21B/P+j6azj4J1A3M9nRiUgpdBeTJJ4Z7HcynDoZTpkcBgb8+Jdhzoq5f4aCTUkOUEQqSglCKl/rE+GUN+DUd6BZD/jkOnihI8z5E2zfkOzoRKSclCAkcVodDydPhNPehxY5MOOGkChm3w7byz2iu4gkiRKEJF72sXDSq3D6VGh5bJgu9YWOMPOW8OS2iFRLShBSdVr2hn4vQf9caNUXZv4+JIrPRmlObZFqSAlCql6Lo6Dv8zDgE9jvlHCL7PMHwKe/ha2rkx2diESUICR5mveEE56BAZ/C/v3DtYkXOsKMG2HLqmRHJ1LrKUFI8jXvDsc/CWfOhLZnh7udxneET34NW1YmOzqRWksJQqqPZofDcWPhrDlhlrvP7wktio9/BZtXJDs6kVpHCUKqn6aHwfceh7PmQofvw7z7wgN306+F75YlOzqRWkMJQqqvJofAsY/B2Z/DARfCF/fD+AMh9+fwXV6yoxNJeUoQUv01PhiOeRgGfgmdLoUvH4LxB8FHP4VNXyc7OpGUpQQhNUejTtBnDAyaDwdeAQseDsOMf/QT2Lgo2dGJpJyEJggz629m88xsvpndEGd7UzN70cw+NbPZZnZ5efeVWqzhAdD7QRj4FRw0DBY8Ci92hg+vhA1fJTs6kZSRsARhZmnAA8AAoCtwgZl1LVFsBDDH3XsA/YB7zKxeOfeV2q5hezj6fhi0ADr/DBY/AS8dCh/8CL79MtnRidR4iWxB9Abmu/sCd98GjAMGlyjjQGMLcw02AtYABeXcVyTIbAs594VEccgv4Osn4eXD4P1L4Nt5yY5OpMZKZIJoCyyJeZ8XrYt1P9AFWAbMBK5x96Jy7guAmQ0zs1wzy83Pz6+s2KUmatAGjvoLDFoIh/0SljwLL3WB9y6E9XOSHZ1IjZPIBBFvBvKS09edAcwA9gd6AvebWZNy7htWuo9x9xx3z8nOzt77aCV1NGgNve6GwYvCrHZLx8PL3eDdH8K6mcmOTqTGSGSCyAPax7xvR2gpxLoceNaD+cBC4LBy7itStvrZ0PNOGLQIDr8Jlr0Kr3SHd86HtZ8mOzqRai+RCWIa0NnMOplZPWAoML5Ema+BUwDMrDVwKLCgnPuKlE/9ltDj1tCi6PZ7WDEJXu0JU86BNR8nOzqRaithCcLdC4CrgQnAXOBJd59tZsPNbHhU7Bbge2Y2E3gDuN7dV5W2b6JilVoiowV0/0NIFEf8Ab6ZDK8dBW8PgtXTkh2dSLVj7nG79muknJwcz83NTXYYUlNsWx+G7/j8L7BtDbQZAEfcDC37JDsykSpjZtPdPSfeNj1JLbVXvabQ7TehRdHjDljzEUw8Bt48A/LfT3Z0IkmnBCGS3hgOvyFczO55F6z9BF4/Dt44FVa+k+zoRJJGCUKkWHoj6HpdaFEc+RdYPxsm9YVJJ8E3b0EKdceKlIcShEhJdTPhsJHhyeyj7oMN8+CNk2HSibDiDSUKqTWUIERKU7cBHPqLkChy7odNC+HNU+H142HZBCUKSXlKECJ7klYfDhkBA+fD0Q+GyYom94eJx8LSV5QoJGUpQYiUV1oGdB4eJi7qPQa2fANvnwUTekPei0oUknKUIEQqKq0eHHwVDPwC+jwcnqGYMig8dLfkefCiZEcoUimUIET2Vp10OOgKOHseHPMobN8A75wDr/aCr59RopAaTwlCZF/VqQsHXgZnz4Vj/w1FW+Hd8+GVHrD4SSgqTHaEIntFCUKkstSpC50uhjNnw/fGAkXw3g/hlSPg8/tg1VQo3JrsKEXKrW6yAxBJOXXSoONQOOAHsOQZmHUrfHxttK0eNO8FWX3CmE9ZfaDRgWDxpkARSS4N1idSFb7LCy2I1cVLLhR+F7ZltISs3iFZZPWBlr2hXvPkxiu1RlmD9akFIVIVMttBh3bQ4bzwvqggDOWxeurOxLHsVXZMnNj4kF1bGc26h7unRKqQEoRIMtSpC817hOXgYWHd9m9Dy6K4lbHidVj076h8BrQ4MqaV0QcadlTXlCSUEoRIdZHeBPY7OSwQHrz7bsmurYz5f4d594btGdm7tjKyjoZ6zZIVvaQgJQiR6soMGnYIS4fvh3VF22HdrJ2tjFVTYdlLO/dpcliJrqkjwvMaIntBCUKkJqmTDi16haVzNHPvtvWwZtrOVsbyV2HhY2FbWoPdu6YyO6hrSspFCUKkpqvXFPY7NSwQuqY2LY7pmvoQvngAiv4SttdvvXvXVHqT5MUv1ZYShEiqMYNGHcNywA/DusJtsO6zXa9nLB1fvAM07bJrK6Npt3AhXWo1PQchUlttWwurPop5NmMqbF0dtqVlQoujdrYyWh4TbtWV5CjaDlvyYWs+bFm5+19Lg94P7dWh9RyEiOyuXnPY/4ywQOia2rhg11bGvNFQtC1sb7D/rl1TLXLCNK1ScUUFsHXVzhP8lnzYWsrfLSth+7r4x7G0cDdbowMTEmZCE4SZ9QfuA9KA/3P3O0tsvw64KCaWLkC2u68xs2uAqwAD/uHu9yYyVpFazwwaHxSWjheGdYVbYe2nu941lfdcVL4OND18166pJl3DUCO1TVFhGPY93q/7eH+LW2olWR2olwX1W4Wlec/wNyM7/t96zcI+CZKwLiYzSwO+AE4D8oBpwAXuPqeU8gOBke5+spl1A8YBvYFtwGvAT939y7I+U11MIlVg62pY/VHM0CEfhZMjQN1GoWWx4wJ4H8jcP7nx7g0vCl1w8U7w8X7lb1tdyvDuBhktIKMV1M/e/W/Jk369FlWeYJPVxdQbmO/uC6IgxgGDgbgJArgAGBu97gJ86O7fRfu+DZwD3JXAeEWkPDKyYP8BYYHQNbVh/q6tjM//EvrNIVy7iG1ltDgK6jas2pjdQzfNlnL8ut+yMnT/eCnDtNdrvvOE3uQwyD6h9F/5GVk1+mJ/IiNvCyyJeZ8H9IlX0Mwygf7A1dGqWcBtZpYFbAbOBOI2DcxsGDAMoEOHDpUSuIhUgBk06RyWTheHdYVbYO2MXQcoXPJMVD4t3CUV28po2qViXSXuULCh9F/0W1buum7rqp0Jq6T0pjtP6o0OhKxj4vzKL/7l37JWPXiYyAQR70mc0vqzBgLvufsaAHefa2Z/Al4HNgKfAgXxdnT3McAYCF1M+xq0iFSCtPrhzqeWx+xctyU/dEcVtzIWPwnzx4RtdRuH5zGy+oSRbS1tz7/yiy+el1S38c4TfMMOkJVTeh9+Rssw17jElcgEkQe0j3nfDlhWStmh7OxeAsDdHwYeBjCz26PjiUhNVT8b2p4VFgh99hu+3LWVMfdu8BK/BdMyd57UG+wfBjjMaLXriT72F39a/aqvW4pKZIKYBnQ2s07AUkISuLBkITNrCpwIXFxifSt3X2lmHYBzgWMTGKuIVDWrA00ODcuBl4Z1BZvDA31WJzr5Z1f99QrZIWEJwt0LzOxqYALhNtdH3H22mQ2Pthc/1XEOMNHdN5U4xDPRNYjtwAh3X5uoWEWkmqjbIFybkGpBT1KLiNRiZd3mmrgnLEREpEZTghARkbiUIEREJC4lCBERiUsJQkRE4lKCEBGRuJQgREQkLiUIERGJSwlCRETiUoIQEZG4lCBERCQuJQgREYlrj6O5mtkZhLkc3nD3RTHrr3D3RxIYm4hIreMOGzfC6tXlXzIzYdasyo+lzAQRTdRzPPAxcJOZ3evu/xttvhpQghARKUVBAaxdu+cT/KpVO1+vWQPbSpksD6BpU8jKCkvLlnDoodC2bWLi31MLYiDQK5rbYRTwhJkd6O4jiT+lqIhISvruuz2f3Esu69aVfry6dXee6LOyoHNnOPbYXdeVXFq0CPtVlT19VF33MP+fu68zs4HAGDN7CqiX8OhERCpZUVH5ftWXXLZsKf2YjRvveiI/6KCyT/RZWWEfq+Y/s/eUIL4ys5OA+e6+xN0LgSvN7FbgvMSHJyJSui1bKn6iX7s2JIl40tLCr/Tik3jHjnDUUXs+2ddL0Z/Le0oQ3yd0Jb0DHFW80t1/a2YPJjIwEaldNm2C/PywlNZtU3L9d9+VfrzMzF1P4u3bxz+5t2y583WTJlBH93buUGaCcPfNAGb2oZkd7e7TYrYtTXRwIlIzue884a9cufPEX9b7zZvjH8sMmjffeRJv2xa6d9/1xB5vqV+/auucisp7ueMk4CdmthjYRGhVuLt3T1hkIlJtFN96Wd6TfX5+6X329etDq1aQnR2WLl12fZ+dvevJv1mz0PUjVa+8CWJAQqMQkSrlDhs2VOyEv3Vr/GM1aLDzBN+6NXTrtvsJP/Z9w4bV/+KsBOVKEO6+eG8Obmb9gfuANOD/3P3OEtuvAy6KiaULkO3ua8xsJPBjwIGZwOXuXsZ9BCK1lzusX1/+k31+fun32mdm7jyht2kTunP2dMKX1JSwO2rNLA14ADgNyAOmmdl4d59TXMbd7wbujsoPBEZGyaEt8Augq7tvNrMngaHAo4mKV6Q6cQ/30FfkhL99e/xjNWy484Teti307Fn2CT8zsyprKtVZIh+56E24PXYBgJmNAwYDc0opfwEwtkRsDcxsO5AJLEtgrCIJVVS06wm/5Mm95LpVq0o/4TduvPNk3r59uA2ztJN9dnboAhLZG4lMEG2BJTHv84A+8QqaWSbQnzB8B+6+1Mz+DHwNbAYmuvvEUvYdBgwD6NChQ6UFL1IeW7fCihWwfPmuS8l1+flh2IV4mjTZeTI/4AA4+uiyT/i6O0eqSiITRLzLUF5K2YHAe+6+BsDMmhNaG52AdcBTZnaxuz++2wHdxwBjAHJycko7vki5ucO338Y/0Zdct3bt7vubhZN6mzZh6dkzXLwt7YSfkVHlVRQpl0QmiDygfcz7dpTeTTSUXbuXTgUWuns+gJk9C3wP2C1BiJRXUVH4JV/ar/zY9/Huyc/ICCf8/fYLA6T167fzfXEyaNMmnPSrcrwckURJ5P/G04DOZtYJWEpIAheWLGRmTYETgYtjVn8NHBN1PW0GTgFyExir1GAlu3lK+9X/zTdQWLj7/k2b7jy59+mz68k+9uTfrJluz5TaJWEJIhoB9mpgAuE210fcfbaZDY+2PxQVPYdwjWFTzL5TzexpwjDjBcAnRN1IUjsU36dfVr9+8bo1a3bfP143T8lf+vvtFxbdtSMSn7mnTrd9Tk6O5+aqoVGdFRWFO3T2dFF3xYr44+xkZMQ/0ce+VzePSPmZ2XR3z4m3Tf+EpFIUd/OU1a+/fPmeu3n222/Xbp6SJ39184hUHSUIqZDNm+Gee2DevF1P/uXp5unePf4vf3XziFRPShBSbtu2wfnnwyuvhPv127SBQw6BE0+M3+3TqpW6eURqMv3zlXIpLIRLLw3J4e9/h2HDkh2RiCSapsaQPXKHn/4U/vtfuOsuJQeR2kIJQsrkDr/+NfzjH3DTTXDddcmOSESqihKElOn22+HPf4YRI+DWW5MdjYhUJSUIKdX998NvfwsXXwyjR+v2UpHaRglC4vrXv+DnP4fBg+Gf/9RE7iK1kf7Zy26eew4uvxxOOQXGjdOtqiK1lRKE7GLSJBg6FHr3huef19wDIrWZEoTs8MEHoUvp0EPD8w6NGiU7IhFJJiUIAeDTT+HMM2H//WHiRGjePNkRiUiyKUEIX3wBp58eWgyTJoVhMkRElCBqua+/hlNPDQ/Evf56GGNJRAQ0FlOttnIlnHYarF8PkyfDYYclOyIRqU6UIGqpdevgjDNgyZJwzaFXr2RHJCLVjRJELbRpE5x1FsyeDS++CMcfn+yIRKQ6UoKoZbZuhXPOgQ8/hCefDK0IEZF4lCBqkYICuPDCcDH6kUfgvPOSHZGIVGe6i6mWKCqCq66CZ5+Fe+8NQ2mIiJRFCaIWcIeRI+HRR2HUKLjmmmRHJCI1QUIThJn1N7N5ZjbfzG6Is/06M5sRLbPMrNDMWpjZoTHrZ5jZt2Z2bSJjTWWjRoXhukeOhN//PtnRiEhNkbBrEGaWBjwAnAbkAdPMbLy7zyku4+53A3dH5QcCI919DbAG6BlznKXAc4mKNZX95S/wxz/CFVfAPfdoTgcRKb9EtiB6A/PdfYG7bwPGAYPLKH8BMDbO+lOAr9x9cQJiTGkPPwy/+hV8//swZoySg4hUTCITRFtgScz7vGjdbswsE+gPPBNn81DiJ47ifYeZWa6Z5ebn5+9DuKnlySfDRen+/eHxxyEtLdkRiUhNk8gEEe/3qpdSdiDwXtS9tPMAZvWAQcBTpX2Iu49x9xx3z8nOzt7rYFPJq6+GaUKPOw6eeQbq1Ut2RCJSEyUyQeQB7WPetwOWlVK2tFbCAOBjd/+mkmNLWVOmwLnnQrdu8NJLkJmZ7IhEpKZKZIKYBnQ2s05RS2AoML5kITNrCpwIvBDnGKVdl5A4pk+Hs8+Gjh1hwgRo2jTZEYlITZawu5jcvcDMrgYmAGnAI+4+28yGR9sfioqeA0x0902x+0fXJU4DfpKoGFPJ3LnhekOLFuFJafW2ici+MvfSLgvUPDk5OZ6bm5vsMKrcwoVhwL3CQnj3XTj44GRHJCI1hZlNd/eceNs0FlMNt3x5mNNh82Z4+20lBxGpPEoQNdjq1SE5rFgBb7wBRxyR7IhEJJUoQdRQGzbAmWfC/PnwyivQp0+yIxKRVKMEUQNt2QKDB4e7lp59Fk4+OdkRiUgqUoKoYbZvhx/8IMwh/e9/w6BByY5IRFKVEkQNUlQEP/pRmCb0gQfgoouSHZGIpDLNB1FDuMOIEfDEE3DHHfCznyU7IhFJdUoQNcRNN8FDD8H118MNu82sISJS+ZQgaoA77wzL8OGh9SAiUhWUIKq5Bx+EG2+ECy8M1x00p4OIVBUliGrs8cfDdYeBA8N80nX0X0tEqpBOOdXU+PHhjqV+/cLkP+npyY5IRGobJYhq6M03w7MORx4JL7wA9esnOyIRqY2UIKqZqVPDw2+dO4eZ4Ro3TnZEIlJbKUFUIzNnwoAB0Lo1TJwIWVnJjkhEajMliGpi/vwwMmtmJkyaBG3aJDsiEantNNRGNZCXB6eeCgUF8NZb0KlTsiMSEVGCSLr8/NByWLMmJIcuXZIdkYhIoASRROvXwxlnwKJFMGECHHVUsiMSEdlJCSJJvvsOzj47XJgePx769k12RCIiu1KCSIJt2+C88+D992Hs2HDnkohIdZPQu5jMrL+ZzTOz+Wa22xikZnadmc2IlllmVmhmLaJtzczsaTP73MzmmtmxiYy1qhQWwsUXw2uvwZgx4YE4EZHqKGEJwszSgAeAAUBX4AIz6xpbxt3vdvee7t4TuBF4293XRJvvA15z98OAHsDcRMVaVdxh2DB46im45x648spkRyQiUrpEtiB6A/PdfYG7bwPGAYPLKH8BMBbAzJoAfYGHAdx9m7uvS2CsCecOv/oVPPII/O538MtfJjsiEZGyJTJBtAWWxLzPi9btxswygf7AM9GqA4F84J9m9omZ/Z+ZNSxl32Fmlmtmufn5+ZUXfSW75Rb461/hF7+AP/wh2dGIiOxZIhNEvJkLvJSyA4H3YrqX6gJHAg+6ey9gExB3HjV3H+PuOe6ek52dva8xJ8R998HNN4fRWf/6V83pICI1QyITRB7QPuZ9O2BZKWWHEnUvxeyb5+5To/dPExJGjfPPf8K118K558I//qE5HUSk5kjk6Woa0NnMOplZPUISGF+ykJk1BU4EXihe5+4rgCVmdmi06hRgTgJjTYhnnoEf/zg8Kf3EE1BXNxWLSA2SsFOWuxeY2dXABCANeMTdZ5vZ8Gj7Q1HRc4CJ7r6pxCF+DvwnSi4LgMsTFWsiTJgAF1wAxxwDzz0HGRnJjkhEpGLMvbTLAjVPTk6O5+bmJjsM3n0XTj8dDj00jK/UrFmyIxIRic/Mprt7Trxt6hGvZJ98AmedBe3bh1aEkoOI1FRKEJXo88/D4HvNmoU5HVq1SnZEIiJ7TwmikixeHC5Gm8Hrr4cWhIhITab7airBihVhwp+NG2HyZDjkkGRHJCKy75Qg9tHateGC9LJloVupR49kRyQiUjmUIPbBxo1w5pkwbx689BIcmxLjzYqIBEoQe2nLFhgyBKZNC6OznnZasiMSEalcShB7oaAAhg6FN96Axx6Dc85JdkQiIpVPdzFVUFERXHEFvPAC/O//wqWXJjsiEZHEUIKoAPcwXPe//w233gpXX53siEREEkcJogJ+9zt44AH4n/+Bm25KdjQiIomlBFFOd98Nt90GV10Fd92lOR1EJPUpQZTDmDHw61/DD38IDz6o5CAitYMSxB6MHQvDh4fnHf71L0hLS3ZEIiJVQwmiDC+9FO5SOuEEePppqFcv2RGJiFQdJYhSTJ4M3/9+GDrjxRehQYNkRyQiUrWUIOKYNg0GDoQDD4TXXoMmTZIdkYhI1VOCKGH2bOjfH7KzYeJEaNky2RGJiCSHEkSMBQvCmEoZGWFk1rZtkx2RiEjyaCymyNKlYU6HrVthypTQvSQiUpspQQCrVoU5HfLz4c034fDDkx2RiEjy1foEsWEDDBgQupdefRWOPjrZEYmIVA8JvQZhZv3NbJ6ZzTezG+Jsv87MZkTLLDMrNLMW0bZFZjYz2pabqBgzMuDQQ8NzDv36JepTRERqnoS1IMwsDXgAOA3IA6aZ2Xh3n1Ncxt3vBu6Oyg8ERrr7mpjDnOTuqxIVI4SH3x5/PJGfICJSMyWyBdEbmO/uC9x9GzAOGFxG+QuAsQmMR0REKiCRCaItsCTmfV60bjdmlgn0B56JWe3ARDObbmbDSvsQMxtmZrlmlpufn18JYYuICCQ2QcQb89RLKTsQeK9E99Jx7n4kMAAYYWZ94+3o7mPcPcfdc7Kzs/ctYhER2SGRCSIPaB/zvh2wrJSyQynRveTuy6K/K4HnCF1WIiJSRRKZIKYBnc2sk5nVIySB8SULmVlT4ETghZh1Dc2scfFr4HRgVgJjFRGREhJ2F5O7F5jZ1cAEIA14xN1nm9nwaPtDUdFzgInuvilm99bAcxZm5qkLPOHuryUqVhER2Z25l3ZZoObJycnx3NyEPTIhIpJyzGy6u+fE26bB+kREJK6UakGY2QZgXrLjqGItgYQ+TFgNqc61R22sd1XX+QB3j3sLaKqNxTSvtKZSqjKzXNU59dXGOkPtrHd1qrO6mEREJC4lCBERiSvVEsSYZAeQBKpz7VAb6wy1s97Vps4pdZFaREQqT6q1IEREpJIoQYiISFw1KkGY2SNmttLMZsWsa2Fmr5vZl9Hf5jHbboxms5tnZmckJ+q9Z2btzewtM5trZrPN7JpofcrWGcDM6pvZR2b2aVTvP0TrU73eaWb2iZm9FL1P6fpC/JkjU73eZtbMzJ42s8+jf9vHVts6u3uNWYC+wJHArJh1dwE3RK9vAP4Uve4KfApkAJ2Ar4C0ZNehgvVtAxwZvW4MfBHVK2XrHNXDgEbR63RgKnBMLaj3L4EngJei9yld36gui4CWJdaldL2Bx4AfR6/rAc2qa51rVAvC3acAa0qsHkz4won+DolZP87dt7r7QmA+NWzIcHdf7u4fR683AHMJky6lbJ0BPNgYvU2PFieF621m7YCzgP+LWZ2y9d2DlK23mTUh/NB9GMDdt7n7OqppnWtUgihFa3dfDuGECrSK1pd7RruawMw6Ar0Iv6ZTvs5Rd8sMYCXwuruner3vBX4NFMWsS+X6Fos3c2Qq1/tAIB/4Z9Sd+H/RlAbVss6pkCBKU5EZ7ao1M2tEmI71Wnf/tqyicdbVyDq7e6G79yRMNNXbzLqVUbxG19vMzgZWuvv08u4SZ12NqW8J5Zo5MpIK9a5L6CZ/0N17AZsIXUqlSWqdUyFBfGNmbQCivyuj9RWZ0a7aMrN0QnL4j7s/G61O6TrHiprfkwlzlqdqvY8DBpnZImAccLKZPU7q1ncHjz9zZCrXOw/Ii1rEAE8TEka1rHMqJIjxwGXR68vYOTPdeGComWWYWSegM/BREuLbaxZmTHoYmOvuf4nZlLJ1BjCzbDNrFr1uAJwKfE6K1tvdb3T3du7ekTDz4pvufjEpWt9iVvrMkSlbb3dfASwxs0OjVacAc6iudU72Ff0KXv0fCywHthMy65VAFvAG8GX0t0VM+d8QrvrPAwYkO/69qO/xhObkZ8CMaDkzlesc1aE78ElU71nA76P1KV3vqB792HkXU0rXl9Af/2m0zAZ+U0vq3RPIjf7/fh5oXl3rrKE2REQkrlToYhIRkQRQghARkbiUIEREJC4lCBERiUsJQkRE4lKCEImYWVY0qugMM1thZktj3tfbw745Zja6EmL4o5mduq/HEakMus1VJA4zGwVsdPc/x6yr6+4FyYtKpGqpBSFSBjN71Mz+YmZvAX8ys95m9n400Nr7xU/Emlm/mHkcRlmYu2SymS0ws1/EOW5adOxZ0XwII2M+7/yoRVLceplpZh5tP8jMXosGt3vHzA6rwq9Dapm6yQ5ApAY4BDjV3QuLh2t294KoK+h24Lw4+xwGnESYx2OemT3o7ttjtvcE2rp7NwiTyMTu7O65URnM7G7gtWjTGGC4u39pZn2AvwEnV0otRUpQghDZs6fcvTB63RR4zMw6E4ZBSS9ln5fdfSuw1cxWAq0Jw8MUWwAcaGb/C7wMTIx3EDP7AWEwt9OjUX2/BzwVhukCwkQyIgmhBCGyZ5tiXt8CvOXu50RzdEwuZZ+tMa8LKfFvzd3XmlkP4AxgBPAD4IrYMmZ2OPAHQoul0MzqAOs8DIMuknC6BiFSMU2BpdHrH+3tQcysJVDH3Z8BfkdoJcRub0oY+vtSd88H8DAXyEIz+35UxqIkI5IQShAiFXMXcIeZvQek7cNx2gKTo1nzHgVuLLF9CHAA8I/ii9XR+ouAK82seATUwfsQg0iZdJuriIjEpRaEiIjEpQQhIiJxKUGIiEhcShAiIhKXEoSIiMSlBCEiInEpQYiISFz/DzdHmjnci2E/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.7837079401236853\n",
      "Best Parameters {'sgdregressor__alpha': 0.01, 'sgdregressor__l1_ratio': 0.001}\n",
      "Retraining the model:\n",
      " ...\n",
      "Train Score:  0.7905337234479783\n",
      "Val Score:  0.7838003759362099\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = {'sgdregressor__alpha':[0.000001,0.0001,0.01,0.1,0.3,0.7],\n",
    "        'sgdregressor__l1_ratio':[0.001,0.01,0.5,0.9,1]\n",
    "       }\n",
    "\n",
    "sgd_new=optimize(sgd,grid=grid)\n",
    "plot_learning_curve(sgd_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7840521750766574"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_new.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score:  0.756394708844045\n",
      "Val Score:  0.7432259157061021\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "svm = make_pipeline(preprocessor,SVR())\n",
    "svm.fit(X_train,Y_train)\n",
    "plot_learning_curve(svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.8969588417028307\n",
      "Best Parameters {'svr__C': 60, 'svr__epsilon': 1}\n",
      "Retraining the model:\n",
      " ...\n",
      "Train Score:  0.9466204068240233\n",
      "Val Score:  0.8969230895064874\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqrklEQVR4nO3deXhV1b3/8feXEGYQBEQlVKj1yhgiBMShCGIRrVOdinUqjrRg1d5fHTooVlutta2larlcq9VqtVVra6vXCUFQUQmDyCCKDBJACcg8Zvj+/lg75BB2QgI5OcnJ5/U858k5e+29z1oHyIe11tlrm7sjIiJSXqNUV0BEROomBYSIiMRSQIiISCwFhIiIxFJAiIhILAWEiIjEUkCIVIOZfd3MFqW6HiK1wXQdhNQXZrYMuMrdX091XUQaAvUgRBKYWUaq63Cg0qENUjcoIKTeM7NGZnaLmX1qZuvM7O9mdnBC+TNm9rmZbTSzqWbWK6Hsz2b2RzN7ycy2AkPNbJmZ/T8zmxsd8zczaxbtP8TM8hOOr3DfqPwmM1ttZqvM7CozczP7WgXtONjMHo32XW9m/4y2f9fM3iq37+7zxLTh1qi9GQn7f8vM5u7r8zKzZmb2RLR9g5nNMLNOB/DHI/WYAkLSwQ+Ac4CTgMOB9cCDCeX/BxwFHALMAp4sd/x3gF8ArYHSX8QXAiOAbkA28N1K3j92XzMbAfwQOAX4WlS/yvwFaAH0iur6u33sX1Eb7gO2AieXK/9r9Lyyz+ty4CCgC9AeGA1sr0Y9JI0oICQdXAv8xN3z3X0nMA4438waA7j7I+6+OaGsr5kdlHD8v9z9bXcvcfcd0bbx7r7K3b8E/g3kVPL+Fe17IfCou893923AHRWdwMwOA04DRrv7encvdPc3q/EZlG/DU8BF0blbA6dH26Dyz6uQEAxfc/did5/p7puqUQ9JIwoISQdHAM9HQyIbgIVAMdDJzDLM7J5oOGUTsCw6pkPC8Stizvl5wvNtQKtK3r+ifQ8vd+649ynVBfjS3ddXsk9lyp/7r8C5ZtYUOBeY5e7Lo7IKPy9CL+YV4OloqOteM8vczzpJPaeAkHSwAjjN3dsmPJq5+0rC0MrZhGGeg4Cu0TGWcHyyvsq3GshKeN2lkn1XAAebWduYsq2EoScAzOzQmH32aIO7LwCWE3olicNLpe8V+3lFPZc73L0ncDxwBnBZJfWWNKaAkPomM5pILX00BiYAvzCzIwDMrKOZnR3t3xrYCawj/JL9ZS3W9e/AKDPrYWYtgNsq2tHdVxPmSh4ys3Zmlmlmg6PiD4BeZpYTTYCPq+L7/5Uw3zAYeCZhe4Wfl5kNNbM+0QT3JsKQU3EV30/SjAJC6puXCJOmpY9xwO+BF4BXzWwz8C5wbLT/44T/Sa8EFkRltcLd/w8YD0wGFgPTo6KdFRxyKeEX8kfAGuCG6DwfAz8HXgc+oWwifV+eAoYAb7j72oTtlX1ehwLPEsJhIfAm8EQV30/SjC6UE6klZtYDmAc0dfeiVNdHZF/UgxBJouj6gyZm1g74FfBvhYPUFwoIkeS6FigAPiWM5X8vtdURqToNMYmISCz1IEREJFbjVFegJnXo0MG7du2a6mqIiNQbM2fOXOvuHePK0iogunbtSl5eXqqrISJSb5jZ8orKNMQkIiKxFBAiIhJLASEiIrEUECIiEksBISIisRQQIiISSwEhIiKx0uo6iP324Z1gBhktoHHz6GcLyIieZzSPXieUZzSHjGbhOBGRNKSAAFj4Kyjauh8HWhQe1QiV0m37Ki+/rZHu+igitUsBAXDhFigphKJtULwdirdBUenPfWwr3h69Tigv3bZrXUJ5wn77wxpXI3QqCKJKyxN+mkYeRUQBUaZRJjQ5iHDb4iRyh5Kde4dG7PMqbtu1AYpXlSvfHt5nfzRqWnmoNG4BmQdBk3YVPzLbQpO20Eh/xUTqK/3rrW1mYe4io1ny36ukOOrp1ECvqLS88PPwvHAD7FoPxTsqr0Pj1pUHyV6PtmXPNawmklIKiHTWKAMatYLMVsl7j+IdISj2+dgQfm7+pGzbvobbGresoHeyj2Bp0g4ymiavzSINhAJCDkxGM2h+WHhUV/HOsuCo6FGYEC5blsKuWeF50ZZ91Kt51XosmW333ta4+X58ECLpRwEhqZPRFJp3Co/qKincd7jsWl82FLZtBWyYG23bVPm5GzWt3lDYHj2XFvXvq8/ugIMXg5fs+ZOSMFRJSXx54n6VlVdln8T3ssbQqEl4ZDQpe574yGgasz1TX7KoQQoIqZ8aZUKzjuFRXSVFULix6sNi21fDxgVRuGwEKrlNb6PM+KGwjKb794u1VspL9uMPoA7b73CJ228/j6/ouNJjLTMMAddxCghpeBo1hqbtw6O6SoqhaFPVw2VnAWz+GEp2hf/ZWgbQKPrlEL22cj/LlzdqXHl5+eMT90tJ+YHWtVEIr5JdULwr/NzjsbOC7eUeVTm2aFsVjt/PbwPui2VUP1wqCqcm7aDPbTVeRQWESHU0yijrFUjD4NHwW8nOSoKnmsFU3WMLN1d+XJODFRAiIrXOLBq2agy0THVtapVmc0REJJYCQkREYikgREQklgJCRERiKSBERCSWAkJERGIlNSDMbISZLTKzxWZ2S0x5OzN73szmmtn7Zta7XHmGmc02s/8ks54iIrK3pAWEmWUADwKnAT2Bi8ysZ7ndfgzMcfds4DLg9+XKrwcWJquOIiJSsWT2IAYCi919ibvvAp4Gzi63T09gEoC7fwR0NbNOAGaWBXwTeDiJdRQRkQokMyA6AysSXudH2xJ9AJwLYGYDgSOArKjsfuAmoNKVxMzsGjPLM7O8goKCGqi2iIhAcgMibs3j8stg3gO0M7M5wHXAbKDIzM4A1rj7zH29ibtPdPdcd8/t2HE/VvYUEZFYyVyLKR/okvA6C1iVuIO7bwJGAZiZAUujx0jgLDM7HWgGtDGzJ9z9kiTWV0REEiSzBzEDOMrMuplZE8Iv/RcSdzCztlEZwFXAVHff5O63unuWu3eNjntD4SAiUruS1oNw9yIzGwu8AmQAj7j7fDMbHZVPAHoAj5tZMbAAuDJZ9RERkeox90rujlXP5Obmel5eXqqrISJSb5jZTHfPjSvTldQiIhJLASEiIrEUECIiEksBISIisRQQIiISSwEhIiKxFBAiIhJLASEiIrEUECIiEksBISIisRQQIiISSwEhIiKxFBAiIhJLASEiIrEUECIiEksBISIisRQQIiISSwEhIiKxFBAiIhJLASEiIrEUECIiEksBISIisRQQIiISSwEhIiKxkhoQZjbCzBaZ2WIzuyWmvJ2ZPW9mc83sfTPrHW3vYmaTzWyhmc03s+uTWU8REdlb0gLCzDKAB4HTgJ7ARWbWs9xuPwbmuHs2cBnw+2h7EfDf7t4DGASMiTlWRESSKJk9iIHAYndf4u67gKeBs8vt0xOYBODuHwFdzayTu69291nR9s3AQqBzEusqIiLlJDMgOgMrEl7ns/cv+Q+AcwHMbCBwBJCVuIOZdQWOAd6LexMzu8bM8swsr6CgoGZqLiIiSQ0Ii9nm5V7fA7QzsznAdcBswvBSOIFZK+A54AZ33xT3Ju4+0d1z3T23Y8eONVJxERGBxkk8dz7QJeF1FrAqcYfol/4oADMzYGn0wMwyCeHwpLv/I4n1FBGRGMnsQcwAjjKzbmbWBBgJvJC4g5m1jcoArgKmuvumKCz+BCx0998msY4iIlKBpPUg3L3IzMYCrwAZwCPuPt/MRkflE4AewONmVgwsAK6MDj8BuBT4MBp+Avixu7+UrPqKiMiekjnERPQL/aVy2yYkPJ8OHBVz3FvEz2GIiEgt0ZXUIiISSwEhIiKxFBAiIhJLASEiIrEUECIiEksBISIisRQQIiISSwEhIiKxFBAiIhJLASEiIrEUECIiEksBISIisRQQIiISSwEhIiKxFBAiIhJLASEiIrEUECIiEksBISIisRQQIiISSwEhIiKxFBAiIhJLASEiIrEUECIiEksBISIisZIaEGY2wswWmdliM7slprydmT1vZnPN7H0z613VY0VEJLmSFhBmlgE8CJwG9AQuMrOe5Xb7MTDH3bOBy4DfV+NYERFJomT2IAYCi919ibvvAp4Gzi63T09gEoC7fwR0NbNOVTxWRESSKJkB0RlYkfA6P9qW6APgXAAzGwgcAWRV8Vii464xszwzyysoKKihqouISDIDwmK2ebnX9wDtzGwOcB0wGyiq4rFho/tEd89199yOHTseQHVFRCRR4ySeOx/okvA6C1iVuIO7bwJGAZiZAUujR4t9HSsiIsmVzB7EDOAoM+tmZk2AkcALiTuYWduoDOAqYGoUGvs8VkREkitpPQh3LzKzscArQAbwiLvPN7PRUfkEoAfwuJkVAwuAKys7Nll1FRGpj4qL4eOPYeVKOOWUmj+/uccO7ddLubm5npeXl+pqiIjUuKIiWLgQZs6EWbPCY84c2LoVDj4Y1q4Fi5u93Qczm+nuuXFlyZyDEBGR/bBrF8ybVxYEM2fC3LmwY0cob9kScnLgyiuhX7/wSAYFhIhICm3fDh9+WBYEs2aF14WFobxNmxAA3/9++Nm/Pxx1FGRkJL9uCggRkVqydSt88MGew0Tz54e5BIB27UIA3Hhj+NmvH3z1q9AoRavmKSBERJJg06YwR1AaBjNnwqJFUFISyjt2DCFwxhllYXDEEfs3j5AsCggRkQO0fn1Zj6A0DD75pKz88MNDAFx4YdmcQefOdSsM4uwzIMzsVMKFapPcfVnC9ivc/ZEk1k1EpM4pKNgzCGbNgqVLy8q/8pXQI7jssrIwOPTQ1NX3QFQaEGb2S+BEYBbwYzO7393/EBWPBRQQIpK2Vq/eMwhmzYIVCavEHXkk5ObCNdeEUDjmGOjQIXX1rWn76kGcCRwTXbg2DvirmX3V3W8kfr0kEZF6xx3y8/cMgpkz4fPPQ7kZ/Nd/wYknls0XHHMMtG2b0mon3b4CorG7FwG4+wYzOxOYaGbPAE0qP1REpO5xD0NC5YeJ1q4N5Y0aQY8eMHx42RBRTg60bp3SaqfEvgLiUzMbSrg3wwp3LwauNLO7gPOSXz0Rkf1XUgKLF+8dBhs2hPLGjaF3bzjrrLJrDLKzoUWLlFa7zthXQFxAGEqaBvQv3ejuPzWzPyazYiIi1VFcHL5GmhgEs2fD5s2hvEmT8Mu/9JtE/fuHcGjWLLX1rssqDQh33w5gZu+a2QB3n5FQtjLZlRMRiVNYGL8u0bZtobx5c+jbFy69tGzOoGfPEBJSdVW9DmIocK2ZLQe2EnoVHt1LWkQkaXbujF+XaOfOUN6yZZgwvvrqsjmD7t3D8JEcmKp+hKcltRYiIoQVS+fMgRkzynoH8+aVrUt00EEhAMaO3XNdolQtRZHuqhQQ7r482RURkYanpCQsTPfGGzB5Mrz5ZliiAsIS1v37ww9/uOe6RHX96uN0ok6YiNQa9zCR/MYb4TFlCqxbF8q+9jUYORKGDoXjjgtXJCsMUksBISJJU3rNweTJZaFQevFZly5hobqTTw6h0KVL5eeS2qeAEJEalZ9fFgiTJ8PyaIC6U6eyMDj5ZA0X1QcKCBE5IGvWhKGi0h5C6SqmBx8MQ4bAj34UAqF7dwVCfaOAEJFqWb8+TCaX9hLmzQvbW7eGk06C0aNDIGRn69tF9Z0CQkQqtXkzvPVW2ZDRrFlhbqF587B43cUXh2Gj/v117UG60R+niOxh+3Z4552yHsL774dlLJo0gUGD4PbbQw9h4EBo2jTVtZVkUkCINHC7doUQKO0hvPNO2JaRAQMGwE03hUA4/ngtYtfQKCBEGpiiorCIXemk8ltvhTWMzMKy1tddF4aMvv51aNMm1bWVVEpqQJjZCOD3QAbwsLvfU678IOAJ4CtRXe5z90ejshuBqwAHPgRGufuOZNZXJB2VXq1cOmSUeLVyr15wxRWhh3DSSeGbRyKlkhYQZpYBPAh8A8gHZpjZC+6+IGG3McACdz/TzDoCi8zsSaAj8AOgp7tvN7O/AyOBPyerviLpoqpXKw8ZUn/vlSy1I5k9iIGEGw0tATCzp4GzgcSAcKC1mRnQCvgSKEqoW3MzKwRaAKuSWFeRem3p0rJAmDw53EsZdLWyHJhkBkRnIOH23uQDx5bb5wHgBcIv/9bAt929BFhpZvcBnwHbgVfd/dW4NzGza4BrAL7yla/UaANE6qrSq5VLh410tbIkQzIDIu6vpZd7fSowBzgZOBJ4zcymEeYszga6ARuAZ8zsEnd/Yq8Tuk8EJgLk5uaWP79IWki8WnnyZPj447A98WrloUPDvZQVCFJTkhkQ+UBihzaLvYeJRgH3uLsDi81sKdAdOAJY6u4FAGb2D+B4woS2SNpbvx6mTi0bNip/tfK11+pqZUm+ZAbEDOAoM+sGrCRMMn+n3D6fAcOAaWbWCTgaWELofQwysxaEIaZhQF4S6yqSUlu2wLRpZUNGulpZ6oKk/VVz9yIzGwu8QhgyesTd55vZ6Kh8AnAn8Gcz+5AQCje7+1pgrZk9C8wiTFrPJhpGEkkH27fD9OllPYQZM8L1CbpaWeoSC6M76SE3N9fz8tTRkLqn9Grl0h7C9OnhnsqlVyuXTirramWpbWY2091z48rUWRVJgtJrEV55BV5+OcwnJF6tPHasrlaWuk8BIVJDNm6ESZPKQuGzz8L2o4+GUaNg2DBdrSz1iwJCZD+VlITJ5JdfDqEwfXpY9bR16xAGP/4xnHoqdO2a6pqK7B8FhEg1fP45vPpqCIRXX4W1a8P2/v3h5pthxIgwyZyZmdp6itQEBYRIJXbtCstfl/YS5swJ2w85BE47LfQQvvGN8Fok3SggRMr59NMQBq+8Er5xtGVLuPbghBPgl78MvYS+fXWBmqQ/BYQ0eFu2hGUsSnsJixeH7d26waWXhl7C0KH6tpE0PAoIaXDcYe7csm8bvfUWFBaG6w+GDoXrrw+h8LWvaV0jadgUENIgrFsHr70WAuHVV8uWw87OhhtuCMNGJ5ygq5ZFEikgJC0VFcF775X1EvLyQs/h4IPDpPKIETB8OBx+eKprKlJ3KSAkbXz2Wdnk8uuvhwvXGjUKXzsdNy6EQv/+YXkLEdk3BYTUW9u3hyUsSnsJCxeG7VlZcMEFYR5h2DBo1y619RSprxQQUm+4w0cflX3b6M03YceOMG9w0klw1VWhl6Cb5ojUDAWE1GkbNuy5vtGK6Ca23bvD6NGhlzB4sFZAFUkGBYTUKcXFMHNm2VzCu++GbW3awCmnwM9+FkJBtx8XST4FhKTc6tXhq6cvvxy+irpuXRgiys2FW28NgXDssVrfSKS2KSCk1u3cCW+/XdZL+OCDsL1TJ/jmN8M8wje+AR06pLaeIg2dAkJqxeLFZfMIkyfD1q2hR3DiiXDPPSEUsrM1uSxSlyggJCk2bw5BUBoKS5aE7UceCd/9btn6Rq1apbSaIlIJBYTUCPcwVFQaCG+/HdY3atky3Gv5hz8sW99IROoHBYTst4KCMKlcOpfwxRdhe9++ZYFwwgnQpElq6yki+0cBIdWycSP87nfw4ovh66ju0L59WNeodH2jQw9NdS1FpCYoIKTKXn0VrrwSVq2C44+Hn/889BL69dP6RiLpSAEh+7RpE/z3f8PDD4dlLKZPh4EDU10rEUm2pN400cxGmNkiM1tsZrfElB9kZv82sw/MbL6ZjUooa2tmz5rZR2a20MyOS2ZdJd5rr0Hv3vDII3DzzTBrlsJBpKFIWkCYWQbwIHAa0BO4yMx6ltttDLDA3fsCQ4DfmFnplObvgZfdvTvQF1iYrLrK3jZtgmuvDXMKLVvCO++E6xWaNUt1zUSktiSzBzEQWOzuS9x9F/A0cHa5fRxobWYGtAK+BIrMrA0wGPgTgLvvcvcNSayrJHj9dejTJwwp/ehHMHt2WOpCRBqWZAZEZ2BFwuv8aFuiB4AewCrgQ+B6dy8BvgoUAI+a2Wwze9jMWiaxrkK4uG306LDMRbNm4V7N996rXoNIQ5XMgIhbNMHLvT4VmAMcDuQAD0S9h8ZAP+CP7n4MsBXYaw4DwMyuMbM8M8srKCiooao3PJMmhV7DxIlhQnrOHDhOsz4iDVoyAyIf6JLwOovQU0g0CviHB4uBpUD36Nh8d38v2u9ZQmDsxd0nunuuu+d27NixRhvQEGzZAt//flhKu2nT0Gu47z5o3jzVNRORVEtmQMwAjjKzbtHE80jghXL7fAYMAzCzTsDRwBJ3/xxYYWZHR/sNAxYksa4N0uTJodcwYUK48nnOnHB9g4gIJPE6CHcvMrOxwCtABvCIu883s9FR+QTgTuDPZvYhYUjqZndfG53iOuDJKFyWEHobUgO2bAlfWX3oITjqKJg2LSyJISKSyNzLTwvUX7m5uZ6Xl5fqatRpU6bAFVfAsmVwww1w1126XadIQ2ZmM909N64sqRfKSd2xdStcd11YYjsjA6ZOhd/+VuEgIhVTQDQAb74Zbsbz4INw/fVhWe4TT0x1rUSkrlNApLGtW+EHP4AhQ8LrKVPg/vvVaxCRqlFApKlp08J9Gf7whzC0NHcuDB6c6lqJSH2igEgz27aFyeeTTgr3apgyBcaPD+spiYhUh5b7TiNvvQWjRsHixTB2bFhcT8EgIvtLPYg0sG0b3HhjGEIqLg4XwP3hDwoHETkw6kHUc2+/HXoNn3wCY8aEXkOrVqmulYikA/Ug6qnt28Oiel//OhQWhsX2HnhA4SAiNUc9iHronXdCr+Hjj+F734Nf/Qpat051rUQk3Sgg6pHt2+G22+A3v4GvfCXc2GfYsFTXSuTAFBYWkp+fz44dO1JdlbTWrFkzsrKyyMzMrPIxCoh64t134bvfhUWLwq1Af/1r9RokPeTn59O6dWu6du1KuLmk1DR3Z926deTn59OtW7cqH6c5iDpuxw646aaw2ur27fDaa2F5boWDpIsdO3bQvn17hUMSmRnt27evdi9NPYg67L33Qq/ho4/gmmtCr6FNm1TXSqTmKRySb38+Y/Ug6qAdO8L9Go4/Pqyn9Oqr8D//o3AQkdqlHkQd8/77odewcCFcdVWYkFYwiEgqqAdRR+zcCbfeCscdB5s3w8svw//+r8JBJNk2bNjAQw89VO3jTj/9dDZs2FDzFapD1IOoA2bMCL2GBQvgyitDr+Ggg1JdK5EUmHkDrJ9Ts+dslwP976+wuDQgvv/97++xvbi4mIyMjAqPe+mll2qoggdmX/U8EOpBpNDOnfCTn4Rew8aN8NJL8PDDCgeR2nTLLbfw6aefkpOTw4ABAxg6dCjf+c536NOnDwDnnHMO/fv3p1evXkycOHH3cV27dmXt2rUsW7aMHj16cPXVV9OrVy+GDx/O9u3bK3y/8ePH07NnT7Kzsxk5ciQAW7ZsYdSoUfTp04fs7Gyee+45AJ566in69OlD7969ufnmm3efo1WrVtx2220ce+yxTJ8+nSeeeIKBAweSk5PDtddeS3Fxcc18OO6eNo/+/ft7fZGX5967tzu4jxrlvn59qmskkhoLFixI6fsvXbrUe/Xq5e7ukydP9hYtWviSJUt2l69bt87d3bdt2+a9evXytWvXurv7EUcc4QUFBb506VLPyMjw2bNnu7v7BRdc4H/5y18qfL/DDjvMd+zY4e7u66N/+DfddJNff/31u/f58ssvfeXKld6lSxdfs2aNFxYW+tChQ/355593d3fA//a3v7l7+PzOOOMM37Vrl7u7f+973/PHHnss9r3jPmsgzyv4naoeRC3buRN++lM49lj48kt48UV45BFo2zbVNRMRgIEDB+5xMdn48ePp27cvgwYNYsWKFXzyySd7HdOtWzdycnIA6N+/P8uWLavw/NnZ2Vx88cU88cQTNG4cRvlff/11xowZs3ufdu3aMWPGDIYMGULHjh1p3LgxF198MVOnTgUgIyOD8847D4BJkyYxc+ZMBgwYQE5ODpMmTWLJkiUH+jEAmoOoVbNmweWXw7x5Yc7hd79TMIjUNS0T1smfMmUKr7/+OtOnT6dFixYMGTIk9mKzpk2b7n6ekZFR6RDTiy++yNSpU3nhhRe48847mT9/Pu6+13UK4T/38Zo1a7Z73sHdufzyy7n77rur3MaqUg+iFuzaBT/7GQwcCOvWwX/+A48+qnAQqQtat27N5s2bY8s2btxIu3btaNGiBR999BHvvvvuAb1XSUkJK1asYOjQodx7771s2LCBLVu2MHz4cB544IHd+61fv55jjz2WN998k7Vr11JcXMxTTz3FSSedtNc5hw0bxrPPPsuaNWsA+PLLL1m+fPkB1bOUehBJNnt26C3MnQuXXQb33w/t2qW6ViJSqn379pxwwgn07t2b5s2b06lTp91lI0aMYMKECWRnZ3P00UczaNCgA3qv4uJiLrnkEjZu3Ii7c+ONN9K2bVt++tOfMmbMGHr37k1GRga333475557LnfffTdDhw7F3Tn99NM5++yz9zpnz549ueuuuxg+fDglJSVkZmby4IMPcsQRRxxQXQGssm5MfZObm+t5eXmprgYQeg2/+AX88pfQsWO4EvrMM1NdK5G6Z+HChfTo0SPV1WgQ4j5rM5vp7rlx+yd1iMnMRpjZIjNbbGa3xJQfZGb/NrMPzGy+mY0qV55hZrPN7D/JrGdNmzMnDCf9/Odw0UVhzkHhICL1TdICwswygAeB04CewEVm1rPcbmOABe7eFxgC/MbMmiSUXw8sTFYda1phIdxxBwwYAF98Af/6Fzz+OBx8cKprJiK1bcyYMeTk5OzxePTRR1NdrWpJ5hzEQGCxuy8BMLOngbOBBQn7ONDawvR9K+BLoCjaPwv4JvAL4IdJrGeN+OCDMNcwZw5cfDGMH69gEGnIHnzwwVRX4YAlc4ipM7Ai4XV+tC3RA0APYBXwIXC9u5dEZfcDNwElVMLMrjGzPDPLKygoqIl6V0thYRhKys2F1avhn/+EJ55QOIhI/ZfMgIhbfLz8jPipwBzgcCAHeMDM2pjZGcAad5+5rzdx94nunuvuuR07djzAKlfP3Lnhgrfbb4cLL4T58yHmSwYiIvVSMgMiH+iS8DqL0FNINAr4R3TF92JgKdAdOAE4y8yWAU8DJ5vZE0msa7UUFsJdd4Vew8qV8I9/wJNPQvv2qa6ZiEjNSWZAzACOMrNu0cTzSOCFcvt8BgwDMLNOwNHAEne/1d2z3L1rdNwb7n5JEutaZfPmwaBB4cK3884LvYZvfSvVtRIRqXlJCwh3LwLGAq8Qvon0d3efb2ajzWx0tNudwPFm9iEwCbjZ3dcmq04HoqgoXNfQrx+sWAHPPQdPPQUdOqS6ZiJS21q1apXqKtSKpF5J7e4vAS+V2zYh4fkqYPg+zjEFmJKE6lXZ/PnhG0p5efDtb8MDDygYRJLhhhvCNwFrUk5OWMGgvioqKtq9qF9t01pMlSgqgrvvDr2G5cvhmWfg6acVDiLp5uabb97jrnLjxo3jjjvuYNiwYfTr148+ffrwr3/9q0rnWr16NYMHDyYnJ4fevXszbdo0AF5++WX69etH3759GTZsGBDWTTrnnHPIzs5m0KBBzJ07d/f7X3PNNQwfPpzLLruMgoICzjvvPAYMGMCAAQN4++23a/gTqEBF64DXx0dN3g9i/nz3AQPC/RouuMB9zZoaO7WIJEj1/SDc3WfNmuWDBw/e/bpHjx6+fPly37hxo7u7FxQU+JFHHuklJSXu7t6yZcsKz3Xffff5XXfd5e7uRUVFvmnTJl+zZo1nZWXtvs9E6T0mxo4d6+PGjXN390mTJnnfvn3d3f3222/3fv36+bZt29zd/aKLLvJp06a5u/vy5cu9e/fu+9XO6t4PQov1lVNUBPfdF7662qYN/P3vcMEFqa6ViCTTMcccw5o1a1i1ahUFBQW0a9eOww47jBtvvJGpU6fSqFEjVq5cyRdffMGhhx5a6bkGDBjAFVdcQWFhIeeccw45OTlMmTKFwYMH777PxMHRhVJvvfXW7rvHnXzyyaxbt46NGzcCcNZZZ9G8eXMg3C9iwYKya4w3bdrE5s2bad26dY1/FokUEAkWLgxzDe+/H76h9NBDcMghqa6ViNSG888/n2effZbPP/+ckSNH8uSTT1JQUMDMmTPJzMyka9eusfeCKG/w4MFMnTqVF198kUsvvZQf/ehHtG3bdq/7PUD8PR9K90u8L0VJSQnTp0/fHRi1RXMQQHEx3HsvHHMMfPppmGd45hmFg0hDMnLkSJ5++mmeffZZzj//fDZu3MghhxxCZmYmkydPrvI9FpYvX84hhxzC1VdfzZVXXsmsWbM47rjjePPNN1m6dCkQ5h4ghMmTTz4JhJsTdejQgTZt2ux1zvL3i5hT0zP5FWjwPYj16+G00+C99+Dcc0OvIWE5eBFpIHr16sXmzZvp3Lkzhx12GBdffDFnnnkmubm55OTk0L179yqdZ8qUKfz6178mMzOTVq1a8fjjj9OxY0cmTpzIueeeS0lJCYcccgivvfYa48aNY9SoUWRnZ9OiRQsee+yx2HOOHz+eMWPGkJ2dTVFREYMHD2bChAmx+9akBn8/CHe45JKwHPe3vw0xvUARSSLdD6L2VPd+EA2+B2EWlskQEZE9NfiAEBHZHx9++CGXXnrpHtuaNm3Ke++9l6Ia1TwFhIiknLvHfsunLuvTp0+tTRbXhP2ZTtC3mEQkpZo1a8a6dev26xeYVI27s27dOpo1a1at49SDEJGUysrKIj8/n1Tc8KshadasGVlZWdU6RgEhIimVmZm5+wpjqVs0xCQiIrEUECIiEksBISIisdLqSmoz2wwsSnU9alkHoE7ehS+J1OaGoyG2u7bbfIS7d4wrSLdJ6kUVXTKerswsT21Ofw2xzdAw212X2qwhJhERiaWAEBGRWOkWEBNTXYEUUJsbhobYZmiY7a4zbU6rSWoREak56daDEBGRGqKAEBGRWPUqIMzsETNbY2bzErYdbGavmdkn0c92CWW3mtliM1tkZqemptb7z8y6mNlkM1toZvPN7Ppoe9q2GcDMmpnZ+2b2QdTuO6Lt6d7uDDObbWb/iV6ndXsBzGyZmX1oZnPMLC/altbtNrO2ZvasmX0U/ds+rs622d3rzQMYDPQD5iVsuxe4JXp+C/Cr6HlP4AOgKdAN+BTISHUbqtnew4B+0fPWwMdRu9K2zVE7DGgVPc8E3gMGNYB2/xD4K/Cf6HVatzdqyzKgQ7ltad1u4DHgquh5E6BtXW1zvepBuPtU4Mtym88mfOBEP89J2P60u+9096XAYmBgbdSzprj7anefFT3fDCwEOpPGbQbwYEv0MjN6OGncbjPLAr4JPJywOW3buw9p224za0P4j+6fANx9l7tvoI62uV4FRAU6uftqCL9QgUOi7Z2BFQn75Ufb6iUz6wocQ/jfdNq3ORpumQOsAV5z93Rv9/3ATUBJwrZ0bm8pB141s5lmdk20LZ3b/VWgAHg0Gk582MxaUkfbnA4BUZG4+xfWy+/0mlkr4DngBnffVNmuMdvqZZvdvdjdc4AsYKCZ9a5k93rdbjM7A1jj7jOrekjMtnrT3nJOcPd+wGnAGDMbXMm+6dDuxoRh8j+6+zHAVsKQUkVS2uZ0CIgvzOwwgOjnmmh7PtAlYb8sYFUt1+2AmVkmIRyedPd/RJvTus2Jou73FGAE6dvuE4CzzGwZ8DRwspk9Qfq2dzd3XxX9XAM8Txg+Sed25wP5UY8Y4FlCYNTJNqdDQLwAXB49vxz4V8L2kWbW1My6AUcB76egfvvNwl3c/wQsdPffJhSlbZsBzKyjmbWNnjcHTgE+Ik3b7e63unuWu3cFRgJvuPslpGl7S5lZSzNrXfocGA7MI43b7e6fAyvM7Oho0zBgAXW1zame0a/m7P9TwGqgkJCsVwLtgUnAJ9HPgxP2/wlh1n8RcFqq678f7T2R0J2cC8yJHqenc5ujNmQDs6N2zwNui7andbujdgyh7FtMad1ewnj8B9FjPvCTBtLuHCAv+vv9T6BdXW2zltoQEZFY6TDEJCIiSaCAEBGRWAoIERGJpYAQEZFYCggREYmlgBCJmFn7aFXROWb2uZmtTHjdZB/H5prZ+Bqow8/N7JQDPY9ITdDXXEVimNk4YIu735ewrbG7F6WuViK1Sz0IkUqY2Z/N7LdmNhn4lZkNNLN3ooXW3im9ItbMhiTcx2GchXuXTDGzJWb2g5jzZkTnnhfdD+HGhPc7P+qRlPZePjQzj8qPNLOXo8XtpplZ91r8OKSBaZzqCojUA/8FnOLuxaXLNbt7UTQU9EvgvJhjugNDCffxWGRmf3T3woTyHKCzu/eGcBOZxIPdPS/aBzP7NfByVDQRGO3un5jZscBDwMk10kqRchQQIvv2jLsXR88PAh4zs6MIy6BkVnDMi+6+E9hpZmuAToTlYUotAb5qZn8AXgRejTuJmV1IWMxteLSq7/HAM2GZLiDcSEYkKRQQIvu2NeH5ncBkd/9WdI+OKRUcszPheTHl/q25+3oz6wucCowBLgSuSNzHzHoBdxB6LMVm1gjY4GEZdJGk0xyESPUcBKyMnn93f09iZh2ARu7+HPAzQi8hsfwgwtLfl7l7AYCHe4EsNbMLon0sChmRpFBAiFTPvcDdZvY2kHEA5+kMTInumvdn4NZy5ecARwD/WzpZHW2/GLjSzEpXQD37AOogUil9zVVERGKpByEiIrEUECIiEksBISIisRQQIiISSwEhIiKxFBAiIhJLASEiIrH+P4kgdZq91/3aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_svm = {'svr__C':[42,45,50,55,60,50],'svr__epsilon':[0.2,0.4,0.6,0.9,1,10]}\n",
    "new_svm = optimize(svm,grid = grid_svm)\n",
    "plot_learning_curve(new_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9140057530712283"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_svm.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('pipeline',\n",
       "                 Pipeline(steps=[('columntransformer',\n",
       "                                  ColumnTransformer(transformers=[('pipeline',\n",
       "                                                                   Pipeline(steps=[('functiontransformer',\n",
       "                                                                                    FunctionTransformer(func=<function log_func at 0x7f3620364a60>)),\n",
       "                                                                                   ('robustscaler',\n",
       "                                                                                    RobustScaler(unit_variance=True))]),\n",
       "                                                                   ['Age', 'BF',\n",
       "                                                                    'SP', 'FA',\n",
       "                                                                    'Cement']),\n",
       "                                                                  ('robustscaler',\n",
       "                                                                   RobustScaler(unit_variance=True),\n",
       "                                                                   ['W', 'CA',\n",
       "                                                                    'FAgg'])]))])),\n",
       "                ('gradientboostingregressor',\n",
       "                 GradientBoostingRegressor(n_estimators=65))])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor,GradientBoostingRegressor\n",
    "bgr = make_pipeline(preprocessor,GradientBoostingRegressor(n_estimators=65))\n",
    "bgr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score:  0.9328360575468793\n",
      "Val Score:  0.8826218333821144\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(bgr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gbr.p']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(bgr,'apps/gbr.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.70178277, 5.10370392])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_ci(new_svm,X_test,Y_test,err='se')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The model built has 0.91 R² score on the new data set\n",
    "- The RMSE for estimating the strength is about 4.7 to 5.1 MPa\n",
    "- As W/C increase, the strength decrease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}