capstone-project/target_encoding/target_encoding_dummy_data.ipynb

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   "cell_type": "code",
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   "id": "50d34368",
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    {
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     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in /home/cristiandugacicu/anaconda3/lib/python3.8/site-packages (1.4.2)\n",
      "Requirement already satisfied: numpy in /home/cristiandugacicu/anaconda3/lib/python3.8/site-packages (from xgboost) (1.19.5)\n",
      "Requirement already satisfied: scipy in /home/cristiandugacicu/anaconda3/lib/python3.8/site-packages (from xgboost) (1.6.2)\n"
     ]
    }
   ],
   "source": [
    "#Model training with XGboost\n",
    "!pip install xgboost\n",
    "\n",
    "import xgboost as xgb\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "\n",
    "\n",
    "# from sklearn.tree import DecisionTreeClassifier\n",
    "# from sklearn.metrics import roc_auc_score\n",
    "\n",
    "def train(dataFrame, y, max_depth, eta):\n",
    "    # Hot Encoding\n",
    "    dicts = dataFrame.to_dict(orient=\"records\")\n",
    "    dv = DictVectorizer(sparse=False)\n",
    "    X = dv.fit_transform(dicts)\n",
    "    features = dv.get_feature_names()\n",
    "    dtrain = xgb.DMatrix(X, label=y, feature_names=features)\n",
    "\n",
    "    # train\n",
    "    xgb_params = {\n",
    "        'eta': eta,\n",
    "        'max_depth': max_depth,\n",
    "        'min_child_weight': 1,\n",
    "        'objective': 'reg:squarederror',\n",
    "        'nthread': 8,\n",
    "        'seed':1,\n",
    "        'verbosity':0\n",
    "    }\n",
    "    model = xgb.train(xgb_params, dtrain, num_boost_round=10)\n",
    "    return dv, model\n",
    "\n",
    "\n",
    "def predict(dataFrame, dv, model):\n",
    "    dicts = dataFrame.to_dict(orient=\"records\")\n",
    "    X = dv.transform(dicts)\n",
    "    features = dv.get_feature_names()\n",
    "    dval = xgb.DMatrix(X, feature_names=features)\n",
    "    y_pred = model.predict(dval)\n",
    "    return y_pred, X\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "169bf44d",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurantId</th>\n",
       "      <th>menu_items</th>\n",
       "      <th>bill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
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       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>1002</td>\n",
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      "text/plain": [
       "  restaurantId  menu_items  bill\n",
       "0         1001           3    50\n",
       "1         1001           2    30\n",
       "2         1001           1    10\n",
       "3         1002           1    15\n",
       "4         1003           2    25"
      ]
     },
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     "metadata": {},
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame([\n",
    "    {'restaurantId': \"1001\", 'menu_items': 3, 'bill': 50},\n",
    "    {'restaurantId': \"1001\", 'menu_items': 2, 'bill': 30},\n",
    "    {'restaurantId': \"1001\", 'menu_items': 1, 'bill': 10},\n",
    "    {'restaurantId': \"1002\", 'menu_items': 1, 'bill': 15},\n",
    "    {'restaurantId': \"1003\", 'menu_items': 2, 'bill': 25},\n",
    "])\n",
    "\n",
    "df\n"
   ]
  },
  {
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       "'Train'"
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       "      <th></th>\n",
       "      <th>restaurantId</th>\n",
       "      <th>menu_items</th>\n",
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      "text/plain": [
       "  restaurantId  menu_items\n",
       "0         1001           3\n",
       "1         1001           2\n",
       "2         1001           1\n",
       "3         1002           1\n",
       "4         1003           2"
      ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
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       "   bill\n",
       "0    50\n",
       "1    30\n",
       "2    10\n",
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       "      <th></th>\n",
       "      <th>restaurantId</th>\n",
       "      <th>menu_items</th>\n",
       "      <th>bill</th>\n",
       "      <th>predicted_bill</th>\n",
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       "      <td>1001</td>\n",
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       "      <th>2</th>\n",
       "      <td>1001</td>\n",
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      "text/plain": [
       "  restaurantId  menu_items  bill  predicted_bill\n",
       "0         1001           3    50              50\n",
       "1         1001           2    30              30\n",
       "2         1001           1    10              10\n",
       "3         1002           1    15              15\n",
       "4         1003           2    25              25"
      ]
     },
     "execution_count": 96,
     "metadata": {},
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    }
   ],
   "source": [
    "#Training after  hotenrestaurantId \n",
    "df_train = df[['restaurantId', 'menu_items']]\n",
    "y_train = df[['bill']]\n",
    "\n",
    "display(\"Train\", df_train)\n",
    "display(\"Y\", y_train)\n",
    "\n",
    "\n",
    "dv, model = train(df_train, y_train, 6, 1)\n",
    "y_pred_val, X_val = predict(df_train, dv, model)\n",
    "\n",
    "\n",
    "df.assign(predicted_bill = np.round(y_pred_val).astype('int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e44c0b20",
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       "'Train'"
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       "   menu_items\n",
       "0           3\n",
       "1           2\n",
       "2           1\n",
       "3           1\n",
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       "      <th></th>\n",
       "      <th>bill</th>\n",
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       "   bill\n",
       "0    50\n",
       "1    30\n",
       "2    10\n",
       "3    15\n",
       "4    25"
      ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurantId</th>\n",
       "      <th>menu_items</th>\n",
       "      <th>bill</th>\n",
       "      <th>predicted_bill</th>\n",
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       "      <th>0</th>\n",
       "      <td>1001</td>\n",
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       "      <td>50</td>\n",
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       "      <th>1</th>\n",
       "      <td>1001</td>\n",
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       "      <td>28</td>\n",
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       "      <th>2</th>\n",
       "      <td>1001</td>\n",
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       "      <th>3</th>\n",
       "      <td>1002</td>\n",
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       "  restaurantId  menu_items  bill  predicted_bill\n",
       "0         1001           3    50              50\n",
       "1         1001           2    30              28\n",
       "2         1001           1    10              12\n",
       "3         1002           1    15              12\n",
       "4         1003           2    25              28"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training without restaurantId\n",
    "df_train = df[['menu_items']]\n",
    "y_train = df[['bill']]\n",
    "\n",
    "display(\"Train\", df_train)\n",
    "display(\"Y\", y_train)\n",
    "\n",
    "\n",
    "dv, model = train(df_train, y_train, 6, 1)\n",
    "y_pred_val, X_val = predict(df_train, dv, model)\n",
    "\n",
    "\n",
    "df.assign(predicted_bill = np.round(y_pred_val).astype('int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "44d9673a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Target encoding formula\n",
    "def get_target_encoding(df, field, target_field):\n",
    "    unique_fields = df[field].unique()\n",
    "    targets = {}\n",
    "    for f in unique_fields:\n",
    "        targets[f] = df[df[field]==f][target_field].mean()\n",
    "    return targets    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d819f1d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1001': 30.0, '1002': 15.0, '1003': 25.0}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'df'"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurantId</th>\n",
       "      <th>menu_items</th>\n",
       "      <th>bill</th>\n",
       "      <th>mean_bill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1001</td>\n",
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       "      <td>30.0</td>\n",
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       "  restaurantId  menu_items  bill  mean_bill\n",
       "0         1001           3    50       30.0\n",
       "1         1001           2    30       30.0\n",
       "2         1001           1    10       30.0\n",
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       "   menu_items  mean_bill\n",
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       "  restaurantId  menu_items  bill  mean_bill  predicted_bill\n",
       "0         1001           3    50       30.0              50\n",
       "1         1001           2    30       30.0              30\n",
       "2         1001           1    10       30.0              10\n",
       "3         1002           1    15       15.0              15\n",
       "4         1003           2    25       25.0              25"
      ]
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     "execution_count": 101,
     "metadata": {},
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   ],
   "source": [
    "#Training after target encoding restaurantId\n",
    "df2 = df.copy()\n",
    "targets = get_target_encoding(df2, 'restaurantId', 'bill')\n",
    "df2['mean_bill'] = df2['restaurantId'].apply(lambda x: targets[x])\n",
    "display(targets)\n",
    "\n",
    "\n",
    "df_train2 = df2[['menu_items', 'mean_bill']]\n",
    "y_train2 = df2[['bill']]\n",
    "\n",
    "display(\"df\", df2)\n",
    "display(\"Train\", df_train2)\n",
    "display(\"Y\", y_train2)\n",
    "\n",
    "dv2, model2 = train(df_train2, y_train2, 6, 1)\n",
    "y_pred_val2, X_val2 = predict(df_train2, dv2, model2)\n",
    "\n",
    "\n",
    "df2.assign(predicted_bill = np.round(y_pred_val2).astype('int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fff40891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1001': 30.0, '1002': 15.0, '1003': 25.0, '1004': 30.0}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "  restaurantId  menu_items  bill  mean_bill\n",
       "0         1001           3    50       30.0\n",
       "1         1001           2    30       30.0\n",
       "2         1001           1    10       30.0\n",
       "3         1002           1    15       15.0\n",
       "4         1003           2    25       25.0\n",
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       "   menu_items  mean_bill\n",
       "0           3       30.0\n",
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       "   bill\n",
       "0    50\n",
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       "  restaurantId  menu_items  bill  mean_bill  predicted_bill\n",
       "0         1001           3    50       30.0              50\n",
       "1         1001           2    30       30.0              30\n",
       "2         1001           1    10       30.0              20\n",
       "3         1002           1    15       15.0              15\n",
       "4         1003           2    25       25.0              25\n",
       "5         1004           1    30       30.0              20"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training after target encoding restaurantId with another restaurant having same mean_bill\n",
    "df3 = df.copy()\n",
    "df3 = df3.append({'restaurantId': \"1004\", 'menu_items': 1, 'bill': 30}, ignore_index = True)\n",
    "\n",
    "\n",
    "targets = get_target_encoding(df3, 'restaurantId', 'bill')\n",
    "df3['mean_bill'] = df3['restaurantId'].apply(lambda x: targets[x])\n",
    "display(targets)\n",
    "\n",
    "\n",
    "df_train3 = df3[['menu_items', 'mean_bill']]\n",
    "y_train3 = df3[['bill']]\n",
    "\n",
    "display(\"df3\", df3)\n",
    "display(\"Train3\", df_train3)\n",
    "display(\"Y3\", y_train3)\n",
    "\n",
    "\n",
    "dv3, model3 = train(df_train3, y_train3, 6, 1)\n",
    "y_pred_val3, X_val3 = predict(df_train3, dv3, model3)\n",
    "\n",
    "\n",
    "df3.assign(predicted_bill = np.round(y_pred_val3).astype('int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f3534826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1001': 30.0, '1002': 15.0, '1003': 25.0, '1004': 30.05}"
      ]
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurantId</th>\n",
       "      <th>menu_items</th>\n",
       "      <th>bill</th>\n",
       "      <th>mean_bill</th>\n",
       "      <th>predicted_bill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>3</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003</td>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>31.1</td>\n",
       "      <td>30.05</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.05</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  restaurantId  menu_items  bill  mean_bill  predicted_bill\n",
       "0         1001           3  50.0      30.00              50\n",
       "1         1001           2  30.0      30.00              30\n",
       "2         1001           1  10.0      30.00              10\n",
       "3         1002           1  15.0      15.00              15\n",
       "4         1003           2  25.0      25.00              25\n",
       "5         1004           1  31.1      30.05              30\n",
       "6         1004           1  29.0      30.05              30"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training after target encoding restaurantId with another restaurant having mean_bill very very close\n",
    "df4 = df.copy()\n",
    "df4 = df4.append({'restaurantId': \"1004\", 'menu_items': 1, 'bill': 31.1}, ignore_index = True)\n",
    "df4 = df4.append({'restaurantId': \"1004\", 'menu_items': 1, 'bill': 29}, ignore_index = True)\n",
    "\n",
    "\n",
    "targets = get_target_encoding(df4, 'restaurantId', 'bill')\n",
    "df4['mean_bill'] = df4['restaurantId'].apply(lambda x: targets[x])\n",
    "display(targets)\n",
    "\n",
    "\n",
    "df_train4 = df4[['menu_items', 'mean_bill']]\n",
    "y_train4 = df4[['bill']]\n",
    "\n",
    "display(\"df4\", df4)\n",
    "display(\"Train4\", df_train4)\n",
    "display(\"Y4\", y_train4)\n",
    "\n",
    "\n",
    "dv4, model4 = train(df_train4, y_train4, 6, 1)\n",
    "y_pred_val4, X_val4 = predict(df_train4, dv4, model4)\n",
    "\n",
    "\n",
    "df4.assign(predicted_bill = np.round(y_pred_val4).astype('int'))\n",
    "\n",
    "#a slight difference in `mean_bill` will keep the model accurate but this depends on the hyper parameters of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "85748d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1001': 30.0, '1002': 15.0, '1003': 25.0, '1004': 30.0}"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'df5'"
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       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>7</th>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  restaurantId  menu_items  bill  mean_bill  mean_bill_deviation\n",
       "0         1001           3    50       30.0                 20.0\n",
       "1         1001           2    30       30.0                  0.0\n",
       "2         1001           1    10       30.0                -20.0\n",
       "3         1002           1    15       15.0                  0.0\n",
       "4         1003           2    25       25.0                  0.0\n",
       "5         1004           1    30       30.0                  0.0\n",
       "6         1004           1    31       30.0                  1.0\n",
       "7         1004           1    29       30.0                 -1.0"
      ]
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       "'Train5'"
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      "text/plain": [
       "   menu_items  mean_bill  mean_bill_deviation\n",
       "0           3       30.0                 20.0\n",
       "1           2       30.0                  0.0\n",
       "2           1       30.0                -20.0\n",
       "3           1       15.0                  0.0\n",
       "4           2       25.0                  0.0\n",
       "5           1       30.0                  0.0\n",
       "6           1       30.0                  1.0\n",
       "7           1       30.0                 -1.0"
      ]
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     "data": {
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       "'Y5'"
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       "   bill\n",
       "0    50\n",
       "1    30\n",
       "2    10\n",
       "3    15\n",
       "4    25\n",
       "5    30\n",
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       "      <th></th>\n",
       "      <th>restaurantId</th>\n",
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       "      <td>1004</td>\n",
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       "      <td>30.0</td>\n",
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       "      <th>7</th>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-1.0</td>\n",
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      ],
      "text/plain": [
       "  restaurantId  menu_items  bill  mean_bill  mean_bill_deviation  \\\n",
       "0         1001           3    50       30.0                 20.0   \n",
       "1         1001           2    30       30.0                  0.0   \n",
       "2         1001           1    10       30.0                -20.0   \n",
       "3         1002           1    15       15.0                  0.0   \n",
       "4         1003           2    25       25.0                  0.0   \n",
       "5         1004           1    30       30.0                  0.0   \n",
       "6         1004           1    31       30.0                  1.0   \n",
       "7         1004           1    29       30.0                 -1.0   \n",
       "\n",
       "   predicted_bill  \n",
       "0              50  \n",
       "1              30  \n",
       "2              10  \n",
       "3              15  \n",
       "4              25  \n",
       "5              30  \n",
       "6              31  \n",
       "7              29  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training after target encoding restaurantId with another restaurant having same mean_bill and mean_bill_deviation\n",
    "df5 = df.copy()\n",
    "df5 = df5.append({'restaurantId': \"1004\", 'menu_items': 1, 'bill': 30}, ignore_index = True)\n",
    "df5 = df5.append({'restaurantId': \"1004\", 'menu_items': 1, 'bill': 31}, ignore_index = True)\n",
    "df5 = df5.append({'restaurantId': \"1004\", 'menu_items': 1, 'bill': 29}, ignore_index = True)\n",
    "\n",
    "\n",
    "targets = get_target_encoding(df5, 'restaurantId', 'bill')\n",
    "df5['mean_bill'] = df5['restaurantId'].apply(lambda x: targets[x])\n",
    "df5['mean_bill_deviation'] = df5['bill'] - df5['mean_bill']\n",
    "display(targets)\n",
    "\n",
    "\n",
    "df_train5 = df5[['menu_items', 'mean_bill', 'mean_bill_deviation']]\n",
    "y_train5 = df5[['bill']]\n",
    "\n",
    "display(\"df5\", df5)\n",
    "display(\"Train5\", df_train5)\n",
    "display(\"Y5\", y_train5)\n",
    "\n",
    "\n",
    "dv5, model5 = train(df_train5, y_train5, 6, 1)\n",
    "y_pred_val5, X_val5 = predict(df_train5, dv5, model5)\n",
    "\n",
    "\n",
    "df5.assign(predicted_bill = np.round(y_pred_val5).astype('int'))\n",
    "\n",
    "# using both `mean_bill` and `mean_bill_deviation` keeps the model accurate"
   ]
  }
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