Concrete-ANN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cement</th>\n",
       "      <th>Slag</th>\n",
       "      <th>FlyAsh</th>\n",
       "      <th>Water</th>\n",
       "      <th>Superplasticizer</th>\n",
       "      <th>CoarseAggregate</th>\n",
       "      <th>FineAggregate</th>\n",
       "      <th>Age</th>\n",
       "      <th>ConcreteStrength</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    Slag   FlyAsh   Water    Superplasticizer   CoarseAggregate    \\\n",
       "0       540.0     0.0      0.0    162.0                2.5             1040.0   \n",
       "1       540.0     0.0      0.0    162.0                2.5             1055.0   \n",
       "2       332.5   142.5      0.0    228.0                0.0              932.0   \n",
       "3       332.5   142.5      0.0    228.0                0.0              932.0   \n",
       "4       198.6   132.4      0.0    192.0                0.0              978.4   \n",
       "...       ...     ...      ...      ...                ...                ...   \n",
       "1025    276.4   116.0     90.3    179.6                8.9              870.1   \n",
       "1026    322.2     0.0    115.6    196.0               10.4              817.9   \n",
       "1027    148.5   139.4    108.6    192.7                6.1              892.4   \n",
       "1028    159.1   186.7      0.0    175.6               11.3              989.6   \n",
       "1029    260.9   100.5     78.3    200.6                8.6              864.5   \n",
       "\n",
       "      FineAggregate   Age  ConcreteStrength  \n",
       "0              676.0   28             79.99  \n",
       "1              676.0   28             61.89  \n",
       "2              594.0  270             40.27  \n",
       "3              594.0  365             41.05  \n",
       "4              825.5  360             44.30  \n",
       "...              ...  ...               ...  \n",
       "1025           768.3   28             44.28  \n",
       "1026           813.4   28             31.18  \n",
       "1027           780.0   28             23.70  \n",
       "1028           788.9   28             32.77  \n",
       "1029           761.5   28             32.40  \n",
       "\n",
       "[1030 rows x 9 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('/Volumes/General Har/Data_Sets/rpcsv')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "ds=pd.read_csv('concrete.csv')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cement               float64\n",
       " Slag                float64\n",
       "FlyAsh               float64\n",
       "Water                float64\n",
       "Superplasticizer     float64\n",
       "CoarseAggregate      float64\n",
       "FineAggregate        float64\n",
       "Age                    int64\n",
       "ConcreteStrength     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cement               0\n",
       " Slag                0\n",
       "FlyAsh               0\n",
       "Water                0\n",
       "Superplasticizer     0\n",
       "CoarseAggregate      0\n",
       "FineAggregate        0\n",
       "Age                  0\n",
       "ConcreteStrength     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "z = np.abs(stats.zscore(ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1030, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ds[(z<3).all(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Cement</th>\n",
       "      <th>Slag</th>\n",
       "      <th>FlyAsh</th>\n",
       "      <th>Water</th>\n",
       "      <th>Superplasticizer</th>\n",
       "      <th>CoarseAggregate</th>\n",
       "      <th>FineAggregate</th>\n",
       "      <th>Age</th>\n",
       "      <th>ConcreteStrength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>277.489399</td>\n",
       "      <td>73.106218</td>\n",
       "      <td>56.749745</td>\n",
       "      <td>180.760041</td>\n",
       "      <td>6.194495</td>\n",
       "      <td>974.729256</td>\n",
       "      <td>776.003976</td>\n",
       "      <td>36.448522</td>\n",
       "      <td>35.294220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102.594951</td>\n",
       "      <td>85.197793</td>\n",
       "      <td>64.439192</td>\n",
       "      <td>20.139643</td>\n",
       "      <td>5.486470</td>\n",
       "      <td>77.765000</td>\n",
       "      <td>77.651828</td>\n",
       "      <td>36.871075</td>\n",
       "      <td>16.733154</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>190.300000</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>739.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>23.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>266.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>968.000000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>33.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>349.000000</td>\n",
       "      <td>144.200000</td>\n",
       "      <td>118.300000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>1038.000000</td>\n",
       "      <td>822.200000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>45.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>540.000000</td>\n",
       "      <td>316.100000</td>\n",
       "      <td>200.100000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>1145.000000</td>\n",
       "      <td>992.600000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>82.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Cement        Slag      FlyAsh      Water    Superplasticizer   \\\n",
       "count  981.000000  981.000000  981.000000  981.000000         981.000000   \n",
       "mean   277.489399   73.106218   56.749745  180.760041           6.194495   \n",
       "std    102.594951   85.197793   64.439192   20.139643           5.486470   \n",
       "min    102.000000    0.000000    0.000000  121.800000           0.000000   \n",
       "25%    190.300000    0.000000    0.000000  164.900000           0.000000   \n",
       "50%    266.000000   22.000000    0.000000  184.000000           6.700000   \n",
       "75%    349.000000  144.200000  118.300000  192.000000          10.300000   \n",
       "max    540.000000  316.100000  200.100000  237.000000          23.400000   \n",
       "\n",
       "       CoarseAggregate    FineAggregate          Age  ConcreteStrength  \n",
       "count         981.000000      981.000000  981.000000        981.000000  \n",
       "mean          974.729256      776.003976   36.448522         35.294220  \n",
       "std            77.765000       77.651828   36.871075         16.733154  \n",
       "min           801.000000      594.000000    1.000000          2.330000  \n",
       "25%           932.000000      739.000000    7.000000         23.220000  \n",
       "50%           968.000000      780.000000   28.000000         33.690000  \n",
       "75%          1038.000000      822.200000   28.000000         45.300000  \n",
       "max          1145.000000      992.600000  180.000000         82.600000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Cement</th>\n",
       "      <th>Slag</th>\n",
       "      <th>FlyAsh</th>\n",
       "      <th>Water</th>\n",
       "      <th>Superplasticizer</th>\n",
       "      <th>CoarseAggregate</th>\n",
       "      <th>FineAggregate</th>\n",
       "      <th>Age</th>\n",
       "      <th>ConcreteStrength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cement</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.266328</td>\n",
       "      <td>-0.389618</td>\n",
       "      <td>-0.071321</td>\n",
       "      <td>0.030318</td>\n",
       "      <td>-0.092010</td>\n",
       "      <td>-0.237042</td>\n",
       "      <td>0.030073</td>\n",
       "      <td>0.485174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slag</th>\n",
       "      <td>-0.266328</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.334016</td>\n",
       "      <td>0.105951</td>\n",
       "      <td>0.067628</td>\n",
       "      <td>-0.293014</td>\n",
       "      <td>-0.277048</td>\n",
       "      <td>-0.051423</td>\n",
       "      <td>0.149047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlyAsh</th>\n",
       "      <td>-0.389618</td>\n",
       "      <td>-0.334016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.256133</td>\n",
       "      <td>0.430345</td>\n",
       "      <td>-0.027427</td>\n",
       "      <td>0.059940</td>\n",
       "      <td>-0.054422</td>\n",
       "      <td>-0.081538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Water</th>\n",
       "      <td>-0.071321</td>\n",
       "      <td>0.105951</td>\n",
       "      <td>-0.256133</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.642165</td>\n",
       "      <td>-0.202194</td>\n",
       "      <td>-0.400566</td>\n",
       "      <td>0.115496</td>\n",
       "      <td>-0.327835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Superplasticizer</th>\n",
       "      <td>0.030318</td>\n",
       "      <td>0.067628</td>\n",
       "      <td>0.430345</td>\n",
       "      <td>-0.642165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.239669</td>\n",
       "      <td>0.163481</td>\n",
       "      <td>-0.080760</td>\n",
       "      <td>0.384011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoarseAggregate</th>\n",
       "      <td>-0.092010</td>\n",
       "      <td>-0.293014</td>\n",
       "      <td>-0.027427</td>\n",
       "      <td>-0.202194</td>\n",
       "      <td>-0.239669</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.187816</td>\n",
       "      <td>0.022298</td>\n",
       "      <td>-0.158351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FineAggregate</th>\n",
       "      <td>-0.237042</td>\n",
       "      <td>-0.277048</td>\n",
       "      <td>0.059940</td>\n",
       "      <td>-0.400566</td>\n",
       "      <td>0.163481</td>\n",
       "      <td>-0.187816</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.036370</td>\n",
       "      <td>-0.162665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.030073</td>\n",
       "      <td>-0.051423</td>\n",
       "      <td>-0.054422</td>\n",
       "      <td>0.115496</td>\n",
       "      <td>-0.080760</td>\n",
       "      <td>0.022298</td>\n",
       "      <td>-0.036370</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.432874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConcreteStrength</th>\n",
       "      <td>0.485174</td>\n",
       "      <td>0.149047</td>\n",
       "      <td>-0.081538</td>\n",
       "      <td>-0.327835</td>\n",
       "      <td>0.384011</td>\n",
       "      <td>-0.158351</td>\n",
       "      <td>-0.162665</td>\n",
       "      <td>0.432874</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Cement      Slag    FlyAsh    Water    Superplasticizer   \\\n",
       "Cement             1.000000 -0.266328 -0.389618 -0.071321           0.030318   \n",
       " Slag             -0.266328  1.000000 -0.334016  0.105951           0.067628   \n",
       "FlyAsh            -0.389618 -0.334016  1.000000 -0.256133           0.430345   \n",
       "Water             -0.071321  0.105951 -0.256133  1.000000          -0.642165   \n",
       "Superplasticizer   0.030318  0.067628  0.430345 -0.642165           1.000000   \n",
       "CoarseAggregate   -0.092010 -0.293014 -0.027427 -0.202194          -0.239669   \n",
       "FineAggregate     -0.237042 -0.277048  0.059940 -0.400566           0.163481   \n",
       "Age                0.030073 -0.051423 -0.054422  0.115496          -0.080760   \n",
       "ConcreteStrength   0.485174  0.149047 -0.081538 -0.327835           0.384011   \n",
       "\n",
       "                   CoarseAggregate    FineAggregate        Age  \\\n",
       "Cement                     -0.092010       -0.237042  0.030073   \n",
       " Slag                      -0.293014       -0.277048 -0.051423   \n",
       "FlyAsh                     -0.027427        0.059940 -0.054422   \n",
       "Water                      -0.202194       -0.400566  0.115496   \n",
       "Superplasticizer           -0.239669        0.163481 -0.080760   \n",
       "CoarseAggregate             1.000000       -0.187816  0.022298   \n",
       "FineAggregate              -0.187816        1.000000 -0.036370   \n",
       "Age                         0.022298       -0.036370  1.000000   \n",
       "ConcreteStrength           -0.158351       -0.162665  0.432874   \n",
       "\n",
       "                   ConcreteStrength  \n",
       "Cement                     0.485174  \n",
       " Slag                      0.149047  \n",
       "FlyAsh                    -0.081538  \n",
       "Water                     -0.327835  \n",
       "Superplasticizer           0.384011  \n",
       "CoarseAggregate           -0.158351  \n",
       "FineAggregate             -0.162665  \n",
       "Age                        0.432874  \n",
       "ConcreteStrength           1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>978</th>\n",
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       "</table>\n",
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       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
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       "\n",
       "            7         8  \n",
       "0    0.150838  0.967485  \n",
       "1    0.150838  0.741996  \n",
       "2    0.497207  0.556871  \n",
       "3    0.150838  0.425065  \n",
       "4    0.150838  0.542170  \n",
       "..        ...       ...  \n",
       "976  0.150838  0.522611  \n",
       "977  0.150838  0.359412  \n",
       "978  0.150838  0.266226  \n",
       "979  0.150838  0.379220  \n",
       "980  0.150838  0.374611  \n",
       "\n",
       "[981 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "x=ds.values\n",
    "scaler=preprocessing.MinMaxScaler()\n",
    "x_scaled=scaler.fit_transform(x)\n",
    "ds=pd.DataFrame(x_scaled)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Will test concrete strength in the following ways-\n",
    "\n",
    "\n",
    "Model 1 - Cement , SuperPlas. , Age\n",
    "\n",
    "Model 2- Cement, SuperPlas\n",
    "\n",
    "Model 3- Cement, Age\n",
    "\n",
    "Model 4 - All Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>979</th>\n",
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       "      <td>0.150838</td>\n",
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       "    <tr>\n",
       "      <th>980</th>\n",
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       "      <td>0.150838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>981 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         4         7\n",
       "0    1.000000  0.106838  0.150838\n",
       "1    1.000000  0.106838  0.150838\n",
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       "4    0.374429  0.000000  0.150838\n",
       "..        ...       ...       ...\n",
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       "977  0.502740  0.444444  0.150838\n",
       "978  0.106164  0.260684  0.150838\n",
       "979  0.130365  0.482906  0.150838\n",
       "980  0.362785  0.367521  0.150838\n",
       "\n",
       "[981 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1=ds.iloc[:,[0,4,7]]\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=ds.iloc[:,[0,4,7]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.10683761, 0.15083799],\n",
       "       [1.        , 0.10683761, 0.15083799],\n",
       "       [0.37442922, 0.        , 0.4972067 ],\n",
       "       ...,\n",
       "       [0.10616438, 0.26068376, 0.15083799],\n",
       "       [0.1303653 , 0.48290598, 0.15083799],\n",
       "       [0.36278539, 0.36752137, 0.15083799]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1.shape # Model 1 x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "<p>981 rows × 2 columns</p>\n",
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      "text/plain": [
       "            0         4\n",
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       "..        ...       ...\n",
       "976  0.398174  0.380342\n",
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       "[981 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2=ds.iloc[:,[0,4]]\n",
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2=ds.iloc[:,[0,4]].values # Model 2 x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "x2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3=ds.iloc[:,[0,7]].values #Model 3 x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981, 2)"
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "x3.shape"
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  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
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    "x4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x4=ds.iloc[:,0:8].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981, 8)"
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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   "metadata": {},
   "outputs": [
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       "       0.48374237, 0.63074623, 0.52261119, 0.47115984, 0.68145011,\n",
       "       0.5243553 , 0.6065778 , 0.63610315, 0.41148623, 0.28267099,\n",
       "       0.2618662 , 0.61891117, 0.44462439, 0.46057057, 0.73178024,\n",
       "       0.67036253, 0.65927495, 0.65329513, 0.5968606 , 0.55251028,\n",
       "       0.55251028, 0.25439143, 0.28877538, 0.40413604, 0.42992401,\n",
       "       0.54279307, 0.49009593, 0.67285412, 0.56858104, 0.73663884,\n",
       "       0.5194967 , 0.66176654, 0.66301233, 0.69253769, 0.29002118,\n",
       "       0.38818986, 0.71334247, 0.33910552, 0.4433786 , 0.74162203,\n",
       "       0.74398904, 0.36975209, 0.86919148, 0.45683319, 0.78946057,\n",
       "       0.37099788, 0.47290395, 0.89734646, 0.24467422, 0.09368382,\n",
       "       0.15871434, 0.26062041, 0.51575931, 0.13790956, 0.27158341,\n",
       "       0.5256011 , 0.75507662, 0.71209667, 0.28391678, 0.26547901,\n",
       "       0.41634484, 0.45197459, 0.82745733, 0.80291516, 0.26809518,\n",
       "       0.37049956, 0.11610814, 0.21016569, 0.57954404, 0.46430796,\n",
       "       0.51837548, 0.40164445, 0.64768905, 0.27494705, 0.16556621,\n",
       "       0.24330385, 0.09829326, 0.1528591 , 0.20455961, 0.36925377,\n",
       "       0.2624891 , 0.28952286, 0.49022051, 0.31655662, 0.07312819,\n",
       "       0.09081849, 0.16643827, 0.31904821, 0.37747602, 0.1857481 ,\n",
       "       0.21726672, 0.29176529, 0.3830821 , 0.27083593, 0.24280553,\n",
       "       0.23346207, 0.15584901, 0.3702504 , 0.1199701 , 0.28802791,\n",
       "       0.25115236, 0.28516258, 0.13404759, 0.4530958 , 0.0645322 ,\n",
       "       0.1899838 , 0.23483244, 0.19770774, 0.18524978, 0.25289647,\n",
       "       0.24330385, 0.12507786, 0.29151613, 0.44200822, 0.38258378,\n",
       "       0.15211162, 0.30621652, 0.0706366 , 0.36838171, 0.09978821,\n",
       "       0.21726672, 0.43739878, 0.11399028, 0.27544537, 0.19023296,\n",
       "       0.10464682, 0.36190358, 0.13566712, 0.23221627, 0.30783605,\n",
       "       0.37012583, 0.40911922, 0.4250654 , 0.05157593, 0.1309331 ,\n",
       "       0.20044849, 0.24442507, 0.27120967, 0.23582908, 0.29450604,\n",
       "       0.11249533, 0.36028404, 0.37373863, 0.08695652, 0.30659026,\n",
       "       0.31518625, 0.40064781, 0.12719571, 0.31394045, 0.38108883,\n",
       "       0.09542793, 0.06864333, 0.12358291, 0.11012832, 0.1869939 ,\n",
       "       0.18948549, 0.35181263, 0.14787592, 0.27619285, 0.1650679 ,\n",
       "       0.30546904, 0.29562726, 0.4469914 , 0.51538557, 0.34645571,\n",
       "       0.12956273, 0.23097047, 0.22362028, 0.39815622, 0.2144014 ,\n",
       "       0.56534197, 0.51139903, 0.34334122, 0.05693285, 0.38445247,\n",
       "       0.03201694, 0.02790582, 0.28815249, 0.27357668, 0.39392052,\n",
       "       0.11311823, 0.22748225, 0.29350941, 0.33511897, 0.35741871,\n",
       "       0.10041111, 0.39030771, 0.31817616, 0.21203438, 0.11286907,\n",
       "       0.1303102 , 0.46081973, 0.03052199, 0.17167061, 0.51139903,\n",
       "       0.2253644 , 0.05743117, 0.15821602, 0.14002741, 0.37623022,\n",
       "       0.16718575, 0.06665006, 0.4543416 , 0.38208546, 0.18624642,\n",
       "       0.2734521 , 0.27058677, 0.42680952, 0.60296499, 0.14114862,\n",
       "       0.14712844, 0.56621403, 0.        , 0.18138782, 0.26398405,\n",
       "       0.05581164, 0.46555376, 0.19471783, 0.38059051, 0.24940825,\n",
       "       0.10539429, 0.06714837, 0.49022051, 0.09007101, 0.05668369,\n",
       "       0.6004734 , 0.18487604, 0.3501931 , 0.09218886, 0.01233337,\n",
       "       0.29886633, 0.50977949, 0.04920892, 0.3708733 , 0.43141896,\n",
       "       0.65117728, 0.23856983, 0.09169054, 0.07524604, 0.46505544,\n",
       "       0.09667373, 0.03114489, 0.09991279, 0.51376604, 0.61704248,\n",
       "       0.11859973, 0.1857481 , 0.31767784, 0.41646942, 0.4530958 ,\n",
       "       0.14924629, 0.18823969, 0.36638844, 0.44325402, 0.46156721,\n",
       "       0.1686807 , 0.08321914, 0.38968481, 0.47751339, 0.48000498,\n",
       "       0.12109132, 0.18948549, 0.20655288, 0.28466426, 0.35667123,\n",
       "       0.51775259, 0.64719073, 0.4311698 , 0.15161331, 0.25052946,\n",
       "       0.12844151, 0.29562726, 0.38470163, 0.4311698 , 0.52024418,\n",
       "       0.62638595, 0.71546032, 0.80951788, 0.83879407, 0.86321166,\n",
       "       0.19683568, 0.25165068, 0.31157344, 0.34446244, 0.36152984,\n",
       "       0.37859723, 0.04908434, 0.1541049 , 0.26024667, 0.31879905,\n",
       "       0.36152984, 0.45683319, 0.1577177 , 0.13940451, 0.26996387,\n",
       "       0.43714962, 0.1138657 , 0.25052946, 0.23458328, 0.36763423,\n",
       "       0.15535069, 0.28466426, 0.18948549, 0.14787592, 0.24068768,\n",
       "       0.33711225, 0.13940451, 0.1735393 , 0.36277563, 0.43478261,\n",
       "       0.19683568, 0.37859723, 0.46293759, 0.48237199, 0.19683568,\n",
       "       0.30409867, 0.7423695 , 0.55923757, 0.60682696, 0.48972219,\n",
       "       0.14177152, 0.21627009, 0.36526722, 0.20904447, 0.4640588 ,\n",
       "       0.57879656, 0.26996387, 0.43714962, 0.1138657 , 0.21627009,\n",
       "       0.1577177 , 0.31879905, 0.40301482, 0.4360284 , 0.39205183,\n",
       "       0.49943939, 0.57393796, 0.66787094, 0.70325153, 0.22972468,\n",
       "       0.28466426, 0.33960384, 0.24193347, 0.4579544 , 0.27482247,\n",
       "       0.60022424, 0.90519497, 0.43403513, 0.39155351, 0.17652921,\n",
       "       0.22000747, 0.42381961, 0.39068145, 0.16307462, 0.3873178 ,\n",
       "       0.45745609, 0.31580914, 0.30559362, 0.5353183 , 0.34620655,\n",
       "       0.16494332, 0.52672231, 0.63772269, 0.68369254, 0.79207674,\n",
       "       0.62900212, 0.38706864, 0.19558988, 0.43640214, 0.38009219,\n",
       "       0.50218014, 0.47003862, 0.49345957, 0.73377351, 0.48012956,\n",
       "       0.38582285, 0.62401894, 0.1589635 , 0.45010589, 0.43515635,\n",
       "       0.4098667 , 0.49582659, 0.36800797, 0.48847639, 0.46243927,\n",
       "       0.44325402, 0.52261119, 0.35941198, 0.26610191, 0.37909555,\n",
       "       0.37461069, 0.42942569, 0.19870437, 0.38283294, 0.36240189,\n",
       "       0.35754329, 0.52398156, 0.12271085, 0.28939828, 0.42494082,\n",
       "       0.38158714, 0.2679706 , 0.29774511, 0.19459325, 0.47776255,\n",
       "       0.20779868, 0.39105519, 0.0773639 , 0.13865703, 0.37261742,\n",
       "       0.26398405, 0.34122337, 0.59100536, 0.6242681 , 0.48087704,\n",
       "       0.5298368 , 0.13541796, 0.4372742 , 0.34309206, 0.67621777,\n",
       "       0.12619908, 0.2673477 , 0.13653918, 0.46206553, 0.5469042 ,\n",
       "       0.5256011 , 0.2667248 , 0.29662389, 0.16432042, 0.51376604,\n",
       "       0.41771521, 0.48237199, 0.33212906, 0.54702878, 0.3063411 ,\n",
       "       0.10227981, 0.28366762, 0.3331257 , 0.09231344, 0.39205183,\n",
       "       0.46729787, 0.306964  , 0.31020306, 0.35280927, 0.38270836,\n",
       "       0.27718949, 0.24392675, 0.3556746 , 0.16207799, 0.27419958,\n",
       "       0.2685935 , 0.25663386, 0.33736141, 0.42942569, 0.19882895,\n",
       "       0.37859723, 0.36240189, 0.33150617, 0.48087704, 0.12271085,\n",
       "       0.28939828, 0.42494082, 0.38158714, 0.2679706 , 0.29774511,\n",
       "       0.19471783, 0.45222374, 0.20779868, 0.39105519, 0.0773639 ,\n",
       "       0.13865703, 0.372742  , 0.26398405, 0.34134795, 0.59100536,\n",
       "       0.62439267, 0.48087704, 0.52996138, 0.13541796, 0.4372742 ,\n",
       "       0.34309206, 0.67634234, 0.12619908, 0.2673477 , 0.13653918,\n",
       "       0.46206553, 0.5469042 , 0.5256011 , 0.2667248 , 0.29674847,\n",
       "       0.164445  , 0.51389062, 0.41783979, 0.48237199, 0.33212906,\n",
       "       0.54715336, 0.3063411 , 0.10227981, 0.28366762, 0.3331257 ,\n",
       "       0.09231344, 0.39205183, 0.43403513, 0.39155351, 0.17652921,\n",
       "       0.22000747, 0.42381961, 0.44711598, 0.16307462, 0.3873178 ,\n",
       "       0.45758067, 0.31580914, 0.30559362, 0.5353183 , 0.34620655,\n",
       "       0.16494332, 0.52672231, 0.63772269, 0.68381712, 0.79207674,\n",
       "       0.6291267 , 0.38706864, 0.19558988, 0.43640214, 0.41086334,\n",
       "       0.50218014, 0.47003862, 0.51663137, 0.73389809, 0.48012956,\n",
       "       0.38594743, 0.62414352, 0.1589635 , 0.45010589, 0.43528093,\n",
       "       0.4098667 , 0.49595116, 0.36813255, 0.48847639, 0.46256385,\n",
       "       0.4433786 , 0.52261119, 0.35941198, 0.26622649, 0.37922013,\n",
       "       0.37461069])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=ds.iloc[:,8].values\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since everything is quantatative, there is no need to encode anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1_train,x1_test,y_train,y_test=train_test_split(x1,y,test_size=0.3,random_state=0) # Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(686, 3)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 1 Evaluation-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(input_dim=3,activation='relu',units=100, kernel_initializer='normal')) #Input Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(2):\n",
    "    model.add(Dense(activation='relu',units=100, kernel_initializer='normal')) #Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(activation='linear',units=1,kernel_initializer='normal')) #Output Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss=\"mean_absolute_error\",metrics=['mean_absolute_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.3074 - mean_absolute_error: 0.3074\n",
      "Epoch 2/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1431 - mean_absolute_error: 0.1431\n",
      "Epoch 3/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1195 - mean_absolute_error: 0.1195\n",
      "Epoch 4/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1113 - mean_absolute_error: 0.1113\n",
      "Epoch 5/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1031 - mean_absolute_error: 0.1031\n",
      "Epoch 6/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1025 - mean_absolute_error: 0.1025\n",
      "Epoch 7/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0987 - mean_absolute_error: 0.0987\n",
      "Epoch 8/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0972 - mean_absolute_error: 0.0972\n",
      "Epoch 9/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0959 - mean_absolute_error: 0.0959\n",
      "Epoch 10/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0938 - mean_absolute_error: 0.0938\n",
      "Epoch 11/300\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.0911 - mean_absolute_error: 0.0911\n",
      "Epoch 12/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0900 - mean_absolute_error: 0.0900\n",
      "Epoch 13/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0872 - mean_absolute_error: 0.0872\n",
      "Epoch 14/300\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.0877 - mean_absolute_error: 0.0877\n",
      "Epoch 15/300\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.0868 - mean_absolute_error: 0.0868\n",
      "Epoch 16/300\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.0850 - mean_absolute_error: 0.0850\n",
      "Epoch 17/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0843 - mean_absolute_error: 0.0843\n",
      "Epoch 18/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0808 - mean_absolute_error: 0.0808\n",
      "Epoch 19/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0806 - mean_absolute_error: 0.0806\n",
      "Epoch 20/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0797 - mean_absolute_error: 0.0797\n",
      "Epoch 21/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0789 - mean_absolute_error: 0.0789\n",
      "Epoch 22/300\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.0795 - mean_absolute_error: 0.0795\n",
      "Epoch 23/300\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.0796 - mean_absolute_error: 0.0796\n",
      "Epoch 24/300\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.0809 - mean_absolute_error: 0.0809\n",
      "Epoch 25/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0801 - mean_absolute_error: 0.0801\n",
      "Epoch 26/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0787 - mean_absolute_error: 0.0787\n",
      "Epoch 27/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0791 - mean_absolute_error: 0.0791\n",
      "Epoch 28/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0804 - mean_absolute_error: 0.0804\n",
      "Epoch 29/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0793 - mean_absolute_error: 0.0793\n",
      "Epoch 30/300\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.0788 - mean_absolute_error: 0.0788\n",
      "Epoch 31/300\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.0864 - mean_absolute_error: 0.0864\n",
      "Epoch 32/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0801 - mean_absolute_error: 0.0801\n",
      "Epoch 33/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0792 - mean_absolute_error: 0.0792\n",
      "Epoch 34/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0840 - mean_absolute_error: 0.0840\n",
      "Epoch 35/300\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.0817 - mean_absolute_error: 0.0817\n",
      "Epoch 36/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0792 - mean_absolute_error: 0.0792\n",
      "Epoch 37/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0841 - mean_absolute_error: 0.0841\n",
      "Epoch 38/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0786 - mean_absolute_error: 0.0786\n",
      "Epoch 39/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0794 - mean_absolute_error: 0.0794\n",
      "Epoch 40/300\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.0768 - mean_absolute_error: 0.0768\n",
      "Epoch 41/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0804 - mean_absolute_error: 0.0804\n",
      "Epoch 42/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0771 - mean_absolute_error: 0.0771\n",
      "Epoch 43/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0772 - mean_absolute_error: 0.0772\n",
      "Epoch 44/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0792 - mean_absolute_error: 0.0792\n",
      "Epoch 45/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0799 - mean_absolute_error: 0.0799\n",
      "Epoch 46/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0783 - mean_absolute_error: 0.0783\n",
      "Epoch 47/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0773 - mean_absolute_error: 0.0773\n",
      "Epoch 48/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0782 - mean_absolute_error: 0.0782\n",
      "Epoch 49/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0772 - mean_absolute_error: 0.0772A: 0s - loss: 0.0787 - mean_absolute_error: 0.078\n",
      "Epoch 50/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0777 - mean_absolute_error: 0.0777\n",
      "Epoch 51/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0820 - mean_absolute_error: 0.0820\n",
      "Epoch 52/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0808 - mean_absolute_error: 0.0808\n",
      "Epoch 53/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0787 - mean_absolute_error: 0.0787\n",
      "Epoch 54/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0779 - mean_absolute_error: 0.0779\n",
      "Epoch 55/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0830 - mean_absolute_error: 0.0830\n",
      "Epoch 56/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0784 - mean_absolute_error: 0.0784\n",
      "Epoch 57/300\n",
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      "Epoch 218/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0763 - mean_absolute_error: 0.0763\n",
      "Epoch 219/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0726 - mean_absolute_error: 0.0726\n",
      "Epoch 220/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0740 - mean_absolute_error: 0.0740\n",
      "Epoch 221/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0714 - mean_absolute_error: 0.0714\n",
      "Epoch 222/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0731 - mean_absolute_error: 0.0731\n",
      "Epoch 223/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0739 - mean_absolute_error: 0.0739\n",
      "Epoch 224/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0725 - mean_absolute_error: 0.0725\n",
      "Epoch 225/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0726 - mean_absolute_error: 0.0726\n",
      "Epoch 226/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0722 - mean_absolute_error: 0.0722\n",
      "Epoch 227/300\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1023 - mean_absolute_error: 0.102 - 0s 2ms/step - loss: 0.0724 - mean_absolute_error: 0.0724\n",
      "Epoch 228/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0722 - mean_absolute_error: 0.0722\n",
      "Epoch 229/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0727 - mean_absolute_error: 0.0727\n",
      "Epoch 230/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0727 - mean_absolute_error: 0.0727\n",
      "Epoch 231/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0720 - mean_absolute_error: 0.0720\n",
      "Epoch 232/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0746 - mean_absolute_error: 0.0746\n",
      "Epoch 233/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0734 - mean_absolute_error: 0.0734\n",
      "Epoch 234/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0712 - mean_absolute_error: 0.0712\n",
      "Epoch 235/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0722 - mean_absolute_error: 0.0722\n",
      "Epoch 236/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0715 - mean_absolute_error: 0.0715\n",
      "Epoch 237/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0708 - mean_absolute_error: 0.0708\n",
      "Epoch 238/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0727 - mean_absolute_error: 0.0727\n",
      "Epoch 239/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0718 - mean_absolute_error: 0.0718\n",
      "Epoch 240/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0726 - mean_absolute_error: 0.0726\n",
      "Epoch 241/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0724 - mean_absolute_error: 0.0724\n",
      "Epoch 242/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0719 - mean_absolute_error: 0.0719\n",
      "Epoch 243/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0714 - mean_absolute_error: 0.0714\n",
      "Epoch 244/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0716 - mean_absolute_error: 0.0716\n",
      "Epoch 245/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0710 - mean_absolute_error: 0.0710\n",
      "Epoch 246/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0757 - mean_absolute_error: 0.0757\n",
      "Epoch 247/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0741 - mean_absolute_error: 0.0741\n",
      "Epoch 248/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0722 - mean_absolute_error: 0.0722\n",
      "Epoch 249/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0715 - mean_absolute_error: 0.0715\n",
      "Epoch 250/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0751 - mean_absolute_error: 0.0751\n",
      "Epoch 251/300\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.0731 - mean_absolute_error: 0.0731\n",
      "Epoch 252/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0704 - mean_absolute_error: 0.0704\n",
      "Epoch 253/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0713 - mean_absolute_error: 0.0713\n",
      "Epoch 254/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0712 - mean_absolute_error: 0.0712\n",
      "Epoch 255/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0719 - mean_absolute_error: 0.0719\n",
      "Epoch 256/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0726 - mean_absolute_error: 0.0726\n",
      "Epoch 257/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0733 - mean_absolute_error: 0.0733\n",
      "Epoch 258/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0719 - mean_absolute_error: 0.0719\n",
      "Epoch 259/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0708 - mean_absolute_error: 0.0708\n",
      "Epoch 260/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0711 - mean_absolute_error: 0.0711\n",
      "Epoch 261/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0711 - mean_absolute_error: 0.0711\n",
      "Epoch 262/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0739 - mean_absolute_error: 0.0739\n",
      "Epoch 263/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0710 - mean_absolute_error: 0.0710\n",
      "Epoch 264/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0736 - mean_absolute_error: 0.0736\n",
      "Epoch 265/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0719 - mean_absolute_error: 0.0719\n",
      "Epoch 266/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0733 - mean_absolute_error: 0.0733\n",
      "Epoch 267/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0711 - mean_absolute_error: 0.0711\n",
      "Epoch 268/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0709 - mean_absolute_error: 0.0709\n",
      "Epoch 269/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0729 - mean_absolute_error: 0.0729\n",
      "Epoch 270/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0717 - mean_absolute_error: 0.0717\n",
      "Epoch 271/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0711 - mean_absolute_error: 0.0711\n",
      "Epoch 272/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0727 - mean_absolute_error: 0.0727\n",
      "Epoch 273/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0700 - mean_absolute_error: 0.0700\n",
      "Epoch 274/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0696 - mean_absolute_error: 0.0696\n",
      "Epoch 275/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0703 - mean_absolute_error: 0.0703\n",
      "Epoch 276/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0704 - mean_absolute_error: 0.0704\n",
      "Epoch 277/300\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0704 - mean_absolute_error: 0.0704\n",
      "Epoch 278/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0697 - mean_absolute_error: 0.0697\n",
      "Epoch 279/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0735 - mean_absolute_error: 0.0735\n",
      "Epoch 280/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0714 - mean_absolute_error: 0.0714\n",
      "Epoch 281/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0709 - mean_absolute_error: 0.0709\n",
      "Epoch 282/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0721 - mean_absolute_error: 0.0721\n",
      "Epoch 283/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0713 - mean_absolute_error: 0.0713\n",
      "Epoch 284/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0699 - mean_absolute_error: 0.0699\n",
      "Epoch 285/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0698 - mean_absolute_error: 0.0698\n",
      "Epoch 286/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0715 - mean_absolute_error: 0.0715\n",
      "Epoch 287/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0698 - mean_absolute_error: 0.0698\n",
      "Epoch 288/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0726 - mean_absolute_error: 0.0726\n",
      "Epoch 289/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0703 - mean_absolute_error: 0.0703\n",
      "Epoch 290/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0711 - mean_absolute_error: 0.0711\n",
      "Epoch 291/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0715 - mean_absolute_error: 0.0715\n",
      "Epoch 292/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0706 - mean_absolute_error: 0.0706\n",
      "Epoch 293/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0714 - mean_absolute_error: 0.0714\n",
      "Epoch 294/300\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0733 - mean_absolute_error: 0.0733\n",
      "Epoch 295/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0726 - mean_absolute_error: 0.0726\n",
      "Epoch 296/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0703 - mean_absolute_error: 0.0703\n",
      "Epoch 297/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0703 - mean_absolute_error: 0.0703\n",
      "Epoch 298/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0707 - mean_absolute_error: 0.0707\n",
      "Epoch 299/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0699 - mean_absolute_error: 0.0699\n",
      "Epoch 300/300\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0716 - mean_absolute_error: 0.0716\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc094148250>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x1_train,y_train,epochs=300,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "y_train_prediction = model.predict(x1_train)\n",
    "y_test_prediction = model.predict(x1_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training dataset evaluation ( Model 1) : 0.09582301853633904 0.7877792951506475\n"
     ]
    }
   ],
   "source": [
    "rmse_training = np.sqrt(mean_squared_error(y_train, y_train_prediction))\n",
    "r2_training = r2_score(y_train, y_train_prediction)\n",
    "print(\"training dataset evaluation ( Model 1) :\", rmse_training, r2_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing dataset evaluation (Model 1) : 0.10979250380178884 0.722539785607015\n"
     ]
    }
   ],
   "source": [
    "rmse_testing = np.sqrt(mean_squared_error(y_test, y_test_prediction))\n",
    "r2_testing = r2_score(y_test, y_test_prediction)\n",
    "print(\"testing dataset evaluation (Model 1) :\", rmse_testing, r2_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 2 Evaluation -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2_train,x2_test,y_train,y_test=train_test_split(x2,y,test_size=0.3,random_state=0) # Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.add(Dense(input_dim=2,activation='relu',units=100, kernel_initializer='normal')) #Input Layer\n",
    "for i in range(2):\n",
    "    model2.add(Dense(activation='relu',units=100, kernel_initializer='normal')) #Hidden Layer\n",
    "model2.add(Dense(activation='linear',units=1,kernel_initializer='normal')) #Output Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.3265 - mean_absolute_error: 0.3265\n",
      "Epoch 2/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1459 - mean_absolute_error: 0.1459\n",
      "Epoch 3/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1391 - mean_absolute_error: 0.1391\n",
      "Epoch 4/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1322 - mean_absolute_error: 0.1322\n",
      "Epoch 5/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1312 - mean_absolute_error: 0.1312\n",
      "Epoch 6/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1296 - mean_absolute_error: 0.1296\n",
      "Epoch 7/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1293 - mean_absolute_error: 0.1293\n",
      "Epoch 8/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1275 - mean_absolute_error: 0.1275\n",
      "Epoch 9/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1286 - mean_absolute_error: 0.1286\n",
      "Epoch 10/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1284 - mean_absolute_error: 0.1284\n",
      "Epoch 11/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1285 - mean_absolute_error: 0.1285\n",
      "Epoch 12/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1274 - mean_absolute_error: 0.1274\n",
      "Epoch 13/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1297 - mean_absolute_error: 0.1297\n",
      "Epoch 14/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1280 - mean_absolute_error: 0.1280\n",
      "Epoch 15/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1314 - mean_absolute_error: 0.1314\n",
      "Epoch 16/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1272 - mean_absolute_error: 0.1272\n",
      "Epoch 17/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1274 - mean_absolute_error: 0.1274\n",
      "Epoch 18/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1279 - mean_absolute_error: 0.1279\n",
      "Epoch 19/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1291 - mean_absolute_error: 0.1291\n",
      "Epoch 20/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1280 - mean_absolute_error: 0.1280\n",
      "Epoch 21/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1291 - mean_absolute_error: 0.1291\n",
      "Epoch 22/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1272 - mean_absolute_error: 0.1272\n",
      "Epoch 23/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1264 - mean_absolute_error: 0.1264\n",
      "Epoch 24/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1264 - mean_absolute_error: 0.1264\n",
      "Epoch 25/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1264 - mean_absolute_error: 0.1264\n",
      "Epoch 26/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1262 - mean_absolute_error: 0.1262\n",
      "Epoch 27/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1266 - mean_absolute_error: 0.1266\n",
      "Epoch 28/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1287 - mean_absolute_error: 0.1287\n",
      "Epoch 29/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1258 - mean_absolute_error: 0.1258\n",
      "Epoch 30/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1275 - mean_absolute_error: 0.1275\n",
      "Epoch 31/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1269 - mean_absolute_error: 0.1269\n",
      "Epoch 32/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1271 - mean_absolute_error: 0.1271\n",
      "Epoch 33/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1272 - mean_absolute_error: 0.1272\n",
      "Epoch 34/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1282 - mean_absolute_error: 0.1282\n",
      "Epoch 35/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1250 - mean_absolute_error: 0.1250\n",
      "Epoch 36/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 37/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1267 - mean_absolute_error: 0.1267\n",
      "Epoch 38/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1271 - mean_absolute_error: 0.1271\n",
      "Epoch 39/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1259 - mean_absolute_error: 0.1259\n",
      "Epoch 40/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1263 - mean_absolute_error: 0.1263\n",
      "Epoch 41/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1244 - mean_absolute_error: 0.1244\n",
      "Epoch 42/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1259 - mean_absolute_error: 0.1259\n",
      "Epoch 43/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1249 - mean_absolute_error: 0.1249\n",
      "Epoch 44/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1242 - mean_absolute_error: 0.1242\n",
      "Epoch 45/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1253 - mean_absolute_error: 0.1253\n",
      "Epoch 46/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1316 - mean_absolute_error: 0.1316\n",
      "Epoch 47/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1246 - mean_absolute_error: 0.1246\n",
      "Epoch 48/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1258 - mean_absolute_error: 0.1258\n",
      "Epoch 49/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1250 - mean_absolute_error: 0.1250\n",
      "Epoch 50/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 51/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1242 - mean_absolute_error: 0.1242\n",
      "Epoch 52/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1263 - mean_absolute_error: 0.1263\n",
      "Epoch 53/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1274 - mean_absolute_error: 0.1274\n",
      "Epoch 54/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1265 - mean_absolute_error: 0.1265\n",
      "Epoch 55/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 56/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1249 - mean_absolute_error: 0.1249\n",
      "Epoch 57/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1265 - mean_absolute_error: 0.1265\n",
      "Epoch 58/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1258 - mean_absolute_error: 0.1258\n",
      "Epoch 59/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1297 - mean_absolute_error: 0.1297\n",
      "Epoch 60/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1243 - mean_absolute_error: 0.1243\n",
      "Epoch 61/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1250 - mean_absolute_error: 0.1250\n",
      "Epoch 62/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1244 - mean_absolute_error: 0.1244\n",
      "Epoch 63/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1254 - mean_absolute_error: 0.1254\n",
      "Epoch 64/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1263 - mean_absolute_error: 0.1263\n",
      "Epoch 65/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1258 - mean_absolute_error: 0.1258\n",
      "Epoch 66/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1312 - mean_absolute_error: 0.1312\n",
      "Epoch 67/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1264 - mean_absolute_error: 0.1264\n",
      "Epoch 68/200\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1304 - mean_absolute_error: 0.1304\n",
      "Epoch 69/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1246 - mean_absolute_error: 0.1246\n",
      "Epoch 70/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1262 - mean_absolute_error: 0.1262\n",
      "Epoch 71/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1250 - mean_absolute_error: 0.1250\n",
      "Epoch 72/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1260 - mean_absolute_error: 0.1260\n",
      "Epoch 73/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1257 - mean_absolute_error: 0.1257\n",
      "Epoch 74/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1252 - mean_absolute_error: 0.1252\n",
      "Epoch 75/200\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1252 - mean_absolute_error: 0.1252\n",
      "Epoch 76/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1248 - mean_absolute_error: 0.1248\n",
      "Epoch 77/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1239 - mean_absolute_error: 0.1239\n",
      "Epoch 78/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1260 - mean_absolute_error: 0.1260\n",
      "Epoch 79/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1257 - mean_absolute_error: 0.1257\n",
      "Epoch 80/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1246 - mean_absolute_error: 0.1246\n",
      "Epoch 81/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1259 - mean_absolute_error: 0.1259\n",
      "Epoch 82/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1273 - mean_absolute_error: 0.1273\n",
      "Epoch 83/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1267 - mean_absolute_error: 0.1267\n",
      "Epoch 84/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1250 - mean_absolute_error: 0.1250\n",
      "Epoch 85/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1254 - mean_absolute_error: 0.1254\n",
      "Epoch 86/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 87/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1243 - mean_absolute_error: 0.1243\n",
      "Epoch 88/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1243 - mean_absolute_error: 0.1243\n",
      "Epoch 89/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1239 - mean_absolute_error: 0.1239\n",
      "Epoch 90/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1248 - mean_absolute_error: 0.1248\n",
      "Epoch 91/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1245 - mean_absolute_error: 0.1245\n",
      "Epoch 92/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1242 - mean_absolute_error: 0.1242\n",
      "Epoch 93/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 94/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1244 - mean_absolute_error: 0.1244\n",
      "Epoch 95/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1241 - mean_absolute_error: 0.1241\n",
      "Epoch 96/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1248 - mean_absolute_error: 0.1248\n",
      "Epoch 97/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1266 - mean_absolute_error: 0.1266\n",
      "Epoch 98/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 99/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1251 - mean_absolute_error: 0.1251\n",
      "Epoch 100/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1272 - mean_absolute_error: 0.1272\n",
      "Epoch 101/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1292 - mean_absolute_error: 0.1292\n",
      "Epoch 102/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1257 - mean_absolute_error: 0.1257\n",
      "Epoch 103/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1253 - mean_absolute_error: 0.1253\n",
      "Epoch 104/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1263 - mean_absolute_error: 0.1263\n",
      "Epoch 105/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1243 - mean_absolute_error: 0.1243\n",
      "Epoch 106/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1253 - mean_absolute_error: 0.1253\n",
      "Epoch 107/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1253 - mean_absolute_error: 0.1253\n",
      "Epoch 108/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1257 - mean_absolute_error: 0.1257\n",
      "Epoch 109/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1239 - mean_absolute_error: 0.1239\n",
      "Epoch 110/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1244 - mean_absolute_error: 0.1244\n",
      "Epoch 111/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1243 - mean_absolute_error: 0.1243\n",
      "Epoch 112/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1241 - mean_absolute_error: 0.1241\n",
      "Epoch 113/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1260 - mean_absolute_error: 0.1260\n",
      "Epoch 114/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1238 - mean_absolute_error: 0.1238\n",
      "Epoch 115/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1266 - mean_absolute_error: 0.1266\n",
      "Epoch 116/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1278 - mean_absolute_error: 0.1278\n",
      "Epoch 117/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1322 - mean_absolute_error: 0.1322\n",
      "Epoch 118/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1269 - mean_absolute_error: 0.1269\n",
      "Epoch 119/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 120/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 121/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1264 - mean_absolute_error: 0.1264\n",
      "Epoch 122/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1261 - mean_absolute_error: 0.1261\n",
      "Epoch 123/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1246 - mean_absolute_error: 0.1246\n",
      "Epoch 124/200\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1255 - mean_absolute_error: 0.1255\n",
      "Epoch 125/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1238 - mean_absolute_error: 0.1238\n",
      "Epoch 126/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1278 - mean_absolute_error: 0.1278\n",
      "Epoch 127/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1263 - mean_absolute_error: 0.1263\n",
      "Epoch 128/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1246 - mean_absolute_error: 0.1246\n",
      "Epoch 129/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1237 - mean_absolute_error: 0.1237\n",
      "Epoch 130/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1237 - mean_absolute_error: 0.1237\n",
      "Epoch 131/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 132/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1263 - mean_absolute_error: 0.1263\n",
      "Epoch 133/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1245 - mean_absolute_error: 0.1245\n",
      "Epoch 134/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1250 - mean_absolute_error: 0.1250\n",
      "Epoch 135/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1271 - mean_absolute_error: 0.1271\n",
      "Epoch 136/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 137/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1263 - mean_absolute_error: 0.1263\n",
      "Epoch 138/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1258 - mean_absolute_error: 0.1258\n",
      "Epoch 139/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1245 - mean_absolute_error: 0.1245\n",
      "Epoch 140/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1245 - mean_absolute_error: 0.1245\n",
      "Epoch 141/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1266 - mean_absolute_error: 0.1266\n",
      "Epoch 142/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1242 - mean_absolute_error: 0.1242\n",
      "Epoch 143/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1243 - mean_absolute_error: 0.1243\n",
      "Epoch 144/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1239 - mean_absolute_error: 0.1239\n",
      "Epoch 145/200\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1241 - mean_absolute_error: 0.1241\n",
      "Epoch 146/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1238 - mean_absolute_error: 0.1238\n",
      "Epoch 147/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1252 - mean_absolute_error: 0.1252\n",
      "Epoch 148/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1246 - mean_absolute_error: 0.1246\n",
      "Epoch 149/200\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1246 - mean_absolute_error: 0.1246\n",
      "Epoch 150/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1255 - mean_absolute_error: 0.1255\n",
      "Epoch 151/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1254 - mean_absolute_error: 0.1254\n",
      "Epoch 152/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1242 - mean_absolute_error: 0.1242\n",
      "Epoch 153/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1239 - mean_absolute_error: 0.1239\n",
      "Epoch 154/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1242 - mean_absolute_error: 0.1242\n",
      "Epoch 155/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1256 - mean_absolute_error: 0.1256\n",
      "Epoch 156/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1248 - mean_absolute_error: 0.1248\n",
      "Epoch 157/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1248 - mean_absolute_error: 0.1248\n",
      "Epoch 158/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1235 - mean_absolute_error: 0.1235\n",
      "Epoch 159/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1251 - mean_absolute_error: 0.1251\n",
      "Epoch 160/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1239 - mean_absolute_error: 0.1239\n",
      "Epoch 161/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1275 - mean_absolute_error: 0.1275\n",
      "Epoch 162/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1272 - mean_absolute_error: 0.1272\n",
      "Epoch 163/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1241 - mean_absolute_error: 0.1241\n",
      "Epoch 164/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1248 - mean_absolute_error: 0.1248\n",
      "Epoch 165/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1247 - mean_absolute_error: 0.1247\n",
      "Epoch 166/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1248 - mean_absolute_error: 0.1248\n",
      "Epoch 167/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1237 - mean_absolute_error: 0.1237\n",
      "Epoch 168/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1232 - mean_absolute_error: 0.1232\n",
      "Epoch 169/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1261 - mean_absolute_error: 0.1261\n",
      "Epoch 170/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1235 - mean_absolute_error: 0.1235\n",
      "Epoch 171/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1240 - mean_absolute_error: 0.1240\n",
      "Epoch 172/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1244 - mean_absolute_error: 0.1244\n",
      "Epoch 173/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1243 - mean_absolute_error: 0.1243\n",
      "Epoch 174/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1237 - mean_absolute_error: 0.1237\n",
      "Epoch 175/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1244 - mean_absolute_error: 0.1244\n",
      "Epoch 176/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1261 - mean_absolute_error: 0.1261\n",
      "Epoch 177/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1242 - mean_absolute_error: 0.1242\n",
      "Epoch 178/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1238 - mean_absolute_error: 0.1238\n",
      "Epoch 179/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1265 - mean_absolute_error: 0.1265\n",
      "Epoch 180/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1276 - mean_absolute_error: 0.1276\n",
      "Epoch 181/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1253 - mean_absolute_error: 0.1253\n",
      "Epoch 182/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1241 - mean_absolute_error: 0.1241\n",
      "Epoch 183/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1248 - mean_absolute_error: 0.1248\n",
      "Epoch 184/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1248 - mean_absolute_error: 0.1248\n",
      "Epoch 185/200\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1267 - mean_absolute_error: 0.1267\n",
      "Epoch 186/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1249 - mean_absolute_error: 0.1249\n",
      "Epoch 187/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1240 - mean_absolute_error: 0.1240\n",
      "Epoch 188/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1236 - mean_absolute_error: 0.1236\n",
      "Epoch 189/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1252 - mean_absolute_error: 0.1252\n",
      "Epoch 190/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1239 - mean_absolute_error: 0.1239\n",
      "Epoch 191/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1242 - mean_absolute_error: 0.1242\n",
      "Epoch 192/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1233 - mean_absolute_error: 0.1233\n",
      "Epoch 193/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1246 - mean_absolute_error: 0.1246\n",
      "Epoch 194/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1245 - mean_absolute_error: 0.1245\n",
      "Epoch 195/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1241 - mean_absolute_error: 0.1241\n",
      "Epoch 196/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1238 - mean_absolute_error: 0.1238\n",
      "Epoch 197/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1253 - mean_absolute_error: 0.1253\n",
      "Epoch 198/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1243 - mean_absolute_error: 0.1243\n",
      "Epoch 199/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1241 - mean_absolute_error: 0.1241\n",
      "Epoch 200/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1244 - mean_absolute_error: 0.1244\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc094b0afa0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.compile(optimizer='adam', loss=\"mean_absolute_error\",metrics=['mean_absolute_error'])\n",
    "model2.fit(x2_train,y_train,epochs=200,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training dataset evaluation (Model 2) : 0.15512714410995276 0.4438093906734665\n",
      "testing dataset evaluation ( Model 2) : 0.17547079854813402 0.29129573867152525\n"
     ]
    }
   ],
   "source": [
    "y_train_prediction = model2.predict(x2_train)\n",
    "y_test_prediction = model2.predict(x2_test)\n",
    "\n",
    "rmse_training = np.sqrt(mean_squared_error(y_train, y_train_prediction))\n",
    "r2_training = r2_score(y_train, y_train_prediction)\n",
    "print(\"training dataset evaluation (Model 2) :\", rmse_training, r2_training)\n",
    "\n",
    "rmse_testing = np.sqrt(mean_squared_error(y_test, y_test_prediction))\n",
    "r2_testing = r2_score(y_test, y_test_prediction)\n",
    "print(\"testing dataset evaluation ( Model 2) :\", rmse_testing, r2_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 3 Evaluation -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.3140 - mean_absolute_error: 0.3140\n",
      "Epoch 2/200\n",
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      "Epoch 162/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1057 - mean_absolute_error: 0.1057\n",
      "Epoch 163/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1006 - mean_absolute_error: 0.1006\n",
      "Epoch 164/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1025 - mean_absolute_error: 0.1025\n",
      "Epoch 165/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1017 - mean_absolute_error: 0.1017\n",
      "Epoch 166/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1027 - mean_absolute_error: 0.1027\n",
      "Epoch 167/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1013 - mean_absolute_error: 0.1013\n",
      "Epoch 168/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1014 - mean_absolute_error: 0.1014\n",
      "Epoch 169/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1011 - mean_absolute_error: 0.1011\n",
      "Epoch 170/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1013 - mean_absolute_error: 0.1013\n",
      "Epoch 171/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1039 - mean_absolute_error: 0.1039\n",
      "Epoch 172/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1023 - mean_absolute_error: 0.1023\n",
      "Epoch 173/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1033 - mean_absolute_error: 0.1033\n",
      "Epoch 174/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1012 - mean_absolute_error: 0.1012\n",
      "Epoch 175/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1005 - mean_absolute_error: 0.1005\n",
      "Epoch 176/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1046 - mean_absolute_error: 0.1046\n",
      "Epoch 177/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1045 - mean_absolute_error: 0.1045\n",
      "Epoch 178/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1028 - mean_absolute_error: 0.1028\n",
      "Epoch 179/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1018 - mean_absolute_error: 0.1018\n",
      "Epoch 180/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1032 - mean_absolute_error: 0.1032\n",
      "Epoch 181/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1063 - mean_absolute_error: 0.1063\n",
      "Epoch 182/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1026 - mean_absolute_error: 0.1026\n",
      "Epoch 183/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1019 - mean_absolute_error: 0.1019\n",
      "Epoch 184/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1042 - mean_absolute_error: 0.1042\n",
      "Epoch 185/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1029 - mean_absolute_error: 0.1029\n",
      "Epoch 186/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1008 - mean_absolute_error: 0.1008\n",
      "Epoch 187/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1013 - mean_absolute_error: 0.1013\n",
      "Epoch 188/200\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1005 - mean_absolute_error: 0.1005\n",
      "Epoch 189/200\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1008 - mean_absolute_error: 0.1008\n",
      "Epoch 190/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1011 - mean_absolute_error: 0.1011\n",
      "Epoch 191/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1003 - mean_absolute_error: 0.1003\n",
      "Epoch 192/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1008 - mean_absolute_error: 0.1008\n",
      "Epoch 193/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1015 - mean_absolute_error: 0.1015\n",
      "Epoch 194/200\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1022 - mean_absolute_error: 0.1022\n",
      "Epoch 195/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1031 - mean_absolute_error: 0.1031\n",
      "Epoch 196/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1020 - mean_absolute_error: 0.1020\n",
      "Epoch 197/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1009 - mean_absolute_error: 0.1009\n",
      "Epoch 198/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1017 - mean_absolute_error: 0.1017\n",
      "Epoch 199/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1014 - mean_absolute_error: 0.1014\n",
      "Epoch 200/200\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.1007 - mean_absolute_error: 0.1007\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc094d76f70>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3_train,x3_test,y_train,y_test=train_test_split(x3,y,test_size=0.3,random_state=0) # Model 1\n",
    "model3=Sequential()\n",
    "\n",
    "model3.add(Dense(input_dim=2,activation='relu',units=100, kernel_initializer='normal')) #Input Layer\n",
    "for i in range(2):\n",
    "    model3.add(Dense(activation='relu',units=100, kernel_initializer='normal')) #Hidden Layer\n",
    "model3.add(Dense(activation='linear',units=1,kernel_initializer='normal')) #Output Layer\n",
    "\n",
    "model3.compile(optimizer='adam', loss=\"mean_absolute_error\",metrics=['mean_absolute_error'])\n",
    "model3.fit(x3_train,y_train,epochs=200,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training dataset evaluation (Model 3) : 0.13079690357259613 0.6045942475625196\n",
      "testing dataset evaluation ( Model 3) : 0.1336773875877799 0.5886881342242845\n"
     ]
    }
   ],
   "source": [
    "y_train_prediction = model3.predict(x3_train)\n",
    "y_test_prediction = model3.predict(x3_test)\n",
    "\n",
    "rmse_training = np.sqrt(mean_squared_error(y_train, y_train_prediction))\n",
    "r2_training = r2_score(y_train, y_train_prediction)\n",
    "print(\"training dataset evaluation (Model 3) :\", rmse_training, r2_training)\n",
    "\n",
    "rmse_testing = np.sqrt(mean_squared_error(y_test, y_test_prediction))\n",
    "r2_testing = r2_score(y_test, y_test_prediction)\n",
    "print(\"testing dataset evaluation ( Model 3) :\", rmse_testing, r2_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 4 Evaluation -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.2873 - mean_absolute_error: 0.2873\n",
      "Epoch 2/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1535 - mean_absolute_error: 0.1535\n",
      "Epoch 3/600\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1334 - mean_absolute_error: 0.1334\n",
      "Epoch 4/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.1158 - mean_absolute_error: 0.1158\n",
      "Epoch 5/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0971 - mean_absolute_error: 0.0971\n",
      "Epoch 6/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0878 - mean_absolute_error: 0.0878\n",
      "Epoch 7/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0855 - mean_absolute_error: 0.0855\n",
      "Epoch 8/600\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.0824 - mean_absolute_error: 0.0824\n",
      "Epoch 9/600\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0814 - mean_absolute_error: 0.0814\n",
      "Epoch 10/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0812 - mean_absolute_error: 0.0812\n",
      "Epoch 11/600\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0787 - mean_absolute_error: 0.0787\n",
      "Epoch 12/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0760 - mean_absolute_error: 0.0760\n",
      "Epoch 13/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0741 - mean_absolute_error: 0.0741\n",
      "Epoch 14/600\n",
      "22/22 [==============================] - 0s 14ms/step - loss: 0.0723 - mean_absolute_error: 0.0723\n",
      "Epoch 15/600\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.0708 - mean_absolute_error: 0.0708\n",
      "Epoch 16/600\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.0698 - mean_absolute_error: 0.0698\n",
      "Epoch 17/600\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.0685 - mean_absolute_error: 0.0685\n",
      "Epoch 18/600\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0663 - mean_absolute_error: 0.0663\n",
      "Epoch 19/600\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.0661 - mean_absolute_error: 0.0661\n",
      "Epoch 20/600\n",
      "22/22 [==============================] - 0s 12ms/step - loss: 0.0632 - mean_absolute_error: 0.0632\n",
      "Epoch 21/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0622 - mean_absolute_error: 0.0622\n",
      "Epoch 22/600\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.0612 - mean_absolute_error: 0.0612A: 0s - loss: 0.0584 - mean_absolute_error: 0.05\n",
      "Epoch 23/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0613 - mean_absolute_error: 0.0613\n",
      "Epoch 24/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0596 - mean_absolute_error: 0.0596\n",
      "Epoch 25/600\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0590 - mean_absolute_error: 0.0590\n",
      "Epoch 26/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0589 - mean_absolute_error: 0.0589\n",
      "Epoch 27/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0600 - mean_absolute_error: 0.0600\n",
      "Epoch 28/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0589 - mean_absolute_error: 0.0589\n",
      "Epoch 29/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0571 - mean_absolute_error: 0.0571\n",
      "Epoch 30/600\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.0567 - mean_absolute_error: 0.0567\n",
      "Epoch 31/600\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.0583 - mean_absolute_error: 0.0583\n",
      "Epoch 32/600\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0555 - mean_absolute_error: 0.0555\n",
      "Epoch 33/600\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0572 - mean_absolute_error: 0.0572\n",
      "Epoch 34/600\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.0553 - mean_absolute_error: 0.0553\n",
      "Epoch 35/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0548 - mean_absolute_error: 0.0548\n",
      "Epoch 36/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0549 - mean_absolute_error: 0.0549\n",
      "Epoch 37/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0548 - mean_absolute_error: 0.0548\n",
      "Epoch 38/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0553 - mean_absolute_error: 0.0553\n",
      "Epoch 39/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0548 - mean_absolute_error: 0.0548\n",
      "Epoch 40/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0535 - mean_absolute_error: 0.0535\n",
      "Epoch 41/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0532 - mean_absolute_error: 0.0532\n",
      "Epoch 42/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0531 - mean_absolute_error: 0.0531\n",
      "Epoch 43/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0567 - mean_absolute_error: 0.0567\n",
      "Epoch 44/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0568 - mean_absolute_error: 0.0568\n",
      "Epoch 45/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0582 - mean_absolute_error: 0.0582\n",
      "Epoch 46/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0526 - mean_absolute_error: 0.0526\n",
      "Epoch 47/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0529 - mean_absolute_error: 0.0529\n",
      "Epoch 48/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0531 - mean_absolute_error: 0.0531\n",
      "Epoch 49/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0518 - mean_absolute_error: 0.0518\n",
      "Epoch 50/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0520 - mean_absolute_error: 0.0520\n",
      "Epoch 51/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0521 - mean_absolute_error: 0.0521\n",
      "Epoch 52/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0514 - mean_absolute_error: 0.0514\n",
      "Epoch 53/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0509 - mean_absolute_error: 0.0509\n",
      "Epoch 54/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0516 - mean_absolute_error: 0.0516\n",
      "Epoch 55/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0504 - mean_absolute_error: 0.0504\n",
      "Epoch 56/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0513 - mean_absolute_error: 0.0513\n",
      "Epoch 57/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0509 - mean_absolute_error: 0.0509\n",
      "Epoch 58/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0506 - mean_absolute_error: 0.0506\n",
      "Epoch 59/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0507 - mean_absolute_error: 0.0507\n",
      "Epoch 60/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0496 - mean_absolute_error: 0.0496\n",
      "Epoch 61/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0496 - mean_absolute_error: 0.0496\n",
      "Epoch 62/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0494 - mean_absolute_error: 0.0494\n",
      "Epoch 63/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0487 - mean_absolute_error: 0.0487\n",
      "Epoch 64/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0493 - mean_absolute_error: 0.0493\n",
      "Epoch 65/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0518 - mean_absolute_error: 0.0518\n",
      "Epoch 66/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0490 - mean_absolute_error: 0.0490\n",
      "Epoch 67/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0493 - mean_absolute_error: 0.0493\n",
      "Epoch 68/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0496 - mean_absolute_error: 0.0496\n",
      "Epoch 69/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0502 - mean_absolute_error: 0.0502\n",
      "Epoch 70/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0486 - mean_absolute_error: 0.0486\n",
      "Epoch 71/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0474 - mean_absolute_error: 0.0474\n",
      "Epoch 72/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0478 - mean_absolute_error: 0.0478\n",
      "Epoch 73/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0485 - mean_absolute_error: 0.0485\n",
      "Epoch 74/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0476 - mean_absolute_error: 0.0476\n",
      "Epoch 75/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0478 - mean_absolute_error: 0.0478\n",
      "Epoch 76/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0468 - mean_absolute_error: 0.0468\n",
      "Epoch 77/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0481 - mean_absolute_error: 0.0481\n",
      "Epoch 78/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0483 - mean_absolute_error: 0.0483\n",
      "Epoch 79/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0477 - mean_absolute_error: 0.0477\n",
      "Epoch 80/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0465 - mean_absolute_error: 0.0465\n",
      "Epoch 81/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0472 - mean_absolute_error: 0.0472\n",
      "Epoch 82/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0471 - mean_absolute_error: 0.0471\n",
      "Epoch 83/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0476 - mean_absolute_error: 0.0476\n",
      "Epoch 84/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0467 - mean_absolute_error: 0.0467\n",
      "Epoch 85/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0464 - mean_absolute_error: 0.0464\n",
      "Epoch 86/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0462 - mean_absolute_error: 0.0462\n",
      "Epoch 87/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0476 - mean_absolute_error: 0.0476\n",
      "Epoch 88/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0459 - mean_absolute_error: 0.0459\n",
      "Epoch 89/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0458 - mean_absolute_error: 0.0458\n",
      "Epoch 90/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0459 - mean_absolute_error: 0.0459\n",
      "Epoch 91/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0454 - mean_absolute_error: 0.0454\n",
      "Epoch 92/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0459 - mean_absolute_error: 0.0459\n",
      "Epoch 93/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0453 - mean_absolute_error: 0.0453\n",
      "Epoch 94/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0455 - mean_absolute_error: 0.0455\n",
      "Epoch 95/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0461 - mean_absolute_error: 0.0461\n",
      "Epoch 96/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0466 - mean_absolute_error: 0.0466\n",
      "Epoch 97/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0472 - mean_absolute_error: 0.0472\n",
      "Epoch 98/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0454 - mean_absolute_error: 0.0454\n",
      "Epoch 99/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0442 - mean_absolute_error: 0.0442\n",
      "Epoch 100/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0454 - mean_absolute_error: 0.0454\n",
      "Epoch 101/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0452 - mean_absolute_error: 0.0452\n",
      "Epoch 102/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0438 - mean_absolute_error: 0.0438\n",
      "Epoch 103/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0443 - mean_absolute_error: 0.0443\n",
      "Epoch 104/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0453 - mean_absolute_error: 0.0453\n",
      "Epoch 105/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0444 - mean_absolute_error: 0.0444\n",
      "Epoch 106/600\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 0.0446 - mean_absolute_error: 0.0446\n",
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     ]
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     ]
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     ]
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     ]
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     ]
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      "22/22 [==============================] - 0s 3ms/step - loss: 0.0320 - mean_absolute_error: 0.0320\n",
      "Epoch 589/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0311 - mean_absolute_error: 0.0311\n",
      "Epoch 590/600\n",
      "22/22 [==============================] - 0s 3ms/step - loss: 0.0311 - mean_absolute_error: 0.0311\n",
      "Epoch 591/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0310 - mean_absolute_error: 0.0310\n",
      "Epoch 592/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0297 - mean_absolute_error: 0.0297\n",
      "Epoch 593/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0297 - mean_absolute_error: 0.0297\n",
      "Epoch 594/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0294 - mean_absolute_error: 0.0294\n",
      "Epoch 595/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0308 - mean_absolute_error: 0.0308\n",
      "Epoch 596/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0309 - mean_absolute_error: 0.0309\n",
      "Epoch 597/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0297 - mean_absolute_error: 0.0297\n",
      "Epoch 598/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0294 - mean_absolute_error: 0.0294\n",
      "Epoch 599/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0296 - mean_absolute_error: 0.0296\n",
      "Epoch 600/600\n",
      "22/22 [==============================] - 0s 2ms/step - loss: 0.0304 - mean_absolute_error: 0.0304\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc095147c70>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x4_train,x4_test,y_train,y_test=train_test_split(x4,y,test_size=0.3,random_state=0) # Model 1\n",
    "model4=Sequential()\n",
    "model4.add(Dense(input_dim=8,activation='relu',units=150, kernel_initializer='normal')) #Input Layer\n",
    "for i in range(3):\n",
    "    model3.add(Dense(activation='relu',units=150, kernel_initializer='normal')) #Hidden Layer\n",
    "model4.add(Dense(activation='linear',units=1,kernel_initializer='normal')) #Output Layer\n",
    "\n",
    "model4.compile(optimizer='adam', loss=\"mean_absolute_error\",metrics=['mean_absolute_error'])\n",
    "model4.fit(x4_train,y_train,epochs=600,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(686, 8)\n",
      "(295, 8)\n",
      "training dataset evaluation (Model 4) : 0.04840364897024824 0.9458492765652978\n",
      "testing dataset evaluation ( Model 4) : 0.07615787240645466 0.8664988690766469\n"
     ]
    }
   ],
   "source": [
    "y_train_prediction = model4.predict(x4_train)\n",
    "y_test_prediction = model4.predict(x4_test)\n",
    "\n",
    "print(x4_train.shape)\n",
    "print(x4_test.shape)\n",
    "\n",
    "\n",
    "rmse_training = np.sqrt(mean_squared_error(y_train, y_train_prediction))\n",
    "r2_training = r2_score(y_train, y_train_prediction)\n",
    "print(\"training dataset evaluation (Model 4) :\", rmse_training, r2_training)\n",
    "\n",
    "rmse_testing = np.sqrt(mean_squared_error(y_test, y_test_prediction))\n",
    "r2_testing = r2_score(y_test, y_test_prediction)\n",
    "print(\"testing dataset evaluation ( Model 4) :\", rmse_testing, r2_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualizations : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "# Using plotly + cufflinks in offline mode\n",
    "import cufflinks\n",
    "cufflinks.go_offline(connected = True)\n",
    "init_notebook_mode(connected = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=9, step=1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    float64\n",
       "1    float64\n",
       "2    float64\n",
       "3    float64\n",
       "4    float64\n",
       "5    float64\n",
       "6    float64\n",
       "7    float64\n",
       "8    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "[981 rows x 9 columns]"
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     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Cement</th>\n",
       "      <th>Slag</th>\n",
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       "      <td>0.266226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.130365</td>\n",
       "      <td>0.590636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.467014</td>\n",
       "      <td>0.482906</td>\n",
       "      <td>0.548256</td>\n",
       "      <td>0.488961</td>\n",
       "      <td>0.150838</td>\n",
       "      <td>0.379220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>0.362785</td>\n",
       "      <td>0.317937</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.684028</td>\n",
       "      <td>0.367521</td>\n",
       "      <td>0.184593</td>\n",
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       "      <td>0.374611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>981 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cement      Slag    FlyAsh    Water    Superplasticizer   \\\n",
       "0    1.000000  0.000000  0.000000  0.348958           0.106838   \n",
       "1    1.000000  0.000000  0.000000  0.348958           0.106838   \n",
       "2    0.374429  0.360645  0.000000  0.921875           0.000000   \n",
       "3    0.634703  0.300538  0.000000  0.921875           0.000000   \n",
       "4    0.374429  0.360645  0.000000  0.921875           0.000000   \n",
       "..        ...       ...       ...       ...                ...   \n",
       "976  0.398174  0.366972  0.451274  0.501736           0.380342   \n",
       "977  0.502740  0.000000  0.577711  0.644097           0.444444   \n",
       "978  0.106164  0.441000  0.542729  0.615451           0.260684   \n",
       "979  0.130365  0.590636  0.000000  0.467014           0.482906   \n",
       "980  0.362785  0.317937  0.391304  0.684028           0.367521   \n",
       "\n",
       "     CoarseAggregate    FineAggregate        Age  ConcreteStrength  \n",
       "0             0.694767        0.205720  0.150838          0.967485  \n",
       "1             0.738372        0.205720  0.150838          0.741996  \n",
       "2             0.380814        0.190667  0.497207          0.556871  \n",
       "3             0.380814        0.000000  0.150838          0.425065  \n",
       "4             0.380814        0.190667  0.150838          0.542170  \n",
       "..                 ...             ...       ...               ...  \n",
       "976           0.200872        0.437280  0.150838          0.522611  \n",
       "977           0.049128        0.550426  0.150838          0.359412  \n",
       "978           0.265698        0.466633  0.150838          0.266226  \n",
       "979           0.548256        0.488961  0.150838          0.379220  \n",
       "980           0.184593        0.420221  0.150838          0.374611  \n",
       "\n",
       "[981 rows x 9 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.rename(columns={0:'Cement ',4:'Superplasticizer ',7:'Age',8:'ConcreteStrength',1:' Slag ',\n",
    "                  2:'FlyAsh ',3 :'Water  ',5:'CoarseAggregate  ',6:'FineAggregate '})"
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   "source": [
    "Concrete Strength vs Factors"
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   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc096d3ab50>"
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     },
     "execution_count": 57,
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(y='ConcreteStrength',x='Cement ',data=ds.sample(300)) # This type worked passing column as x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc09627f670>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(y='ConcreteStrength',x='Cement ',data=ds.sample(300), kind='line',ci=None) # This type worked passing column as x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc0966b7cd0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(y='ConcreteStrength',x='Superplasticizer ',data=ds.sample(400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc096da8220>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(y='ConcreteStrength',x='Superplasticizer ',data=ds.sample(400),kind='line', ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc09783f8b0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(y='ConcreteStrength',x='Age',data=ds.sample(400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc097a714c0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(y='ConcreteStrength',x='Age',data=ds,kind=\"line\", ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cement ', ' Slag ', 'FlyAsh ', 'Water  ', 'Superplasticizer ',\n",
       "       'CoarseAggregate  ', 'FineAggregate ', 'Age', 'ConcreteStrength'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "histfunc": "count",
         "histnorm": "",
         "marker": {
          "color": "rgba(255, 153, 51, 1.0)",
          "line": {
           "color": "#4D5663",
           "width": 1.3
          }
         },
         "name": "Cement ",
         "opacity": 0.8,
         "orientation": "v",
         "type": "histogram",
         "x": [
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\"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"font\": {\"color\": \"#4D5663\"}}, \"xaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Concrete Strength\"}, \"zerolinecolor\": \"#E1E5ED\"}, \"yaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Count\"}, \"zerolinecolor\": \"#E1E5ED\"}},\n",
       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('d9578c40-4bf2-44e0-8b91-745f76acb812');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds[['ConcreteStrength']].iplot(kind='hist', yTitle='Count',xTitle='Concrete Strength')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regression Models Views-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Cement \n",
       "1                Slag \n",
       "2              FlyAsh \n",
       "3              Water  \n",
       "4    Superplasticizer \n",
       "5    CoarseAggregate  \n",
       "6       FineAggregate \n",
       "7                  Age\n",
       "8     ConcreteStrength\n",
       "dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(ds.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981, 3)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc097d153a0>"
      ]
     },
     "execution_count": 70,
     "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": [
    "sns.distplot(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc097bf6ee0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(y='ConcreteStrength',x='Age',data=ds.sample(300)) #Water vs Concrete Strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc096d99370>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(y='ConcreteStrength',x= 'Water  ',data=ds.sample(300),kind='line')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}