Ecommerce spending.ipynb

{
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "## **Notebook Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac0aea4f",
   "metadata": {
    "papermill": {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 50)\n",
    "\n",
    "from scipy.stats.mstats import normaltest\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import Ridge, Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "\n",
    "import optuna\n",
    "\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49023a39",
   "metadata": {
    "papermill": {
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     "start_time": "2022-08-26T13:20:12.254452",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Loading Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83b5546b",
   "metadata": {
    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Email</th>\n",
       "      <th>Address</th>\n",
       "      <th>Avatar</th>\n",
       "      <th>Avg. Session Length</th>\n",
       "      <th>Time on App</th>\n",
       "      <th>Time on Website</th>\n",
       "      <th>Length of Membership</th>\n",
       "      <th>Yearly Amount Spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mstephenson@fernandez.com</td>\n",
       "      <td>835 Frank Tunnel\\r\\nWrightmouth, MI 82180-9605</td>\n",
       "      <td>Violet</td>\n",
       "      <td>34.497268</td>\n",
       "      <td>12.655651</td>\n",
       "      <td>39.577668</td>\n",
       "      <td>4.082621</td>\n",
       "      <td>587.951054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hduke@hotmail.com</td>\n",
       "      <td>4547 Archer Common\\r\\nDiazchester, CA 06566-8576</td>\n",
       "      <td>DarkGreen</td>\n",
       "      <td>31.926272</td>\n",
       "      <td>11.109461</td>\n",
       "      <td>37.268959</td>\n",
       "      <td>2.664034</td>\n",
       "      <td>392.204933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pallen@yahoo.com</td>\n",
       "      <td>24645 Valerie Unions Suite 582\\r\\nCobbborough,...</td>\n",
       "      <td>Bisque</td>\n",
       "      <td>33.000915</td>\n",
       "      <td>11.330278</td>\n",
       "      <td>37.110597</td>\n",
       "      <td>4.104543</td>\n",
       "      <td>487.547505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>riverarebecca@gmail.com</td>\n",
       "      <td>1414 David Throughway\\r\\nPort Jason, OH 22070-...</td>\n",
       "      <td>SaddleBrown</td>\n",
       "      <td>34.305557</td>\n",
       "      <td>13.717514</td>\n",
       "      <td>36.721283</td>\n",
       "      <td>3.120179</td>\n",
       "      <td>581.852344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mstephens@davidson-herman.com</td>\n",
       "      <td>14023 Rodriguez Passage\\r\\nPort Jacobville, PR...</td>\n",
       "      <td>MediumAquaMarine</td>\n",
       "      <td>33.330673</td>\n",
       "      <td>12.795189</td>\n",
       "      <td>37.536653</td>\n",
       "      <td>4.446308</td>\n",
       "      <td>599.406092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Email  \\\n",
       "0      mstephenson@fernandez.com   \n",
       "1              hduke@hotmail.com   \n",
       "2               pallen@yahoo.com   \n",
       "3        riverarebecca@gmail.com   \n",
       "4  mstephens@davidson-herman.com   \n",
       "\n",
       "                                             Address            Avatar  \\\n",
       "0     835 Frank Tunnel\\r\\nWrightmouth, MI 82180-9605            Violet   \n",
       "1   4547 Archer Common\\r\\nDiazchester, CA 06566-8576         DarkGreen   \n",
       "2  24645 Valerie Unions Suite 582\\r\\nCobbborough,...            Bisque   \n",
       "3  1414 David Throughway\\r\\nPort Jason, OH 22070-...       SaddleBrown   \n",
       "4  14023 Rodriguez Passage\\r\\nPort Jacobville, PR...  MediumAquaMarine   \n",
       "\n",
       "   Avg. Session Length  Time on App  Time on Website  Length of Membership  \\\n",
       "0            34.497268    12.655651        39.577668              4.082621   \n",
       "1            31.926272    11.109461        37.268959              2.664034   \n",
       "2            33.000915    11.330278        37.110597              4.104543   \n",
       "3            34.305557    13.717514        36.721283              3.120179   \n",
       "4            33.330673    12.795189        37.536653              4.446308   \n",
       "\n",
       "   Yearly Amount Spent  \n",
       "0           587.951054  \n",
       "1           392.204933  \n",
       "2           487.547505  \n",
       "3           581.852344  \n",
       "4           599.406092  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data0 = pd.read_csv('Data/Ecommerce Customers.csv')\n",
    "data0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8502d26",
   "metadata": {
    "papermill": {
     "duration": 0.021878,
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     "exception": false,
     "start_time": "2022-08-26T13:20:12.348759",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 500\n",
      "Number of columns: 8\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows:', data0.shape[0])\n",
    "print('Number of columns:', data0.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043ec177",
   "metadata": {
    "papermill": {
     "duration": 0.011851,
     "end_time": "2022-08-26T13:20:12.394668",
     "exception": false,
     "start_time": "2022-08-26T13:20:12.382817",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Checking For Missing Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1568d250",
   "metadata": {
    "papermill": {
     "duration": 0.02443,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Email                   0\n",
       "Address                 0\n",
       "Avatar                  0\n",
       "Avg. Session Length     0\n",
       "Time on App             0\n",
       "Time on Website         0\n",
       "Length of Membership    0\n",
       "Yearly Amount Spent     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data0.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2d581a",
   "metadata": {
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     "start_time": "2022-08-26T13:20:12.443888",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "There are no missing values in our dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5828ef8",
   "metadata": {
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     "duration": 0.01223,
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    },
    "tags": []
   },
   "source": [
    "`Email`, `Address` and `Avatar` column won't be much useful in our analysis, therefore I will just drop them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8b5554a",
   "metadata": {
    "papermill": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data1 = data0.drop(['Email', 'Address', 'Avatar'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "be8afe72",
   "metadata": {
    "papermill": {
     "duration": 0.04961,
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Session Length</th>\n",
       "      <th>Time on App</th>\n",
       "      <th>Time on Website</th>\n",
       "      <th>Length of Membership</th>\n",
       "      <th>Yearly Amount Spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.053194</td>\n",
       "      <td>12.052488</td>\n",
       "      <td>37.060445</td>\n",
       "      <td>3.533462</td>\n",
       "      <td>499.314038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.992563</td>\n",
       "      <td>0.994216</td>\n",
       "      <td>1.010489</td>\n",
       "      <td>0.999278</td>\n",
       "      <td>79.314782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.532429</td>\n",
       "      <td>8.508152</td>\n",
       "      <td>33.913847</td>\n",
       "      <td>0.269901</td>\n",
       "      <td>256.670582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.341822</td>\n",
       "      <td>11.388153</td>\n",
       "      <td>36.349257</td>\n",
       "      <td>2.930450</td>\n",
       "      <td>445.038277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.082008</td>\n",
       "      <td>11.983231</td>\n",
       "      <td>37.069367</td>\n",
       "      <td>3.533975</td>\n",
       "      <td>498.887875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.711985</td>\n",
       "      <td>12.753850</td>\n",
       "      <td>37.716432</td>\n",
       "      <td>4.126502</td>\n",
       "      <td>549.313828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36.139662</td>\n",
       "      <td>15.126994</td>\n",
       "      <td>40.005182</td>\n",
       "      <td>6.922689</td>\n",
       "      <td>765.518462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Avg. Session Length  Time on App  Time on Website  \\\n",
       "count           500.000000   500.000000       500.000000   \n",
       "mean             33.053194    12.052488        37.060445   \n",
       "std               0.992563     0.994216         1.010489   \n",
       "min              29.532429     8.508152        33.913847   \n",
       "25%              32.341822    11.388153        36.349257   \n",
       "50%              33.082008    11.983231        37.069367   \n",
       "75%              33.711985    12.753850        37.716432   \n",
       "max              36.139662    15.126994        40.005182   \n",
       "\n",
       "       Length of Membership  Yearly Amount Spent  \n",
       "count            500.000000           500.000000  \n",
       "mean               3.533462           499.314038  \n",
       "std                0.999278            79.314782  \n",
       "min                0.269901           256.670582  \n",
       "25%                2.930450           445.038277  \n",
       "50%                3.533975           498.887875  \n",
       "75%                4.126502           549.313828  \n",
       "max                6.922689           765.518462  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac505983",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1080x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figures, (fig1, fig2, fig3, fig4, fig5) = plt.subplots(1, 5, figsize=(15,6))\n",
    "fig1.boxplot(data1['Avg. Session Length'])\n",
    "fig1.set_title('Avg. Session Length')\n",
    "fig2.boxplot(data1['Time on App'])\n",
    "fig2.set_title('Time on App')\n",
    "fig3.boxplot(data1['Time on Website'])\n",
    "fig3.set_title('Time on Website')\n",
    "fig4.boxplot(data1['Length of Membership'])\n",
    "fig4.set_title('Length of Membership')\n",
    "fig5.boxplot(data1['Yearly Amount Spent'])\n",
    "fig5.set_title('Yearly Amount Spent')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.012223,
     "end_time": "2022-08-26T13:20:12.608729",
     "exception": false,
     "start_time": "2022-08-26T13:20:12.596506",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:12.635370Z",
     "iopub.status.busy": "2022-08-26T13:20:12.634990Z",
     "iopub.status.idle": "2022-08-26T13:20:12.998834Z",
     "shell.execute_reply": "2022-08-26T13:20:12.997832Z"
    },
    "papermill": {
     "duration": 0.38023,
     "end_time": "2022-08-26T13:20:13.001508",
     "exception": false,
     "start_time": "2022-08-26T13:20:12.621278",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (15, 5))\n",
    "sns.heatmap(data1.corr(), annot= True, cmap='YlGnBu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.012927,
     "end_time": "2022-08-26T13:20:13.028088",
     "exception": false,
     "start_time": "2022-08-26T13:20:13.015161",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "`Time on Website` doesn't seem to be a useful feature. Also, `length of membership` seems to correlated with yearly amount spent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:13.057017Z",
     "iopub.status.busy": "2022-08-26T13:20:13.055837Z",
     "iopub.status.idle": "2022-08-26T13:20:13.062286Z",
     "shell.execute_reply": "2022-08-26T13:20:13.061436Z"
    },
    "papermill": {
     "duration": 0.023188,
     "end_time": "2022-08-26T13:20:13.064526",
     "exception": false,
     "start_time": "2022-08-26T13:20:13.041338",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data1 = data1.drop('Time on Website', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.012877,
     "end_time": "2022-08-26T13:20:13.091140",
     "exception": false,
     "start_time": "2022-08-26T13:20:13.078263",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let us also check the distribution of our target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:13.119839Z",
     "iopub.status.busy": "2022-08-26T13:20:13.118987Z",
     "iopub.status.idle": "2022-08-26T13:20:13.409752Z",
     "shell.execute_reply": "2022-08-26T13:20:13.408597Z"
    },
    "papermill": {
     "duration": 0.30782,
     "end_time": "2022-08-26T13:20:13.412213",
     "exception": false,
     "start_time": "2022-08-26T13:20:13.104393",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.161"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xiJZj9T6Bf/3rX/jXv/713LLQ0FCEhoayuVuiQ6ora7Hjq+Po7GGPsZMGcB1HY3j0cMT5k7eQk1HIdRSi5bRzDj6i0ywsjCH661z3N9/9gcfFFfj3t6/Bzt6S22AaxM7JGuYWJrh36yHNRUxahYoA0TgikQCbVh/Gk5IK/H7oGjp72OPUiUScOpHYZNu3Pwprh4Tc4/F4cPd2xI0L95F0NR0OnZt3gQUhz9DYQUQjMQyDGxfuQWgogHd/N67jaCSXrnYQGQqxb3s811GIFqMiQDRSZuojFEvL0GtAFxga6fYAcS0lEBigi6cDLvx+G/nZxVzHIVqKigDROKWPK5F0JQ3WthZwdbfjOo5G6+LVCQYCPo7vu8h1FKKlqAgQjfPd+ljIZXXoO8Sj2XeU6xtjE0OMDu2L08duoqKMhpkmzUdFgGiU+3eyEfvzJbj3cISljs4W1tamzhmBmmoZfj9yjesoRAtRESAaQ6lUYtv6Y7ASm8PLx5XrOFqja/dO8O7nhuP7LqGuTsF1HKJlqAgQjXH62E2kJediwQcTVGPiEPWMjxiCksIyXDmTzHUUomXUKgJLly59YdmiRYvaPAzRX5Xl1diz5SQ8vJ0wJqwf13G0jo+vOyQOHRH3y2WuoxAt0+jHrWXLlqGgoADXr19HSUmJanldXR3S09NZD0f0R8x/41H+pAoff/kqfRncAgYGfIydPBC7N/2KrAeP4NKVrqoi6mm0CISHhyM1NRX37t3D2LFjVcsNDAxUcwUQ0lo5mVKciLmEURP6oYtnJ67jaK2R4/vi5//7A3H7r2D+kglcxyFaotEi0LNnT/Ts2RNDhgyBnR19siBtj2EYbP/qBAyNRZg2fzTXcbSauYUJhgX2wtm4P/HKW2Ngam7MdSSiBdT69u3hw4d4//33UVpa+txgVUePHmUtGNEPV8+lIPHyA8yJDIZFR1Ou42i9oPBBOBV7A/HH/0TIy75cxyFaQK0isGLFCkyZMgVeXl50vpa0GbmsDjs2nICTmwRjJw/kOo5OcOvmAA9vJ8Ttv4xxUweBz6cLAEnj1CoCQqEQc+bMYTsL0TNx+69AmvcYUV/NgkBgwHUcnTEufBA2LP8FSVfT0WdQV67jEA2n1scEd3d33Lt3j+0sRI9Ulldj/4549BrYhf5QtbHBAT1g0dEUcb9c4joK0QJqHQlkZ2djypQpcHBwgKGhoWo5fSdAWurQ7nOoKKvGK28Fch1F5whFAowO648DO8+iIO8xbB06ch2JaDC1ikBkZCTbOYgeKSooxbGfL8IvqDfcujlwHUcnBU4agIO7z+G3A1fw6ttjm25A9JZaRcDDw6PFO/j888/x+PFjrFmzBsnJyYiKikJFRQX69++Pzz77DAIBDQ+gb37+7hSUDIOIN0ZxHUVnWUssMGC4J04fu4GIN0bRMBykQWp9JzB48GD4+vqq/u/r64sJE5q+GeXixYs4ePCg6vH777+PTz75BL/++isYhsG+fftanpxopZxMKeKP38S48EGQ2NNpCjYFThqAsidVuBx/l+soRIOpVQRSUlKQnJyMlJQUJCUlYd26dQgLa3wu1ydPniA6OhoLFiwAAOTm5qKmpgZ9+vQBAEyePBlxcXGtS0+0zr5tp2FoLMSkmX5cR9F5Pfu7wbZTR/x28CrXUYgGa/YxolAoREhICLZt24b33nuvwe0+/fRTREZGIj8/HwAglUohFotV68ViMQoKCpod2Npa88eYF4vNuY7QrtTtb9q9PFz84w5mLBiNLk3MGGZqImpxHrbbNrRNa/bb2vYNvQYTZwzD1rVHUVlaCdcWjiekT+9nferrM2oVgSdPnqj+zTAMbt++jbKysga3j4mJgb29PXx9fXHgwAFVu39qyY1nxcUVUCpffC5NIRabo7CwnOsY7aY5/d36RSyMTUQYFda/0TZisTkqq2QtzsRmW1MTUYPbtGa/rW3f0M9zoL8Xtn15HPv+ewZzIoOb/bz69H7W1b7y+bxGPzyrVQQGDx4MHo+n+kNubW2Njz/+uMHtjx8/jsLCQoSFhaG0tBRVVVXg8XgoKipSbVNYWAiJRKJuP4iWS7+Xh8vxdzH1tQCYW5hwHUdvWFiZYVCAF+JP3MT0N8fA0EjIdSSiYdQqAikpKc160u3bt6v+feDAAVy5cgWrV6/G+PHjcf36dfTr1w+HDh2Cnx+dF9YX+7adhqm5EcbTeDbtbszEAbhw8hYu/nEbI0Jo9F/yPLWKgFKpxLZt23D27FnU1dVh6NChWLBgQbMv71y3bh2ioqJQWVkJLy8vzJw5s0WhiXZJv5eHa+dS8PK8kTSyJQd6+LjCwdkGvx26SkWAvECtv+Lr169HSkoKZs2aBaVSiZ9//hlr166td8axf5o8eTImT54MAPD09MQvv/zSusRE6xzcdRYmpoYIfomOArjA4/EQOGkAdmw4gczUfLi623MdiWgQtYrAuXPnsH//fgiFT88njhgxAhMmTFCrCBD9ZGFhDJFIgOwMKS6dvovpb4yEa2dx0w0JK/zH9cGeLSdx8tA1zHs/lOs4RIOoVQQYhlEVAAAQiUTPPSbkn0QiATatPoxr51LA4/NQVl6DTasPq9X27Y8avweFNJ+5hQmGjPLG2bhEvLIwEMYmhk03InpBrSLg6emJ//znP3jllVcAAD/88EOrhpIg+qGqsgaZDx7BrZsDjIxbdw09aVhdnUKt69tfmuOPMyf+ROLFVIRGPD01J5PVobS0mu2IRIOpVQSWLVuGVatWISIiAkqlEsOHD8cnn3zCdjai5e7fygYYoFtPJ66j6DSBwECtoyyGYWDR0RTbvjqOrAwpADrqIk0MGyGTybBkyRJcunQJa9asQUJCAnr16gUDAwOYmWn+nbuEO6WPK5F+Lx9ObhK6IkhD8Hg8uHk64HFROUoKG77Zk+iXRovAxo0bUVFRAR+f/11WtnLlSpSVleHrr79mPRzRXgd2nYWiTgHP3i5cRyF/49LVDgYCPtJT8riOQjREo0UgPj4e69evh7W1tWqZra0t1q5di99//531cEQ7VVfVYv+uc3BwtqHJ4zWMUCSAs5stHqYXQC6r4zoO0QCNFgGhUAgjI6MXlpuZmUEkoi/6SP1+P3wN5aVV8OztzHUUUg83Twco6pTIetD8ARyJ7mm0CPD5fFRUVLywvKKiAnV19CmCvEhRp8Cxny+iz8AusJZYcB2H1MNK3AGW1mZIT8mtd2BHol8aLQLjx49HVFQUqqqqVMuqqqoQFRWFwECaG5a86HL8XRQVlOKluQFcRyGN6OLZCaWPK3HnZibXUQjHGi0Cs2bNgrm5OYYOHYqXXnoJ4eHhGDp0KDp06ICFCxe2V0aiRWJ/vgi7TlbwHenFdRTSCOcuEgiEBjjyUwLXUQjHGr1PgM/nY+XKlZg/fz7u3r0LPp+Pnj17wtbWtr3yES1y/0427t/Oxtx3Q8DnqzVpHeGIQCiASxdbnD52ExHzR9Pw3npMrZvFHB0d4ejoyHYWouVi9ybAxMwIATRSpVZw8+yEtJQ8xB//E6HThnAdh3Ck2dNLEv3wbAA4dYjF5pDmPcbl03cRPtsfzi42LKcjbcHS2gw9fFxx8tBVjI/wbdFMf0T7UREg9Xo2AFxTnk23mHQlDQqlEuV/DRRHwxFohwnThmD1Bz/izs1MePftzHUcwgE6cUtarU6uQPq9PDi6iGFq/uJ9JURzBQT3gam5EX47eIXrKIQjVARIq2WmPoJcVgd3bxooTtsYGokwItgHV+KTUVry4j1BRPdRESCtwjAMUu9mo6ONOawlHbiOQ1pgzMT+qKtT4NSxG1xHIRxgtQhs2LABwcHBCAkJUU0+n5CQgNDQUAQGBiI6OprN3ZN2kJNZhIrSanh4O9EXi1rK0VUCLx9X/H74GpRKJddxSDtjrQhcuXIFly5dwpEjR7B//37s3r0bKSkpWLp0KTZv3ozjx4/j9u3bOHPmDFsRSDu4m5gFIxMRHGnqSK0WOGkACnIfI+lqOtdRSDtjrQgMHDgQu3btgkAgQHFxMRQKBcrKyuDi4gInJycIBAKEhoYiLi6OrQiEZaUlFcjPLkZXL0e6OUzLDfL3QoeOpvQFsR5i9TdXKBRi48aNCAkJga+vL6RSKcTi/31ilEgkKCigkQy1VeqdHBgI+HDr5sB1FNJKQpEAASE+uHb+Hk04o2dYv09g8eLFmDdvHhYsWIDMzMwX1jf3PLK1tebPaKbOfK/awNSk4eHCa6plyEorQNfuDrCyqn/OgMbat2bfXLdtaJvW7Le17VvT9tn79eU5I3D4h/O4dOo2Zr099oX1+kCf+voMa0UgLS0NMpkM3bt3h7GxMQIDAxEXFwcDAwPVNlKpFBKJpFnPW1xcAaVSc4e/FYvNUVhYznWMVhOLzVFZJWtwffKfmVAqlPDq7dLgdo21b4qmtn12c1xb77e17VvT9tn7VWRiiN6DuuLwjxcwdsogGAgMdOb9rA5d7Sufz2v0wzNrp4NycnIQFRUFmUwGmUyGP/74AxEREcjIyEBWVhYUCgViY2Ph5+fHVgTCEqVSibTkPEgcOsLSSvOPzIj6xkzsj2JpGW5eTOU6CmknrB0J+Pv7IzExERMnToSBgQECAwMREhICKysrLFq0CLW1tfD390dQUBBbEQhLcjMLUV1Vi75DPbiOQtpY/2Ge6Ghjjt8OXUX/4Z5cxyHtgNXvBBYvXozFixc/t8zX1xdHjhxhc7eEZal3cmBqbgx7J+umNyZaRSAwwKjQfti/4wwK85/o5TlyfUPX9ZFmKSksQ7G0DF29OtHNYTpq1IR+4PGA349c4zoKaQdUBEizPLibA4HQAJ097LmOQlgitrOEj68H/jh6HXIZzSWu66gIELXVVNXiYboUru52EKo51wDRTmOnDMST4grExyVyHYWwjIoAUVtaSh4YJYOuXjTLnK7rM6grHJytcWDnWa6jEJZRESBqUSqUSEvJg52jFc1Hqwf4fD7GhQ/G3cQs3L+TzXUcwiI6pidqyc6QorZaBvcedBSgS+rqFA1eARQ+czh+2vo7Th+5jqEjvF5YL5PVobS0mu2IhGVUBEiTGIZB6p0cmFuYwLaTFddxSBsSCAwanUbUpastfo+9AUNTQxibGD63jqYQ1Q10Oog0qaSwDI+LyumyUD3UvZczGCWD9JQ8rqMQllARIE1KvfP0slBXdzuuo5B21sHSFPZO1khLzoVCQRPO6CIqAqRR1ZW1yMkoRGcPewiEdPZQH7n3cERtjRzZ6VKuoxAWUBEgjUpLyQXD0GWh+kzi0BHmliZ4cDcHDKO5I/iSlqEiQBqkqFMgPSUPDs42MOtgzHUcwhEejwd3L0c8LipHsZQmnNE1VARIgx6mS1FbI0fXHp24jkI45tL16V3i92/TPQO6hooAqRfDMLh/KxsWHU0hse/IdRzCMYHQAF26OyA3sxDlpVVcxyFtiIoAqdfls8koe1IJj57OdFkoAQC493AC34CPe7cech2FtCEqAqRee787DWMTQzi7NW/6T6K7jIxFcHW3Q1bqI1RX1XIdh7QRKgLkBWkpubh5KRXu3o7gG9BbhPxPt57OUDIMUm/ncB2FtBH6DScvOPLjBZiYGsKtmwPXUYiGMetgDCdXCdJSclFRTuMG6QJWi8CmTZsQEhKCkJAQrF27FgCQkJCA0NBQBAYGIjo6ms3dkxaQ5j/GxVN3MGHaEJozgNSrWy9n1MkVOLznAtdRSBtgrQgkJCTg/PnzOHjwIA4dOoQ7d+4gNjYWS5cuxebNm3H8+HHcvn0bZ86cYSsCaYFjey+CB2DKLH+uoxAN1dHGHHaOVvh522lUV9J3A9qOtSIgFovx4YcfQiQSQSgUokuXLsjMzISLiwucnJwgEAgQGhqKuLg4tiKQZqooq8YfR69j6JiekNhbch2HaDAvH1eUPq7EiV8ucR2FtBJrRcDd3R19+vQBAGRmZuL48ePg8XgQi8WqbSQSCQoKCtiKQJrpt4NXUFMtw4QZw7iOQjSctcQCvgFeOLLnAqoqa7iOQ1qB9ZO+qampmD9/PpYsWQKBQICMjIzn1jf3GnRra7O2jMeKhibp0GS1tXLE7b+CgcM9McDXHQBgaiJSq21D26nbXtvastHf1rZns21D6+e8Mw5vTFyP+NgbmPX22BbvX5No4+9ua7FaBK5fv47Fixdj6dKlCAkJwZUrV1BUVKRaL5VKIZE07zr04uIKKJWaO4iVWGyOwsJyrmM024lfLqOksAzjPp2CwsJyiMXmqKySNdnO1ETU4HbqtG+IprZlq7+tbc9W28b6283bCQP8PLH3+9PwD/aBqbl2jy+lrb+7TeHzeY1+eGbtdFB+fj4WLlyIdevWISQkBADQu3dvZGRkICsrCwqFArGxsfDz82MrAlGTXFaHQ7vPwrOXM7z7deY6DtEiL702ElUVNTj6UwLXUUgLsXYksG3bNtTW1mLNmjWqZREREVizZg0WLVqE2tpa+Pv7IygoiK0IRE3xx2+iWFqGN5dOoiEiSLN09rCH78geOPrTBQROGgArcQeuI5FmYq0IREVFISoqqt51R44cYWu3pJnq6hQ4sPMs3Hs4ovfALlzHIVpoxpuBuHouBXv/7w+89fEkruOQZqI7hvXcmRN/ovDRE4TPGUFHAaRF7BytEDx1ME4fu4mM+/lcxyHNREVAjynqFDi48yzcujmg7xAPruMQLTZltj/MOhhj58Y4mn1My1AR0GOnjt3Eo9wSTH2NjgJI65iaG+Ol1wJw+3o6rp+/x3Uc0gxUBPRUbY0cMd+fgoe3E/oP8+Q6DtEBYyYNQCcXG+zYcAK1NXKu4xA1URHQUyd+uYSSonK88lYgHQWQNiEQGOC198bjUW4J9u+I5zoOURMVAT1UUVaNg7vOwsfXHV4+rlzHITqk14AuGBHsg8M/nMfDNBoSRhtQEdBDh384h8ryGsx4cwzXUYgOmrloLEzMjPDtmsNQKpVcxyFNoCKgZ4qlZTi27xKGBfaCq7s913GIDupgaYpZi4Nw/3Y2Th66xnUc0gQqAnpm96anl/BNmz+a6yhEh/mP64NeA7pg9ze/Ij+7mOs4pBFUBPTI3T8zcf7kLYTNGAZbh45cxyE6jMfj4a2PJ8HAgI8Ny39BXZ2C60ikAVQE9IRCocR/vzwGG1sLTJo5nOs4RA/Y2Fpg/pIwPLibg5htp7mOQxpAk8jqiZOHriIz9RHeXfUyDI1aN+49IcDTcaeaGn8/LMIXyX9m4MDOs/AP7PXc+FQyWR1KS2myeq5REdADZU8qsXfrH/Du1xm+I3twHYfoCIHAAJtWH25yOyMTQ5iaG2HJ6/+H0WH9YGRiCAB4+6MwtiMSNdDpID2w7ctjqKmW4bV3Q+jGMNLuBEIBfEf2gKxWjoQ/bkOhoMtGNQkVAR13Kf4OLpy8hfC5I+DkZst1HKKnLK3NMcDPE8XSMtxMuE+DzGkQOh2kw8pLq/Dd2qPo7GGPia/Sl8GEW05utnhSUomUxCxYasFc4fqCjgR02LYvj6GirBoLP5kMgcCA6ziEwLtfZ9g7WePmpVScO5nEdRwCKgI6K+H3Wzj/WxLC54yAa1c7ruMQAuDp/QODA7xgZdMBny3eiaSraVxH0nusF4GKigqMHz8eOTk5AICEhASEhoYiMDAQ0dHRbO9eL+VmFWLz6kPw8HbCpFl+XMch5DkCoQDDx/aCY2cJ1i75Eal3criOpNdYLQKJiYmYNm0aMjMzAQA1NTVYunQpNm/ejOPHj+P27ds4c+YMmxH0Tm2NDOuX7oVQKMC7q16m00BEI4kMhVi3fQE6dDTFvyN34UFyLteR9BarRWDfvn1YtmwZJBIJACApKQkuLi5wcnKCQCBAaGgo4uLi2Iygd75fF4vsjEK8szwcNrYWXMchpEE2thZY9vUcmJgZ4rO3t+PuzUyuI+klVovAv//9b/Tv31/1WCqVQiwWqx5LJBIUFNCY423lRMwlnD52E1Nm+6PPYHeu4xDSJFuHjlj57TxYic2xKnIXbl5M5TqS3mnXS0Truza4uTcvWWvBpWVN3UrPhrO/JuK/0ccxdJQ33vpwAgwMWl/fTU3UG16ioe3Uba9tbdnob2vbs9m2sfWt7bNYbA6x2Byb9/0L783egjUf/ID3VryEkJcGt+p5W5NH37RrEbC1tUVRUZHqsVQqVZ0qUldxcQWUSs290UQsNkdhYXm77jM5MQsrInfDvYcj3oqahJKSylY/p1hsjsoqWZPbmZqIGtxOnfYN0dS2bPW3te3ZattYf1u7XwDP/a58snE2voz6GWuX7kXyrWy8sjCwTT7MqIuL3932wOfzGv3w3K6XiPbu3RsZGRnIysqCQqFAbGws/Pzo6pXWyHrwCJ+/vwdiO0t8tO4VGhyOaC1TMyMsXfcKxk0djKM/XcDnH+xBRRkNMMe2dj0SMDQ0xJo1a7Bo0SLU1tbC398fQUFB7RlB61hYGEMkqv9luvtnJpa/vR1GJiJ8ufNN2DtZP7e+Tq6AQEhXBxHN1NAopB+ujoCntyM2rjyAD+duwSfRM+Hdt/Nz29AIpG2nXYrAqVOnVP/29fXFkSNH2mO3OkEkEtQ7UqM07zHOn7wFI2MhBvp3x/4fzr+wzdsfhak1ymN9aIRHwramRiEdEeyDS6fv4O2XN8K7X2d06+Ws+g6R3p9th+4Y1kLZ6QU491sSTM2MEBDSF6bmxlxHIqTNWYk7YHRYf3RyscGta+k4fewmykuruI6lc2gAOS2iVCiReCUND+7mwFrSAUPH9IKhkZDrWISwRmQoxOCRPfDwQQFuXkrFbwevwrtfZ5qusg3RkYCWqCyvQfzxm3hwNwfuPRwxItiHCgDRCzweDy7udhg7eSDsOlkh6Uoa5k9aj+TELK6j6QQ6EtBwdXUK3Lv1EHduZILHAwYH9ICTW/MuqyVEFxibGmLIaG/kZhYiLTkXnyz4Hn5BvTF9wRi6O74V6EhAg92+kYH5k9Yj6UoaJPaWCJw0kAoA0Ws8Hg+OnSXY9etHmDLbHwl/3MbbU6OxPfo4npRUcB1PK9GRgIZhGAa3rqYjZvtpJP+ZBbGtBYaM8oaDiw1NDUnIX4xNDDFt/miMDuuPX7bH48T+y/j9yDWEvOyLCdOHwawDXSyhLioCGqKirBrnfkvCH0euITP1EazEHfDauyF4ea4/vos+wXU8QjSS2M4Sb340EWEzhuHn70/hwM6zOBFzGaMm9EPwS4Mhse/IdUSNR0WAQ5Xl1biRcB9Xzibj+vl7kMnq0NnDHm98MAEBIT4QigR0BzAhanBwtkHkipcw6dXhOPTDeZyIuYTj+y5i0AgvhE4fCo8eTlxH1FhUBNpRVWUN7t/KRnJiFpITs3Av6SEUCiUsrc0wMrQfRob2hVs3B65jEqK1XN3t8a/PpuKVtwJx4pdLOHnoGi6euoPOHvYYOb4vhgX2grmFCdcxNQoVARYVS8uQkpSF9ORc3LmZifR7eVAqGRgY8NHVqxNeem0Eho/phe69ncHn03f0hLQVG1sLvLpwLMLnjED88T9x6uh1bPvyGHZ+HYeho7wRPHUQ+g/p9sKwKk2NIqqLw1VQEWgjSqUSuVlFuPjHLVw9fw8piVmQ5j8BABibiGBuaQrP3i6wsbOAtbgDBEIB6hQMTscl4nRcYoPPS7fHE9JyxiaGGBc+COPCByHjfj5OH7uB878l4UxcIoQiATq52MCxswS2Dh1hbm7U5Kiouvj7SEVATf8cyE1WW4f7d7KRdC0dt66l4/aNDJQ9eXpLu5WNOXr2d8PLrwWgZ383dPF0wLdfxHIVnRCd09Dgc40Ri80xcKgHIpeFY9k7O5CTUYiczEJkpj6CUCSAk6sYNvaWsHO00qvv4qgIqElRp8An7+1GYf4TFD0qRXFhGZQKJQDA3MIENrYW6NbTGU6uNuALBeDxeHiU/wSPjt5AN2/6UoqQttTU4HONefujMHRyEaOTixgKhRLS3BLkZBYiL7sY6ffzATwdt8jeyQp2jtboaGOu05dnUxFoQFVlDVKSHiL5Zibu3MxEWkouFHVKgAd0tDZH1+6dYGNrAWtbCxgZ/+9TQ1OTcBBCNIeBAR/2zjawd7aBibEQOQ+LkZ9djEc5JbhzIxN3bmRCZCiE2N4SEntLZKY+gomFiU4VBb0pAo2Nyw8AZU8qcetaBv688gCJV9OQeifn6Ze4Aj48ezoj4rUAZGUUwsbWAsJGnocQop14PB6sxB1gJe6AHn07o7Zahke5JSjIe4zCvMfIzSzErHFrYGllBq++rujh0xke3k5wdpPAQKC983bozV+zf47LX1MtQ9GjJyj867/Sv6Zk5BvwYS3ugG69nCG2t4S12AICoQHeeD+0xYefhBDtY2gsgktXO7h0tQPDMKgsr0FPH1dcPHMXd65nIOH32wAAI2MRunp1goe3Ezx6OMHd2wkWHU05Tq8+vSkCRQWleJhWoPqjX/7Xl7gGAj6sJRbo0VcCsZ0lrMTmWl3VCSFtj8fjwayDMUJeGoyBAT3AMAyk+U9w/9ZD3L+djXu3s3H4h/NQ/PU9YUcbc7h0tUNndzu4uNvB1d0O9o7WGvm3RS+KQOKVB1j5zk4AgEBoABtbC7h2tYPYzhIdbczBb8fJrAkh2o/H48HWoSNsHTpi+NjeAIDaGhnSUvKQlpyLzNRHyHrwCEevpavmPhCKBHD865JU585iOHaWwKmzGE6uElhYmTb5PQNb9yjoRRHo7GGPD/4TgRuXH8DCypRuzCKEtDlDIxG8+rjCq4+raplcXofcvy5DLcp/jLO/3cKflx/g/O+3wCgZ1XYGAgOYmhnB1NwIJn/939Tsf/8WGQqxaOlEVnJzUgSOHj2KLVu2QC6XY/bs2ZgxYwar++tgaYqQlwYjI62A1f0QQsjfCYUCuLrbw9XdHmKxORR//d1XKpWoqqhFeWkVKkqrUFlRg6qKGlRW1KCooBRyWd3zzyMSYOyEfuhoa9nmGdu9CBQUFCA6OhoHDhyASCRCREQEBg0ahK5du7Z3FEIIaZaW3KRWHz6fD7MOxk+HvHayfmG9rFaOqopaVFZUo6q8BrW1ctjYWaqKSFtq9yKQkJCAwYMHw9LSEgAwduxYxMXF4e2331arPZ/f8utzzS1aN8a4Ou1NjETgC1/88qc1+9bktg31tz32zUVbtvrb2vZstW2sv63db2vbt3XbpvoKPL1Jbefm31q0z1lvBTYjszGs/zF/lJWNOYqLmz9xTlN/M3kMw7BQWxq2detWVFVVITIyEgAQExODpKQkrFy5sj1jEEIIAQfTS9ZXc3Tp7jtCCNEm7V4EbG1tUVRUpHoslUohkdC8uYQQwoV2LwJDhgzBxYsXUVJSgurqavz222/w8/Nr7xiEEELAwRfDtra2iIyMxMyZMyGXyxEeHo5evXq1dwxCCCHg4IthQgghmoNunSWEED1GRYAQQvQYFQFCCNFjVAQIIUSPURFopg0bNiA4OBghISHYvn07gKdDYYSGhiIwMBDR0dGqbZOTkzFlyhSMHTsWH3/8Merq6hp6Wo33+eef48MPPwTQcL/y8vIwY8YMBAUF4c0330RlZSWXkVtk5syZCAkJQVhYGMLCwpCYmIijR48iODgYY8aMwZ49e1TbNvS6a5NTp05h8uTJCAoKwqpVqwDo7vs5JiZG9bqGhYWhX79+WLFihc72V20MUdvly5eZiIgIRi6XM9XV1UxAQACTnJzM+Pv7Mw8fPmTkcjkzd+5cJj4+nmEYhgkJCWFu3rzJMAzDfPTRR8yePXs4TN9yCQkJzKBBg5glS5YwDNNwv9544w0mNjaWYRiG2bRpE7N27VpO8raUUqlkhg4dysjlctWyR48eMQEBAczjx4+ZyspKJjQ0lElNTWWqq6sbfN21xcOHD5lhw4Yx+fn5jEwmY6ZNm8bEx8fr/PuZYRjm/v37zJgxY5i8vDy96G9j6EigGQYOHIhdu3ZBIBCguLgYCoUCZWVlcHFxgZOTEwQCAUJDQxEXF4fc3FzU1NSgT58+AIDJkycjLi6O2w60wJMnTxAdHY0FCxYAQIP9ksvluHr1KsaOHfvccm2Snp4OHo+HefPmYcKECfjhhx+eG/DQxMRENeBhUlJSva+7Njl58iSCg4NhZ2cHoVCI6OhoGBsb6/T7+Znly5cjMjIS2dnZetHfxlARaCahUIiNGzciJCQEvr6+kEqlEIvFqvUSiQQFBQUvLBeLxSgo0L75DD799FNERkaiQ4cOANBgvx4/fgwzMzMIBILnlmuTsrIy+Pr64ptvvsGOHTuwd+9e5OXlqfX6PluuTbKysqBQKPDaa69hwoQJ+PHHH3X+/Qw8Pd1VU1ODcePG6UV/m0JFoAUWL16MixcvIj8/H5mZmS+s5/F4OjFQXkxMDOzt7eHr66ta1lC/dKG/Pj4+WLt2LUxMTGBlZYXw8HBs3Ljxhe10pb8KhQIXL17EF198gX379uHWrVvIycl5YTtd6e8ze/fuxZw5cwDo9vtZXXoxvWRbSUtLg0wmQ/fu3WFsbIzAwEDExcXBwOB/Y5A/GxDvnwPlFRYWat1AecePH0dhYSHCwsJQWlqKqqoq8Hi8evtlZWWFiooKKBQKGBgYaGV/r127Brlcrip6DMOgU6dO9Q54qAsDIdrY2MDX1xdWVlYAgFGjRun0+xkAZDIZrl69ijVr1gBoeEBLXemvOuhIoBlycnIQFRUFmUwGmUyGP/74AxEREcjIyFAdWsfGxsLPzw+dOnWCoaEhrl+/DgA4dOiQ1g2Ut337dsTGxuLw4cNYvHgxRo4cidWrV9fbL6FQiP79++P48ePPLdcm5eXlWLt2LWpra1FRUYGDBw/iiy++qHfAw969e9f7umuTgIAAnD9/HmVlZVAoFDh37hyCgoJ09v0MAPfu3YOrqytMTEwAoMHXUVf6qw46EmgGf39/JCYmYuLEiTAwMEBgYCBCQkJgZWWFRYsWoba2Fv7+/ggKCgIArFu3DlFRUaisrISXlxdmzpzJcQ/aRkP9WrZsGT788ENs2bIF9vb2+PLLLzlO2jwBAQGq11epVGL69Ono169fgwMerlmzpt7XXVv07t0br7/+OqZPnw65XI6hQ4di2rRpcHNz09n3c3Z2Nuzs7FSPDQ0NG3wddaG/6qAB5AghRI/R6SBCCNFjVAQIIUSPUREghBA9RkWAEEL0GBUBQgjRY1QESLtZsWIFXnrpJSgUCtUyhUKBiIiINhuFs1u3bigpKWlWm1OnTqFbt244duxYm2RoraSkJHz66af1rquoqEBUVBRCQ0MxYcIETJw4ETExMaxliYmJeW7kVKJ7qAiQdrNkyRJUVVVh69atqmVbt26FgYEBFi9ezFmun376CaGhodi5cydnGf7uwYMHDY5Ts379epiYmODIkSM4cuQItm7dim+++Qbnz59nJcv169dRU1PDynMTzUA3i5F2Y2hoiPXr12PatGkICAgAwzD48ccfsX//fhgYGCAmJgY//fQTlEolLC0t8cknn6BLly7IyMjAihUrUFVVBalUCk9PT3z11VcwNDSEt7c3Ro0ahZSUFKxbt061rzlz5iAoKAgvv/wyAGDLli14/Pgxli5d+lym7OxsXL58GadPn0ZwcDBu3rwJHx8fAMCHH34IQ0ND3Lp1C0VFRRg3bhysrKxw+vRpFBYWYtWqVfD19UV5eTk+++wzpKSkgMfjYfjw4Xj33XchEAjQrVs3XLx4UTU0w7PHqampiI6OhpOTE1JTUyGTyfDpp5/CxcUFGzduRHl5OT766COsXr36ubyFhYWwtraGXC6HSCSCra0tvv76a1haWgIARo4cidGjR+PatWsoLy/HnDlzMH36dABPj3i2bNkCuVwOIyMjLFmyBD4+Pvj666+Rm5uLwsJC5ObmwsrKCtHR0UhKSsKpU6dw4cIFGBkZYcaMGay8LwjHOBrCmuixXbt2MZMmTWLCwsJUY7dfvnyZmT59OlNVVcUwDMOcO3eOGTduHMMwDLNmzRrm0KFDDMMwjEwmY8aPH8/ExcUxDMMwHh4ezMGDB1XP7eHhwRQXFzMnT55kpkyZwjAMwygUCiYgIIBJS0t7IcvatWuZRYsWMQzDMMuXL2feeecd1bolS5YwU6dOZWQyGSOVShkPDw9m165dDMMwzI4dO5g5c+YwDMMwH3zwAbNy5UpGqVQytbW1zNy5c5mtW7c+l+ef+S5dusR0796duXv3LsMwDLNt2zZmxowZDMMwzP79+5k33nij3p9dcnIyExgYyPj4+DBz585lNm3axKSnp6vWBwQEMJ988gmjVCqZ/Px8ZtCgQUxKSgqTkZHBjB8/nikpKWEY5ul4+kOHDmUqKyuZjRs3MqNGjWLKy8sZhmGY+fPnMxs2bFD9DL7//vt6sxDdQEcCpN29+uqr+PXXX9GlSxf4+/sDAOLj45GVlYWIiAjVdqWlpXjy5Anef/99XLhwAd999x0yMzMhlUpRVVWl2q5///4v7CMgIACrVq1CSkoKCgoK4OjoCDc3t+e2kclk2L9/P/7zn/8AACZNmoRp06YhPz8f9vb2qucRCoUQi8UwMTHB8OHDAQDOzs548uQJAODs2bP46aefwOPxIBKJEBERgZ07d+KNN95o9Ofg4OCA7t27AwC8vLxw8ODBJn92np6eiIuLw507d3D16lVcuHAB3377LTZs2ICRI0cCAKZPnw4ejwc7OzsMHz4cFy5cgKGhIaRSKWbPnq16Lh6Ph4cPHwJ4OleGmZmZKktpaWmTWYhuoCJAOOHo6AhnZ2fVY6VSibCwMLz//vuqx1KpFBYWFoiMjIRCocC4ceMwYsQI5OfnPzfU77PBwP7OwMAAERER+OWXXyCVSp8rLs+cOHECZWVlWLlypWpqRR6Ph927d+ODDz4AAIhEoufaPJsv4e+USuULj+ubilAmkz332MjISPXvhoYv/ru6ujp89tlneO+99+Dt7Q1vb2/MmTMHmzdvxs8//6wqAn/PqFQqwefzoVQq4evri6+++kq1Lj8/HxKJBCdPnmx2FqI76IthohGGDh2KY8eOQSqVAnj6Ze2sWbMAAOfPn8fChQsRHBwMHo+HxMTE564wasjUqVPx+++/486dOxgzZswL63/66ScsWLAAp0+fxqlTp3Dq1CksX74cMTExzx1pNGXYsGHYs2cPGIaBTCbDvn37MGTIEACAlZUVbt26BeDpTF7qMDAwqLeICAQCZGZmYvPmzZDL5QCeFobs7Gx4eXmptjt06BCAp3M+X7hwAX5+fhg8eDAuXLiAtLQ0AMCZM2cwYcIE1NbWtigL0R10JEA0wvDhwzFv3jzMnTsXPB4PZmZm2LRpE3g8HiIjI7Fw4UJYWFjA2NgYAwYMUJ3GaIy1tTW8vb3RpUsXCIXC59alpKQgOTkZmzdvfm75xIkTsWXLFrVOzTwTFRWFVatWITQ0FHK5HMOHD1dNxxkVFYUVK1agQ4cOGDJkyHOzVTXEx8cHX331FRYuXIhvvvnmuXUbNmzAF198gbFjx8LY2BgMw2D06NFYuHChapucnBxMnjwZNTU1iIqKUp0GW7FiBd59910wDAOBQIAtW7bUexT1d35+fli5ciUAYP78+Wr/TIj2oFFEic4qKSlBeHg49uzZozrHr+tGjhyJDRs2oGfPnlxHIVqCTgcRnbRv3z4EBwdj5syZelMACGkJOhIghBA9RkcChBCix6gIEEKIHqMiQAgheoyKACGE6DEqAoQQoseoCBBCiB77f/hRrIEtFufHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data1['Yearly Amount Spent'], kde= True, color = '#3e236f');\n",
    "normaltest(data1['Yearly Amount Spent']).pvalue.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.013384,
     "end_time": "2022-08-26T13:20:13.439288",
     "exception": false,
     "start_time": "2022-08-26T13:20:13.425904",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Distribution of `Yearly Amount Spent` seems to be normal, so I don't think there is a need to apply log transformation to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:13.469089Z",
     "iopub.status.busy": "2022-08-26T13:20:13.467909Z",
     "iopub.status.idle": "2022-08-26T13:20:18.589441Z",
     "shell.execute_reply": "2022-08-26T13:20:18.588553Z"
    },
    "papermill": {
     "duration": 5.140067,
     "end_time": "2022-08-26T13:20:18.593080",
     "exception": false,
     "start_time": "2022-08-26T13:20:13.453013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.PairGrid(data1, vars=['Avg. Session Length', 'Time on App', 'Length of Membership', 'Yearly Amount Spent'],\n",
    "                    height=2, aspect = 2)\n",
    "\n",
    "grid = grid.map_diag(plt.hist)\n",
    "grid = grid.map_lower(sns.regplot, scatter_kws = {'s': 15, 'alpha': 0.7, 'color': '#005b96'}, \n",
    "                      line_kws = {'color':'orange', 'linewidth': 2})\n",
    "grid = grid.map_upper(sns.kdeplot, n_levels = 10, cmap= 'coolwarm', shade = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.015355,
     "end_time": "2022-08-26T13:20:18.624216",
     "exception": false,
     "start_time": "2022-08-26T13:20:18.608861",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There is definitely a relationship between our target variable and length of membership and also all of our features and our target variable have a bell shaped curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:18.658332Z",
     "iopub.status.busy": "2022-08-26T13:20:18.657274Z",
     "iopub.status.idle": "2022-08-26T13:20:19.593302Z",
     "shell.execute_reply": "2022-08-26T13:20:19.592077Z"
    },
    "papermill": {
     "duration": 0.956483,
     "end_time": "2022-08-26T13:20:19.596543",
     "exception": false,
     "start_time": "2022-08-26T13:20:18.640060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize= (20, 10))\n",
    "fig.suptitle('Scatterplot and Boxplot of Time on App and Length of Membership', fontsize = 20)\n",
    "\n",
    "sns.regplot(x = data1['Length of Membership'], y= data1['Yearly Amount Spent'], scatter_kws = {'s': 20, 'color': '#005b96', 'alpha': 0.7}, line_kws = {'linewidth': 2, 'color': 'orange'}, ax = axs[0, 0])\n",
    "sns.boxplot(x = data1['Length of Membership'], ax= axs[0, 1])\n",
    "\n",
    "sns.regplot(x = data1['Time on App'], y= data1['Yearly Amount Spent'], scatter_kws = {'s': 20, 'color': '#005b96','alpha': 0.7}, line_kws = {'linewidth': 2, 'color': 'orange'}, ax = axs[1, 0])\n",
    "sns.boxplot(x = data1['Time on App'], ax= axs[1, 1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:19.634110Z",
     "iopub.status.busy": "2022-08-26T13:20:19.633334Z",
     "iopub.status.idle": "2022-08-26T13:20:19.637749Z",
     "shell.execute_reply": "2022-08-26T13:20:19.636959Z"
    },
    "papermill": {
     "duration": 0.025819,
     "end_time": "2022-08-26T13:20:19.639893",
     "exception": false,
     "start_time": "2022-08-26T13:20:19.614074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data2 = data1.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017144,
     "end_time": "2022-08-26T13:20:19.674507",
     "exception": false,
     "start_time": "2022-08-26T13:20:19.657363",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Scaling and Splitting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:19.712467Z",
     "iopub.status.busy": "2022-08-26T13:20:19.711731Z",
     "iopub.status.idle": "2022-08-26T13:20:19.717512Z",
     "shell.execute_reply": "2022-08-26T13:20:19.716649Z"
    },
    "papermill": {
     "duration": 0.027855,
     "end_time": "2022-08-26T13:20:19.719830",
     "exception": false,
     "start_time": "2022-08-26T13:20:19.691975",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = data2.drop(['Yearly Amount Spent'], axis=1)\n",
    "y = data2['Yearly Amount Spent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:19.758353Z",
     "iopub.status.busy": "2022-08-26T13:20:19.757619Z",
     "iopub.status.idle": "2022-08-26T13:20:19.767251Z",
     "shell.execute_reply": "2022-08-26T13:20:19.766322Z"
    },
    "papermill": {
     "duration": 0.031288,
     "end_time": "2022-08-26T13:20:19.769716",
     "exception": false,
     "start_time": "2022-08-26T13:20:19.738428",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:19.807299Z",
     "iopub.status.busy": "2022-08-26T13:20:19.806574Z",
     "iopub.status.idle": "2022-08-26T13:20:19.812366Z",
     "shell.execute_reply": "2022-08-26T13:20:19.811498Z"
    },
    "papermill": {
     "duration": 0.027233,
     "end_time": "2022-08-26T13:20:19.814672",
     "exception": false,
     "start_time": "2022-08-26T13:20:19.787439",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state= 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017109,
     "end_time": "2022-08-26T13:20:19.849431",
     "exception": false,
     "start_time": "2022-08-26T13:20:19.832322",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Hyperparameter optimization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:19.887487Z",
     "iopub.status.busy": "2022-08-26T13:20:19.886810Z",
     "iopub.status.idle": "2022-08-26T13:20:19.891434Z",
     "shell.execute_reply": "2022-08-26T13:20:19.890628Z"
    },
    "papermill": {
     "duration": 0.026543,
     "end_time": "2022-08-26T13:20:19.893484",
     "exception": false,
     "start_time": "2022-08-26T13:20:19.866941",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "kf = KFold(n_splits= 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2022-08-26T13:20:19.930871Z",
     "iopub.status.busy": "2022-08-26T13:20:19.930154Z",
     "iopub.status.idle": "2022-08-26T13:26:12.988155Z",
     "shell.execute_reply": "2022-08-26T13:26:12.986916Z"
    },
    "papermill": {
     "duration": 353.079823,
     "end_time": "2022-08-26T13:26:12.990841",
     "exception": false,
     "start_time": "2022-08-26T13:20:19.911018",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-10-01 15:09:15,472]\u001b[0m A new study created in memory with name: no-name-196df53f-b7d4-4c64-b2bb-374df4bbe9ce\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:09:20,232]\u001b[0m Trial 0 finished with value: 353.31681780127167 and parameters: {'n_estimators': 263, 'max_depth': 27, 'bootstrap': 'False'}. Best is trial 0 with value: 353.31681780127167.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:09:26,106]\u001b[0m Trial 1 finished with value: 351.03523462570524 and parameters: {'n_estimators': 316, 'max_depth': 17, 'bootstrap': 'True'}. Best is trial 1 with value: 351.03523462570524.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:09:31,871]\u001b[0m Trial 2 finished with value: 451.40888478179977 and parameters: {'n_estimators': 355, 'max_depth': 6, 'bootstrap': 'True'}. Best is trial 1 with value: 351.03523462570524.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:09:38,509]\u001b[0m Trial 3 finished with value: 357.99925573547654 and parameters: {'n_estimators': 355, 'max_depth': 47, 'bootstrap': 'True'}. Best is trial 1 with value: 351.03523462570524.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:09:40,564]\u001b[0m Trial 4 finished with value: 351.79388135801884 and parameters: {'n_estimators': 106, 'max_depth': 45, 'bootstrap': 'True'}. Best is trial 1 with value: 351.03523462570524.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:09:47,211]\u001b[0m Trial 5 finished with value: 355.31458660006547 and parameters: {'n_estimators': 358, 'max_depth': 19, 'bootstrap': 'True'}. Best is trial 1 with value: 351.03523462570524.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:09:51,422]\u001b[0m Trial 6 finished with value: 350.49493902821234 and parameters: {'n_estimators': 235, 'max_depth': 47, 'bootstrap': 'False'}. Best is trial 6 with value: 350.49493902821234.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:09:56,022]\u001b[0m Trial 7 finished with value: 352.83363123531706 and parameters: {'n_estimators': 251, 'max_depth': 19, 'bootstrap': 'True'}. Best is trial 6 with value: 350.49493902821234.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:02,589]\u001b[0m Trial 8 finished with value: 353.8879573994377 and parameters: {'n_estimators': 360, 'max_depth': 41, 'bootstrap': 'False'}. Best is trial 6 with value: 350.49493902821234.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:04,056]\u001b[0m Trial 9 finished with value: 372.3207771396569 and parameters: {'n_estimators': 79, 'max_depth': 50, 'bootstrap': 'True'}. Best is trial 6 with value: 350.49493902821234.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "def random_forest_objective(trial):\n",
    "    n_estimators = trial.suggest_int('n_estimators', 50, 600)\n",
    "    max_depth = trial.suggest_int('max_depth', 5, 50)\n",
    "    bootstrap = trial.suggest_categorical('bootstrap', ['True', 'False'])\n",
    "\n",
    "    model = RandomForestRegressor(\n",
    "        n_estimators= n_estimators,\n",
    "        max_depth= max_depth,\n",
    "        bootstrap= bootstrap\n",
    "    )\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    cv_score = - cross_val_score(model, X_train, y_train, scoring= 'neg_mean_squared_error', cv= kf)\n",
    "\n",
    "    return np.mean(cv_score)\n",
    "\n",
    "study = optuna.create_study(direction= 'minimize')\n",
    "study.optimize(random_forest_objective, n_trials= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2022-08-26T13:26:13.040128Z",
     "iopub.status.busy": "2022-08-26T13:26:13.039739Z",
     "iopub.status.idle": "2022-08-26T13:28:18.945052Z",
     "shell.execute_reply": "2022-08-26T13:28:18.943793Z"
    },
    "papermill": {
     "duration": 125.932917,
     "end_time": "2022-08-26T13:28:18.947423",
     "exception": false,
     "start_time": "2022-08-26T13:26:13.014506",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-10-01 15:10:04,109]\u001b[0m A new study created in memory with name: no-name-8c6251e9-bf32-4bfe-81c7-e46534e35484\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:05,512]\u001b[0m Trial 0 finished with value: 178.04111948607448 and parameters: {'learning_rate': 0.19783091726415541, 'iterations': 82, 'depth': 4}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:13,471]\u001b[0m Trial 1 finished with value: 411.9018481883321 and parameters: {'learning_rate': 0.422635301817595, 'iterations': 123, 'depth': 9}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:16,283]\u001b[0m Trial 2 finished with value: 183.6915630974404 and parameters: {'learning_rate': 0.26347524319025806, 'iterations': 296, 'depth': 4}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:19,889]\u001b[0m Trial 3 finished with value: 351.8513803030822 and parameters: {'learning_rate': 0.03609331023165052, 'iterations': 156, 'depth': 7}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:28,307]\u001b[0m Trial 4 finished with value: 290.85541627266116 and parameters: {'learning_rate': 0.2839488947054183, 'iterations': 243, 'depth': 8}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:32,683]\u001b[0m Trial 5 finished with value: 373.0857596225547 and parameters: {'learning_rate': 0.34412147503519785, 'iterations': 64, 'depth': 9}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:34,643]\u001b[0m Trial 6 finished with value: 733.1228631175994 and parameters: {'learning_rate': 0.027564822867307825, 'iterations': 99, 'depth': 6}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:40,101]\u001b[0m Trial 7 finished with value: 340.68224805454355 and parameters: {'learning_rate': 0.1844795003057123, 'iterations': 81, 'depth': 9}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:10:42,705]\u001b[0m Trial 8 finished with value: 216.81148733530125 and parameters: {'learning_rate': 0.22496712132500873, 'iterations': 146, 'depth': 6}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,459]\u001b[0m Trial 9 finished with value: 392.6371255430903 and parameters: {'learning_rate': 0.21366740140520812, 'iterations': 262, 'depth': 10}. Best is trial 0 with value: 178.04111948607448.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "def catboost_objective(trial):\n",
    "    learning_rate = trial.suggest_float('learning_rate', 0, 0.5)\n",
    "    iterations = trial.suggest_int('iterations', 50, 300)\n",
    "    depth = trial.suggest_int('depth', 3, 10)\n",
    "\n",
    "    model = CatBoostRegressor(\n",
    "        learning_rate= learning_rate,\n",
    "        iterations= iterations,\n",
    "        depth= depth,\n",
    "        verbose= 0\n",
    "    )\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    cv_score = - cross_val_score(model, X_train, y_train, scoring= 'neg_mean_squared_error', cv= kf)\n",
    "\n",
    "    return np.mean(cv_score)\n",
    "\n",
    "study = optuna.create_study(direction= 'minimize')\n",
    "study.optimize(catboost_objective, n_trials= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:19.010232Z",
     "iopub.status.busy": "2022-08-26T13:28:19.009138Z",
     "iopub.status.idle": "2022-08-26T13:28:20.960679Z",
     "shell.execute_reply": "2022-08-26T13:28:20.959096Z"
    },
    "papermill": {
     "duration": 1.985192,
     "end_time": "2022-08-26T13:28:20.963100",
     "exception": false,
     "start_time": "2022-08-26T13:28:18.977908",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-10-01 15:11:17,523]\u001b[0m A new study created in memory with name: no-name-7f7494b2-00a8-4c82-8624-da77766c0694\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,605]\u001b[0m Trial 0 finished with value: 98.41924799048499 and parameters: {'tol': 0.00154830159454611, 'alpha': 0.25711831400933083}. Best is trial 0 with value: 98.41924799048499.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,621]\u001b[0m Trial 1 finished with value: 98.41722043416284 and parameters: {'tol': 0.0017092756958127127, 'alpha': 0.10614631899073712}. Best is trial 1 with value: 98.41722043416284.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,637]\u001b[0m Trial 2 finished with value: 98.41720139655433 and parameters: {'tol': 1.8208117500288134e-06, 'alpha': 0.10236252365777043}. Best is trial 2 with value: 98.41720139655433.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,653]\u001b[0m Trial 3 finished with value: 98.42921291668156 and parameters: {'tol': 0.0017818358631896947, 'alpha': 0.5307555808339995}. Best is trial 2 with value: 98.41720139655433.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,669]\u001b[0m Trial 4 finished with value: 98.42062279991444 and parameters: {'tol': 0.00020539227611702713, 'alpha': 0.3129188015911567}. Best is trial 2 with value: 98.41720139655433.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,685]\u001b[0m Trial 5 finished with value: 98.41751674265458 and parameters: {'tol': 1.0422934800019965e-05, 'alpha': 0.1461407410925608}. Best is trial 2 with value: 98.41720139655433.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,701]\u001b[0m Trial 6 finished with value: 98.42800504318905 and parameters: {'tol': 0.0013969583841860575, 'alpha': 0.5065439003992015}. Best is trial 2 with value: 98.41720139655433.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,717]\u001b[0m Trial 7 finished with value: 98.44434656113691 and parameters: {'tol': 0.037598581263330405, 'alpha': 0.7674485808766772}. Best is trial 2 with value: 98.41720139655433.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,733]\u001b[0m Trial 8 finished with value: 98.44061394059463 and parameters: {'tol': 2.166698671245862e-05, 'alpha': 0.7169761634811515}. Best is trial 2 with value: 98.41720139655433.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:17,749]\u001b[0m Trial 9 finished with value: 98.45592935429197 and parameters: {'tol': 0.002953681385454811, 'alpha': 0.9052444348112854}. Best is trial 2 with value: 98.41720139655433.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "def ridge_objective(trial):\n",
    "    tol = trial.suggest_loguniform('tol', 1e-7, 0.1)\n",
    "    alpha = trial.suggest_float('alpha', 0, 1)\n",
    "\n",
    "    model = Ridge(\n",
    "        tol= tol,\n",
    "        alpha= alpha\n",
    "    )\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    cv_score = - cross_val_score(model, X_train, y_train, scoring= 'neg_mean_squared_error', cv= kf)\n",
    "\n",
    "    return np.mean(cv_score)\n",
    "\n",
    "study = optuna.create_study(direction= 'minimize')\n",
    "study.optimize(ridge_objective, n_trials= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:21.037575Z",
     "iopub.status.busy": "2022-08-26T13:28:21.037173Z",
     "iopub.status.idle": "2022-08-26T13:28:22.941711Z",
     "shell.execute_reply": "2022-08-26T13:28:22.940498Z"
    },
    "papermill": {
     "duration": 1.945181,
     "end_time": "2022-08-26T13:28:22.944472",
     "exception": false,
     "start_time": "2022-08-26T13:28:20.999291",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-10-01 15:11:46,904]\u001b[0m A new study created in memory with name: no-name-7b452f2c-d101-4869-862f-94f16e5af280\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:46,928]\u001b[0m Trial 0 finished with value: 100.35975333867034 and parameters: {'tol': 1.056346458309951e-06, 'alpha': 0.8253497848147254}. Best is trial 0 with value: 100.35975333867034.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:46,941]\u001b[0m Trial 1 finished with value: 101.00725486877457 and parameters: {'tol': 0.0005862842103467059, 'alpha': 0.9515305723445278}. Best is trial 0 with value: 100.35975333867034.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:46,953]\u001b[0m Trial 2 finished with value: 99.19520539644626 and parameters: {'tol': 0.07279749949517357, 'alpha': 0.5914781339487793}. Best is trial 2 with value: 99.19520539644626.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:46,969]\u001b[0m Trial 3 finished with value: 101.23586975007508 and parameters: {'tol': 0.01864479563764619, 'alpha': 0.992491058979065}. Best is trial 2 with value: 99.19520539644626.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:46,980]\u001b[0m Trial 4 finished with value: 99.4758551853489 and parameters: {'tol': 0.00013547357534608308, 'alpha': 0.6118524966980705}. Best is trial 2 with value: 99.19520539644626.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:46,988]\u001b[0m Trial 5 finished with value: 98.67847518531451 and parameters: {'tol': 0.002105471840156431, 'alpha': 0.30931318855294987}. Best is trial 5 with value: 98.67847518531451.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:46,997]\u001b[0m Trial 6 finished with value: 98.41703389540575 and parameters: {'tol': 4.292877040293863e-05, 'alpha': 0.007094542594516251}. Best is trial 6 with value: 98.41703389540575.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:47,006]\u001b[0m Trial 7 finished with value: 100.98752824822336 and parameters: {'tol': 0.004693203580119338, 'alpha': 0.9482056306423715}. Best is trial 6 with value: 98.41703389540575.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:47,019]\u001b[0m Trial 8 finished with value: 100.40396221917828 and parameters: {'tol': 6.236228026270901e-07, 'alpha': 0.8345784838747404}. Best is trial 6 with value: 98.41703389540575.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:11:47,029]\u001b[0m Trial 9 finished with value: 98.8660142111301 and parameters: {'tol': 2.1851745756150355e-07, 'alpha': 0.40179830412949646}. Best is trial 6 with value: 98.41703389540575.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "def lasso_objective(trial):\n",
    "    tol = trial.suggest_loguniform('tol', 1e-7, 0.1)\n",
    "    alpha = trial.suggest_float('alpha', 0, 1)\n",
    "\n",
    "    model = Lasso(\n",
    "        tol= tol,\n",
    "        alpha= alpha\n",
    "    )\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    cv_score = - cross_val_score(model, X_train, y_train, scoring= 'neg_mean_squared_error', cv= kf)\n",
    "\n",
    "    return np.mean(cv_score)\n",
    "\n",
    "study = optuna.create_study(direction= 'minimize')\n",
    "study.optimize(lasso_objective, n_trials= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:23.031284Z",
     "iopub.status.busy": "2022-08-26T13:28:23.030889Z",
     "iopub.status.idle": "2022-08-26T13:28:25.259793Z",
     "shell.execute_reply": "2022-08-26T13:28:25.258757Z"
    },
    "papermill": {
     "duration": 2.274759,
     "end_time": "2022-08-26T13:28:25.262421",
     "exception": false,
     "start_time": "2022-08-26T13:28:22.987662",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-10-01 15:12:11,900]\u001b[0m A new study created in memory with name: no-name-07a09907-8e84-4506-a50a-4d55356e0131\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:11,926]\u001b[0m Trial 0 finished with value: 425.6050484132579 and parameters: {'n_neighbors': 4, 'leaf_size': 50}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:11,939]\u001b[0m Trial 1 finished with value: 702.2045065123122 and parameters: {'n_neighbors': 17, 'leaf_size': 5}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:11,949]\u001b[0m Trial 2 finished with value: 525.3913904334229 and parameters: {'n_neighbors': 8, 'leaf_size': 40}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:11,960]\u001b[0m Trial 3 finished with value: 614.4159743887241 and parameters: {'n_neighbors': 12, 'leaf_size': 32}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:11,972]\u001b[0m Trial 4 finished with value: 525.3913904334229 and parameters: {'n_neighbors': 8, 'leaf_size': 5}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:11,986]\u001b[0m Trial 5 finished with value: 577.8293289851359 and parameters: {'n_neighbors': 10, 'leaf_size': 2}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:11,997]\u001b[0m Trial 6 finished with value: 710.883035106661 and parameters: {'n_neighbors': 18, 'leaf_size': 46}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:12,009]\u001b[0m Trial 7 finished with value: 427.5334982407073 and parameters: {'n_neighbors': 2, 'leaf_size': 23}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:12,021]\u001b[0m Trial 8 finished with value: 635.4539037001998 and parameters: {'n_neighbors': 13, 'leaf_size': 18}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n",
      "\u001b[32m[I 2022-10-01 15:12:12,031]\u001b[0m Trial 9 finished with value: 553.7367546748193 and parameters: {'n_neighbors': 9, 'leaf_size': 50}. Best is trial 0 with value: 425.6050484132579.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "def knn_objective(trial):\n",
    "    n_neighbors = trial.suggest_int('n_neighbors', 1, 20)\n",
    "    leaf_size = trial.suggest_int('leaf_size', 1, 50)\n",
    "\n",
    "    model = KNeighborsRegressor(\n",
    "        n_neighbors= n_neighbors,\n",
    "        leaf_size= leaf_size\n",
    "    )\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    cv_score = - cross_val_score(model, X_train, y_train, scoring= 'neg_mean_squared_error', cv= kf)\n",
    "\n",
    "    return np.mean(cv_score)\n",
    "\n",
    "study = optuna.create_study(direction= 'minimize')\n",
    "study.optimize(knn_objective, n_trials= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:25.360303Z",
     "iopub.status.busy": "2022-08-26T13:28:25.359923Z",
     "iopub.status.idle": "2022-08-26T13:28:25.367428Z",
     "shell.execute_reply": "2022-08-26T13:28:25.366542Z"
    },
    "papermill": {
     "duration": 0.059152,
     "end_time": "2022-08-26T13:28:25.369550",
     "exception": false,
     "start_time": "2022-08-26T13:28:25.310398",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "random_forest_params = {\n",
    "    'n_estimators': 479, \n",
    "    'max_depth': 18, \n",
    "    'bootstrap': 'False'\n",
    "}\n",
    "\n",
    "catboost_params = {\n",
    "    'learning_rate': 0.09283646947015993, \n",
    "    'iterations': 286, \n",
    "    'depth': 3,\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "ridge_params = {\n",
    "    'tol': 2.6773399620833603e-07, \n",
    "    'alpha': 0.058158941739886394\n",
    "}\n",
    "\n",
    "lasso_params = {\n",
    "    'tol': 0.0748529183574545, \n",
    "    'alpha': 0.03140815868349302\n",
    "}\n",
    "\n",
    "knn_params = {\n",
    "    'n_neighbors': 4, \n",
    "    'leaf_size': 33\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.047527,
     "end_time": "2022-08-26T13:28:25.465431",
     "exception": false,
     "start_time": "2022-08-26T13:28:25.417904",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Modelling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:25.563458Z",
     "iopub.status.busy": "2022-08-26T13:28:25.562640Z",
     "iopub.status.idle": "2022-08-26T13:28:25.568755Z",
     "shell.execute_reply": "2022-08-26T13:28:25.567742Z"
    },
    "papermill": {
     "duration": 0.057252,
     "end_time": "2022-08-26T13:28:25.571039",
     "exception": false,
     "start_time": "2022-08-26T13:28:25.513787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "models = {\n",
    "    'random forest': RandomForestRegressor(**random_forest_params),\n",
    "    'catboost' : CatBoostRegressor(**catboost_params),\n",
    "    'ridge' : Ridge(**ridge_params),\n",
    "    'lasso' : Lasso(**lasso_params),\n",
    "    'knn' : KNeighborsRegressor(**knn_params)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:25.668354Z",
     "iopub.status.busy": "2022-08-26T13:28:25.667993Z",
     "iopub.status.idle": "2022-08-26T13:28:26.621155Z",
     "shell.execute_reply": "2022-08-26T13:28:26.619900Z"
    },
    "papermill": {
     "duration": 1.005365,
     "end_time": "2022-08-26T13:28:26.623783",
     "exception": false,
     "start_time": "2022-08-26T13:28:25.618418",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forest trained\n",
      "catboost trained\n",
      "ridge trained\n",
      "lasso trained\n",
      "knn trained\n"
     ]
    }
   ],
   "source": [
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    print(name + \" trained\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.048076,
     "end_time": "2022-08-26T13:28:26.719509",
     "exception": false,
     "start_time": "2022-08-26T13:28:26.671433",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:26.816710Z",
     "iopub.status.busy": "2022-08-26T13:28:26.815974Z",
     "iopub.status.idle": "2022-08-26T13:28:36.186240Z",
     "shell.execute_reply": "2022-08-26T13:28:36.185096Z"
    },
    "papermill": {
     "duration": 9.421844,
     "end_time": "2022-08-26T13:28:36.188946",
     "exception": false,
     "start_time": "2022-08-26T13:28:26.767102",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "results = {}\n",
    "\n",
    "kf = KFold(n_splits= 10)\n",
    "\n",
    "for name, model in models.items():\n",
    "    result = np.sqrt(-cross_val_score(model, X_train, y_train, scoring = 'neg_mean_squared_error', cv= kf))\n",
    "    results[name] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:36.286135Z",
     "iopub.status.busy": "2022-08-26T13:28:36.285728Z",
     "iopub.status.idle": "2022-08-26T13:28:36.292576Z",
     "shell.execute_reply": "2022-08-26T13:28:36.291450Z"
    },
    "papermill": {
     "duration": 0.058793,
     "end_time": "2022-08-26T13:28:36.295180",
     "exception": false,
     "start_time": "2022-08-26T13:28:36.236387",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------\n",
      "random forest\n",
      "18.505547741216514\n",
      "2.9976883994517816\n",
      "-------\n",
      "catboost\n",
      "12.722068315071883\n",
      "1.0615927682094657\n",
      "-------\n",
      "ridge\n",
      "9.838427779101284\n",
      "0.9720939474624083\n",
      "-------\n",
      "lasso\n",
      "9.834160279463012\n",
      "0.9704192428457076\n",
      "-------\n",
      "knn\n",
      "19.774600170956752\n",
      "3.6143911885792286\n"
     ]
    }
   ],
   "source": [
    "for name, result in results.items():\n",
    "    print(\"-------\\n\" + name)\n",
    "    print(np.mean(result))\n",
    "    print(np.std(result))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.047405,
     "end_time": "2022-08-26T13:28:36.390333",
     "exception": false,
     "start_time": "2022-08-26T13:28:36.342928",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Clearly, ridge and lasso along with catboost seems to be performing the best. So we will combine these 3 to predict final results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.047578,
     "end_time": "2022-08-26T13:28:36.485495",
     "exception": false,
     "start_time": "2022-08-26T13:28:36.437917",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Final Predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:36.583405Z",
     "iopub.status.busy": "2022-08-26T13:28:36.582224Z",
     "iopub.status.idle": "2022-08-26T13:28:36.591339Z",
     "shell.execute_reply": "2022-08-26T13:28:36.590384Z"
    },
    "papermill": {
     "duration": 0.060916,
     "end_time": "2022-08-26T13:28:36.594153",
     "exception": false,
     "start_time": "2022-08-26T13:28:36.533237",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "final_predictions = (\n",
    "    0.2 * models['catboost'].predict(X_test) +\n",
    "    0.4 * models['ridge'].predict(X_test) +\n",
    "    0.4 * models['lasso'].predict(X_test) \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:36.692537Z",
     "iopub.status.busy": "2022-08-26T13:28:36.692123Z",
     "iopub.status.idle": "2022-08-26T13:28:36.702315Z",
     "shell.execute_reply": "2022-08-26T13:28:36.701184Z"
    },
    "papermill": {
     "duration": 0.062422,
     "end_time": "2022-08-26T13:28:36.704985",
     "exception": false,
     "start_time": "2022-08-26T13:28:36.642563",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 10.527443162303632\n",
      "R-square: 0.9776185211758321\n"
     ]
    }
   ],
   "source": [
    "r2 = r2_score(y_test, final_predictions)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, final_predictions))\n",
    "\n",
    "print(\"RMSE:\", rmse)\n",
    "print(\"R-square:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.047464,
     "end_time": "2022-08-26T13:28:36.800964",
     "exception": false,
     "start_time": "2022-08-26T13:28:36.753500",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We have an R-Square of 0.977, which is very close to 1. Therefore, our model is doing a very good job. Let us also visualise the final results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.047878,
     "end_time": "2022-08-26T13:28:36.897147",
     "exception": false,
     "start_time": "2022-08-26T13:28:36.849269",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **Visualising results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:36.997318Z",
     "iopub.status.busy": "2022-08-26T13:28:36.996511Z",
     "iopub.status.idle": "2022-08-26T13:28:37.223493Z",
     "shell.execute_reply": "2022-08-26T13:28:37.222254Z"
    },
    "papermill": {
     "duration": 0.280016,
     "end_time": "2022-08-26T13:28:37.226271",
     "exception": false,
     "start_time": "2022-08-26T13:28:36.946255",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of error\n",
    "sns.histplot(y_test - final_predictions, color = '#005b96', kde= True)\n",
    "plt.xlabel('Error')\n",
    "normaltest(final_predictions).pvalue.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:37.325429Z",
     "iopub.status.busy": "2022-08-26T13:28:37.325019Z",
     "iopub.status.idle": "2022-08-26T13:28:37.557688Z",
     "shell.execute_reply": "2022-08-26T13:28:37.556635Z"
    },
    "papermill": {
     "duration": 0.285113,
     "end_time": "2022-08-26T13:28:37.560223",
     "exception": false,
     "start_time": "2022-08-26T13:28:37.275110",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10, 6))\n",
    "sns.scatterplot(x= y_test, y= final_predictions, color= '#005b96')\n",
    "plt.xlabel('Actual Yearly Amount Spent')\n",
    "plt.ylabel('Predicted Yearly Amount Spent')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:37.659249Z",
     "iopub.status.busy": "2022-08-26T13:28:37.658838Z",
     "iopub.status.idle": "2022-08-26T13:28:37.874968Z",
     "shell.execute_reply": "2022-08-26T13:28:37.873835Z"
    },
    "papermill": {
     "duration": 0.268848,
     "end_time": "2022-08-26T13:28:37.877802",
     "exception": false,
     "start_time": "2022-08-26T13:28:37.608954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10, 6))\n",
    "sns.residplot(x= y_test, y = final_predictions, color= '#005b96')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.048993,
     "end_time": "2022-08-26T13:28:37.975849",
     "exception": false,
     "start_time": "2022-08-26T13:28:37.926856",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **ANN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:38.076661Z",
     "iopub.status.busy": "2022-08-26T13:28:38.076244Z",
     "iopub.status.idle": "2022-08-26T13:28:50.536633Z",
     "shell.execute_reply": "2022-08-26T13:28:50.535639Z"
    },
    "papermill": {
     "duration": 12.514248,
     "end_time": "2022-08-26T13:28:50.539178",
     "exception": false,
     "start_time": "2022-08-26T13:28:38.024930",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "12/12 [==============================] - 0s 12ms/step - loss: 502.0629 - mae: 502.0629 - val_loss: 497.4916 - val_mae: 497.4916\n",
      "Epoch 2/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 500.7982 - mae: 500.7982 - val_loss: 495.4580 - val_mae: 495.4580\n",
      "Epoch 3/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 497.6338 - mae: 497.6338 - val_loss: 490.1129 - val_mae: 490.1129\n",
      "Epoch 4/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 489.8180 - mae: 489.8180 - val_loss: 477.6733 - val_mae: 477.6733\n",
      "Epoch 5/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 472.3302 - mae: 472.3302 - val_loss: 451.1887 - val_mae: 451.1887\n",
      "Epoch 6/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 437.1599 - mae: 437.1599 - val_loss: 399.8509 - val_mae: 399.8509\n",
      "Epoch 7/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 371.6933 - mae: 371.6933 - val_loss: 308.9920 - val_mae: 308.9920\n",
      "Epoch 8/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 265.5211 - mae: 265.5211 - val_loss: 193.4006 - val_mae: 193.4006\n",
      "Epoch 9/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 172.9038 - mae: 172.9038 - val_loss: 166.1843 - val_mae: 166.1843\n",
      "Epoch 10/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 156.0911 - mae: 156.0911 - val_loss: 158.0976 - val_mae: 158.0976\n",
      "Epoch 11/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 146.0571 - mae: 146.0571 - val_loss: 143.4356 - val_mae: 143.4356\n",
      "Epoch 12/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 136.3470 - mae: 136.3470 - val_loss: 137.2447 - val_mae: 137.2447\n",
      "Epoch 13/100\n",
      "12/12 [==============================] - 0s 5ms/step - loss: 128.7524 - mae: 128.7524 - val_loss: 130.7425 - val_mae: 130.7425\n",
      "Epoch 14/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 121.6377 - mae: 121.6377 - val_loss: 124.0531 - val_mae: 124.0531\n",
      "Epoch 15/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 114.0638 - mae: 114.0638 - val_loss: 116.3413 - val_mae: 116.3413\n",
      "Epoch 16/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 105.7309 - mae: 105.7309 - val_loss: 107.9501 - val_mae: 107.9501\n",
      "Epoch 17/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 96.9394 - mae: 96.9394 - val_loss: 100.0104 - val_mae: 100.0104\n",
      "Epoch 18/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 86.7124 - mae: 86.7124 - val_loss: 92.1981 - val_mae: 92.1981\n",
      "Epoch 19/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 77.1896 - mae: 77.1896 - val_loss: 83.1857 - val_mae: 83.1857\n",
      "Epoch 20/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 67.7740 - mae: 67.7740 - val_loss: 72.7070 - val_mae: 72.7070\n",
      "Epoch 21/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 59.4422 - mae: 59.4422 - val_loss: 64.6444 - val_mae: 64.6444\n",
      "Epoch 22/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 51.6430 - mae: 51.6430 - val_loss: 57.9195 - val_mae: 57.9195\n",
      "Epoch 23/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 46.3000 - mae: 46.3000 - val_loss: 50.7372 - val_mae: 50.7372\n",
      "Epoch 24/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 40.0857 - mae: 40.0857 - val_loss: 43.5866 - val_mae: 43.5866\n",
      "Epoch 25/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 35.4561 - mae: 35.4561 - val_loss: 40.2682 - val_mae: 40.2682\n",
      "Epoch 26/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 31.8441 - mae: 31.8441 - val_loss: 32.8710 - val_mae: 32.8710\n",
      "Epoch 27/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 27.0804 - mae: 27.0804 - val_loss: 28.5279 - val_mae: 28.5279\n",
      "Epoch 28/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 24.2632 - mae: 24.2632 - val_loss: 24.6484 - val_mae: 24.6484\n",
      "Epoch 29/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 22.6869 - mae: 22.6869 - val_loss: 26.4245 - val_mae: 26.4245\n",
      "Epoch 30/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 20.9277 - mae: 20.9277 - val_loss: 20.4214 - val_mae: 20.4214\n",
      "Epoch 31/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 17.7056 - mae: 17.7056 - val_loss: 18.8413 - val_mae: 18.8413\n",
      "Epoch 32/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 16.7282 - mae: 16.7282 - val_loss: 16.7049 - val_mae: 16.7049\n",
      "Epoch 33/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 15.4960 - mae: 15.4960 - val_loss: 15.6843 - val_mae: 15.6843\n",
      "Epoch 34/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 13.8518 - mae: 13.8518 - val_loss: 14.7001 - val_mae: 14.7001\n",
      "Epoch 35/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 13.2410 - mae: 13.2410 - val_loss: 13.5734 - val_mae: 13.5734\n",
      "Epoch 36/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 12.2834 - mae: 12.2834 - val_loss: 13.9279 - val_mae: 13.9279\n",
      "Epoch 37/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 12.0993 - mae: 12.0993 - val_loss: 13.3179 - val_mae: 13.3179\n",
      "Epoch 38/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 11.2887 - mae: 11.2887 - val_loss: 12.1584 - val_mae: 12.1584\n",
      "Epoch 39/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 11.1889 - mae: 11.1889 - val_loss: 11.7195 - val_mae: 11.7195\n",
      "Epoch 40/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 10.8813 - mae: 10.8813 - val_loss: 11.2489 - val_mae: 11.2489\n",
      "Epoch 41/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 10.9521 - mae: 10.9521 - val_loss: 11.0024 - val_mae: 11.0024\n",
      "Epoch 42/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 10.4211 - mae: 10.4211 - val_loss: 10.8015 - val_mae: 10.8015\n",
      "Epoch 43/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 9.8379 - mae: 9.8379 - val_loss: 10.8997 - val_mae: 10.8997\n",
      "Epoch 44/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 9.7414 - mae: 9.7414 - val_loss: 10.1180 - val_mae: 10.1180\n",
      "Epoch 45/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 9.9290 - mae: 9.9290 - val_loss: 10.1547 - val_mae: 10.1547\n",
      "Epoch 46/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 9.5991 - mae: 9.5991 - val_loss: 9.6980 - val_mae: 9.6980\n",
      "Epoch 47/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 9.2859 - mae: 9.2859 - val_loss: 9.7243 - val_mae: 9.7243\n",
      "Epoch 48/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 9.3278 - mae: 9.3278 - val_loss: 9.4931 - val_mae: 9.4931\n",
      "Epoch 49/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 9.2020 - mae: 9.2020 - val_loss: 10.2162 - val_mae: 10.2162\n",
      "Epoch 50/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 9.1259 - mae: 9.1259 - val_loss: 10.2033 - val_mae: 10.2033\n",
      "Epoch 51/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 9.2391 - mae: 9.2391 - val_loss: 9.3352 - val_mae: 9.3352\n",
      "Epoch 52/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 9.3493 - mae: 9.3493 - val_loss: 9.0954 - val_mae: 9.0954\n",
      "Epoch 53/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.8245 - mae: 8.8245 - val_loss: 9.0484 - val_mae: 9.0484\n",
      "Epoch 54/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.6258 - mae: 8.6258 - val_loss: 9.1916 - val_mae: 9.1916\n",
      "Epoch 55/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.6100 - mae: 8.6100 - val_loss: 8.9040 - val_mae: 8.9040\n",
      "Epoch 56/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.5918 - mae: 8.5918 - val_loss: 8.8283 - val_mae: 8.8283\n",
      "Epoch 57/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.6355 - mae: 8.6355 - val_loss: 8.6704 - val_mae: 8.6704\n",
      "Epoch 58/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.5467 - mae: 8.5467 - val_loss: 8.8365 - val_mae: 8.8365\n",
      "Epoch 59/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.4315 - mae: 8.4315 - val_loss: 8.8358 - val_mae: 8.8358\n",
      "Epoch 60/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.4774 - mae: 8.4774 - val_loss: 9.0255 - val_mae: 9.0255\n",
      "Epoch 61/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.3255 - mae: 8.3255 - val_loss: 8.6696 - val_mae: 8.6696\n",
      "Epoch 62/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.5201 - mae: 8.5201 - val_loss: 8.6282 - val_mae: 8.6282\n",
      "Epoch 63/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.3998 - mae: 8.3998 - val_loss: 9.1224 - val_mae: 9.1224\n",
      "Epoch 64/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.3917 - mae: 8.3917 - val_loss: 9.0099 - val_mae: 9.0099\n",
      "Epoch 65/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.3454 - mae: 8.3454 - val_loss: 8.7939 - val_mae: 8.7939\n",
      "Epoch 66/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.1743 - mae: 8.1743 - val_loss: 8.5349 - val_mae: 8.5349\n",
      "Epoch 67/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.2413 - mae: 8.2413 - val_loss: 8.5850 - val_mae: 8.5850\n",
      "Epoch 68/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.2969 - mae: 8.2969 - val_loss: 9.4766 - val_mae: 9.4766\n",
      "Epoch 69/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.5246 - mae: 8.5246 - val_loss: 10.4066 - val_mae: 10.4066\n",
      "Epoch 70/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.4892 - mae: 8.4892 - val_loss: 9.7265 - val_mae: 9.7265\n",
      "Epoch 71/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.2843 - mae: 8.2843 - val_loss: 9.1029 - val_mae: 9.1029\n",
      "Epoch 72/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.3995 - mae: 8.3995 - val_loss: 8.7758 - val_mae: 8.7758\n",
      "Epoch 73/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.2925 - mae: 8.2925 - val_loss: 8.6255 - val_mae: 8.6255\n",
      "Epoch 74/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.1970 - mae: 8.1970 - val_loss: 8.6762 - val_mae: 8.6762\n",
      "Epoch 75/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.0163 - mae: 8.0163 - val_loss: 9.2062 - val_mae: 9.2062\n",
      "Epoch 76/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.7224 - mae: 8.7224 - val_loss: 8.4217 - val_mae: 8.4217\n",
      "Epoch 77/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.2161 - mae: 8.2161 - val_loss: 8.6873 - val_mae: 8.6873\n",
      "Epoch 78/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.0139 - mae: 8.0139 - val_loss: 8.5745 - val_mae: 8.5745\n",
      "Epoch 79/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.0942 - mae: 8.0942 - val_loss: 8.8925 - val_mae: 8.8925\n",
      "Epoch 80/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.0938 - mae: 8.0938 - val_loss: 8.6993 - val_mae: 8.6993\n",
      "Epoch 81/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.0115 - mae: 8.0115 - val_loss: 8.6696 - val_mae: 8.6696\n",
      "Epoch 82/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 7.9315 - mae: 7.9315 - val_loss: 9.5321 - val_mae: 9.5321\n",
      "Epoch 83/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.3135 - mae: 8.3135 - val_loss: 9.5816 - val_mae: 9.5816\n",
      "Epoch 84/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 7.9340 - mae: 7.9340 - val_loss: 10.2588 - val_mae: 10.2588\n",
      "Epoch 85/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.3810 - mae: 8.3810 - val_loss: 8.6613 - val_mae: 8.6613\n",
      "Epoch 86/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.4608 - mae: 8.4608 - val_loss: 8.9487 - val_mae: 8.9487\n",
      "Epoch 87/100\n",
      "12/12 [==============================] - 0s 5ms/step - loss: 8.6340 - mae: 8.6340 - val_loss: 8.8429 - val_mae: 8.8429\n",
      "Epoch 88/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.0705 - mae: 8.0705 - val_loss: 8.5295 - val_mae: 8.5295\n",
      "Epoch 89/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.0844 - mae: 8.0844 - val_loss: 8.9988 - val_mae: 8.9988\n",
      "Epoch 90/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.2168 - mae: 8.2168 - val_loss: 9.1366 - val_mae: 9.1366\n",
      "Epoch 91/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.1683 - mae: 8.1683 - val_loss: 8.9980 - val_mae: 8.9980\n",
      "Epoch 92/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.1785 - mae: 8.1785 - val_loss: 8.9498 - val_mae: 8.9498\n",
      "Epoch 93/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.5632 - mae: 8.5632 - val_loss: 9.0052 - val_mae: 9.0052\n",
      "Epoch 94/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.4908 - mae: 8.4908 - val_loss: 9.3031 - val_mae: 9.3031\n",
      "Epoch 95/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.0813 - mae: 8.0813 - val_loss: 8.5028 - val_mae: 8.5028\n",
      "Epoch 96/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.4244 - mae: 8.4244 - val_loss: 9.7876 - val_mae: 9.7876\n",
      "Epoch 97/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.3499 - mae: 8.3499 - val_loss: 9.4552 - val_mae: 9.4552\n",
      "Epoch 98/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.1136 - mae: 8.1136 - val_loss: 8.7917 - val_mae: 8.7917\n",
      "Epoch 99/100\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 8.0540 - mae: 8.0540 - val_loss: 10.1174 - val_mae: 10.1174\n",
      "Epoch 100/100\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 8.6069 - mae: 8.6069 - val_loss: 8.9221 - val_mae: 8.9221\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(3),\n",
    "    tf.keras.layers.Dense(80, activation = 'relu'),\n",
    "    tf.keras.layers.Dense(80, activation = 'relu'),\n",
    "    tf.keras.layers.Dense(80, activation = 'relu'),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss', min_delta=0, patience=5, verbose=0,\n",
    "    mode='auto', baseline=None, restore_best_weights=False\n",
    ")\n",
    "\n",
    "model.compile(\n",
    "    loss = tf.keras.losses.mae,\n",
    "    optimizer = 'adam',\n",
    "    metrics = 'mae'\n",
    ")\n",
    "\n",
    "history = model.fit(X_train, y_train, validation_split = 0.10, epochs = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:50.669297Z",
     "iopub.status.busy": "2022-08-26T13:28:50.668172Z",
     "iopub.status.idle": "2022-08-26T13:28:50.674920Z",
     "shell.execute_reply": "2022-08-26T13:28:50.673890Z"
    },
    "papermill": {
     "duration": 0.073519,
     "end_time": "2022-08-26T13:28:50.677121",
     "exception": false,
     "start_time": "2022-08-26T13:28:50.603602",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'mae', 'val_loss', 'val_mae'])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:50.806769Z",
     "iopub.status.busy": "2022-08-26T13:28:50.805995Z",
     "iopub.status.idle": "2022-08-26T13:28:51.022488Z",
     "shell.execute_reply": "2022-08-26T13:28:51.021250Z"
    },
    "papermill": {
     "duration": 0.284029,
     "end_time": "2022-08-26T13:28:51.024958",
     "exception": false,
     "start_time": "2022-08-26T13:28:50.740929",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "epochs = range(1, 101)\n",
    "train_loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.plot(epochs, train_loss, label=\"Training Loss\")\n",
    "plt.plot(epochs, val_loss, label=\"Validation Loss\")\n",
    "\n",
    "plt.title(\"Training and Validation Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:51.155309Z",
     "iopub.status.busy": "2022-08-26T13:28:51.154903Z",
     "iopub.status.idle": "2022-08-26T13:28:51.235949Z",
     "shell.execute_reply": "2022-08-26T13:28:51.234949Z"
    },
    "papermill": {
     "duration": 0.149808,
     "end_time": "2022-08-26T13:28:51.238448",
     "exception": false,
     "start_time": "2022-08-26T13:28:51.088640",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 8.9379 - mae: 8.9379\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[8.937858581542969, 8.937858581542969]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:51.369798Z",
     "iopub.status.busy": "2022-08-26T13:28:51.369350Z",
     "iopub.status.idle": "2022-08-26T13:28:51.529897Z",
     "shell.execute_reply": "2022-08-26T13:28:51.528631Z"
    },
    "papermill": {
     "duration": 0.229677,
     "end_time": "2022-08-26T13:28:51.532709",
     "exception": false,
     "start_time": "2022-08-26T13:28:51.303032",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T13:28:51.664204Z",
     "iopub.status.busy": "2022-08-26T13:28:51.663785Z",
     "iopub.status.idle": "2022-08-26T13:28:51.670214Z",
     "shell.execute_reply": "2022-08-26T13:28:51.669042Z"
    },
    "papermill": {
     "duration": 0.07351,
     "end_time": "2022-08-26T13:28:51.672307",
     "exception": false,
     "start_time": "2022-08-26T13:28:51.598797",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-square: 0.97554181261684\n"
     ]
    }
   ],
   "source": [
    "print('R-square:', r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.062939,
     "end_time": "2022-08-26T13:28:51.798645",
     "exception": false,
     "start_time": "2022-08-26T13:28:51.735706",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We have an R-square of 0.975, so our Artificial neural network is also doing a pretty good job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.1 64-bit",
   "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": "3.10.1"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 540.492629,
   "end_time": "2022-08-26T13:28:55.145618",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-08-26T13:19:54.652989",
   "version": "2.3.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "a9cff5a362bc38ef45d817ae74b1af54d6a076e3d773891282bce078b815ba34"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}