test_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import category_encoders as ce\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47803, 36)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv('train.csv', header=0)\n",
    "\n",
    "train_data.drop('animal_id_outcome', axis=1, inplace=True)\n",
    "\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31869, 36)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv('test.csv', header=0)\n",
    "\n",
    "test_data['outcome_type'] = 'Missing'\n",
    "animal_id = test_data.animal_id_outcome\n",
    "test_data.drop('animal_id_outcome', axis=1, inplace=True)\n",
    "\n",
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79672, 36)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([train_data, test_data])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('outcome_datetime', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna('Unknown', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dob_year                      0\n",
       "dob_month                     0\n",
       "age_upon_intake               0\n",
       "animal_type                   0\n",
       "breed                         0\n",
       "color                         0\n",
       "intake_condition              0\n",
       "intake_type                   0\n",
       "sex_upon_intake               0\n",
       "count                         0\n",
       "age_upon_intake_(days)        0\n",
       "age_upon_intake_(years)       0\n",
       "age_upon_intake_age_group     0\n",
       "intake_datetime               0\n",
       "intake_month                  0\n",
       "intake_year                   0\n",
       "intake_monthyear              0\n",
       "intake_weekday                0\n",
       "intake_hour                   0\n",
       "intake_number                 0\n",
       "time_in_shelter               0\n",
       "time_in_shelter_days          0\n",
       "age_upon_outcome              0\n",
       "date_of_birth                 0\n",
       "sex_upon_outcome              0\n",
       "age_upon_outcome_(days)       0\n",
       "age_upon_outcome_(years)      0\n",
       "age_upon_outcome_age_group    0\n",
       "outcome_month                 0\n",
       "outcome_year                  0\n",
       "outcome_monthyear             0\n",
       "outcome_weekday               0\n",
       "outcome_hour                  0\n",
       "outcome_number                0\n",
       "outcome_type                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = data.outcome_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['age_upon_intake', 'count', 'age_upon_intake_(years)',\n",
    "           'intake_datetime', 'intake_monthyear', 'time_in_shelter',\n",
    "           'age_upon_outcome', 'date_of_birth', 'age_upon_outcome_(years)',\n",
    "           'outcome_monthyear'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category Columns:  ['animal_type', 'breed', 'color', 'intake_condition', 'intake_type', 'sex_upon_intake', 'age_upon_intake_age_group', 'intake_weekday', 'sex_upon_outcome', 'age_upon_outcome_age_group', 'outcome_weekday', 'outcome_type'] \n",
      "\n",
      "Numerical Columns:  ['dob_year', 'dob_month', 'age_upon_intake_(days)', 'intake_month', 'intake_year', 'intake_hour', 'intake_number', 'time_in_shelter_days', 'age_upon_outcome_(days)', 'outcome_month', 'outcome_year', 'outcome_hour', 'outcome_number']\n"
     ]
    }
   ],
   "source": [
    "categoryColumns = []\n",
    "numericalColumns = []\n",
    "for col in data.columns:\n",
    "    if data[col].dtype == 'object':\n",
    "        categoryColumns.append(col)\n",
    "    else:\n",
    "        numericalColumns.append(col)\n",
    "print('Category Columns: ',categoryColumns, '\\n')\n",
    "print('Numerical Columns: ',numericalColumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>dob_year</th>\n",
       "      <th>dob_month</th>\n",
       "      <th>animal_type</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>intake_condition</th>\n",
       "      <th>intake_type</th>\n",
       "      <th>sex_upon_intake</th>\n",
       "      <th>age_upon_intake_(days)</th>\n",
       "      <th>age_upon_intake_age_group</th>\n",
       "      <th>...</th>\n",
       "      <th>time_in_shelter_days</th>\n",
       "      <th>sex_upon_outcome</th>\n",
       "      <th>age_upon_outcome_(days)</th>\n",
       "      <th>age_upon_outcome_age_group</th>\n",
       "      <th>outcome_month</th>\n",
       "      <th>outcome_year</th>\n",
       "      <th>outcome_weekday</th>\n",
       "      <th>outcome_hour</th>\n",
       "      <th>outcome_number</th>\n",
       "      <th>outcome_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>527</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3650</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.588194</td>\n",
       "      <td>2</td>\n",
       "      <td>3650</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>527</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2555</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.259722</td>\n",
       "      <td>2</td>\n",
       "      <td>2555</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>527</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2190</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.113889</td>\n",
       "      <td>2</td>\n",
       "      <td>2190</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>830</td>\n",
       "      <td>449</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3650</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>4.970139</td>\n",
       "      <td>2</td>\n",
       "      <td>3650</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1997</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1880</td>\n",
       "      <td>183</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5840</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119444</td>\n",
       "      <td>2</td>\n",
       "      <td>5840</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   dob_year  dob_month  animal_type  breed  color  intake_condition  \\\n",
       "0      2007          7            2   1982    527                 3   \n",
       "1      2007          7            2   1982    527                 3   \n",
       "2      2007          7            2   1982    527                 3   \n",
       "3      2004          4            2    830    449                 3   \n",
       "4      1997         10            2   1880    183                 2   \n",
       "\n",
       "   intake_type  sex_upon_intake  age_upon_intake_(days)  \\\n",
       "0            3                2                    3650   \n",
       "1            2                2                    2555   \n",
       "2            2                2                    2190   \n",
       "3            1                2                    3650   \n",
       "4            2                2                    5840   \n",
       "\n",
       "   age_upon_intake_age_group  ...  time_in_shelter_days  sex_upon_outcome  \\\n",
       "0                          9  ...              0.588194                 2   \n",
       "1                          8  ...              1.259722                 2   \n",
       "2                          8  ...              1.113889                 2   \n",
       "3                          9  ...              4.970139                 2   \n",
       "4                          3  ...              0.119444                 2   \n",
       "\n",
       "   age_upon_outcome_(days)  age_upon_outcome_age_group  outcome_month  \\\n",
       "0                     3650                           9             12   \n",
       "1                     2555                           8             12   \n",
       "2                     2190                           8              3   \n",
       "3                     3650                           9              4   \n",
       "4                     5840                           3             11   \n",
       "\n",
       "   outcome_year  outcome_weekday  outcome_hour  outcome_number  outcome_type  \n",
       "0          2017                4             0             1.0             6  \n",
       "1          2014                2            16             2.0             6  \n",
       "2          2014                2            17             3.0             6  \n",
       "3          2014                1            15             1.0             4  \n",
       "4          2013                2            11             1.0             6  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = preprocessing.LabelEncoder() \n",
    "data[categoryColumns] = data[categoryColumns].apply(le.fit_transform)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['dob_year', 'age_upon_intake_(days)', 'intake_month', 'intake_year', 'intake_number'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['age_upon_intake_age_group'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "irrelevant = ['dob_month', 'color', 'intake_weekday', 'outcome_month']\n",
    "data.drop(irrelevant, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79672, 15)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape (47803, 14) (47803,)\n",
      "Test Shape (31869, 14)\n"
     ]
    }
   ],
   "source": [
    "X, y = data.drop('outcome_type',axis=1), data['outcome_type']\n",
    "X_train, X_test, y_train = X.iloc[0:47803, :], X.iloc[47803:, :], y[0:47803]\n",
    "\n",
    "# sm = SMOTE(random_state=42)\n",
    "# X_train, y_train = sm.fit_resample(X_train, y_train)\n",
    "\n",
    "print('Train Shape', X_train.shape, y_train.shape)\n",
    "print('Test Shape', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature_list = ['animal_type', 'breed', 'intake_condition', 'intake_type', 'outcome_number']\n",
    "\n",
    "# MEE_encoder = ce.MEstimateEncoder()\n",
    "# X_train[feature_list] = MEE_encoder.fit_transform(X_train[feature_list], y_train)\n",
    "# X_test[feature_list] = MEE_encoder.transform(X_test[feature_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBClassifier(colsample_bytree= 0.5, max_depth= 10, n_estimators= 100)\n",
    "model = model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_type = pd.DataFrame(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = pd.concat([animal_id, outcome_type],axis=1)\n",
    "prediction.columns = ['animal_id_outcome', 'outcome_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>animal_id_outcome</th>\n",
       "      <th>outcome_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A723133</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A723134</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A723135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A723136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A723137</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal_id_outcome  outcome_type\n",
       "0           A723133             8\n",
       "1           A723134             0\n",
       "2           A723135             0\n",
       "3           A723136             1\n",
       "4           A723137             0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_names = [''] * 9\n",
    "for val, indx in zip(targets, data.outcome_type.unique()):\n",
    "    target_names[indx] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 4, 8, 0, 3, 7, 5, 1, 2])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.outcome_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction.outcome_type = prediction.outcome_type.apply(lambda x : target_names[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>animal_id_outcome</th>\n",
       "      <th>outcome_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A723133</td>\n",
       "      <td>Transfer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A723134</td>\n",
       "      <td>Adoption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A723135</td>\n",
       "      <td>Adoption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A723136</td>\n",
       "      <td>Died</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A723137</td>\n",
       "      <td>Adoption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A723138</td>\n",
       "      <td>Return to Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A723139</td>\n",
       "      <td>Transfer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A723140</td>\n",
       "      <td>Adoption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A723141</td>\n",
       "      <td>Adoption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A723142</td>\n",
       "      <td>Transfer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal_id_outcome     outcome_type\n",
       "0           A723133         Transfer\n",
       "1           A723134         Adoption\n",
       "2           A723135         Adoption\n",
       "3           A723136             Died\n",
       "4           A723137         Adoption\n",
       "5           A723138  Return to Owner\n",
       "6           A723139         Transfer\n",
       "7           A723140         Adoption\n",
       "8           A723141         Adoption\n",
       "9           A723142         Transfer"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction.to_csv('prediction15.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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