Titanic_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 讀取訓練和測試資料\n",
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of women who survived: 0.7420382165605095\n",
      "% of men who survived: 0.18890814558058924\n"
     ]
    },
    {
     "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": [
    "# 檢查女人存活的機率\n",
    "women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\n",
    "rate_women = sum(women)/len(women)\n",
    "print(\"% of women who survived:\", rate_women)\n",
    "\n",
    "# 檢查男人存活的機率\n",
    "men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\n",
    "rate_men = sum(men)/len(men)\n",
    "print(\"% of men who survived:\", rate_men)\n",
    "\n",
    "# 畫出女人和男人的存活率: 發現女性存活率較高\n",
    "X = np.array(['Female', 'male'])\n",
    "Y = np.array([rate_women, rate_men])\n",
    "for x, y in zip(X, Y):\n",
    "\tplt.text(x, y, '%.2f'%y, ha='center', va='bottom')\n",
    "plt.xlabel('Sex')\n",
    "plt.ylabel('Survival rate')\n",
    "plt.bar(X, Y, color=['#FFAE33', '#33A4FF'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of class1 who survived: 0.6296296296296297\n",
      "% of class2 who survived: 0.4027777777777778\n",
      "% of class3 who survived: 0.24236252545824846\n"
     ]
    },
    {
     "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": [
    "# 檢查艙等(Pclass)是否影響存活的機率\n",
    "class1 = train_data.loc[train_data.Pclass == 1][\"Survived\"]\n",
    "rate_class1 = sum(class1)/len(class1)\n",
    "print(\"% of class1 who survived:\", rate_class1)\n",
    "\n",
    "class2 = train_data.loc[train_data.Pclass == 2][\"Survived\"]\n",
    "rate_class2 = sum(class2)/len(class1)\n",
    "print(\"% of class2 who survived:\", rate_class2)\n",
    "\n",
    "class3 = train_data.loc[train_data.Pclass == 3][\"Survived\"]\n",
    "rate_class3 = sum(class3)/len(class3)\n",
    "print(\"% of class3 who survived:\", rate_class3)\n",
    "\n",
    "# 畫出各艙等的存活率: 發現艙等高，存活率較高\n",
    "X = np.array(['class1', 'class2', 'class3'])\n",
    "Y = np.array([rate_class1, rate_class2, rate_class3])\n",
    "for x, y in zip(X, Y):\n",
    "\tplt.text(x, y, '%.2f'%y, ha='center', va='bottom')\n",
    "plt.xlabel('Pclass')\n",
    "plt.ylabel('Survival rate')\n",
    "plt.bar(X, Y, color=['#FFAE33', '#33A4FF', '#41B605'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 檢查兄弟姊妹數量(SibSp)是否影響存活的機率\n",
    "SibSp = np.unique(train_data['SibSp'])\n",
    "rate_SibSp = []\n",
    "for i in range(len(SibSp)):\n",
    "\tSib = train_data.loc[train_data.SibSp == SibSp[i]][\"Survived\"]\n",
    "\trate = sum(Sib)/len(Sib)\n",
    "\trate_SibSp.append(rate)\n",
    "\n",
    "# 畫出兄弟姊妹數量(SibSp)之存活率: 發現登船兄弟姊妹的數量過多不利於逃生\n",
    "for x, y in zip(SibSp, rate_SibSp):\n",
    "\tplt.text(x, y, '%.2f'%y, ha='center', va='bottom')\n",
    "plt.xlabel('SibSp')\n",
    "plt.ylabel('Survival rate')\n",
    "plt.xticks(np.arange(9))\n",
    "plt.bar(SibSp, rate_SibSp, color=['#FFAE33', '#33A4FF', '#41B605'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 檢查直系血親數量(Parch)是否影響存活的機率\n",
    "Parch = np.unique(train_data['Parch'])\n",
    "rate_Parch = []\n",
    "for i in range(len(Parch)):\n",
    "\tPar = train_data.loc[train_data.Parch == Parch[i]][\"Survived\"]\n",
    "\trate = sum(Par)/len(Par)\n",
    "\trate_Parch.append(rate)\n",
    "\n",
    "# 畫出直系血親數量(Parch)之存活率\n",
    "for x, y in zip(Parch, rate_Parch):\n",
    "\tplt.text(x, y, '%.2f'%y, ha='center', va='bottom')\n",
    "plt.xlabel('Parch')\n",
    "plt.ylabel('Survival rate')\n",
    "plt.bar(Parch, rate_Parch, color=['#FFAE33', '#33A4FF', '#41B605'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of S who survived: 0.33695652173913043\n",
      "% of C who survived: 0.5535714285714286\n",
      "% of Q who survived: 0.38961038961038963\n"
     ]
    },
    {
     "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": [
    "# 檢查登船港口(Embarked)是否影響存活的機率\n",
    "S = train_data.loc[train_data.Embarked == 'S'][\"Survived\"]\n",
    "rate_S = sum(S)/len(S)\n",
    "print(\"% of S who survived:\", rate_S)\n",
    "\n",
    "C = train_data.loc[train_data.Embarked == 'C'][\"Survived\"]\n",
    "rate_C = sum(C)/len(C)\n",
    "print(\"% of C who survived:\", rate_C)\n",
    "\n",
    "Q = train_data.loc[train_data.Embarked == 'Q'][\"Survived\"]\n",
    "rate_Q = sum(Q)/len(Q)\n",
    "print(\"% of Q who survived:\", rate_Q)\n",
    "\n",
    "# 登船港口(Embarked)之存活率\n",
    "X = np.array(['S', 'C', 'Q'])\n",
    "Y = np.array([rate_S, rate_C, rate_Q])\n",
    "for x, y in zip(X, Y):\n",
    "\tplt.text(x, y, '%.2f'%y, ha='center', va='bottom')\n",
    "plt.xlabel('Embarked')\n",
    "plt.ylabel('Survival rate')\n",
    "plt.bar(X, Y, color=['#FFAE33', '#33A4FF', '#41B605'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Fare1 who survived: 0.19940476190476192\n",
      "% of Fare2 who survived: 0.4245810055865922\n",
      "% of Fare3 who survived: 0.43\n",
      "% of Fare4 who survived: 0.5740740740740741\n",
      "% of Fare5 who survived: 0.6721311475409836\n"
     ]
    },
    {
     "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": [
    "# 檢查票價(Fare)是否影響存活的機率\n",
    "Fare = train_data['Fare']\n",
    "x = np.linspace(min(Fare), max(Fare), 5)\n",
    "count = []\n",
    "count1 = 0\n",
    "count2 = 0\n",
    "count3 = 0\n",
    "count4 = 0\n",
    "count5 = 0\n",
    "for i in range(len(Fare)):\n",
    "\tif Fare[i] <= 10:\n",
    "\t\tcount1 += 1\n",
    "\telif 10 < Fare[i] <= 20:\n",
    "\t\tcount2 += 1\n",
    "\telif 20 < Fare[i] <= 40:\n",
    "\t\tcount3 += 1\n",
    "\telif 40 < Fare[i] <= 60:\n",
    "\t\tcount4 += 1\n",
    "\telse:\n",
    "\t\tcount5 += 1\n",
    "\t\n",
    "count = [count1, count2, count3, count4, count5]\n",
    "\n",
    "Fare1 = train_data.loc[train_data.Fare <= 10][\"Survived\"]\n",
    "rate_Fare1 = sum(Fare1)/len(Fare1)\n",
    "print(\"% of Fare1 who survived:\", rate_Fare1)\n",
    "\n",
    "Fare2 = train_data.loc[(10 < train_data.Fare) & (train_data.Fare <= 20)][\"Survived\"]\n",
    "rate_Fare2 = sum(Fare2)/len(Fare2)\n",
    "print(\"% of Fare2 who survived:\", rate_Fare2)\n",
    "\n",
    "Fare3 = train_data.loc[(20 < train_data.Fare) & (train_data.Fare <= 40)][\"Survived\"]\n",
    "rate_Fare3 = sum(Fare3)/len(Fare3)\n",
    "print(\"% of Fare3 who survived:\", rate_Fare3)\n",
    "\n",
    "Fare4 = train_data.loc[(40 < train_data.Fare) & (train_data.Fare <= 60)][\"Survived\"]\n",
    "rate_Fare4 = sum(Fare4)/len(Fare4)\n",
    "print(\"% of Fare4 who survived:\", rate_Fare4)\n",
    "\n",
    "Fare5 = train_data.loc[60 < train_data.Fare][\"Survived\"]\n",
    "rate_Fare5 = sum(Fare5)/len(Fare5)\n",
    "print(\"% of Fare5 who survived:\", rate_Fare5)\n",
    "\n",
    "# 畫出各票價(Fare)區間之存活率: 發現票價高存活率高\n",
    "X = np.array(['0-10', '10-20', '20-40', '40-60', '60-'])\n",
    "Y = np.array([rate_Fare1, rate_Fare2, rate_Fare3, rate_Fare4, rate_Fare5])\n",
    "for x, y in zip(X, Y):\n",
    "\tplt.text(x, y, '%.2f'%y, ha='center', va='bottom')\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Survival rate')\n",
    "plt.bar(X, Y, color=['#FFAE33', '#33A4FF', '#41B605'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 檢查年齡(Age)是否影響存活的機率\n",
    "Age = np.unique(train_data['Age'])\n",
    "Age = [x for x in Age if x > 0]\n",
    "rate_Age = []\n",
    "for i in range(len(Age)):\n",
    "\tA = train_data.loc[train_data.Age == Age[i]][\"Survived\"]\n",
    "\trate = sum(A)/len(A)\n",
    "\trate_Age.append(rate)\n",
    "\n",
    "# 畫出各年齡(Age)之存活率: 發現年紀低於15歲存活率高\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Survival rate')\n",
    "plt.bar(Age, rate_Age)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'f(x)')"
      ]
     },
     "execution_count": 30,
     "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": [
    "from scipy.stats.kde import gaussian_kde\n",
    "\n",
    "Age_R = train_data['Age']\n",
    "Age_R = [x for x in Age_R if x > 0]\n",
    "kde = gaussian_kde(Age_R)\n",
    "dist_space = np.linspace(min(Age_R), max(Age_R), 100 )\n",
    "\n",
    "# 畫出原始年齡資料(Age)的機率分布\n",
    "plt.plot(dist_space, kde(dist_space))\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('f(x)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 年齡(Age)資料補遺，並輸出成new_train.csv\n",
    "Age_mean = sum(Age_R) / len(Age_R)\n",
    "Age_mean = round(Age_mean, 2)\n",
    "train_data['Age'] = train_data['Age'].replace(np.nan, Age_mean)\n",
    "train_data.to_csv('new_train.csv', index=False)\n",
    "\n",
    "Age_R2 = train_data['Age']\n",
    "kde = gaussian_kde(Age_R2)\n",
    "dist_space = np.linspace(min(Age_R2), max(Age_R2), 100 )\n",
    "# 畫出補遺後年齡資料(Age)的機率分布\n",
    "plt.plot(dist_space, kde(dist_space))\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('f(x)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特徵選取\n",
    "new_train_data = pd.read_csv('new_train.csv')\n",
    "features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare'] \n",
    "\n",
    "# One-hot encoding: 因電腦只會做數學運算\n",
    "X = pd.get_dummies(new_train_data[features])\n",
    "X_arr = X.values #Dataframe to np array\n",
    "\n",
    "#標準化\n",
    "X_arr_normal = preprocessing.scale(X_arr) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.79888268 0.81564246 0.8258427  0.79213483 0.84745763]\n",
      "0.8159920589746672\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用random forest model建模\n",
    "y = new_train_data[\"Survived\"]\n",
    "\n",
    "# 將訓練資料分成五個part做交叉驗證\n",
    "model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n",
    "scores = cross_val_score(model, X_arr_normal, y, cv=5, scoring='accuracy')\n",
    "print (scores)\n",
    "print (scores.mean())\n",
    "model.fit(X_arr_normal, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 測試資料的Age資料需要補遺，假設也採用平均值補遺\n",
    "Test_Age = test_data['Age']\n",
    "Test_Age_R = [x for x in Test_Age if x > 0]\n",
    "Test_Age_mean = sum(Test_Age_R) / len(Test_Age_R)\n",
    "Test_Age_mean = round(Test_Age_mean, 2)\n",
    "test_data['Age'] = test_data['Age'].replace(np.nan, Test_Age_mean)\n",
    "\n",
    "# 發現test_data['Fare']也有一個nan值，以平均值補上\n",
    "test_data['Fare'] = test_data['Fare'].replace(np.nan, 32.63) \n",
    "X_test = pd.get_dummies(test_data[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your submission was successfully saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\waukin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by the scale function.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# 將測試資料的特徵也標準化\n",
    "X_test_normal = preprocessing.scale(X_test)\n",
    "predictions = model.predict(X_test_normal)\n",
    "\n",
    "# 輸出成csv檔\n",
    "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})\n",
    "output.to_csv('submission_R2.csv', index=False)\n",
    "print(\"Your submission was successfully saved!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1\n",
       "5          897         0\n",
       "6          898         1\n",
       "7          899         0\n",
       "8          900         1\n",
       "9          901         0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head(10)"
   ]
  }
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