Divorce Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "import joblib\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',None, 'display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"divorce.xlsx\")\n",
    "ques = pd.read_excel(\"Divorce Questions.xlsx\", header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "   Atr1  Atr2  Atr3  Atr4  Atr5  Atr6  Atr7  Atr8  Atr9  Atr10  Atr11  Atr12  \\\n",
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       "3     3     2     3     2     3     3     3     3     3      3      4      3   \n",
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       "   Atr13  Atr14  Atr15  Atr16  Atr17  Atr18  Atr19  Atr20  Atr21  Atr22  \\\n",
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       "   Atr23  Atr24  Atr25  Atr26  Atr27  Atr28  Atr29  Atr30  Atr31  Atr32  \\\n",
       "0      0      0      0      0      0      0      0      1      1      2   \n",
       "1      0      2      2      1      2      0      1      1      0      4   \n",
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       "\n",
       "   Atr33  Atr34  Atr35  Atr36  Atr37  Atr38  Atr39  Atr40  Atr41  Atr42  \\\n",
       "0      1      2      0      1      2      1      3      3      2      1   \n",
       "1      2      3      0      2      3      4      2      4      2      2   \n",
       "2      1      1      1      1      2      1      3      3      3      3   \n",
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       "\n",
       "   Atr43  Atr44  Atr45  Atr46  Atr47  Atr48  Atr49  Atr50  Atr51  Atr52  \\\n",
       "0      1      2      3      2      1      3      3      3      2      3   \n",
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       "2      2      3      2      3      2      3      1      1      1      2   \n",
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       "\n",
       "   Atr53  Atr54  Class  \n",
       "0      2      1      1  \n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting Test and Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,0:53]\n",
    "y = df.iloc[:,-1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x,y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection using KBest Algorithm\n",
    "\n",
    "\n",
    "Using the Kbest Algorithm we will get the scores of all the features in relation to our target label **Class** and  and then sort them in descending order and select the top 10, thus selecting the 10 features with the highest scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Atr19</td>\n",
       "      <td>155.229166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Atr17</td>\n",
       "      <td>152.548781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Atr11</td>\n",
       "      <td>151.993322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Atr5</td>\n",
       "      <td>150.218797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Atr20</td>\n",
       "      <td>144.605114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    names      scores\n",
       "35  Atr36  183.797065\n",
       "34  Atr35  181.106124\n",
       "39  Atr40  174.375947\n",
       "37  Atr38  160.588721\n",
       "8    Atr9  160.425659\n",
       "18  Atr19  155.229166\n",
       "16  Atr17  152.548781\n",
       "10  Atr11  151.993322\n",
       "4    Atr5  150.218797\n",
       "19  Atr20  144.605114"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = SelectKBest(score_func=chi2, k= 10)\n",
    "\n",
    "fs.fit(X_train, y_train)\n",
    "\n",
    "X_train_fs = fs.transform(X_train)\n",
    "X_test_fs = fs.transform(X_test)\n",
    "\n",
    "\n",
    "\n",
    "scores = pd.DataFrame(fs.scores_)\n",
    "names = pd.DataFrame(X_train.columns)\n",
    "\n",
    "\n",
    "\n",
    "features = pd.concat([names,scores],axis=1)\n",
    "features.columns = [\"names\", \"scores\"]\n",
    "\n",
    "\n",
    "    \n",
    "features = features.sort_values(by= ['scores'],ascending=False)\n",
    "\n",
    "features = features.head(10)\n",
    "\n",
    "questions = []\n",
    "\n",
    "for _ in range(len(features)):\n",
    "    mask  = features.names.iloc[_]\n",
    "    questions.append(ques[0].loc[ques[1] == mask])\n",
    "\n",
    "    \n",
    "questions = pd.Series(questions)    \n",
    "questions = pd.DataFrame(questions)\n",
    "\n",
    "\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating DataFrame of Selected Features and Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Atr36</th>\n",
       "      <th>Class</th>\n",
       "      <th>Atr35</th>\n",
       "      <th>Atr40</th>\n",
       "      <th>Atr38</th>\n",
       "      <th>Atr9</th>\n",
       "      <th>Atr19</th>\n",
       "      <th>Atr17</th>\n",
       "      <th>Atr11</th>\n",
       "      <th>Atr5</th>\n",
       "      <th>Atr20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Atr36  Class  Atr35  Atr40  Atr38  Atr9  Atr19  Atr17  Atr11  Atr5  Atr20\n",
       "0        1      1      0      3      1     0      0      0      1     0      1\n",
       "1        2      1      0      4      4     4      3      4      4     4      2\n",
       "2        1      1      1      3      1     1      3      3      3     1      2\n",
       "3        1      1      1      4      3     3      3      3      4     3      4\n",
       "4        0      1      0      1      0     0      2      1      0     1      1\n",
       "..     ...    ...    ...    ...    ...   ...    ...    ...    ...   ...    ...\n",
       "165      0      0      0      0      0     0      0      0      0     0      0\n",
       "166      1      0      1      1      1     0      0      0      0     0      0\n",
       "167      0      0      0      2      1     0      0      0      0     0      1\n",
       "168      0      0      0      1      0     0      0      0      0     0      0\n",
       "169      0      0      0      0      0     0      0      0      0     0      1\n",
       "\n",
       "[170 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame()\n",
    "\n",
    "index = len(features.index)\n",
    "\n",
    "for column in range(index):\n",
    "    \n",
    "    mask = features.iloc[column]\n",
    "    df1[mask[0]] = df[mask[0]]\n",
    "    df1[\"Class\"] = df[\"Class\"]\n",
    "    \n",
    "df1   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Final Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1.drop(\"Class\", axis = 1)\n",
    "y = df1[\"Class\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x,y, test_size=0.30, random_state=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will train different models and also record and visualize their testing and training scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score on test: 0.6470588235294118\n",
      "score on train: 0.7058823529411765\n"
     ]
    }
   ],
   "source": [
    "mnb = MultinomialNB().fit(X_train, y_train) #MultiNomial Naive Bayes\n",
    "\n",
    "mnb_score_test = mnb.score(X_test, y_test)\n",
    "mnb_score_train = mnb.score(X_train, y_train)\n",
    "\n",
    "print(\"score on test: \" + str(mnb_score_test))\n",
    "print(\"score on train: \"+ str(mnb_score_train))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score on test: 0.9803921568627451\n",
      "score on train: 0.9915966386554622\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(max_iter=1000)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "lr_score_test = lr.score(X_test, y_test)\n",
    "lr_score_train = lr.score(X_train, y_train)\n",
    "\n",
    "print(\"score on test: \" + str(lr_score_test))\n",
    "print(\"score on train: \"+ str(lr_score_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K - Nearest Neighbour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape: (119, 10)\n",
      "score on test: 0.9607843137254902\n",
      "score on train: 0.9831932773109243\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(algorithm = 'brute', n_jobs=-1)\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print(\"train shape: \" + str(X_train.shape))\n",
    "\n",
    "knn_score_test = knn.score(X_test, y_test)\n",
    "knn_score_train = knn.score(X_train, y_train)\n",
    "\n",
    "\n",
    "print(\"score on test: \" + str(knn_score_test))\n",
    "print(\"score on train: \"+ str(knn_score_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score on test: 0.6274509803921569\n",
      "score on train: 0.7226890756302521\n"
     ]
    }
   ],
   "source": [
    "svm=LinearSVC(C=0.0001)\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "svm_score_test = svm.score(X_test, y_test)\n",
    "svm_score_train = svm.score(X_train, y_train)\n",
    "\n",
    "print(\"score on test: \" + str(svm_score_test))\n",
    "print(\"score on train: \"+ str(svm_score_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score on test: 0.9607843137254902\n",
      "score on train: 1.0\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "clf_score_test = clf.score(X_test, y_test)\n",
    "clf_score_train = clf.score(X_train, y_train)\n",
    "\n",
    "print(\"score on test: \"  + str(clf_score_test))\n",
    "print(\"score on train: \" + str(clf_score_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score on test: 0.9607843137254902\n",
      "score on train: 0.9915966386554622\n"
     ]
    }
   ],
   "source": [
    "# maX_samples: maXimum size 0.5=50% of each sample taken from the full dataset\n",
    "# maX_features: maXimum of features 1=100% taken here all 10K \n",
    "# n_estimators: number of decision trees\n",
    "\n",
    "\n",
    "bg = BaggingClassifier(DecisionTreeClassifier(),max_samples=0.5,max_features=1.0,n_estimators=10)\n",
    "\n",
    "bg.fit(X_train, y_train)\n",
    "\n",
    "bg_score_test = bg.score(X_test, y_test)\n",
    "bg_score_train = bg.score(X_train, y_train)\n",
    "\n",
    "print(\"score on test: \" + str(bg_score_test))\n",
    "print(\"score on train: \"+ str(bg_score_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score on test: 0.9607843137254902\n",
      "score on train: 0.9831932773109243\n"
     ]
    }
   ],
   "source": [
    "# n_estimators = number of decision trees\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=10, max_depth=2)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "rf_score_test = rf.score(X_test, y_test)\n",
    "rf_score_train = rf.score(X_train, y_train)\n",
    "\n",
    "\n",
    "print(\"score on test: \" + str(rf_score_test))\n",
    "print(\"score on train: \"+ str(rf_score_train))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the dataset of our scores to visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = [['Naive Bayes',mnb_score_test,mnb_score_train],\n",
    "          ['Logistic Regression',lr_score_test,lr_score_train],\n",
    "          ['KNN', knn_score_test,knn_score_train],\n",
    "          ['SVM', svm_score_test,svm_score_train],\n",
    "          ['Decision Tree Classifier', clf_score_test,clf_score_train],\n",
    "          ['Bagging Classifier',bg_score_test,bg_score_train],\n",
    "          ['Random Forest',rf_score_test,rf_score_train]]\n",
    "\n",
    "score_df = pd.DataFrame(scores, columns = ['Model Name', 'TestScore' , 'TrainScore']) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot a bar chart to find out which model scored the highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2bPr168fIkSOZN28ey5YtY+LEiQwaNIjLL798tbKXXXYZgwYNYuLEiSxbtox58+YxcuRI+vXrx+zZs5tylyRJH8HAgQM5/fTT6du3Lz/60Y945plnOPjggznggAPo168fr7zyCgCPPPIIRx99NABDhw7le9/7Hoceeii77bZbvcHPnDlzaN++Pe3atQOgXbt2pbtaTZ48mcMPP5z99tuPT3ziE0yZMoWUEhdccAH77LMP++67L6NGjSpt95BDDuErX/kKe+21FytXruSCCy7gwAMPpHfv3vzqV7/a4NfAgEeSJEmF1a1bN3784x/z5z//mYMOOmi9lh0yZAhvvPEGACNGjGDWrFn07dsXgGHDhjF9+nQA/vWvf/GTn/wEgL59+zJr1ixGjBgBZGd2hw4d2kR7I0naEDNnzmTMmDFcc801fPzjH+fxxx/nueee4yc/+Qk//vGP613m5Zdf5qGHHuKZZ55h2LBhLF++fLXn99tvP3bccUd23XVXvvvd7/LnP/+59Ny3v/1tzjjjDGpqahgzZgxdu3blD3/4AxMmTKCmpoa//e1vXHDBBcyaNQuAf/7zn/zsZz/j1Vdf5ZZbbqFjx448++yzPPvss9x0001MmzZtg/bfgEeSJEmFdeCBB3L55Zdz9NFH06ZNm0Yvt2rVqtJZ1V69ejFgwAC6dOnC4MGDAVi+fDmjR48GYPTo0aUG/+DBg+nSpQsDBgygV69eANx1112sWrWqKXdLkvQRHHfccTRv3hyABQsWcNxxx7HPPvtw7rnn8uKLL9a7zJe+9CVatWpF586d2WGHHXjrrbdWe7558+b85S9/YfTo0eyxxx6ce+65DB06lEWLFvHGG2/wta99DYDWrVuzzTbb8MQTT3DCCSfQvHlzdtxxR/r378+zzz4LwEEHHVTq/fPXv/6V22+/nf3335++ffsyf/58XnvttQ3afwMeaSuzPmMN1PXUU09x5JFH0rFjR7bZZhsOPvhg7rnnnjXKzZ07l/PPP5899tiDtm3bUl1dzac//Wnuuuuupt4dSZI+kilTprBgwQIA9thjj9L88unnnnsOyM641vd87fSCBQs2+KyrNr2N0UYCePvttznvvPPYbbfdSj8qDznkEB588MGm3B1pq9S2bdvS9H/8x39w2GGH8cILL/DnP/+ZpUuX1rtMq1atStPNmzevd3yciOCggw7ioosu4q677uL3v//9BtcvpcR1111XGjto2rRpfP7zn/9I661lwCNtRdZnrIG6/vGPf9C/f3/++te/snDhQt5//32efvppjjnmGG6//fZSuVWrVvG5z32Oq6++mtdee40lS5Ywb948xowZwwknnMAvf/nLSu+mJEnrNG/evNJ0hw4d6p2eM2fOepdVMW2MNlLtdvr27cs111zDtGnT+OCDD5g/fz5PPPEETz75ZCV3UdrqLFiwgG7dugFw2223feT1vPnmm6sF/RMmTGCXXXahffv27LTTTvzpT38CYNmyZSxZsoRDDjmEUaNGsXLlSubOnctjjz1W7yXERx55JL/85S9LPURfffVV3nvvvY9cT/A26dJWpe5YA4cffjjHHHMMY8eOZdiwYZx00knsvPPO9S47ePBgli9fzrbbbstjjz3GDjvswNFHH824ceP44Q9/yLHHHku7du2oqanh+eefB7IuiA888AAvvfQShx12GCtXruTWW2/lBz/4wUbbZ0mS1kf5QM0R0WRltXnbGG0kgDPPPJPJkyfTqlUrrrvuOo499liaNWvGuHHj1nuQcGlzsq7bmm8KP/rRj/jOd77DZZddxpe+9NHrt3z5cs4//3zefPPN0l0Ub7zxRiD7vDjttNO49NJLqaqq4ne/+x1f+9rXeOqpp9hvv/2ICK688kq6dOnCyy+/vNp6Bw0axOuvv84nPvEJUkpUV1eXwqKPLKVUqL9PfvKTSdL6W7lyZerYsWMCUq9evUrzR40alYAEpKuvvrreZd95551SmeOOO640/2c/+1lp/qhRo1JKKU2cOLE07+KLLy6V7datWwLSPvvsU6E9lCRt7fr375+A1Lx583WWffXVV0vfV1/96ldL85977rnS/AEDBqSUUjrxxBNL85577rlS2a9+9aul+ZMnT27q3dFGsrHaSDNmzEjNmjVLQLrooosqu1NShb300kubugpbjfpea2Bcqicv8RItaSuxPmMN1PX++++vc/21y+6zzz586lOfAuDhhx9m/vz5PPbYY6WR4zf0ulJtPhzPSVKR9ezZk44dOwJZt/ha5dMHHHAAAJ/4xCfqfb52umPHjqVBM1U8G6uN9Pjjj5cG4543bx577703rVu3Zvfdd+eaa66xB88WwvaRNqn6Up/N+c8ePNJHM2bMmNKZpJNOOqk0f8qUKaX5n//85+tddtWqVWmHHXZIQNp2223TxIkT0+zZs1OfPn1Ky5566qml8gsWLEiHHnpo6Tnys6mnnnpqev/99yu+r6q8WbNmlXpl1f277LLL1rrs3//+91RVVVXvssOHDy+VW7lyZdp3333rLQekG264odK7KakAPvjggzR37tw0d+7c1K9fv9J3Tu28pUuXpmnTppU+O4YMGVJa9vvf/35p/h133JFmzZqV+vbtm4BUVVWVpk+fnlJK6V//+lfpc6tv375p1qxZ6Y477igte9ppp22ivVdT2FhtpJ/+9KcNfqdRp+ezimlrah/Zg2fjsQePpEZLjRg/ICK4+OKLAXj33Xfp3bs3Xbp0Ydy4caUyVVVVQDbI8re//W0eeeSR1daxcuVKXn75ZWbOnNnEe9Awz6BUTt2xCmbNmkXfvn0BGDZsGNOnT29w2fKxCiZOnMjs2bPp06cPAD/84Q9ZvHgxwBrjOc2bN4/HHnusdOvLW2+9tWL7J6k4nnzySaqrq6murmbMmDFA9p1TO+/OO+9scNlhw4aVBuAcMGAAXbt2ZezYsUD2Ode9e3cAdt55Zy699FIAxo4dS9euXRkwYAAA3bp1Y+jQoZXaPW1CTd1Gqh1IFaBHjx5MnTqVqVOnsssuuwBw1VVXsWjRoibfD208to+0qVUs4ImI30TEnIh4oYHnIyJ+HhGTI2JiRHyivnKSmkbnzp1L0wsXLixNlzckqqurG1z+7LPP5te//jUf//jHadmyJbvvvjs//OEPS8/XNoLvvfde7rvvPgC+853vsGDBAiZNmkS3bt14/PHH+drXvtZk+7Q23jGsclatWsWoUaMA6NWrFwMGDKBLly4MHjwYyBqwo0ePrnfZd999l5qaGgCOOOII9t13X3bccUdOOumk0vMPPPAAAC1afHgfgCOOOIJOnTpxyCGH0KVLF6Bx3eIlaW26dOnCmDFjOPHEE+nUqROtWrWid+/e3HzzzaUf7bUuueQSbr75Znr37k2rVq3o1KkTJ554ImPGjCl9LqmYNlYbqVOnTqV5X/va19h1113ZddddOfbYY4HsDjyvvPLKhu+QNgnbR9ocVLIHz23AF9by/FHAx/K/7wNb5i8haTOxPmMNNOTUU09l0qRJLFu2jNdee42Pf/zjpef69+8PsNro8CeeeCIdOnTg4x//OJ/5zGcAeOGFF5g7d+6G79A6eAalchzPSdLm5NBDD13r5f0DBw6kR48epcd1e9vsvPPOpZMBS5cupaamhlNOOaXebZ1yyinU1NSwdOlS5s2bx8iRIxu8s5KKY2O1kda1DoA2bdqsV921+bB9pM1BxQKelNJjwNtrKfJV4Pb8ErKngW0jomul6iNt7Zo1a8bxxx8PwKRJkxg5ciSzZ8/mmmuuAbLuw8cddxyQdTeOCAYOHFha/tlnn+XBBx/k7bffZuHChdx9991cdNFFAPTp04eDDz4YgK5dP/xv/Nvf/paFCxfy8ssv89hjjwHZWYe2bdtWdF89g1JZ8+bNK0136NCh3uk5c+bUu2yXLl3YYYcdgKxR8vzzz/PWW28xYsSIUpn58+cD2XH40EMPceihh/LMM8/QuXNn+vfvT0Rw6qmnrrMnliRJjbGx2kgHHXQQu+22GwB//OMfmTZtGtOmTeMPf/gDADvssMNqwZCKxfaRNgct1l2kYroBM8oez8znzapbMCK+T9bLx7Mk0gYYNmwY999/P2+88UZp7IBa5WMN1KempoZTTz11jfnV1dWrXbZ07LHHcumllzJ9+nSGDx/O8OHDVys/cOBAttlmmw3ck7XbGGdQvvnNb5bOoDz99NM8/PDDnHvuubz44otb7RmU9Rmr4Ic//GFprIK61mc8p913373pdkDSZqHHhfdv6ipsFK9f8aVNXQWV2RhtpIjghhtu4Oijj+b1118vhT21z1199dWlXsDacmw17aOhHZt4fQvW+vT8+fP53Oc+B2RDMzRv3rx0KeUzzzxDy5Yt17r8I488QsuWLenXr98az7311luccsopzJgxg+XLl9OjR4/SCd7NXSEGWU4p/Tql1Cel1Gdt179KWrv1GWugrn322Yf+/fvTuXNnqqqq6NatG4MGDWL8+PH06tWrVK59+/Y8+eSTnHLKKXTv3p0WLVrQpk0bevfuzX//939zww03VHo3PYNSYVvbeE6SpC3fxmgjARx55JE8/PDD9O/fn7Zt27LNNtvw6U9/mvvuu2+NYEnFYvto4+rUqRMTJkxgwoQJnH766Zx77rmlx+sKdyALeGoH5q/r0ksv5YgjjqCmpoaXXnqJK664YoPru2LFig1eR2Nsyh48bwDlUfhO+TxJFVQ71sDalJ9pqPWpT31qjTMFDdlpp524+eabP0r1KmqrOYNSYbVjFSxYsGCDxiooP9t54403lqbXNp5Thw4d+MxnPsOdd95ZGs/J4F+S1BQ2RhsJsnGj1qe8isH20aY3fvx4Bg8ezOLFi+ncuTO33XYbXbt25ec//zk33ngjLVq0YK+99uKKK67gxhtvpHnz5txxxx1cd911HHLIIaX1zJo1a7We+OW/B376059yxx130KxZM4466iiuuOKKUsi0ZMkSevbsyW9+8xu22247Dj30UPbff3+eeOIJTjjhBA499NB669eUNmUPnnuBk/O7aX0KWJBSWuPyLElaX55BqaytaTwnSZKkxrB9tGmllDjrrLMYPXo048eP53vf+16p990VV1zBc889x8SJE7nxxhvp0aPHar1+ysMdgDPOOINTTjmFww47jMsvv5w333wTgAcffJB77rmHsWPHUlNTw49+9CMATj75ZH76058yceJE9t13X4YNG1Za1wcffMC4ceM4++yzG6xfU6pYD56IuBM4FOgcETOBIUAVQErpRuAB4IvAZGAJ8N1K1UXaUjlOQf08g1J5W8t4TpKkYrKNpE3B9tGms2zZMl544QWOOOIIIOtRXxuG9e7dm29/+9scc8wxHHPMMetc15FHHsnUqVP5y1/+woMPPsgBBxzACy+8wN/+9je++93vll7f7bffngULFvDuu++Wfh985zvfKQV5QCn0e+WVVxqsX1Oq5F20TkgpdU0pVaWUdkop3ZJSujEPd8jvnnVGSqlnSmnflNK4StVF0tbFMyiVt7WM5yRJktRYto82nZQSe++9d2kcnueff56//vWvANx///2cccYZ/POf/+TAAw9s1Hg422+/PSeeeCIjRozgwAMPLLXv11ftb4G11a8pFWKQZUlaX8OGDaNbt24ADBgwgK5duzJ27FigcWdQvvjFL9KpUyc6duzI8ccfz7vvvlvvGZTaO/sNHz6cjh070qtXL954IxtObEs/g1I7VsG8efNYunQpNTU1nHLKKauVSSmRUuK2224rzasdq2Du3Ll88MEHzJw5k5tuuqne96R2PKfp06ezfPlylixZQk1NDRdeeGFpLKRKmz59OgMGDKC6uprWrVuz3377ccsttzRq2aeeeoojjzySjh07ss0223DwwQdzzz331Fv27bff5rzzzmO33XajVatWdO7cmUMOOYQHH3ywKXdHUsH5maSm4HFUOVtL+wiyXjNTp05lwoQJFd3OihUrmDFjBs8//zzjx49nwoQJvPzyy6W75gK0atWKuXPn8tRTTwGwfPlyXnzxRVatWsWMGTM47LDD+OlPf8qCBQtYvHgx7du3X234hnL/+Mc/WLJkCZAN8TBlyhR23nlnjjjiCG699dbSc2+//TYdO3Zku+224/HHHwdgxIgRpd485fbcc89669fUNuUgy5JUMbVnUC666CIeeughFi9ezJ577snZZ5+9xpdsXbVnUF588UUWLFjADjvswFFHHcWll1662pds7RmUoUOH8te//pVZs2ZRVVXFxz72MU444QTOO++8Su+mKmz27Nn069evFNoBTJw4kUGDBjF79uy1no37xz/+wRe+8AWWL19emvf0009zzDHHMHz4cE4++eTVtnPIIYcwefLk0rz58+fzxBNP8OSTT3LUUUc18Z5JKiI/k9QUPI7UFGpvKlJ7LIw7+u+l57p167bWy48WLlzIa6+9Vu+g5T169FhtPM3ly5fz8ssvs2zZstK8FStWsHjxYhYvXkzHjtnt2Zs1a8bo0aM5++yzWbBgAStWrOCcc85hjz32YMCAASxYsICUEmeffTbbbrstX/7yl/nGN77BPffcs8Ygy+PHj+fMM8+kRYsWrFq1ikGDBnHggQcCMGHCBPr06UPLli354he/yH/9138xfPjw0iDLu+22G7feeusa+9WyZct667f33ns39iVvlKjvRd2c9enTJ40b59VcEnh9uVRpp512Gr/+9a+B7IzM4YcfzjHHHMPYsWOpqqpi8uTJpV5cde2///7U1NSw7bbb8thjj7HDDjtw9NFHM27cOLbddltmzJhBu3btAPjGN77B73//e1q1asV1113HscceS7NmzRg3bhwppdXu5CBt6fxua5ifSY3ncdQwjyM1hSeeeILWrVsDsOuuu9KhQwcmT57Me++9R0Swzz770KpVq3qXffHFF3n//fdp3rw5e+65J1VVVbz22mssWbKE5s2b07t3b5o3bw7AlClTeOedd4gIdt55Z7bbbjsAlixZQkqpFPBsySZNmrTaZXoAETE+pdSnbll78EjSVm5raQTD+jWEV61axahRowDo1atXabDEwYMHc/zxx7N8+XJGjx7N4MGD11j23XffpaamBoAjjjiCfffdF4CTTjqJcePG8e677/LAAw/wzW9+k5kzZ/LHP/6xtO7yARZrB+KTJD+T1BQ8jtbP1tJGWt+gcNWqVbz33nu0bt2a1q1b06lTJwB23HFHpk6dSkqJd955hy5duqyx7IoVK3j//fcB6NChQ2k4g06dOrFkyRJWrlzJggUL2H777fnggw945513Susuv3FJhw4dPtK+bukMeCRt/oZu+cl8ydAF6y6jjWLKlCmla7v32GOP0vzy6eeee67eZWsbLmvz3HPP8c1vfpPHH3+cVatWATBv3jz23ntvpkyZwk477cT/+3//j3PPPZeI2JBdkbQF8DNJTcHjSE1hypQppcuranvx1J2uHaemrtrjYm2WLFnC9ttvv9oYOStWrOCFF15g2bJltGzZkurqanbccUePozoMeCRJqse8efNK0+Vnicqn58yZU++yXbp0YYcddmDOnDk8/PDDPP/88+ywww6MGDGiVGb+/PkAzJgxozTvpptuKk1PmTKF8847j7fffpvLLrtsw3dIUqH5maSm4HGkpjBv3rxSUFN7KVXd6YbuVFVVVUWLFi1YsWIFCxcuZMmSJVRVVZWOnfJly8d6Kj92ly1bxsyZM1m5cmXppipbqvUdUse7aEmStB7Kv2gbOmsUEaVBKt9991169+5Nly5dKB9DrvYuF+WNlx49ejB16lSmTp3KLrvsAsBVV13V4F0eJMnPJDUFjyOtr8mTJ7NixYrVjp3GhBERURqAeeXKlbz00kvU1NSs1uOn9hgsX1/Lli3Zd9992XfffWnZsiWQDeS9cuXKJtmfzVFKifnz56/WM2pd7MEjSVI9yu/gsHDhwtJ0eYO0/Frwus4++2zatGnDNddcw9SpU9l555350pe+xM9+9jOA0h3Zaq9bB/ja177GrrvuCsCxxx7Ltddey7Jly3jllVfo02eNcfQkbUX8TFJT8DhSU+jcuTNDhw5l6NCh9O7du3SHqw8++KDU0+b9999fa/iyatUqFi1axPLly2nRogVt2rQpHYcrVqxgyZIlLFq0iLfffhvI7l47depUIDtea8s+//zzDQ7mvCVo3bo1O+20U6PLG/BIklSPnj170rFjRxYsWMCrr75aml8+fcABB6x1HaeeeupqA0veeOONpen+/fs3ah0Abdq0aXS9JW2Z/ExSU/A4UlPo2bMnq1at4oc//CG9evXipZdeAuDuu+/m+OOPB+Dqq6+ud7Duhtx444384Ac/AGDMmDH06tWLsWPHctRRRwFw7rnncs011wDZwN3XXnstAC+88MIad5jamnmJliRJ9WjWrFmpkTJp0iRGjhzJ7NmzS42LqqoqjjvuOCDrShwRDBw4sLT8s88+y4MPPsjbb7/NwoULufvuu7nooosA6NOnDwcffDAABx10ELvtthsAf/zjH5k2bRrTpk3jD3/4AwA77LADH//4xzfKPkvafPmZpKbgcaSm4HG0+bIHjyRJDRg2bBj3338/b7zxRulWsrWGDBlS6open5qamtXOcNaqrq7m9ttvLz2OCG644QaOPvpoXn/99VJDpva5q6++erVBCyVtvfxMUlPwOFJT8DjaPNmDR5KkBnTp0oUxY8Zw4okn0qlTJ1q1akXv3r25+eabSwNNNmSfffahf//+dO7cmaqqKrp168agQYMYP378Gl2JjzzySB5++GH69+9P27Zt2Wabbfj0pz/Nfffdt0ajSdLWy88kNQWPIzUFj6PNU6zvbbc2tT59+qTykdqlrVmPC+/f1FXYKF5vfeKmrsLGM3TBRt/k1nIcAbx+xZc2dRUkrcPW8pnk51FleRypKXgcaXMVEeNTSmuMUm4PHkmSJEmSpIJzDB5J0tZjaMdNXYONYxP0BJO0nraWzyPwM6mSPI7UFDyOthj24JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkquIoGPBHxhYh4JSImR8SF9Ty/c0T8X0Q8FxETI+KLlayPJEmSJEnSlqhiAU9ENAd+ARwF7AWcEBF71Sl2CXB3SukA4FvADZWqjyRJkiRJ0paqkj14DgImp5SmppQ+AO4CvlqnTAI65NMdgTcrWB9JkiRJkqQtUosKrrsbMKPs8Uygb50yQ4G/RsRZQFvg8ArWR5IkSZIkaYu0qQdZPgG4LaW0E/BFYERErFGniPh+RIyLiHFz587d6JWUJEmSJEnanFUy4HkD6F72eKd8XrlTgLsBUkpPAa2BznVXlFL6dUqpT0qpT3V1dYWqK0mSJEmSVEyVDHieBT4WEbtGREuyQZTvrVNmOvA5gIjoRRbw2EVHkiRJkiRpPVQs4EkprQDOBB4CJpHdLevFiPhJRHwlL3YecGpE1AB3AgNTSqlSdZIkSZIkSdoSVXKQZVJKDwAP1Jl3adn0S8CnK1kHSZIkSZKkLd2mHmRZkiRJkiRJG8iAR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkbTLTp09nwIABVFdX07p1a/bbbz9uueWWRi9///3389nPfpaOHTvSpk0bevbsycCBA0kplcqcdtpp7Lvvvmy33Xa0aNGC6upqjjrqKB555JEK7JEkSdKm0WJTV0CSJG2dZs+eTb9+/XjjjTdK8yZOnMigQYOYPXs2F1988VqXv+qqq7jgggtWmzd16lSmTp3KzTffTIsWWTNn+PDhLFu2rFRm3rx5/OUvf+Hhhx/mscceo1+/fk24V5IkSZuGPXgkSdImMWTIkFK4M2LECGbNmkXfvn0BGDZsGNOnT29w2ZqaGi688EIADjvsMGpqaliyZAmvvfYaV199Nc2afdjE+fGPf8yECRNYvHgxM2fO5Ctf+QoAK1euZNSoUZXaPUmSpI3KgEeSJG10q1atKoUrvXr1YsCAAXTp0oXBgwcDsHz5ckaPHt3g8r/4xS9YuXIl7dq1Y/To0fTu3Zs2bdqw++67M3jw4NUCnksvvZT99tuPtm3b0q1bNwYNGlR6rqqqqkJ7KEmStHEZ8EiSpI1uypQpLFiwAIA99tijNL98+rnnnmtw+UcffRSA7t27c8YZZ9C5c2fat2/Pl7/8ZV555ZV6l0kpMWPGDG666SYAttlmG04++eQN3hdJkqTNgWPwSJKkjW7evHml6Q4dOtQ7PWfOnAaXnzFjBgCTJk1i0qRJpfn33XcfY8eOpaamhq5du5bmn3/++Vx99dWrbecPf/gDvXv33rAdkSRJ2kzYg0eSJG02yu9+FRENllu+fHlp+rLLLmPRokVcdtllAMydO5frr79+rdtZuHAhxx57LOPHj9/AGkuSJG0eDHgkSdJG17lz59L0woULS9OLFi0qTVdXVze4fKdOnUrTZ511Fu3ateOss84qzaupqVmt/FVXXcXKlSuZOXMm5557bmm7Q4cO/cj7IEmStDkx4CmQ6dOnM2DAAKqrq2ndujX77bcft9xyyzqX69GjBxHR4N9tt91WKjtq1CiOOeYYdthhh9Lz5YNRSpLUFHr27EnHjh0BePXVV0vzy6cPOOCABpdf23MAbdq0WWNes2bN6NatG0OGDCnNe+211xpdZ0mSpM2ZAU9BzJ49m379+jFy5EjmzZvHsmXLmDhxIoMGDeLyyy/foHW3a9euNH3nnXdyzz33MHfu3A2tsiRJDWrWrBnHH388kI2jM3LkSGbPns0111wDZHe3Ou644wBKJxwGDhxYWv5b3/pWafq6665j8eLFXHfddaV5/fv3B+Cee+7h2muv5eWXX2bp0qW89dZb/Od//mep3G677VaxfZQkSdqYDHgKYsiQIbzxxhsAjBgxglmzZtG3b18Ahg0bxvTp0xtc9vXXXyeltNrfJz/5SQA6duzIF7/4xVLZz372s1x77bX8/ve/r+DeSJKUfX9169YNgAEDBtC1a1fGjh0LZN973bt3b3DZk046iUMPPRSASy65hPbt23PJJZcA0Lt3b0455RQApk2bxuDBg+nVqxdt2rShS5cupcGWW7duXVpGkiSp6Ax4CmDVqlWMGjUKgF69ejFgwAC6dOnC4MGDgWygydGjRzd6fePGjSsNKnnyySezzTbblJ47++yzOeecc/jEJz7RhHsgSdKaunTpwpgxYzjxxBPp1KkTrVq1onfv3tx8881cfPHFa122WbNm3HfffVxwwQV0796dqqoqdtppJ84880weffTR0iVaBx98MF//+tfZZZddaNOmDVVVVeyyyy6cdNJJPPPMM/Tr129j7KokSVLFeZv0ApgyZQoLFiwAYI899ijNL59+7rnnGr2+X/3qV6Xp008/vQlqKEnSR7PzzjszcuTItZYpv7NWubZt23LllVdy5ZVXNrhs37591+skiCRJUlHZg6cA5s2bV5ru0KFDvdNz5sxp1LoWLlzInXfeCcBnPvMZ9tprryaqpSRJkiRJ2lTswVNg5Wc0I6JRy9xxxx289957gL13JElNp8eF92/qKmw0r1/xpU1dBUmSpDXYg6cAOnfuXJpeuHBhaXrRokWl6erq6katq/byrOrqar7+9a83UQ0lSZIkSdKmZMBTAD179qRjx44AvPrqq6X55dMHHHDAOtczZswYJk6cCMD3vvc9WrZs2cQ1lSRJkiRJm4IBTwE0a9aM448/HoBJkyYxcuRIZs+ezTXXXANAVVUVxx13HJBdqhURDBw4cI311PbeiQhOO+20ere1ePFi5s2bxzvvvFOat2zZMubNm7faWECSJEmSJGnzYcBTEMOGDaNbt24ADBgwgK5duzJ27FgAhgwZQvfu3de6/DvvvMPdd98NwOc//3l23XXXesudeeaZVFdXr3ab9DvuuIPq6upGXwYmSZIkSZI2LgOegujSpQtjxozhxBNPpFOnTrRq1YrevXtz8803c/HFF69z+eHDh7N06VLAwZUlSZIkSdrSeBetAtl5550ZOXLkWsuU31mr3DnnnMM555yzzm3cdttt3HbbbR+hdpIkSZIkaVOxB48kSZIkSVLB2YNnE+lx4f2bugobxetXfGlTV0GSJEmSpC2ePXgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4tFmaPn06AwYMoLq6mtatW7Pffvtxyy23NHr5+++/n89+9rN07NiRNm3a0LNnTwYOHEhKqVSmR48eRES9fxMmTKjAXkmSJEmSVBktNnUFpLpmz55Nv379eOONN0rzJk6cyKBBg5g9ezYXX3zxWpe/6qqruOCCC1abN3XqVKZOncrNN99MixYe9pIkSZKkLYs9eLTZGTJkSCncGTFiBLNmzaJv374ADBs2jOnTpze4bE1NDRdeeCEAhx12GDU1NSxZsoTXXnuNq6++mmbN1jzkb731VlJKq/3tv//+Tb9jkiRJkiRViAGPNiurVq1i1KhRAPTq1YsBAwbQpUsXBg8eDMDy5csZPXp0g8v/4he/YOXKlbRr147Ro0fTu3dv2rRpw+67787gwYPrDXgkSZIkSSo6f+1qszJlyhQWLFgAwB577FGaXz793HPPNbj8o48+CkD37t0544wz6Ny5M+3bt+fLX/4yr7zySr3LnH/++bRs2ZLtttuOL37xizz11FNNsSuSJEmSJG00BjzarMybN6803aFDh3qn58yZ0+DyM2bMAGDSpEncddddzJ8/n8WLF3PfffdxyCGHMGvWrDWWmT9/PsuXL+fdd9/lwQcfpH///jz22GNNsTuSJEmSJG0UBjwqhPK7X0VEg+WWL19emr7ssstYtGgRl112GQBz587l+uuvLz1/+umn8+STT7JgwQJmz57NaaedVlrHpZde2tS7IEmSJElSxRjwaLPSuXPn0vTChQtL04sWLSpNV1dXN7h8p06dStNnnXUW7dq146yzzirNq6mpKU1feOGF9OvXjw4dOrDjjjty/fXXs8022wDw7LPPbtiOSJIkSZK0ERnwaLPSs2dPOnbsCMCrr75aml8+fcABBzS4/NqeA2jTpg2QDeZcV0SUegetrZeQJEmSJEmbGwMebVaaNWvG8ccfD2Tj6IwcOZLZs2dzzTXXAFBVVcVxxx0HfBjIDBw4sLT8t771rdL0ddddx+LFi7nuuutK8/r37w/Afffdx/HHH88jjzzCkiVLeOuttzjjjDN47733APj0pz9d0f2UJEmSJKkpGfBoszNs2DC6desGwIABA+jatStjx44FYMiQIXTv3r3BZU866SQOPfRQAC655BLat2/PJZdcAkDv3r055ZRTgKwHz913381hhx1G27Zt6dKlC7/61a8AaNu2LVdccUWldk+StBWaPn06AwYMoLq6mtatW7Pffvtxyy23NHr5+++/n89+9rN07NiRNm3a0LNnTwYOHLjaGHWnnXYa++67L9tttx0tWrSgurqao446ikceeaQCeyRJkjY3Bjza7HTp0oUxY8Zw4okn0qlTJ1q1akXv3r25+eabufjii9e6bLNmzbjvvvu44IIL6N69O1VVVey0006ceeaZPProo6VLtA4++GCGDBlC3759qa6upkWLFnTt2pUTTzyR8ePHr/NSL0mSGmv27Nn069ePkSNHMm/ePJYtW8bEiRMZNGgQl19++TqXv+qqqzj66KP5v//7PxYuXMjSpUuZOnUqw4cPZ+XKlaVyw4cP54UXXuDdd99l5cqVzJs3j7/85S8cfvjhjBkzppK7KEmSNgMGPNos7bzzzqWG8NKlS6mpqSn1vqmVUiKlxG233bba/LZt23LllVcyffp0PvjgA2bMmMF1113HtttuWyqz4447MnToUJ5++mnmzJnD8uXLefPNNxk5ciR77rnnRthDSdLWYsiQIbzxxhsAjBgxglmzZtG3b18g67U6ffr0BpetqanhwgsvBOCwww6jpqaGJUuW8Nprr3H11VfTrNmHTbkf//jHTJgwgcWLFzNz5ky+8pWvALBy5UpGjRpVqd2TJEmbCQMeSZKkClm1alUpXOnVqxcDBgygS5cuDB48GIDly5czevToBpf/xS9+wcqVK2nXrh2jR4+md+/etGnTht13353BgwevFvBceuml7LfffrRt25Zu3boxaNCg0nNVVVUV2kNJkrS5aLGpK6At3NCOm7oGG8/QBZu6BpKkzcyUKVNYsCD7fthjjz1K88unn3vuuQaXf/TRRwHo3r07Z5xxBg8//DDLli3j0EMP5aqrrqq312lKiZkzZ3LTTTcBsM0223DyySc3yf5IkqTNlwGPJElShcybN6803aFDh3qn58yZ0+DyM2bMALI7S06aNKk0/7777mPs2LHU1NTQtWvX0vzzzz+fq6++erXt/OEPf6B3794btiOSJGmz5yVakiRJG1n53a8iosFyy5cvL01fdtllLFq0iMsuuwyAuXPncv311691OwsXLuTYY49l/PjxG1hjSZK0uTPgkSRJqpDOnTuXphcuXFiaXrRoUWm6urq6weU7depUmj7rrLNo164dZ511VmleTU3NauWvuuoqVq5cycyZMzn33HNL2x06dOhH3gdJklQMBjySJEkV0rNnTzp2zMaje/XVV0vzy6cPOOCABpdf23MAbdq0WWNes2bN6NatG0OGDCnNe+211xpdZ0mSVEwGPJIkSRXSrFkzjj/+eCAbR2fkyJHMnj2ba665BsjubnXccccB2aVaEcHAgQNLy3/rW98qTV933XUsXryY6667rjSvf//+ANxzzz1ce+21vPzyyyxdupS33nqL//zP/yyV22233Sq2j5IkafNgwCNJklRBw4YNo1u3bgAMGDCArl27MnbsWACGDBlC9+7dG1z2pJNO4tBDDwXgkksuoX379lxyySUA9O7dm1NOOQWAadOmMXjwYHr16kWbNm3o0qVLabDl1q1bl5aRJElbLgMeSZKkCurSpQtjxozhxBNPpFOnTrRq1YrevXtz8803c/HFF6912WbNmnHfffdxwQUX0L17d6qqqthpp50488wzefTRR0uXaB188MF8/etfZ5dddqFNmzZUVVWxyy67cNJJJ/HMM8/Qr1+/jbGrkiRpE/I26ZIkSRW28847M3LkyLWWKb+zVrm2bdty5ZVXcuWVVza4bN++fRk9evQG1VGSJBWbPXgkSZIkSZIKzh48kiRJ62Nox01dg41j6IJNXQNJkrQe7MEjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVX0YAnIr4QEa9ExOSIuLCBMt+MiJci4sWI+G0l6yNJkiRJkrQlalGpFUdEc+AXwBHATODZiLg3pfRSWZmPARcBn04pvRMRO1SqPpIkSZIkSVuqSvbgOQiYnFKamlL6ALgL+GqdMqcCv0gpvQOQUppTwfpIkiRJkiRtkSoZ8HQDZpQ9npnPK7cHsEdEPBkRT0fEF+pbUUR8PyLGRcS4uXPnVqi6kiRJkiRJxbSpB1luAXwMOBQ4AbgpIratWyil9OuUUp+UUp/q6uqNW0NJkiRJkqTNXCUDnjeA7mWPd8rnlZsJ3JtSWp5Smga8Shb4SJIkSZIkqZEqGfA8C3wsInaNiJbAt4B765T5E1nvHSKiM9klW1MrWCdJkiRJkqQtTsUCnpTSCuBM4CFgEnB3SunFiPhJRHwlL/YQMD8iXgL+D7ggpTS/UnWSJEmSJEnaElXsNukAKaUHgAfqzLu0bDoBg/M/SZIkSZIkfQSbepBlSZIkSZIkbSADHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkquEYFPBHRMyJa5dOHRsTZEbFtRWsmSZIkSZKkRmlsD57fAysjYnfg10B34LcVq5UkSZIkSZIarbEBz6qU0grga8B1KaULgK6Vq5YkSZIkSZIaq7EBz/KIOAH4DnBfPq+qMlWSJEmSJEnS+mhswPNd4GDg8pTStIjYFRhRuWpJkiRJkiSpsVo0plBK6aWI+P+AnfPH04CfVrJikiRJkiRJapzG3kXry8AE4C/54/0j4t4K1kuSJEmSJEmN1NhLtIYCBwHvAqSUJgC7VaRGkiRJkiRJWi+NHmQ5pbSgzrxVTV0ZSZIkSZIkrb9GjcEDvBgRJwLNI+JjwNnAmMpVS5IkSZIkSY3V2B48ZwF7A8uA3wILgHMqVCdJkiRJkiSth3X24ImI5sD9KaXDgIsrXyVJkiRJkiStj3X24EkprQRWRUTHjVAfSZIkSZIkrafGjsGzGHg+Ih4G3qudmVI6uyK1kiRJkiRJUqM1NuD5Q/4nSZIkSZKkzUyjAp6U0vCIaAnskc96JaW0vHLVkiRJkiRJUmM1KuCJiEOB4cDrQADdI+I7KaXHKlYzSZIkSZIkNUpjL9G6Gvh8SukVgIjYA7gT+GSlKiZJkiRJkqTGWeddtHJVteEOQErpVaCqMlWSJEmSJEnS+mhsD55xEXEzcEf++NvAuMpUSZIkSZIkSeujsQHPD4AzgNrboj8O3FCRGkmSJEmSJGm9NDbgaQH8LKV0DUBENAdaVaxWkiRJkiRJarTGjsHzd6BN2eM2wN+avjqSJEmSJElaX40NeFqnlBbXPsint6lMlSRJkiRJkrQ+GhvwvBcRn6h9EBF9gPcrUyVJkiRJkiStj8aOwXMO8LuIeDN/3BU4viI1kiRJkiRJ0npZaw+eiDgwIrqklJ4FPg6MApYDfwGmbYT6SZIkSZIkaR3WdYnWr4AP8umDgR8DvwDeAX5dwXpJkiRJkiSpkdZ1iVbzlNLb+fTxwK9TSr8Hfh8REypaM0mSJEmSJDXKunrwNI+I2hDoc8A/yp5r7Pg9kiRJkiRJqqB1hTR3Ao9GxDyyu2Y9DhARuwMLKlw3SZIkSZIkNcJaA56U0uUR8Xeyu2b9NaWU8qeaAWdVunKSJEmSJElat3VeZpVSerqeea9WpjqSJEmSJElaX+sag0eSJEmSJEmbOQMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIIz4JEkSZIkSSo4Ax5JkiRJkqSCM+CRJEmSJEkqOAMeSZIkSZKkgjPgkSRJkiRJKjgDHkmSJEmSpIKraMATEV+IiFciYnJEXLiWcl+PiBQRfSpZH0mSJEmSpC1RxQKeiGgO/AI4CtgLOCEi9qqnXHvgh8DYStVFkiRJkiRpS1bJHjwHAZNTSlNTSh8AdwFfrafcfwI/BZZWsC6SJEmSJElbrEoGPN2AGWWPZ+bzSiLiE0D3lNL9a1tRRHw/IsZFxLi5c+c2fU0lSZIkSZIKbJMNshwRzYBrgPPWVTal9OuUUp+UUp/q6urKV06SJEmSJKlAKhnwvAF0L3u8Uz6vVntgH+CRiHgd+BRwrwMtS5IkSZIkrZ9KBjzPAh+LiF0joiXwLeDe2idTSgtSSp1TSj1SSj2Ap4GvpJTGVbBOkiRJkiRJW5yKBTwppRXAmcBDwCTg7pTSixHxk4j4SqW2K0mSJEmStLVpUcmVp5QeAB6oM+/SBsoeWsm6SJIkSZIkbak22SDLkiRJkiRJahoGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBWfAI0mSJEmSVHAGPJIkSZIkSQVnwCNJkiRJklRwBjySJEmSJEkFZ8AjSZIkSZJUcAY8kiRJkiRJBVfRgCcivhARr0TE5Ii4sJ7nB0fESxExMSL+HhG7VLI+kiRJkiRJW6KKBTwR0Rz4BXAUsBdwQkTsVafYc0CflFJvYDRwZaXqI0mSJEmStKWqZA+eg4DJKaWpKaUPgLuAr5YXSCn9X0ppSf7waWCnCtZHkiRJkiRpi1TJgKcbMKPs8cx8XkNOAR6s74mI+H5EjIuIcXPnzm3CKkqSJEmSJBXfZjHIckQMAPoA/1Pf8ymlX6eU+qSU+lRXV2/cykmSJEmSJG3mWlRw3W8A3cse75TPW01EHA5cDPRPKS2rYH0kSZIkSZK2SJXswfMs8LGI2DUiWgLfAu4tLxARBwC/Ar6SUppTwbpIkiRJkiRtsSoW8KSUVgBnAg8Bk4C7U0ovRsRPIuIrebH/AdoBv4uICRFxbwOrkyRJkiRJUgMqeYkWKaUHgAfqzLu0bPrwSm5fkiRJkiRpa7BZDLIsSZIkSZKkj86AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCs6AR5IkSZIkqeAMeCRJkiRJkgrOgEeSJEmSJKngDHgkSZIkSZIKzoBHkiRJkiSp4Ax4JEmSJEmSCq6iAU9EfCEiXomIyRFxYT3Pt4qIUfnzYyOiRyXrI0mSJEmStCWqWMATEc2BXwBHAXsBJ0TEXnWKnQK8k1LaHbgW+Gml6iNJkiRJkrSlqmQPnoOAySmlqSmlD4C7gK/WKfNVYHg+PRr4XEREBeskSZIkSZK0xYmUUmVWHPEN4AsppUH545OAvimlM8vKvJCXmZk/npKXmVdnXd8Hvp8/3BN4pSKV1tp0Buats5S0dh5HagoeR2oKHkdqCh5HaioeS2oKHkdbj11SStV1Z7bYFDVZXymlXwO/3tT12JpFxLiUUp9NXQ8Vm8eRmoLHkZqCx5GagseRmorHkpqCx5EqeYnWG0D3ssc75fPqLRMRLYCOwPwK1kmSJEmSJGmLU8mA51ngYxGxa0S0BL4F3FunzL3Ad/LpbwD/SJW6ZkySJEmSJGkLVbFLtFJKKyLiTOAhoDnwm5TSixHxE2BcSule4BZgRERMBt4mC4G0efISOTUFjyM1BY8jNQWPIzUFjyM1FY8lNQWPo61cxQZZliRJkiRJ0sZRyUu0JEmSJEmStBEY8EiSJEmSJBWcAU/BRESKiKvLHp8fEUPXscxXIuLCJtj2wIiYGxETIuLFiBgdEdts6Hq1fiJicROso09E/Hwtz/eIiBMbW76e5R+JiFcioiYino2I/Tewyk2mqf4/qOmVH9sR8cWIeDUidomIoRGxJCJ2aKDsen8uassWERfn31MT8++sIRHx33XK7B8Rk/Lp1yPi8TrPT4iIFzZmvbdEEbGyrN1QExHnRcRHan9GxE8i4vC1PH96RJz80WsLEbFvXt8JEfF2REzLp/+2IeutZztHRcS4iHgpIp6r/QzLP+/Ob8LtjCmb/p/8ffifpnitNmdlx11NRPwzIvpVYBvr1TZqxPpOjogXIuL5/Jg4P59/W0R8o4m28W8RMbrs8Z355+S56/r/pTWVHWcvRMSfI2LbJlrvwIi4vinWVWe9te3z2s+4Jjmu6tnOar8jtHFVbJBlVcwy4NiI+O+U0rzGLJAPaF33DmYf1aiU0pkAEfFb4Hjg1iZatzaSlNI4YNxaivQATgR+28jy9fl2SmlcRHwX+B/giI9Q1dVERPOU0soNWUcT/39QBUTE54CfA0emlP4VEQDzgPOA/6+eRdb7c1Fbrog4GDga+ERKaVlEdAb2Am4DLior+i3gzrLH7SOie0ppRkT02mgV3vK9n1LaHyAPaX8LdACGrO+KUkqXruP5Gz9KBeus43lgf8h+WAP3pZRGl5eJiBYppRUfdRsRsQ9wPfCllNLLEdEc+P5HrvRapJTKg43vA9t/lO/RDd3nTaD8uDsS+G+gf1Nu4CO2jeoVEUcB5wCfTym9GRGtgCYP4FJKb5LduZiI6AIcmFLa/aOsq4DHRCWUH2fDgTOAyzdpjdbt2/mx22gf4b3uQdnvCG1c9uApnhVko6OfW/eJiPhyRIzNU/+/RcSO+fyBEXF9RHSMiH/VnjmLiLYRMSMiqiKiZ0T8JSLGR8TjEfHxtVUiIloAbYF3Gtp2RDSLiNciojov0ywiJkdEdf73+8h6dzwbEZ/Oy/QvS5Wfi4j2TfnibakiOxP9dH4W5o8RsV0+/8D48Az2/0R+NjoiDo2I+/Lp+l7zK4BD8nnn1infLiJuzc8wTYyIr6+jek8B3fJl20bEbyLimXxbX83nbxMRd0d2JvOP+bHUJ39ucURcHRE1wMERMSBffkJE/Coimud/t8WHZ77OzZc9O1/nxIi4K59XOiuSn2H4R/783yNi53z+bRHx84gYExFTo0JnOLSmiPgMcBNwdEppStlTvwGOj4jt61mswc9FbZW6AvNSSssAUkrzUkqPAe9ERN+yct9k9YDnbrKTFgAn1HlOTSClNIcsZDgzMs3z76Zn88/h02rLRsT/l3+e10TEFfm8Uk+GiLii7PP9qnxeqffLWr4XH4mIn+bfI69GxCGNqXu+3P9GxDjghxHxyYh4NLJ200MR0TUv15j21I+Ay1NKL+evy8qU0i/r2eap+WtTE1mbaZt8/nH5911NRDyWz9u77LtxYkR8LJ+/OP/3XqAdMD4ijq/zWtVb5/z1vjEixgJXNuZ12kx14MP2arv8+/6f+fH11dpCEfEfkfVueCKyni21r09j2lJDI2vfPJK3G85e13rruAg4Pw9gSCktSyndVLdQRFyaHxMvRMSvI7IzIA20d9Zo30XW7qntmfhXoFv+/CF1/n81dHyv9v9gQ96ULVB5e/egiHgqf93HRMSe+fyBEfGH/P/baxFR+n8VEd/NP5OeAT5dNn9tbdVfRvY5NzU/Hn8TEZMiC6cbJSK2j4g/5et/OiJ65/OHRsSIiHiS7K7X6/PbbbXfERv6wmo9pZT8K9AfsJjsi+p1oCNwPjA0f247Prwz2iDg6nx6IHB9Pn0PcFg+fTxwcz79d+Bj+XRf4B/1bHsgMBeYALwFPA40X8e2hwDn5NOfB36fT/8W+Pd8emdgUj79Z+DT+XQ7oMWmfs03tz9gcT3zJgL98+mfAP+bT78AHJxPXwG8kE8fSnZWst7XvPz5esr/tHb9te99PfV5BOiTT58D/Fc+/V/AgHx6W+BVsqDwfOBX+fx9yH6w1y6fgG/m073y+lblj28gO8P1SeDhsu1vm//7JtCqzrzy/w9/Br6TT38P+FM+fRvwO7IQfC9g8qZ+37eGP2A58DbQu878ofkxcikwLNX5f8BaPhf92/r+8s+xCfnnyw1ln43nA9fm058CxpUt8zqwJzAmf/xc/n//hU29P0X/o/7vrHeBHcnCnkvyea3IekPsChwFjAG2yZ/bPv/3NrLeB52AV/iw3bFt/u9Qsh/J0PD34iN82Eb5IvC3tdT9NuAbZcvdkE9X5fWrzh8fD/wmn25Me+qfwH4NbLN8HzqVzb8MOCuffh7oVmffryM7Mw/QEmhT9/WvM12+nXrrnO//feRtvSL9ASvzz4GXgQXAJ/P5LYAO+XRnYDIQwIF5+dZAe+C1stenMW2pofkx0Spf7/z8OGlwvXXq+zbQsRHH4fZl80cAX86n62vv1Ne+61FW/9J0nf9fazu+HyH/f+Dfh/+ngOZk7cYv5I87kP+GAQ7nw98/A4GpZG2V1sC/gO5kJyamA9Vk/3+fpHFt1bvy4/erwEJgX7K263hg/3rq+wjZZ+eE/K8T2WfHkPz5zwITyo7p8Xz4WdLo327U+R3h38b98xKtAkopLYyI24GzgffLntoJGJWn7C2BafUsPorsg/r/yLqn3xAR7YB+wO/yEwGQfUHVZ1RK6cz8jMEvgAvIvuwa2vZvyEKl/yX7ULo1n384sFfZ9jrk9XgSuCYiRgJ/SCnNXPcrsnWLiI5kX+aP5rOGk72X2wLtU0pP5fN/S3bZQl1rvOZl70t9Dic7dgBIKb3TQLmREdGS7MN+/3ze54GvlJ29ak32JfHvwM/y9b0QERPL1rMS+H0+/TmyMOfZvI5tgDlkXy67RcR1wP1kZ6Uga+CPjIg/AX+qp44HA8fm0yNY/Qzln1JKq4CXIu8Np4pbTtaoPIX6zwz+HJgQ+dn6cmv5XNRWJqW0OCI+CRwCHEb23XQh2fffmIg4jzUvz4LsB9k7EfEtYBKwZCNWe2v1eaB3fNhLsiPwMbLvmVtTSksAUkpv11luAbAUuCXvQXFf+ZMNfS+WFflD/u94sh+5jTUq/3dPspMRD+ffRc2BWevZnmqMfSLiMrITIu2Ah/L5TwK3RcTdfLgvTwEXR8ROZN/lrzVmA42o8+/SBl4avYmUXzpzMHB7ZJfGBfBfkfUWXUXW42JHsh4T96SUlgJLI+LP+bLb0ri2FMD9Kes5uCwi5qxtvRvgsIj4EbANsD3wIlkbqL72zvq272rVe3yXPT+qvoW2Um0iYgLZcTQJeDif3xEYHllPukQWmtX6e0ppAUBEvATsQhYKPpJSmpvPHwXskZdfW1v1zymlFBHPA2+l7BJTIuJFss+2CfXUebVLtCLi34GvA6SU/hERnSKiQ/70vSml2jZVo3+7NfI4U4V4iVZx/S/Zj6C2ZfOuI0t79wVOI/vxXNe9wBciu8zhk8A/yI6Dd1NK+5f9rXX8gZRSIvtC+czatp1SmgG8FRGfBQ4CHszLNwM+Vba9bimlxSmlK8h6ALUBnox1XCqmDVfB1/zbwG5kDevr8nkBfL3sfd85pTRpHetZWta4DGB42fJ7ppSG5iHTfmRnJk4Hbs7Lf4ksiPwEWSi0PqH2srJpv6k2jlVkl80cFBE/rvtkSuldssb1GQ0s/7+s+bmorVDKLnl5JKU0BDiT7HNnBtnJh/5kjdn6fqSMIvvM8PKsComI3ciC+zlkn61nlX2m75pS+uva1wApGwviIGA02Y/tv6xnNWo/31eyfuNRvpf/G8CLZfXeN6X0eRrfnnqRrA22LrcBZ+Ztq2F82LY6HbiE7Mz/+IjolFL6LfAVsoD7gbzd1RjrqvN7DS1YFHk405msd8S3838/mQdAb1F/e/mjKG83rO+xtc5jIiJak/VK/EZ+TNzEh3Vfo72zAe27ho7vWoU/JppQbZC4C9nrVts++U/g/1JK+wBfZvVjbEOOk7pq17WqznpXbeB6a5W/1/52KwgDnoLKz2bdTfZjplZH4I18+jsNLLcYeJast8R9eSN4ITAtIo4DiMx+jajGvwO1Y2Ssbds3A3ew+lmgvwJn1RaI/C5LEdEzpfR8SumneT39kFiH/CzAO/HhOAInAY/mP4YXxYdjTnyrvuUbeM0XkXUlrs/DlP3AjnxcgwbqloD/AD6Vf+A/BJyV9wAjIg7Iiz5J9sOeiNiLrItpff4OfCPyuynl1w3vEtkgqs1SSr8na/R+IrKxprqnlP6PbGDejmRnQMuN4cPX5dtklx1qE8rP2H8J+HZEnFJPkWvIQuQ1Gi4NfC5qKxMRe+ZnTWvtT9YNHrLg5lpgagM9RP9Idnb0oXqe0waKbEy+G8lOCCWy1/kHEVGVP79HRLQl+575bnw45sz2ddbTjuxylgfIxt5arc3S0PdiE+7KK0B13jOEyMYy3Hs92lP/A/w4IvbIyzWLiNPrKdeerGdQFdl3FHn5nimlsSkbdHou0D0PzqamlH5O1nO6d2N2ZAPagIWRtz+ak/XS6wjMSSktj4jDyH6YQ9YO+XJEtM6Pr6OhdGJhnW2ptah3vfX4b+B/Ihv4mIhoGRGD6pSpDQnm5euqHS+n3vbOBrSp6z2+G7nsVilvu5wNnJefTCz/XTSwEasYC/TPe89UAceVPVfpturj+XqJiEPJxrBbWE+59fnttrbfEaowL9EqtqvJzkzWGkrWxfYdsp45uzaw3CiyrsqHls37NvDLiLiErBvhXUBNPcsen3flawbM5MMPrbVt+16yS7NuLZt3NvCLyC7FaQE8Rtbz4pz8C3cV2dmMB1Fd20RE+Q+Ta8hCtRvzxvBU4Lv5c6cAN0XEKrLG7YJ61lffa74KWBnZwMa3kY1HUesysvfuBbIzD8P4sIv4GlJK70d2+9cLyI7X/wUm5g2SaWSNnRvIurK+RHa9/Iv11TWl9FJ+jP41X345Wdj0PnBrfHjr3YvIGnN3RNZVP4Cfp5TerdNt9Kx8uQvIGsnfRZtcSuntiPgC8FhEzK3z3LyI+CMND6hc93NRW592wHWRXVqxgmyMjdo7FP2O7FK/s+pbMKW0iGycMexi3mRqL2GoIns/RpB9b0F2AqgH8M88+J8LHJNS+kv+42FcRHwAPACU9+prD9yT92gIYHA9223oe3GDpZQ+iOyysp/n3zEtyL7bXqQR7amU0sSIOAe4M69fos5lZrn/IPvhNzf/t/YH0//kIWaQnfioIfthf1JELAdmk41511iNbQMWSe1xB9nr9J2U0srILiP5c2SXtIwja3OQUno2soGoJ5L16nmeD9shjWlL1Wsd6y0v90Bkl4P/Lf+/kMiGOSgv825E3EQ2JtBssh/T0HB75z/rad91bUSd13Z8qwEppefy3zUnkJ0oGJ7/n7q/EcvOioihZJdavsvql1ZVuq06FPhNXvclNNBJgPX77bba74iU0rVNXGetRe3gdFLFRHY3pGtTSo26U4WaTkS0y3ttEdkYFF1TSvWNbbJJRXaL2KqU0tKI6An8DdgzpfTBJq6aJEnaCtS2mfLQ7THg+ymlf25oW6qh9VZkJyRt9ezBo4rKvwh/QFnXYm1UX4qIi8j+r/+LxnUT3RS2Af4v75YawP8z3JEkSRvRryO7TLw12Xh/tSHMhralGlqvJDU5e/BIkiRJkiQVnIMsS5IkSZIkFZwBjyRJkiRJUsEZ8EiSJEmSJBWcAY8kSZIkSVLBGfBIkiRJkiQV3P8Pnidv+W8tingAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpos = np.arange(7)\n",
    "\n",
    "plt.figure(figsize =(16,8))\n",
    "\n",
    "plt.xticks(xpos, score_df['Model Name'] )\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.title(\"Scores of Various Binary Classification Models\")\n",
    "testbar = plt.bar(xpos - 0.1, score_df['TrainScore'],width = 0.4, label = \"Train Score\")\n",
    "trainbar = plt.bar(xpos + 0.3, score_df['TestScore'], width = 0.4, label = \"Test Score\")\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "count = 0\n",
    "for count,rect in enumerate(testbar + trainbar):\n",
    "    height = rect.get_height()\n",
    "    \n",
    "    if (count==0 or count%2==0) and (count<=7):\n",
    "        plt.text(rect.get_x() + rect.get_width()/2.0, height, '%0.2f' % score_df[\"TrainScore\"][count], ha='center', va='bottom', weight = \"bold\", fontsize = 15)\n",
    "    elif(count>=7):\n",
    "        cnt = count-7\n",
    "        plt.text(rect.get_x() + rect.get_width()/2.0, height, '%0.2f' % score_df[\"TestScore\"][cnt], ha='center', va='bottom' weight = \"bold\", fontsize = 15)\n",
    "    else:\n",
    "        plt.text(rect.get_x() + rect.get_width()/2.0, height, '%0.2f' % score_df[\"TestScore\"][count], ha='center', va='bottom')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Model Name</th>\n",
       "      <th>TestScore</th>\n",
       "      <th>TrainScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.991597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bagging Classifier</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.991597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.983193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.983193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.627451</td>\n",
       "      <td>0.722689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model Name  TestScore  TrainScore\n",
       "1       Logistic Regression   0.980392    0.991597\n",
       "4  Decision Tree Classifier   0.960784    1.000000\n",
       "5        Bagging Classifier   0.960784    0.991597\n",
       "2                       KNN   0.960784    0.983193\n",
       "6             Random Forest   0.960784    0.983193\n",
       "0               Naive Bayes   0.647059    0.705882\n",
       "3                       SVM   0.627451    0.722689"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df.sort_values(by=[\"TestScore\", \"TrainScore\"], ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, According to the bar plot, we can see that top three models after sorting are:\n",
    "- Logistic Regression\n",
    "- Decision Tree Classifier\n",
    "- Bagging Classifier\n",
    "\n",
    "\n",
    "**but they mostly overfit the data** and if we had long list of observation of the survey they would have just learnt them with the noise and would make no sense.\n",
    "\n",
    "**_I selected Random Forest Model_**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[28  0]\n",
      " [ 2 21]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = rf.predict(X_test)\n",
    "score = rf.score(X_test, y_pred)\n",
    "\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "\n",
    "plot_confusion_matrix(rf, X_test, y_test)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have,\n",
    "\n",
    "- 28, True Positives\n",
    "- 0,  False Positives\n",
    "- 2,  False Negatives\n",
    "- 21, True Negatives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['random_forest_divorce_predictor.pkl']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib_file = \"random_forest_divorce_predictor.pkl\"\n",
    "joblib.dump(rf, joblib_file)\n",
    "\n",
    "#Model was saved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### The model is imported back again into the notebook just to check it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load from file\n",
    "joblib_model = joblib.load(joblib_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 96.08 %\n"
     ]
    }
   ],
   "source": [
    "score = joblib_model.score(X_test, y_test)\n",
    "print(\"Test score: {0:.2f} %\".format(100 * score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = rf.predict([[4,4,4,3,1,4,1,4,2,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnb_cv = cross_val_score(mnb,x,y).mean()\n",
    "lr_cv  = cross_val_score(lr,x,y).mean()\n",
    "knn_cv = cross_val_score(knn,x,y).mean()\n",
    "clf_cv = cross_val_score(clf,x,y).mean()\n",
    "svm_cv = cross_val_score(svm,x,y).mean()\n",
    "bg_cv  = cross_val_score(bg,x,y).mean()\n",
    "rf_cv  = cross_val_score(rf,x,y).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = [['Naive Bayes',mnb_cv],\n",
    "          ['Logistic Regression',lr_cv],\n",
    "          ['KNN', knn_cv],\n",
    "          ['SVM', svm_cv],\n",
    "          ['Decision Tree Classifier', clf_cv],\n",
    "          ['Bagging Classifier',bg_cv],\n",
    "          ['Random Forest',rf_cv]]\n",
    "\n",
    "cv_df = pd.DataFrame(cv, columns = [\"Model Name\", \"Cv_scores\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpos = np.arange(7)\n",
    "\n",
    "plt.figure(figsize =(16,8))\n",
    "\n",
    "plt.xticks(xpos, cv_df['Model Name'] )\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.title(\"CV Scores of Various Binary Classification Models\")\n",
    "testbar = plt.bar(xpos - 0.1, cv_df['Cv_scores'],width = 0.4, label = \"Cross Validation Score\")\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "for count,rect in enumerate(testbar):\n",
    "    \n",
    "    height = rect.get_height()\n",
    "    plt.text(rect.get_x() + rect.get_width()/2.0,\n",
    "             height,\n",
    "             '%0.5f' % cv_df[\"Cv_scores\"][count],\n",
    "             ha='center',\n",
    "             va='bottom',\n",
    "             weight = \"bold\",\n",
    "             fontsize = 15)\n",
    "    \n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Model Name</th>\n",
       "      <th>Cv_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.976471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.976471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bagging Classifier</td>\n",
       "      <td>0.976471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.976471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.947059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.852941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.694118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model Name  Cv_scores\n",
       "1       Logistic Regression   0.976471\n",
       "2                       KNN   0.976471\n",
       "5        Bagging Classifier   0.976471\n",
       "6             Random Forest   0.976471\n",
       "4  Decision Tree Classifier   0.947059\n",
       "0               Naive Bayes   0.852941\n",
       "3                       SVM   0.694118"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df.sort_values(by=[\"Cv_scores\"], ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the **Cross Validation Scores**, it seems that Random Forest model does very silimar to the top three model, being at the fourth number in the list.\n",
    "\n",
    "It also doesn't **overfit** the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project is deployed on Heroku, here is the link : https://divorce-prediction.herokuapp.com/ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **_This project was developed just for fun and is not accurate, since the data  was not enough to even remotely predict the subjective behaviour of the human mind.\n",
    "Thanks!_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 },
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