Introduction_Deep_Learning_1_U.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/navarra/snap/jupyter/common/lib/python3.7/site-packages/joblib/_multiprocessing_helpers.py:45: UserWarning: [Errno 13] Permission denied.  joblib will operate in serial mode\n",
      "  warnings.warn('%s.  joblib will operate in serial mode' % (e,))\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('fake_reg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>461.527929</td>\n",
       "      <td>999.787558</td>\n",
       "      <td>999.766096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>548.130011</td>\n",
       "      <td>998.861615</td>\n",
       "      <td>1001.042403</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>410.297162</td>\n",
       "      <td>1000.070267</td>\n",
       "      <td>998.844015</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>540.382220</td>\n",
       "      <td>999.952251</td>\n",
       "      <td>1000.440940</td>\n",
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       "      <th>4</th>\n",
       "      <td>546.024553</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "        price     feature1     feature2\n",
       "0  461.527929   999.787558   999.766096\n",
       "1  548.130011   998.861615  1001.042403\n",
       "2  410.297162  1000.070267   998.844015\n",
       "3  540.382220   999.952251  1000.440940\n",
       "4  546.024553  1000.446011  1000.338531"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   price     1000 non-null   float64\n",
      " 1   feature1  1000 non-null   float64\n",
      " 2   feature2  1000 non-null   float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 23.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f488de32400>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['feature1', 'feature2']].values\n",
    "y = df['price'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(Sequential)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(4, activation = 'relu'),\n",
    "    Dense(2, activation = 'relu'),\n",
    "    Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(4, activation = 'relu'))\n",
    "model.add(Dense(2, activation = 'relu'))\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(4, activation = 'relu'))\n",
    "model.add(Dense(4, activation = 'relu'))\n",
    "model.add(Dense(4, activation = 'relu'))\n",
    "\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='rmsprop', loss = 'mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "22/22 [==============================] - 0s 854us/step - loss: 256704.4375\n",
      "Epoch 2/250\n",
      "22/22 [==============================] - 0s 893us/step - loss: 256656.0938\n",
      "Epoch 3/250\n",
      "22/22 [==============================] - 0s 793us/step - loss: 256601.8281\n",
      "Epoch 4/250\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 256533.1406\n",
      "Epoch 5/250\n",
      "22/22 [==============================] - 0s 789us/step - loss: 256447.4219\n",
      "Epoch 6/250\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 256345.3281\n",
      "Epoch 7/250\n",
      "22/22 [==============================] - 0s 877us/step - loss: 256225.8125\n",
      "Epoch 8/250\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 256086.8750\n",
      "Epoch 9/250\n",
      "22/22 [==============================] - 0s 762us/step - loss: 255926.2656\n",
      "Epoch 10/250\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 255742.3750\n",
      "Epoch 11/250\n",
      "22/22 [==============================] - 0s 550us/step - loss: 255531.6094\n",
      "Epoch 12/250\n",
      "22/22 [==============================] - 0s 491us/step - loss: 255293.4375\n",
      "Epoch 13/250\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 255024.3594\n",
      "Epoch 14/250\n",
      "22/22 [==============================] - 0s 522us/step - loss: 254721.7656\n",
      "Epoch 15/250\n",
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      "22/22 [==============================] - 0s 820us/step - loss: 26.1138\n",
      "Epoch 214/250\n",
      "22/22 [==============================] - 0s 790us/step - loss: 26.0468\n",
      "Epoch 215/250\n",
      "22/22 [==============================] - 0s 744us/step - loss: 25.5070\n",
      "Epoch 216/250\n",
      "22/22 [==============================] - 0s 740us/step - loss: 25.5381\n",
      "Epoch 217/250\n",
      "22/22 [==============================] - 0s 743us/step - loss: 25.5189\n",
      "Epoch 218/250\n",
      "22/22 [==============================] - 0s 674us/step - loss: 25.0923\n",
      "Epoch 219/250\n",
      "22/22 [==============================] - 0s 706us/step - loss: 25.0569\n",
      "Epoch 220/250\n",
      "22/22 [==============================] - 0s 725us/step - loss: 24.8032\n",
      "Epoch 221/250\n",
      "22/22 [==============================] - 0s 710us/step - loss: 24.5488\n",
      "Epoch 222/250\n",
      "22/22 [==============================] - 0s 756us/step - loss: 24.4126\n",
      "Epoch 223/250\n",
      "22/22 [==============================] - 0s 767us/step - loss: 24.5480\n",
      "Epoch 224/250\n",
      "22/22 [==============================] - 0s 732us/step - loss: 24.5185\n",
      "Epoch 225/250\n",
      "22/22 [==============================] - 0s 878us/step - loss: 24.4398\n",
      "Epoch 226/250\n",
      "22/22 [==============================] - 0s 690us/step - loss: 24.2575\n",
      "Epoch 227/250\n",
      "22/22 [==============================] - 0s 731us/step - loss: 24.7354\n",
      "Epoch 228/250\n",
      "22/22 [==============================] - 0s 754us/step - loss: 24.2442\n",
      "Epoch 229/250\n",
      "22/22 [==============================] - 0s 752us/step - loss: 24.4863\n",
      "Epoch 230/250\n",
      "22/22 [==============================] - 0s 719us/step - loss: 24.3071\n",
      "Epoch 231/250\n",
      "22/22 [==============================] - 0s 737us/step - loss: 24.5250\n",
      "Epoch 232/250\n",
      "22/22 [==============================] - 0s 697us/step - loss: 24.2917\n",
      "Epoch 233/250\n",
      "22/22 [==============================] - 0s 688us/step - loss: 24.1941\n",
      "Epoch 234/250\n",
      "22/22 [==============================] - 0s 770us/step - loss: 24.0133\n",
      "Epoch 235/250\n",
      "22/22 [==============================] - 0s 791us/step - loss: 24.1158\n",
      "Epoch 236/250\n",
      "22/22 [==============================] - 0s 754us/step - loss: 24.0761\n",
      "Epoch 237/250\n",
      "22/22 [==============================] - 0s 838us/step - loss: 24.6476\n",
      "Epoch 238/250\n",
      "22/22 [==============================] - 0s 722us/step - loss: 24.1446\n",
      "Epoch 239/250\n",
      "22/22 [==============================] - 0s 744us/step - loss: 24.1103\n",
      "Epoch 240/250\n",
      "22/22 [==============================] - 0s 717us/step - loss: 24.1575\n",
      "Epoch 241/250\n",
      "22/22 [==============================] - 0s 754us/step - loss: 23.8070\n",
      "Epoch 242/250\n",
      "22/22 [==============================] - 0s 741us/step - loss: 24.1933\n",
      "Epoch 243/250\n",
      "22/22 [==============================] - 0s 722us/step - loss: 24.0288\n",
      "Epoch 244/250\n",
      "22/22 [==============================] - 0s 808us/step - loss: 24.1495\n",
      "Epoch 245/250\n",
      "22/22 [==============================] - 0s 997us/step - loss: 24.2350\n",
      "Epoch 246/250\n",
      "22/22 [==============================] - 0s 1ms/step - loss: 24.6156\n",
      "Epoch 247/250\n",
      "22/22 [==============================] - 0s 882us/step - loss: 24.2520\n",
      "Epoch 248/250\n",
      "22/22 [==============================] - 0s 948us/step - loss: 24.4356\n",
      "Epoch 249/250\n",
      "22/22 [==============================] - 0s 847us/step - loss: 24.1506\n",
      "Epoch 250/250\n",
      "22/22 [==============================] - 0s 957us/step - loss: 24.1931\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f481c6cdba8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x = X_train, y = y_train, epochs = 250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_df = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_10 (Dense)             (None, 4)                 12        \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 57\n",
      "Trainable params: 57\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.980388641357422"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test, verbose = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.80950355529785"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_train, y_train, verbose = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = pd.Series(test_predictions.reshape(300,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df = pd.DataFrame(y_test, columns = ['Test True y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df = pd.concat([pred_df, test_predictions], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.columns = ['Test True Y', 'Model Predictions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test True Y</th>\n",
       "      <th>Model Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>593.474807</td>\n",
       "      <td>586.481140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>554.762667</td>\n",
       "      <td>550.724121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>556.612154</td>\n",
       "      <td>556.302673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>433.423135</td>\n",
       "      <td>437.761261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>547.731982</td>\n",
       "      <td>548.955017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>355.795352</td>\n",
       "      <td>346.077850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>429.214520</td>\n",
       "      <td>437.305206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>497.464727</td>\n",
       "      <td>492.740387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>532.628798</td>\n",
       "      <td>532.236023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>434.275283</td>\n",
       "      <td>427.227478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Test True Y  Model Predictions\n",
       "65    593.474807         586.481140\n",
       "113   554.762667         550.724121\n",
       "238   556.612154         556.302673\n",
       "190   433.423135         437.761261\n",
       "11    547.731982         548.955017\n",
       "158   355.795352         346.077850\n",
       "15    429.214520         437.305206\n",
       "198   497.464727         492.740387\n",
       "141   532.628798         532.236023\n",
       "251   434.275283         427.227478"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Test True Y', ylabel='Model Predictions'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YxCb9DE6VlZW6fv36/m6GMUOG4ygf1TXzcV0LuX4vLeEoE8ryaGyNsGjtgfLWy+dXUBTIYn9rW3yoqLw4wIoFFUwozePDPS1c++hbPdpUx/Q9EalS1VQzQw8eFETkbGA5UAaI+1BVHdbbDT1UFhSM6V11za28t6sp/ov+jMllLJ372XjSuF37vgfNoQh7msLxADJ2eICo4zAsJwtFbM+DAaq7oNCTPtzPgTNVtVBVh6lqwUAICMaY3uU4SjB0oHDdtLFFXHzyMeze35oyR1DbGOIXL21l7PBcyoZlc3RJLnf/+QMuv7cKn9fLUUUBxhTnUlpgCeXBpCdBYbeqvpv2lhhj+lVDMEzEOVC4bvHsCVz/+CbqmsMpcwQt4Sjfm/Mp8vwe/u0P73LarX9m3Qd1tvZgkOvJ8NHtwCjgP4klnAFQ1SfS2rIesOEjY3qH4ygf7GnCI8Le5jB1zWHKCrI56851TBtbxA+/NCmpjMVdF0xn5LAcRuTHSlPUNYcJR6I2VDRIdDd81JMpqcOI1SI6I+GcAv0eFIwxvaMhGKa2McR1j22iND+bq+dMpDjPz+pLTuCOF7fGp5yW5PkZOSyHtqjDiIR1Bla3aOiw2UfGZDjHUXbuC7JzXyttUQePSNLMoRULKvj5s1uobQq5s4vykwKCGXyOqKcgIuXAL4BT3FP/F/ieqlb3XhONMX3FcZQ9TSGCbVGyfR7qmsPx6aarLzkhvmUmxBLK1z22ibWXzeSjuhZGFuRYQBjiepJoXk1sr4Oj3Md/ueeMMYOM4yhbdu3n7LvW8YUVr/DuzsZ4QADI9XtTzjTyeITPjhnG+BF5FhCGuJ4EhVJVXa2qEffxO6A0ze0yxqTBnqYQV3QTBBqCbSlnGmV5PbYLWoboSVCoc0tme93HQqAu3Q0zxvSeSMThk4YgwbYoqy85gWu/OJFVF86gJD+WTJ42tgiAla9sY8WCik47pHktFmSMnsw+uoxYTuE2YrOO1mG7ohkzIDiOxqeDZvk8+DxCMBybGlocyKI+2Iag7Nwf4tv3HShTcdfCGfzixfd4fnNNp2Ryrt/LzWd/jiyvh4ZgG/eu+5Cbzqro71s1fcRmHxkzSHXc36B93wIBWtscAn4vi++rYvUlJ3Dp7/7aqUzF0rmTWbS2Kn583+Un4qjiqHLJ6r9a3aIh7LBmH4nIj1T15yLyC2I9hCSqenUvttEYc4gS9zeYNraIxbMnEI44FAayiDrK4vtiex1keT0pk8dFgayk4937WxldFOCoYTk8ueQUW4yWobobPmovbWF/ihszALXvb3DujHIWz54QX4l891+28ZOvfobS/Gx++KVJRBxN2hkNYj2DhmBb0nFLOEogy4vP57HFaBmsy6Cgqv/lPm1R1UcTXxORc9LaKmPMQfl9Xhb903i+dnw5F/32jaSy1nsaw1w9ZyLXP76Jk48t4c4LprPk/g2dcgpAfNgpP9tnNYtMj2ofbVDV6Qc71x8sp2AyUSTiUNMUwueBcFTZta+VuuYwK1/ZxsbtDZQXB7j57M8xpjjAqbf8GYBzZ5SzaPYEfB6hLar8cdMnTCkv4uiSXLK8HvJzPBTm2KK0THG4OYWvAF8FxojIHQkvDQMivdtEY0xPRCIOf9/dyB0vvsdVp07kqgc2JPUQbnluCxu3N5CT5QWIDxs9UlXNI1XVnDG5jBvOnMK86eV4BQJ+L0UByxmYA7pbp/AJsXxCK1CV8Hga+FL6m2aM6bg1Zm1TiMX3VTF/xth4QIBYovj6xzexePYEyosDDM/zc9fL27jrgulJaw6uOX0So4blMG54LmOKcxmeZ70Dk6wnw0fDgGZVjbrHXiBbVVv6oH3dsuEjM5QlTjktzc/mX776GUYX5dDaFsUjwmm3/rnTex5bPIucLC9r1n3Eug/qeHzxLKKK7YBmkhxp6ezngS8CTe5xwD13cu80zxjTkeMou/a3ctsLW/iP845nVGE2e5vb+Mbdr8UL16WaUTSqMIc7/rQ1ttnNhZWUWmkKc4h6UuYiR1XbAwLu89z0NcmYzOY4ykd1zbRFHX78lc/Q2Bph+95gfPYQwB0vbu1UjmLlwhnkZ3v4zmnH8cS3T2bSKFtwZg5dT3oKzSIyXVU3AIjIDCB4kPcYY3ogsUxF+/BOQzDM7v2t8b2S23c6K83PjgeFjdsb+PmzW1h72Uwagm3kZ/sIRx1awkp5ca4FA3PYehIUvg88KiKfAEJsa87z0tkoYzJBqjIVqy6cwYg8fzwgQCyJ/O37N7Bs3hQu/d1f4++vbQqxoyGIiPDdBzdaWQrTKw46fKSqfwU+DXwbWAx8RlWr0t0wY4a6Pc2heECA2C//RWurCEWclGUpxpXkJg0XrVhQgdcj8b2T26+7Ys166prDfXszZsjobp3Caar6koic3eGlT4kIqmp7NBtzGNqHjFrD0ZS//BVSJpFrG0OsuWwmPo8gIrSEIzS2RlJ+RjgS7YtbMUNQdz2FL7hfv5biMTfN7TJmSGofMjrrzlfxeiTlhjY+j/CrbyavL7jrgumoKtc+8hZ1zWE+aQhy6/NbGFWYk/Iz/D5vn92TGVqsdLYxfWhvc4i3tu8j1+9lfEku1Q1BvvfQm/F8QPu+BqUFfv7lq5PZF2xj1/7WpBIWy+ZN4VMj8+N7JmytbUrKS1hOwRxMd+sUugwKIvKD7j5UVf+9B9/4I6ARiAIRVa0UkeHAw8B44CPgXFWtFxEBbidWWqMFuKR9xlNXLCiYwSBxhlHEUW76/eb45ja/vaSSllCU4jw/HhEEZXt9ML7hzZrLZiYVu1uxoIKRw3IYX3Jgr+RUM5gsIJjuHO7itQL36yTgBGLlLSA2fPTGIXz/U1V1T8Lxj4EXVfVmEfmxe3w98BVgovs4EbjL/WrMoJVqhtHy+RXUNobZuL2BW57bwndOm8gFv3496Rf/z86czI1Pb0YEVl9yAi3hKCPy/SlrFXk8YqWuTa/pMqegqjeq6o1AOTBdVa9V1WuBGcC4I/ie84B73ef3Al9POL9GY14DikRk9BF8H2P6XeJGOJBcowhg/oyxSYvSquuDXPfYJuqb27h6zkTe293Epb/7K16PMLIgx2oVmbTryTqFkUDi/Lawe64nFHheRBRYpap3AyNVdaf7+q6EzxoDbE94b7V7bmfCOUTkSuBKgHHjjiQ2GZMeicM5UUdTzg4qc/+yL8nzp3w91++lbFg2OT4PjyyaRVl+Nj5fTwoQGHNkehIU1gBviMiT7vHXOfCX/sH8D1XdISJlwAsi8vfEF1VV3YDRY25guRtiOYVDea8x6eQ4yv7WMDvqW1l0XxXV9UH+9IMvpJxemp/to7w4QGlBdsrXW8JRfB4PI4cFrGdg+lRPFq/dBFwK1LuPS1X1//Tkw1V1h/u1BngSmAnsbh8Wcr/WuJfvAMYmvL3cPWfMgNder2hvc1s8IAAEwxGWz0+uUbR8fgU5WR6Wzp3M0xt3dCpvvWJBBWOHBxg9zIrZmb7Xk54CxArg7VfV1SJSKiLHqOqH3b1BRPIAj6o2us/PAP43sYT1xcDN7ten3Lc8DXxHRB4ilmDelzDMZMyA1V7R9OO6FgpyfEl/9X+yr5XHq7azdO5kigJZNATbuHfdh8yfMZZFa2OFAV7aUsuDV5yEquL1iG18Y/rVQYOCiPwMqCQ2C2k1kAXcB5xykLeOBJ6MzTTFBzygqs+KyF+BR0TkcuBj4Fz3+j8Qm476PrEpqZce8t0Y08ciEYdP9gWJOMrY4QG21TYnDQetfGUbP/rypKTidu1rEdrVNoXYvHM/U44axphiK0Bs+ldPegpnAdOADQCq+omIFHT/FlDVD4CpKc7XAXNSnFfgqh60x5gBIbZXcitRVRxVPCJs+KiO5fMr4vWIaptClBZkc/PZnyPL66Et6hDwe6ltCgEHhpPuXfch08dV9PMdGdOzoBBOTAi7Q0HGZJzEWUUBv5e9zWFqG0NJvYCVC2eQk+WJry0oyPHRFIrw4yfejvcepo0tYtm8KRxdksvWmibuXfch15w+iZI8fz/foTE922TnERFZRWzdwBXAn4B70tssYwaWxJpFpyx/mbe272P73mCnEteL76tiW21zvMT1w298TCDLw6qFM+LJ5NqmEKOG5VCUm8WUo4Zx01kVVpbCDBjd9hTc0hMPEyudvZ9YXuF/qeoLfdA2YwaM9kVopfnZLJ07maNLcnE09RqEokAW1fVBrnpgAw9ecRKOKo+t/4gHvnUiXo8kl6KwfrcZYLoNCu6w0R9U9XOABQKTscKRKKX52fzwS5Pi+YKu9kluCLYBsQARcZTcLA/f+vxxVpPIDAo9GT7aICInpL0lxgxgedle7jh/Gtm+2PqCaWOLUu6TvHx+BStf2RY/zsv2MrIwtkjNAoIZDHqSaD4RWOhWPG0mtiWnqqpNlTBDVmJSOSfLw859IRa7i9Laf/nf8tyW2D7Jl8+koaWNwkAWN//x3XiJ61ULZzAizwrVmcGlJ0HhS2lvhTEDSMfKpqsvOYGlT73Tqajd0rmTWfbMZlThuw9upDQ/m6vnTOQnX/0Mu/eHGF1kK5LN4NPddpxlwL8AxwFvA/+mqvv7qmHG9JeOeyfn+r0pE8oleX5WLIgNF7WvWG4JR9nTGKYwkEVRwKaYmsGnu57CGqAK+AWx7TfvAC7pgzYZk1YdN6UpDmRRH2yLrz9oCSXvndwQbEuZUB5VmEMwHGHdB3U8UlUdHzIaXZRjZSrMoNVdUBitqj91nz8nIt3ugmbMYJBq05uVC2dwx4vv8fzmGlZfEptT0bFUxYoFFZ1KVextDjOyIJuHrjwJx1FysryMyLeEshncDrZOoZhYYhnAm3isqnvT3DZjel2qTW8W31fF6ktO4Nuzj6M41881D7/JredM5dpH34qXqgj4vfFSFQ3BNn7+7BZuO+94Sgssb2CGlu6CQiGx4aPEf/HtvQUFjk1Xo4xJl3AkmjI/4PUINY0hQhGH2qYQjirL5k0h1+9leJ6fFc/9nec318TfU14cIDfbawHBDDldBgVVHd+H7TCmT/h9XsqLA5x8bAlXfP5YvB7BUWhsbWPZM5v55Tencdu5UxERzr/ndSBWq+iHX5rE5p2N8eGjVRfadFMzNPV0PwVjBjXHURqCseTyE9+exe7GMJf+7q/xX/J3XTCdk48t4TsPbOQX5x/PiPyceF5h4/YGbnluC8vmTWHc8FyyvMJRhbYjmhmabNNXM+Q5jrKjoYWGljZ27mulNaL84sX3kvIK375/A1d8/liq64MUBvzsbQ4lrVaubQpRVpBNcV4W5cW5tl+yGbKsp2CGNMdR9jSFqG9pY8n9G5JWJNc2htm4vQGIBYYsn4czJpfxcV0LAb+X0oJsnlhyMm0RJ7mInTFDWJd/7ojI8O4efdlIYw5H+/TT5nAkHhDgwIrkxbMnxK8tLw4QiSrfOW0if3x7J6UF2RRk+ygryGFMca7VLjIZo7ueQhWxWUap/k+w2UdmQEpcmOYRoak1Qq4/u8sVyRALCHctnMHdf97Gug/qWDZvCv+oa2HKmML+uAVj+lV3s4+O6cuGGHO42gOBoOzeH2JRQuG6lQtn4PNIlyuSX772C0QVmkNtPFJVDcDRJbF9km0nNJOJDpotk5iFIrLUPR4nIjPT3zRjuuc4Sk1jK//Y28K22iaCbdF4QAAozc+mtjGECNyVsPNZeXGAOy+Yzh1/2sqFv32D3ftbueHpzfHXAllexpfk2XCRyUg9STTfCTjAacAyoBF4HLA9Fky/SVWu4q4LplOaHxsqal9b0L4hzhmTy1hz2Uz2BdsIRRyKcrNYcupxXHXacdz0+83xctf3XFTJyGG2Stlkrp7MqztRVa8CWgFUtR6wfrXpVx0rmbZPK716zkQAFs+eEA8IAM9vruGi375BTWOIb9z9GuGIwzUPv8n3H3qT82cezV9+dCpPLjnF9ko2Ga8nPYU2EfESSy4jIqXEeg7G9AvH0U6VTCEWGI4pzWP1JSdwdElul/snlxcH2L43GO8djCrMobzIFqMZAz0LCncATwJlInITsAD4n2ltlTFdcBxl1/5WgJTJYwGWPvUOS+dOTvl6SzjqDhFl8+r1p9r6A2M6OGhQUNX7RaQKmENseurXVfXdtLfMmA4S8wil+dmdylmvWjiDm36/mer6ICtf2cby+RXxIaSUex3k9fcdGTPwiKqmfuEgC9QGQunsyspKXb9+fX83w/SR2sYQZ935avyv/2lji7h6zkTGDo8NB316VD6zbn45fv20sUUsnj2BT48qIOD3MiLPFqAZAyAiVapameq1ni5eGwfUu8+LgH8Ato7BpE0k4lDTFKIt6pDl9TAiN4tQJMovzp9GfraP9n+aTaEIXhH++PZOji09LmnIaOP2BpY9s5knlpxMWUFOv96PMYPFQRevicg9wJOq+gf3+CvA1/ukdSYjRSIOH+1tZvveIEW5WZTk+9la25y0KO3OC6bzy5e28vzmmvjxA6991HnIyEpcG3NIuhw+il8g8raqfu5g5/qDDR8NTTX7W9lW2xTPF6y+5ASWPvVOp6Tx0rmTWbS2Kul45SvbWDx7AkWBLBqCbRxfXsjIwkB/3YoxA9LhDh+1+0RE/idwn3t8AfBJbzXOmESOo6gqowpzuPey2MJ5v1e6nF6aeFyS52fj9oakQPHkklP6rvHGDAE9Wbx2PlBKbFrqk0CZe86YXuM4yt7mEB/saeKDPc1c+Js3mHPrn7n4t2+wt7mNMyaXJV1fXhygIdiWdDw8z59UyuKeiyqtfpExh6gnU1L3At8TkYLYoTalv1lmqEqsYur3eSkOZNEUbqMlHCXqgNcj8WEjiPUArnpgA2sum5m0HWZ7TgGI5w6G5fh4ZNEsVNXWHxhzmA4aFETkc8AaYLh7vAe4WFXfSXPbzBCTql7RqoUzyA/4+KQ+yHWPbeLWc6amHCraF2xj6dzJfHpUAT6PkO3zcMOZU/jZ1ywAGNObejJ8tAr4gaoerapHA9cCd/f0G4iIV0Q2isgz7vExIvK6iLwvIg+LiN89n+0ev+++Pv4w7scMYHXN4U71ihbdV0VbROO9g4ZgW3wIqF15cYCaxhDLntmM3+thTHEuIwpyOKooYBvgGNPLehIU8lQ1viJIVV/h0NaCfg9IXAG9HLhNVY8jtvbhcvf85UC9e/429zozhIQjyfWKpo0tYuncyfi8wtK5k5k2togXN+/mzgumJ+UGls+v4PGq7axcOIPSfJteakw69WT20QfuXgpr3eOFwAc9+XARKQf+GbgJ+IGICLES3N90L7kXuAG4C5jnPgd4DPiliIgebM6sGTT8Pm98cVnH0tbtv/w9Ar98aStL506mrCCb/Gwf4ajDz772WUYW5ODz9eTvGGPM4epJULgMuBF4wj3+v+65nvgP4EdAgXtcAjSoasQ9rgbGuM/HANsBVDUiIvvc6/ckfqCIXAlcCTBu3LgeNsP0l8TEcsDvZdWFM1i0tqpTaev2fZPXXjaT5zfX8PzmmqTPefX6Uy0gGNMHejL7qB64+lA/WETmAjWqWiUisw+9aV22527cnEZlZaX1IgYox1EagmF2NrQmrUT+3aUn8MAVJxKJasqEsqeLrTP9Pm9f34IxGanLoCAiT3f3RlU98yCffQpwpoh8FcgBhgG3A0Ui4nN7C+XADvf6HcBYoFpEfEAhUNejuzADhuMoe5pDtISiRBzl9hffiw8XLZ49gYaWNkYOyyHLm7r09fa9LaxaOCMpkNh6A2P6Tnc9hVnEhnMeBF4nVgyvx1T1J8BPANyewg9V9QIReZTYngwPARcDT7lvedo9/m/39ZcsnzC4pJpyunx+BUUBP/OmjUnKH9x5wXR+c3Ell9+bfO296z7k5vkVPLnklPhaBptuakzf6a50thc4ndjq5Qrg98CDqvq3Q/4mB4LCXBE5llhAGA5sBBaqakhEcogls6cBe4FvqGq3CW2rfTSw1DS2cvad6zr99b/6khO49Hd/7XT+P847nuF5fvY2h6lrDvN41XauOX2SbYlpTJodVu0jVY0CzwLPikg2seDwiojcqKq/PJQGuNNYX3GffwDMTHFNK3DOoXyu6T+RiMPeljDhqEPUUQJZXlraUm+R6fWkrl1UVpBNfo6P/BwfowtzmD6uwnoFxvSzbhPNbjD4Z2IBYTwHtuY0Gay9tHV9c5hrHnkrPvyz9vKZKfMEWV5PyvMBv4/hVtbamAGlyzl+IrKG2Pj+dOBGVT1BVZep6o6u3mMyQ01TiD1NBwICxP7y/7c/vNtp4dnKhTPIz/Gw6sIZVqzOmEGgu5yCAzS7h4kXCbHCeMPS3LaDspxC//i4rpm2qMMX//0vnV77y49m8+7ORooCWbSEo0wdW8jwvOxOhfBsmMiY/nO4OQVbKWTiEn+p+zyCSOohIXEnqf3m/33ANadPoigQ6w14PEJpgQ0VGTPQ2S9+c1DtU03PuvNVTln+Mufd/RrhiMNt505NGhJasaCC6voWlj2zme/N+RQTS/OtN2DMIHPQ7TgHMhs+6hu1jSHOuvPVTr2Ch688ifd2N5Hr99ISjlKcl8WNT29m4/aG+K5n1jswZuDpbvjIegrmoDpWN4VYYjkcVcJRh9KCbMJRJx4Q4q9Hov3QWmPMkbCgYOIcR6ltDLGjvoXaxhCOE+tFZvk8Kfc48HmEZc9sZmtNE8ueORAQ2l+3ekXGDD4WFAzQOW9w1p2vsmV3I5GIQ1NrhBULKpLyB7eeM5X/3FDNr745ncertrN8foVNOTVmCOhJ6WyTAeqaw9z2whaWzp1MUSALR5V9wTZ2NbZy0W/foDQ/O/5aSziKo8qtf9rK25/s44Yzp+AVbH9kY4YACwoGAMdxuPjkY7j+8U2U5mfzoy9P4oePvhXfM7m6PsiitVXx6x++8iTKiwNcc/okRg3LsQBgzBBhw0cGgKgSr2K6ePaEg+6Z3D67yIrXGTO0WFAwAHgFls6dzMNXnsSnyvLjeyGvfGVbynzB6MIApQXZFhCMGWJs+Mi4G+OEWfbM5nhxuxULKvj5s1vYuL2BW57bwrJ5U5hQlk8gy/IFxgxl1lPIYO1TUHfui+ULEovbXffYJq6eMxGA2qYQowpzKC+y3oExQ531FDJEx4J0xYEsttY2ccWa9fFkcqLq+iATyvJ49fpTbTaRMRnEgsIQlhgIoo7yr7/fzPObaygvDvDAt06Mb5vZnkzuWMbC5/FwVFGgm+9gjBlqLCgMUV3tl1zbGGbj9gZqGkPxINCeTE7cQ3n5/Aq81jEwJuNYTmGIqmsOxwMCxIaDrn98E4tnT4i/3j6jKDGZ/KcffJ6lcydz77oP8Xjsn4cxmcb+rx+iuipiV+ZWLX28anvSbmi1TSFysjxc9+gmlj2zmWtOn2RlKozJQDZ8NESJSMo8QX62L74SeWJpPk8uOYVwJEqWz4PPI/zym9MssWxMBrOewhAlop0WnS2fX0FOlodl86Ywclg2Pp+H0oJsxhTnUlaQw/C82HObdmpM5rKewiDW3b7HjgP3rvswXsSuIdjGves+ZP6MsSxaW8Wr158Kef18A8aYAceCwiDVPrvothe2MH/GWEry/LSEI+T5vShCXraXS085Jl7DqL2ncMtzW2yvA2NMlywoDHBd9Qb2NIe47YUt8cqm7b/4f/XN6fzq5a1cc/okyosD3HLOVEoLsvlHXQu3PLeF2qaQ7XVgjOmS7dE8gKVaa7Dqwhl8qjSfHfta2bK7MV6vqF15cYClcyez7JnNPLHkZATBcRyiiu11YIwBbI/mQSvVWoNFa6vY3RTiwz3NlOT5U047LQpkUV0fpC0S2z95ZGGAo4oClkQ2xhyUBYUBrKu1BlFHyfIKI4dlp9zroL1sheUNjDGHyoLCAOb3eVP+0v/7rkZ+/MTb7N4f4jcXV3aadvp41XbLGxhjDovlFAYwx1He3bU/XtY6cQbRxu0NlBcHuPnsz9EWVY4tzSPL68Er4PF4LG9gjOlSdzkFm300gHk8wqSyAh741olEHGVrTVM8IEBsKGnc8FwCfp8FAWNMr7CgMMD5fB7Ki3PZtb815UyjgN9HqVvPyBhjjpTlFAYBj0cYNSyHey6q7LRXsuUNjDG9KW09BRHJAf4CZLvf5zFV/ZmIHAM8BJQAVcCFqhoWkWxgDTADqAPOU9WP0tW+wcbjESaNLIgXsLP1BsaYdEhnTyEEnKaqU4HjgS+LyEnAcuA2VT0OqAcud6+/HKh3z9/mXmcSeDwSL2Bn6w2MMemQtqCgMU3uYZb7UOA04DH3/L3A193n89xj3NfniIj91jPGmD6U1pyCiHhF5E2gBngB2AY0qGrEvaQaGOM+HwNsB3Bf30dsiKnjZ14pIutFZH1tbW06m2+MMRknrUFBVaOqejxQDswEPt0Ln3m3qlaqamVpaemRfpwxxpgEfTL7SFUbgJeBWUCRiLQnuMuBHe7zHcBYAPf1QmIJZ2OMMX0kbUFBREpFpMh9HgBOB94lFhwWuJddDDzlPn/aPcZ9/SUdzMutjTFmEErn4rXRwL0i4iUWfB5R1WdEZDPwkIj8K7AR+I17/W+AtSLyPrAX+EYa22aMMSaFtAUFVd0ETEtx/gNi+YWO51uBc9LVnnTobjtMY4wZjKzMxWFKtQHOPRdVMmlkgQUGY8ygZWUuDlOqDXCuWLOeuuZwP7fMGGMOn/UUDoPjKOFIlFvPmUpDsI2Vr2xj4/YGquuDhCPR/m6eMcYcNgsKhyjVsFH7Hge1TSHb7cwYM6hZUDhEqYaNrn98E8vmTWFUYY5VLTXGDGoWFA5RV/smTyjLp7woYElmY8ygZonmQ9TVvsmBLK8FBGPMoGdB4RCV5PltsxtjzJBlw0eHyDa7McYMZRYUEjiOsqc5RGtbFK8IAb+XokDnX/jtm90YY8xQY0HBlWqq6YoFFYwclsP4kjzrCRhjMoLlFFyppppe99gmPq5rsVXKxpiMkXE9ha6K2HU11TTX77VVysaYjJFRQaG7InbtU00TA0N5cYCWcNRWKRtjMkZGDR91V8Qu1VTTFQsqOLok16abGmMyRkb1FLoaIgpHovGppk8sOZnWNgev0OXsI2OMGaoyKih0NUTUPjzk8QhlBTn91TxjjOl3GTV8ZKuRjTGmexnVU7DVyMYY072MCgpgq5GNMaY7GTV8ZIwxpnsWFIwxxsRZUDDGGBNnQcEYY0ycBQVjjDFxoqr93YbDJiK1wMf93Y4jNALY09+N6Ed2/3b/dv9972hVLU31wqAOCkOBiKxX1cr+bkd/sfu3+7f7H1j3b8NHxhhj4iwoGGOMibOg0P/u7u8G9DO7/8xm9z/AWE7BGGNMnPUUjDHGxFlQMMYYE2dBIc1EJEdE3hCRt0TkbyJyo3v+GBF5XUTeF5GHRcTvns92j993Xx/frzfQC0TEKyIbReQZ9zhj7h1ARD4SkbdF5E0RWe+eGy4iL4jIVvdrsXteROQO92ewSUSm92/rj5yIFInIYyLydxF5V0RmZcr9i8gk9797+2O/iHx/IN+/BYX0CwGnqepU4HjgyyJyErAcuE1VjwPqgcvd6y8H6t3zt7nXDXbfA95NOM6ke293qqoenzAn/cfAi6o6EXjRPQb4CjDRfVwJ3NXnLe19twPPquqnganE/i1kxP2r6hb3v/vxwAygBXiSgXz/qmqPPnoAucAG4ERiqxh97vlZwHPu8+eAWe5zn3ud9Hfbj+Cey4n9oz8NeAaQTLn3hJ/BR8CIDue2AKPd56OBLe7zVcD5qa4bjA+gEPiw43/HTLn/Dvd8BvDqQL9/6yn0AXf45E2gBngB2AY0qGrEvaQaGOM+HwNsB3Bf3weU9GmDe9d/AD8CHPe4hMy593YKPC8iVSJypXtupKrudJ/vAka6z+M/A1fiz2cwOgaoBVa7Q4i/FpE8Muf+E30DeNB9PmDv34JCH1DVqMa6j+XATODT/duiviEic4EaVa3q77b0s/+hqtOJDQ1cJSKfT3xRY38SDtW54T5gOnCXqk4DmjkwVAIM+fsHwM2bnQk82vG1gXb/FhT6kKo2AC8TGzIpEpH27VDLgR3u8x3AWAD39UKgrm9b2mtOAc4UkY+Ah4gNId1OZtx7nKrucL/WEBtPngnsFpHRAO7XGvfy+M/AlfjzGYyqgWpVfd09foxYkMiU+2/3FWCDqu52jwfs/VtQSDMRKRWRIvd5ADidWKLtZWCBe9nFwFPu86fdY9zXX3L/khh0VPUnqlququOJdZ1fUtULyIB7bycieSJS0P6c2LjyOyTfa8efwUXuLJSTgH0JwwyDjqruAraLyCT31BxgMxly/wnO58DQEQzk++/v5MtQfwAVwEZgE7FfBv/LPX8s8AbwPrEuZbZ7Psc9ft99/dj+vode+jnMBp7JtHt37/Ut9/E34Kfu+RJiCfitwJ+A4e55AX5FLO/0NlDZ3/fQCz+D44H17v8D/wkUZ9j95xHr8RYmnBuw929lLowxxsTZ8JExxpg4CwrGGGPiLCgYY4yJs6BgjDEmzoKCMcaYOAsKJqOISElCxcpdIrIj4djfg/fPFpGTU5y/NOFzwglVUW/upXbnuFVGP5dw7joRWdUbn29MO5uSajKWiNwANKnqLb35HncFd6Wq7ulw3quq0cNrLYjIl4GfAp8HjgL+4n6f+sP9TGM6sp6CyXgiMkNE/uwWrHsuofzA1SKy2a1r/5DE9ndYDFzj9gL+qQef3SQit4rIW8Asie2tMMJ9rVJEXnGf54nIbyW298ZGEZnX8bNU9VlgJ3ARsdLiN1hAML3Nd/BLjBnSBPgFME9Va0XkPOAm4DJihduOUdWQiBSpaoOIrOTQehd5wOuqei2AiHR13U+JlfW4zC2L8oaI/ElVmztc931iq723qurant+mMT1jQcFkumxgCvCC+wvbS+yvcYiVZbhfRP6TWHmGwxEFHu/BdWcQKx74Q/c4BxhH8uZEqOonIvISsb0pjOl1FhRMphPgb6o6K8Vr/0xs/P5rwE8Tk7yHoLVDHiHCgWHbnA7tmK+qW3rwmQ4H9qcwpldZTsFkuhBQKiKzAEQkS0Q+KyIeYKyqvgxcT6yMdz7QCBQcwff7iNi2jADzE84/B3xX3O6KiEw7gu9hzGGzoGAynUOsTPdyNxn8JnAysWGk+0TkbWJVbu/Q2H4Y/wWc1dNEcwo3AreLyHpiQ0vtlgFZwCYR+Zt7bEyfsympxhhj4qynYIwxJs6CgjHGmDgLCsYYY+IsKBhjjImzoGCMMSbOgoIxxpg4CwrGGGPi/j95Fzyc0Lk/JwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot('Test True Y', 'Model Predictions', data = pred_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.002596731228611"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(pred_df['Test True Y'], pred_df['Model Predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>498.673029</td>\n",
       "      <td>1000.014171</td>\n",
       "      <td>999.979847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>93.785431</td>\n",
       "      <td>0.974018</td>\n",
       "      <td>0.948330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>223.346793</td>\n",
       "      <td>997.058347</td>\n",
       "      <td>996.995651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>433.025732</td>\n",
       "      <td>999.332068</td>\n",
       "      <td>999.316106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>502.382117</td>\n",
       "      <td>1000.009915</td>\n",
       "      <td>1000.002243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>564.921588</td>\n",
       "      <td>1000.637580</td>\n",
       "      <td>1000.645380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>774.407854</td>\n",
       "      <td>1003.207934</td>\n",
       "      <td>1002.666308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             price     feature1     feature2\n",
       "count  1000.000000  1000.000000  1000.000000\n",
       "mean    498.673029  1000.014171   999.979847\n",
       "std      93.785431     0.974018     0.948330\n",
       "min     223.346793   997.058347   996.995651\n",
       "25%     433.025732   999.332068   999.316106\n",
       "50%     502.382117  1000.009915  1000.002243\n",
       "75%     564.921588  1000.637580  1000.645380\n",
       "max     774.407854  1003.207934  1002.666308"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.980405448572842"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(pred_df['Test True Y'], pred_df['Model Predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.9980401607603"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(pred_df['Test True Y'], pred_df['Model Predictions'])**0.5   # rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_gem = [[998, 1000]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_gem = scaler.transform(new_gem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[419.7429]], dtype=float32)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(new_gem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('my_gem_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "later_model = load_model('my_gem_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[419.7429]], dtype=float32)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "later_model.predict(new_gem)"
   ]
  }
 ],
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