07-Develop-Machine-Learning-Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit Machine Learning Models\n",
    "- Train and Test Models using a Variety of Appropriate [Classification] Algorithms\n",
    "- Tune (Optimize) Each Model''s Hyperparameters\n",
    "- Evaluate the Optimally-Fitted Models Against a Hold-Out (Out-of-Sample) Dataset\n",
    "- Perform K-Fold Cross-Validation Using Each Model\n",
    "- Determine Each Model''s Accuracy and Identify the Best Model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SciKit-Learn Version: 0.19.1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import sklearn\n",
    "print(\"SciKit-Learn Version:\", sklearn.__version__)\n",
    "\n",
    "if sklearn.__version__ >= '0.18.0':  # Starting with sklearn version 18.0\n",
    "    from sklearn.model_selection import train_test_split, cross_val_score\n",
    "    from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "else:\n",
    "    from sklearn.cross_validation import train_test_split, cross_val_score\n",
    "    from sklearn.cross_validation import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import RobustScaler, StandardScaler, LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report \n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Globals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_percent = 0.2\n",
    "CLASSIFICATION_TYPE = 'Binary'\n",
    "\n",
    "###################################################################################################################\n",
    "# Calculate the Null Accuracy Score: Accommodates Binary or Multiple Classification Type\n",
    "###################################################################################################################\n",
    "def null_accuracy_score(labels, classification_type):  #NOTE: 'labels' must be a pd.Series\n",
    "    if classification_type == CLASSIFICATION_TYPE:\n",
    "        return max(labels[:,].astype(int).mean(), 1 - labels[:,].astype(int).mean())\n",
    "    \n",
    "    elif classification_type == CLASSIFICATION_TYPE:\n",
    "        return labels.value_counts().head(1).item() / len(labels) \n",
    "    \n",
    "    else:\n",
    "        raise ValueError(\"Bad input {0}. Must specify either 'Binary' or 'Multiple'.\".format(classification_type))\n",
    "        \n",
    "\n",
    "###################################################################################################################\n",
    "# Print Evaluation Metrics: Accuracy and Null Accuracy Scores\n",
    "###################################################################################################################\n",
    "def show_accuracy(y_actuals, y_predictions, classification_type):\n",
    "    accuracy = accuracy_score(y_actuals, y_predictions)\n",
    "    null_accuracy = null_accuracy_score(y_actuals, classification_type)\n",
    "\n",
    "    print('----------------------------------------------------------------------------------')\n",
    "    print('Accuracy (The Percentage of Correct Predictions): %0.3f' % accuracy)\n",
    "    print('----------------------------------------------------------------------------------')\n",
    "    print('Null Accuracy (Achieved by Always Predicting the Most Frequent Class): %0.3f' % null_accuracy)\n",
    "    print('----------------------------------------------------------------------------------')   \n",
    "    print('True:', list(y_actuals[0:10]))\n",
    "    print('Pred:', y_predictions[0:10].tolist())\n",
    "    print('----------------------------------------------------------------------------------\\n')\n",
    "        \n",
    "\n",
    "###################################################################################################################\n",
    "# Print Skree Plots: Compare the Explained Variances per Component Between Train/Test and Hold-Out Datasets\n",
    "###################################################################################################################\n",
    "def show_skree_plots(components, variances_explained):\n",
    "    fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(7, 7))\n",
    "    \n",
    "    ax0.set_title(\"PCA Explained Variances: Train/Test Data\")\n",
    "    ax0.scatter(components, variances_explained[0])\n",
    "    ax0.set_ylabel('Explained Variance (Eigenvalues)')\n",
    "    ax0.grid(True)\n",
    "\n",
    "    ax1.set_title(\"PCA Explained Variances: Hold-Out Data\")\n",
    "    ax1.scatter(components, variances_explained[1])\n",
    "    ax1.set_ylabel('Explained Variance (Eigenvalues)')\n",
    "    ax1.set_xlabel('Number of Components (Eigenvectors)')\n",
    "    ax1.grid(True)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "        \n",
    "\n",
    "###################################################################################################################\n",
    "# Print a Confusion Matrix:  Accommodates Binary or Multiple Classification Type\n",
    "###################################################################################################################\n",
    "def show_confusion_matrix(y_actuals, y_predictions, y_probabilities, classification_type):\n",
    "    print('-------------------------------------------------------')\n",
    "    print('Confusion Matrix')\n",
    "    print('-------------------------------------------------------')\n",
    "\n",
    "    cm = pd.crosstab(y_actuals, y_predictions, rownames=['Actuals'], colnames=['Predictions'])\n",
    "    print(cm)\n",
    "        \n",
    "    if classification_type == CLASSIFICATION_TYPE:\n",
    "        print('-------------------------------------------------------')\n",
    "        print('Area Under the Curve (AUC): %0.3f' % roc_auc_score(y_actuals, y_probabilities[:, 1]))\n",
    "        \n",
    "    print('-------------------------------------------------------\\n')\n",
    "    \n",
    "    \n",
    "###################################################################################################################\n",
    "# Print a Classification Report\n",
    "###################################################################################################################\n",
    "def show_classification_report(y_actuals, y_predictions):\n",
    "    print('-------------------------------------------------------')\n",
    "    print('Classification Report')\n",
    "    print('-------------------------------------------------------')\n",
    "    print(classification_report(y_actuals, y_predictions))\n",
    "    print('-------------------------------------------------------\\n')\n",
    "    \n",
    "\n",
    "###################################################################################################################\n",
    "# Print a Receiver Operating Characteristic (ROC) Curve Plot (Binary Classification Only).\n",
    "###################################################################################################################\n",
    "def show_roc_plot(y_actuals, y_probabilities, classifier_algorithm):\n",
    "    plt.figure(figsize=(5,5))\n",
    "    plt.title('ROC Curve (' + classifier_algorithm + ')')\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_actuals, y_probabilities[:, 1])\n",
    "    auc = roc_auc_score(y_actuals, y_probabilities[:, 1])\n",
    "\n",
    "    plt.plot(fpr, tpr, color='darkorange', label='ROC Curve (area = %0.2f)' % auc)\n",
    "    plt.plot([0,1], [0,1], color='steelblue', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataSet Dimensions: (7043, 21)\n"
     ]
    }
   ],
   "source": [
    "# Locate the Data File to be Ingested.\n",
    "data_dir = os.path.join(os.getcwd(), 'Data')\n",
    "source_file = os.path.join(data_dir, 'WA-Telco-Customer-Churn-ML.xlsx')\n",
    "\n",
    "# Read the Data from the Source File\n",
    "df = pd.read_excel(source_file, header=0)\n",
    "\n",
    "# Display the Dimensions of the DataSet Being Used.\n",
    "print(\"DataSet Dimensions:\", df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make Appropriate Data Type Assignments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID            object\n",
       "gender              category\n",
       "SeniorCitizen       category\n",
       "Partner             category\n",
       "Dependents          category\n",
       "tenure                  int8\n",
       "PhoneService        category\n",
       "MultipleLines       category\n",
       "InternetService     category\n",
       "OnlineSecurity      category\n",
       "OnlineBackup        category\n",
       "DeviceProtection    category\n",
       "TechSupport         category\n",
       "StreamingTV         category\n",
       "StreamingMovies     category\n",
       "Contract            category\n",
       "PaperlessBilling    category\n",
       "PaymentMethod       category\n",
       "MonthlyCharges       float32\n",
       "TotalCharges         float32\n",
       "Churn               category\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gender = df.gender.astype('category')\n",
    "df.SeniorCitizen = df.SeniorCitizen.astype('category')\n",
    "df.Partner = df.Partner.astype('category')\n",
    "df.Dependents = df.Dependents.astype('category')\n",
    "df.tenure = pd.to_numeric(df.tenure, downcast='integer', errors='coerce')\n",
    "df.PhoneService = df.PhoneService.astype('category')\n",
    "df.MultipleLines = df.MultipleLines.astype('category')\n",
    "df.InternetService = df.InternetService.astype('category')\n",
    "df.OnlineSecurity = df.OnlineSecurity.astype('category')\n",
    "df.OnlineBackup = df.OnlineBackup.astype('category')\n",
    "df.DeviceProtection = df.DeviceProtection.astype('category')\n",
    "df.TechSupport = df.TechSupport.astype('category')\n",
    "df.StreamingTV = df.StreamingTV.astype('category')\n",
    "df.StreamingMovies = df.StreamingMovies.astype('category')\n",
    "df.Contract = df.Contract.astype('category')\n",
    "df.PaperlessBilling = df.PaperlessBilling.astype('category')\n",
    "df.PaymentMethod = df.PaymentMethod.astype('category')\n",
    "df.MonthlyCharges = pd.to_numeric(df.MonthlyCharges, downcast='float', errors='coerce')\n",
    "df.TotalCharges = pd.to_numeric(df.TotalCharges, downcast='float', errors='coerce')\n",
    "df.Churn = df.Churn.astype('category')\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separate the Predictors from the Response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the Dependant Variable (Target, Response, Label) from the Independant Variables (Predictor).\n",
    "X = df.drop(labels=['customerID', 'Churn'], axis=1)\n",
    "y = df.Churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a Hold-Out Data Set for Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------\n",
      "Train/Test Observations: (5634, 19)\n",
      "Evaluation Observations: (1409, 19)\n",
      "-------------------------------------------------------------------\n",
      "\n",
      "Train/Test Label Distribution:\n",
      " 0    4138\n",
      "1    1496\n",
      "Name: Churn, dtype: int64\n",
      "-------------------------------------------------------------------\n",
      "Evaluation Label Distribution:\n",
      " 0    1036\n",
      "1     373\n",
      "Name: Churn, dtype: int64\n",
      "-------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Divide into Train/Test and Evaluation (Hold-Out) Sets.\n",
    "X, X_eval, y, y_eval = train_test_split(X, y, test_size = hold_out_percent, random_state=42)\n",
    "\n",
    "# Use 80% of the observations for training and testing...\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Train/Test Observations:\", X.shape)\n",
    "\n",
    "# ...And hod out the rest for final evaluation.\n",
    "print(\"Evaluation Observations:\", X_eval.shape)\n",
    "print('-------------------------------------------------------------------\\n')\n",
    "\n",
    "# Examine the distribution of Labels for the two datasets.\n",
    "print(\"Train/Test Label Distribution:\\n\", y.value_counts())\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Evaluation Label Distribution:\\n\", y_eval.value_counts())\n",
    "print('-------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Training and Testing Datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------\n",
      "Training Observations: (4507, 19)\n",
      "Testing Observations: (1127, 19)\n",
      "-------------------------------------------------------------------\n",
      "\n",
      "Training Label Distribution:\n",
      " 0    3316\n",
      "1    1191\n",
      "Name: Churn, dtype: int64\n",
      "-------------------------------------------------------------------\n",
      "Testing Label Distribution:\n",
      " 0    822\n",
      "1    305\n",
      "Name: Churn, dtype: int64\n",
      "-------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Use 60% of the observations for training...\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Training Observations:\", X_train.shape)\n",
    "\n",
    "# ...And use the rest for testing.\n",
    "print(\"Testing Observations:\", X_test.shape)\n",
    "print('-------------------------------------------------------------------\\n')\n",
    "\n",
    "# Examine the distribution of Labels for the two datasets.\n",
    "print(\"Training Label Distribution:\\n\", y_train.value_counts())\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Testing Label Distribution:\\n\", y_test.value_counts())\n",
    "print('-------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform Scalar Normalization of Each Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "\n",
    "X = sc.fit_transform(X)\n",
    "X_eval = sc.transform(X_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use Principal Component Analysis (PCA) for Parameter Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Number of Components to Target\n",
    "n_components = 18\n",
    "\n",
    "# Instantiate a List to Hold Collections of PCA Explained Variances.\n",
    "variances = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Perform PCA Feature Reduction on the Train/Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.22109351 0.12178083 0.07855374 0.06439829 0.05586759 0.05281484\n",
      " 0.04937758 0.04587222 0.04240616 0.04123619 0.03776866 0.03702492\n",
      " 0.03675305 0.02998517 0.02966082 0.02514756 0.01701584 0.01041839]\n",
      "-------------------------------------------------------------------\n",
      "Total Variance Explained: 0.9972\n",
      "-------------------------------------------------------------------\n",
      "Wall time: 201 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "pca = PCA(n_components=n_components)\n",
    "\n",
    "# Reduce the data, outputting an ndarray\n",
    "X_train = pca.fit_transform(X_train)\n",
    "X_test = pca.transform(X_test)\n",
    "\n",
    "# Get the explained variance associated with each component.\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "\n",
    "# Append the explained variances to the Variances List.\n",
    "variances.append(explained_variance)\n",
    "\n",
    "# Print out the explained variance associated with each component.\n",
    "print(explained_variance)\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Total Variance Explained: %0.4f\" % sum(explained_variance))\n",
    "print('-------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Perform PCA Parameter Reduction on the Hold-Out Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.22268568 0.12169782 0.07852919 0.06392973 0.05598127 0.05278273\n",
      " 0.04972987 0.0459601  0.04251888 0.04090496 0.03763186 0.03678124\n",
      " 0.03643007 0.03020616 0.02957181 0.02465513 0.01688429 0.01031678]\n",
      "-------------------------------------------------------------------\n",
      "Total Variance Explained: 0.9972\n",
      "-------------------------------------------------------------------\n",
      "Wall time: 926 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "pca_holdout = PCA(n_components=n_components)\n",
    "\n",
    "# Reduce the data, outputting an ndarray\n",
    "X = pca_holdout.fit_transform(X)\n",
    "X_eval = pca_holdout.transform(X_eval)\n",
    "\n",
    "# Get the explained variance associated with each component.\n",
    "explained_variance_holdout = pca_holdout.explained_variance_ratio_\n",
    "\n",
    "# Append the explained variances to the Variances List.\n",
    "variances.append(explained_variance_holdout)\n",
    "\n",
    "# Print out the explained variance associated with each component.\n",
    "print(explained_variance_holdout)\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Total Variance Explained: %0.4f\" % sum(explained_variance_holdout))\n",
    "print('-------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Use Skree Plots to Illustrate the Explained Variance of Each Component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the range of components\n",
    "components = list(range(1, n_components+1))\n",
    "\n",
    "# Display the Skree Plots to Compare the two Variance Distributions\n",
    "show_skree_plots(components, variances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a Classification Model Using the RandomForestsClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 260 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Train using the Training data.\n",
    "rfc = RandomForestClassifier(random_state=42)\n",
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "# Make Predictions using the Test data.\n",
    "y_pred = rfc.predict(X_test)\n",
    "y_probs = rfc.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the Model's Efficacy using Metrics, a Confusion Matrix and Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------\n",
      "Accuracy (The Percentage of Correct Predictions): 0.761\n",
      "----------------------------------------------------------------------------------\n",
      "Null Accuracy (Achieved by Always Predicting the Most Frequent Class): 0.729\n",
      "----------------------------------------------------------------------------------\n",
      "True: [0, 0, 1, 1, 0, 0, 0, 0, 0, 0]\n",
      "Pred: [0, 1, 0, 1, 0, 0, 0, 0, 0, 0]\n",
      "----------------------------------------------------------------------------------\n",
      "\n",
      "-------------------------------------------------------\n",
      "Confusion Matrix\n",
      "-------------------------------------------------------\n",
      "Predictions    0    1\n",
      "Actuals              \n",
      "0            744   78\n",
      "1            191  114\n",
      "-------------------------------------------------------\n",
      "Area Under the Curve (AUC): 0.764\n",
      "-------------------------------------------------------\n",
      "\n",
      "-------------------------------------------------------\n",
      "Classification Report\n",
      "-------------------------------------------------------\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.91      0.85       822\n",
      "          1       0.59      0.37      0.46       305\n",
      "\n",
      "avg / total       0.74      0.76      0.74      1127\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_accuracy(y_test, y_pred, CLASSIFICATION_TYPE)\n",
    "show_confusion_matrix(y_test, y_pred, y_probs, CLASSIFICATION_TYPE)\n",
    "show_classification_report(y_test, y_pred)\n",
    "\n",
    "if CLASSIFICATION_TYPE == 'Binary':\n",
    "    show_roc_plot(y_test, y_probs, 'Random Forests Classifier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform Hyperparameter Tuning for the Random Forests Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Define the parameter values to be searched\n",
    "estimator_range = list(range(2, 21))\n",
    "criterion_options = ['gini','entropy']\n",
    "max_features_options = [None, 'auto','sqrt','log2']\n",
    "max_depth_range = list(range(2, 21))\n",
    "\n",
    "# Create parameter distributions\n",
    "param_dist = dict(n_estimators = estimator_range\n",
    "                  , max_features = max_features_options\n",
    "                  , criterion = criterion_options\n",
    "                  , max_depth = max_depth_range\n",
    "                 )\n",
    "\n",
    "# Instantiate the grid\n",
    "rand = RandomizedSearchCV(rfc, param_dist, cv=10, scoring='accuracy', n_iter=10, random_state=42, n_jobs=-1) \n",
    "rand.return_train_score=False\n",
    "\n",
    "# Finally, fit the grid\n",
    "rand.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.758154</td>\n",
       "      <td>0.011863</td>\n",
       "      <td>{'n_estimators': 7, 'max_features': 'auto', 'm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.758154</td>\n",
       "      <td>0.010716</td>\n",
       "      <td>{'n_estimators': 4, 'max_features': None, 'max...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.769692</td>\n",
       "      <td>0.012995</td>\n",
       "      <td>{'n_estimators': 11, 'max_features': 'log2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.772798</td>\n",
       "      <td>0.007820</td>\n",
       "      <td>{'n_estimators': 14, 'max_features': 'auto', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.762148</td>\n",
       "      <td>0.013032</td>\n",
       "      <td>{'n_estimators': 6, 'max_features': 'sqrt', 'm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.771023</td>\n",
       "      <td>0.015501</td>\n",
       "      <td>{'n_estimators': 5, 'max_features': 'sqrt', 'm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.782117</td>\n",
       "      <td>0.010076</td>\n",
       "      <td>{'n_estimators': 12, 'max_features': None, 'ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.746838</td>\n",
       "      <td>0.013565</td>\n",
       "      <td>{'n_estimators': 5, 'max_features': 'auto', 'm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.785667</td>\n",
       "      <td>0.011275</td>\n",
       "      <td>{'n_estimators': 9, 'max_features': 'auto', 'm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.735523</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>{'n_estimators': 2, 'max_features': 'sqrt', 'm...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_test_score  std_test_score  \\\n",
       "0         0.758154        0.011863   \n",
       "1         0.758154        0.010716   \n",
       "2         0.769692        0.012995   \n",
       "3         0.772798        0.007820   \n",
       "4         0.762148        0.013032   \n",
       "5         0.771023        0.015501   \n",
       "6         0.782117        0.010076   \n",
       "7         0.746838        0.013565   \n",
       "8         0.785667        0.011275   \n",
       "9         0.735523        0.000715   \n",
       "\n",
       "                                              params  \n",
       "0  {'n_estimators': 7, 'max_features': 'auto', 'm...  \n",
       "1  {'n_estimators': 4, 'max_features': None, 'max...  \n",
       "2  {'n_estimators': 11, 'max_features': 'log2', '...  \n",
       "3  {'n_estimators': 14, 'max_features': 'auto', '...  \n",
       "4  {'n_estimators': 6, 'max_features': 'sqrt', 'm...  \n",
       "5  {'n_estimators': 5, 'max_features': 'sqrt', 'm...  \n",
       "6  {'n_estimators': 12, 'max_features': None, 'ma...  \n",
       "7  {'n_estimators': 5, 'max_features': 'auto', 'm...  \n",
       "8  {'n_estimators': 9, 'max_features': 'auto', 'm...  \n",
       "9  {'n_estimators': 2, 'max_features': 'sqrt', 'm...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the results\n",
    "pd.DataFrame(rand.cv_results_)[['mean_test_score', 'std_test_score', 'params']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------\n",
      "Mean Test Scores: [0.75815398 0.75815398 0.76969159 0.77279787 0.76214777 0.77102285\n",
      " 0.78211671 0.74683825 0.78566674 0.73552252]\n",
      "-------------------------------------------------------------------\n",
      "Estimator Values: [2, 4, 5, 5, 6, 7, 9, 11, 12, 14]\n",
      "-------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "dist_mean_scores = rand.cv_results_['mean_test_score']\n",
    "param_estimators = rand.cv_results_['params']\n",
    "items = list(range(0, len(param_estimators)))\n",
    "\n",
    "estimators = []\n",
    "\n",
    "for i in items:\n",
    "    estimators.append(param_estimators[i]['n_estimators'])\n",
    "    estimators.sort()\n",
    "    \n",
    "print('-------------------------------------------------------------------')    \n",
    "print('Mean Test Scores:', dist_mean_scores)\n",
    "print('-------------------------------------------------------------------')\n",
    "print('Estimator Values:', estimators)\n",
    "print('-------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mean Test Scores')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MnDy+32UXTc9jfFoSq61V1Nt09yj7apr6rNz2WTA1m/g4sXqLfgQzWeQD+/xe73ennURECoEiYA2Aqr4MPAMcch9PqurWIMZqTMTpUeX3a97klkc3Uur1cMsnljJhfOqA6yTEx3HO/Mm8vO0ITW2dIYo08h0+5rSEmtZH5bZPSlICc/KzrN6iH8FMFn3VMfTXpu8y4EFV7QYQkRnAXKAAJ8EsF5GzT9qByGdEZL2IrK+utstuEzs6urq5+aE3+MuLu7hg4VS+d9li0lMSA1p3ZUkBnd09vLD1UJCjjB6VffQJ1ZdSr4edhxpotkR7kmAmi/3AVL/XBcDBfpa9jLeKoAA+AKxV1SZVbQIeB07vvZKq3q6qi1R1UV5e3iiFbUx4NbR0cP0fX+G5LYf45PI5XPveYhLiA/9XnTV5PAWedFZvtKIon0q32exUT/9XFuBUcvcoVFTVhSKsqBLMZLEOmCkiRSKShJMQHum9kIjMBrKBl/0mVwHniEiCiCTiVG5bMZSJeQdqm/nSXS+y/WAD3/zgqXxk2XSG2hBQRFhRnE9FVR2H61uCFGl0qapuZML4VNKS+24J5TO3IIukhDgbarUPQUsWqtoFXAM8ifND/4CqbhaR74rIhX6LXg7cr2+/7fRBYBdQAWwANqjqo8GK1ZhIUFFVx7V3vUhzWxc//vjpnD1v8rC3taLYqR607j8cldVNfd6M11tSQjzzpmZbp4J9GDjNjpCqPgY81mvajb1e39THet3AZ4MZmzGRZE3FAW55dCMTs1L53mWLmZKTPqLtTcxKo6Qwh9UbD3D5mTOGfHUSS7p7lH21TZQVeQJavrTQwz3PbqehpYPxaUlBji562B3cxoSRqvLnF3bwo7+XM7cgi1uvPmPEicJnZUkB++ua2XZwbPd3dLi+hY6uvvuE6ktZUS4AG60o6m0sWRgTJp3dPfzs0Y3c8+x2VhTn84OPLiEzdfTOZM+cO4mkhDhWjfGK7qrq/vuE6susyeNJTYq3JrS9WLIwJgya2jq54c+v8vSG/Xzs7Jl87aJSkhLiB19xCNKTE1k6ayLPbT5I5xju/qOqxtcnVGBXFgnxcSyYlmOV3L1YsjAmxA4fa+G6u15iU1UdX7uolCvPmRW0OoWVJQUcb+1k3c6jQdl+NKisbiIvM2XQllD+Sr0e9tc2U9vYFsTIooslC2NC6M0Dx7j2rhepa2rjf654BytLCoK6v9Om55KVnjSm77morG5kWoD1FT5lXqfewrr+eIslC2NC5N9bD/G1e9eSmpTArVcvo9QbWOuckYiPi+Pc+VN4ZcdRGlvH3l3JPerrEyqw+gqfUyZmkpGSYPUWfixZGBNkqsqDL+/m+w++zvSJmdx29Rl9jqkQLL7uP57f0l8HCrHrSH0r7V09A/Y225f4OKGk0GNXFn4sWRgTRN09Pfzi8U3csWorZ86dzI8+djpZ6ckhjWHGpEym5WaMyZ5oA+0Tqi9lXg+H61s5fMzuggdLFsYETUt7F9/+y3r+9VoVHz5jOv/vgwtJThzdFk+BEBFWlhSwed8xDtY1h3z/4VTpNpsdzpVcqa/eotKuLsCShTFBUX28lS/f/RKv7arh2vcW86kVc4gL413Uy4unIIy97j+qahrJzUwJuMdef4V5GWSlJ1lRlMuShTGjbOehBq79/YscqW/l+5cv5j2nTgt3SORlplLq9bCq4gBv74YttlVWDzw63kBEhNJCD+V7a8bUe9YfSxbGjKJXdhzhK/e8TJwIt3xiKadNj5yu81eWFHDoWAtb9o+NYUN7VKmqaRpys1l/ZUW51Da2s792bBXf9cWShTGj5JF1e7npL+sp8KTz808uo2hiZrhDeptlcyaRnBA3Ziq6j9a30t7ZPeRms/5KC53mzVZvYcnCmBHr7lF++9QWfvXEZpbMmMBPr1qKZ1xKuMM6SVpyAmfMmcRzmw/R0dUd7nCCrvJENx/DTxZTctLIzUyhfI8lC0sWxoxAW0cX33/wNR56ZQ8XLfZy44cXkZoU1J7/R2RlSQFNbZ28uiP2u/+oPNGB4PCLoUSEMq+HjZW19IzxegtLFsYMU11TG1+7dy0vbzvC5949j8+fP5/4uMgeN2JhkYecjOQxURRVVd2EZ1wyGcNoCeWv1OuhoaWDvUcbRymy6GTJwphh2Hu0kWt//xKVNU18+8OLuHhJUbhDCkh8XBznLpjCqzuOcrylI9zhBFVlTWPAPc0O5ES9xRhvQmvJwpghen13Ddfd/RJd3T387KqlLJ09MdwhDcnK4gK6epTnYrj7jx5VqgIcSnUwE7PSmJydNua7LLdkYcwQPFm+jxvue5UJman8/JPLmDl5fLhDGrLpkzIpmjAupnuirW5opa2ze0T1Ff7KvB4qKmvp7hm79RaWLIwJQI8qd615k1se3Uip18Mtn1jKhPGp4Q5r2FaU5LP1QD37a5vCHUpQVA5xdLzBlHo9NLd3setww6hsLxpZsjBmEB1d3fzo4XLuf3EXFyycyvcuWzys7iMiyfIF+cQJMVvRXTnE0fEG4+tOfiwXRVmyMGYADS0dXP/HV3h280E+uXwO1763mIT46P+38YxLoawol9UVB2KySWhVdRM5GcmMSx2dpJ6TkcK03AxLFsYMxS8eq+C1XdXhDiPoDtQ286W7XmT7wQa++cFT+ciy6UEb/jQcVhbnc6S+lc37Yq/7j8rqJqaNUhGUT6nXw+aqujE7nrklCzNk/3qtip8+soHm9tgdea2iqo5r73qR5rYufvzx0zl73uRwhzTqls2ZREpiPKs37g93KKNKVamqaaRwlIqgfMq8Hto6u9l+sH5UtxstLFmYYalraucPz+0IdxhBsabiAN/44yuMT0vitqvPYF5BdrhDCoqUpASWzZnE81tiq/uP6uNttHaMrE+ovpQUehAYs11/WLIww5IYH8c/Xt3L7iPHwx3KqFFV/vzCDn7093LmFmRx69VnMCUnPdxhBdXKkgKa27tYuz12uv/wjY43kt5m+5KZlsQpEzPH7LjclizMsJy/cCoZKQn88vFNMdHXf2d3Dz97dCP3PLudFcX5/OCjS8hMTQp3WEFX6vXgGZccU0VRJ5rNBmGc89IiD1v319PeGTtXYoGyZGGGZVxqIp9aMYfN+46xKspv7mpq6+SGP7/K0xv287GzZ/K1i0pJSgj98KfhEB8nLF+Qz7pd1dQ3t4c7nFFRVdNIdnoymWmjn+zLvB46u3vYOkbGBPFnycIM27vKpjI3P4s7Vm2lqS06K7sPH2vhurteYlNVHV+7qJQrz5kVUy2eArGypIDuHuW5zbHR/UdVEFpC+SyYlkOcyJhsQjtoshARr4gkuc/PFJHPi0hkjepiwiJOhGsuWEBjawd3P7Mt3OEM2YHaZq6960Xqmtr4nyvewcqSgnCHFBbeCeOYPjEz6q8Qwal3qqwZnT6h+pKenMisKePHZKeCgVxZ/B1QEZkO3AvMBf4c1KhM1JgxeTzvW1TIv16rZMeh6OkKoaOrm/956HW6e5Rbr1524g7dsWplST7bDzVQVRPd3X/UNLbR0t41andu96XU62HbwXpa2ruCto9IFEiy6FHVTuAS4DZV/SKQH8jGReR8EdkmIjtF5Po+5t8qIuXuY7uI1LvT3+k3vVxE2kTk4qEcmAmdq86dTWZaEr98fFPU3A181zPb2Hn4OF9+f8mIRlKLFecumOJ0/xHlFd2+ym1vkK4sAMq8uXT3KJv31QVtH5EokGTRJSKXAh8D/ulOG/QeehGJB34FXADMAy4XkXn+y6jqdapapqplwC+Ah9zpz/hNXw60AE8FeEwmxDJSEvn0irm8eaCeJ8v3hTucQa3beZSH1u7h/YsKOWP2pHCHExFyMlI49ZQ81mw6GDUJvy9VQWo262/e1GwS4mTMFUUFkiw+CbwT+LGq7haRIuC+ANZbAuxU1d2q2gHcD1w0wPKX97PdDwGPq2pLAPs0YbKyJJ8F03L4/eo3I3pQnbqmNn76yAaKJozjP1bODXc4EWVlST5HG1qpqIzeM+bK6iay0pMYH4SWUD4pifHMLcgec5XcgyYLVd0EfAlY677eo6o/CGDb+YD/aeZ++im+EpFCoAhY08fsy+gnOYnIZ0RkvYisr66O/b6KIpmIcM3582lq6+KuCK3s7lHlp//YQEt7F9+4ZCHJiWOjeWygls6eRFpSAquiuCjKGR0v+MWKZV4POw810Ngana0AhyOQ1lDvBSqAp93XZSLycADb7qv9YX/Xt5cBD6rq2+50EZHJQDHwZF8rqertqrpIVRfl5eUFEJIZqTf21KA4vZb2VjQxk4uXeHn89SrePBB5/ec8tHYPr+2u4T/fNW/UBsWJJSmJ8Zw5dxL/3nqYtii86UxPjI4X/M+21OtBgYrKsXN1EUgx1HeBdwD1AKpaDswIYL39wFS/1wVAfw25+7t6+DDwsFvBbsKsu0e5/emtTByfyrtK+25meuU5M8nOSOYXj1VE1Khi2w/Wc9eaN1k2ZxLvOXVauMOJWCtK8mnp6GLttiPhDmXIahvbaW7vClqzWX+z87NITohjgyWLt+lU1d6niYH8CqwDZopIkXufxmXAI70XEpHZQDbwch/b6K8ew4TB6or97D5ynKuXz+73Duf05EQ+e948dh4+zmOvV4U4wr61tHfxw4ffICsjmS+9r3jM3XQ3FCWFHvIyU1hVEX1FUaM94NFAkhLimT8tZ0x1KhhIstgqIh8G4twf/ttw6y8GoqpdwDU4RUhbgQdUdbOIfFdELvRb9HLgfu3VwZCIeHGuTJ4L6EhMULV1dHH3M9uYPSWLc+dPGXDZc+ZPptTr4e5n3oyILiR+/cRmDh9r4fqLy8ZEf08jESfC8uJ8XttVw7Gm8H92QzHaQ6kOpszrYW91Y0R8x0MhkGRxDXAa0IPTtLUNp8J7UKr6mKrOUtXpvkpxVb1RVR/xW+YmVT3pHgxV3auq+ao6NkcaiTAPrt1DbWM7n33X3EHPzH2V3W0d3dy5+s0QRdi3NRUHeHrjfi4/cybFhWP7xrtArSzOp0eVZ6Ks+4+q6kbGpyWRlZ4ckv35buQcK01oB0wW7r0S31LVr6vqQvdxvTVjHVtqG9v460u7OHPOJOZPzQlonWl547jk9FN4esP+sN28dOhYC794bBPzp2ZzxdmBVLMZcD67mZPHR90NepXVwevmoy8zJ48nLSlhzDShHTBZuK2TloQoFhOh7n1uO13dPXxyxZwhrffRs2aQm5nCLx7bRHdPaC8Qu7p7+OFDbyACX7+4jPg46zNzKFaW5LPz8HH2Hm0MdygB8Y2OF8q78ePj4iguzLErCz+vi8hDInK5iFzoewQ9MhMR9hw5zlPl+3j/Yi/5QxwIKDUpgf981zz2HG3kkXWVQYqwb/c+u51tB+v50vtKmJiVFtJ9x4Jz508hTiRq7rmoa2qnqa0rqHdu96XU6+FAXTPVx1tDut9wCCRZTASagfcAl7qPDwUzKBM57li1lbTkRD561vCKcc6cM4nTpudx73PbqW1sG+Xo+vbGnhoeeGkX5y+cGpNjZ4dCVnoyi2fk8cymgxHVBLo/oa7c9ikbQ/UWgdzB/bE+Hh8PRXAmvNbtPMpru2u44qwZw25FJCJ84d3z6ezq4c5VW0c5wpPVN7fz47+XU+BJ53Pvmjf4CqZfK4rzqWlsY2MU3EtQ5TabLQxBs1l/RRMzGZeaOCbqLQK5g3uKiPxVRA65j7+IyMBtJ03U6+7p4Y5VW5mcncb7FhWOaFv5nnQuXXoKazYdDOoPj6pyy6MbaWzt5BuXnEpKUkLQ9jUWnD5rImnJ0dH9R2V1E5mpiWSlh7ZpdJwIJYUeNuytjYnhhQcSSDHUXTg9vnrdx9PuNBPDnizfT2V1E59aPmdUhhj9yJkzmJiVyi8f30RXd3Aqux9Zt5dXdhzl0yvnMH2Sjc81UsmJ8Zw9b7LT/UdHZI/dUFndyLS8cWG54bLM6+FoQyuH62O73iKgOgtVvUNV293HnTj1GCZGtbR3ce+z25k/NZsz545OF94pifF87l3zqaxu4uFX94zKNv3tOnycO1a9yZKZE7hosXfUtz9WrSzOp62zm5ciuPsPVaWyuils45L46i3K99aEZf+hEkiyqBORy+SXHpghAAAgAElEQVQtHwGitw9jM6i/vrSLY83tfOa8wW/AG4qlsyfyjpkT+ONzO0a19UhbRxc/fOh1xqUm8pX3l1h3HqNo/rQcJo5PjeiiqGPN7TS1dQZ1wKOBTM3NICcjOea7/gh0PIuPAzVANc4gSJ8KZlAmfKqPt/K3tbs5d/4U5uRnj/r2P//u+fSo0yHhaPnNU1vYX9vMf19cFrK7d8eKOBFWFOfzxp6akLVmG6oqtyVUqJvN+ohbb7GxMrbrLQJpDbVXVd+jqh5VzVXV96nq6JcjmIhwzzPb6VG4+p2zg7L9SdlpfGTZDJ7fcojXd4/8sv2FLYd4/I19XHrGdBYW5Y5ChKa3FSX59Cis2XQg3KH0qdIdHS/UzWb9lRV5qGtqZ1+Uj2E+kEBaQ/1ORLL8XmeLyB3BDcuEw85DDazauJ+Ll3iZlB28G9k+fMYpTM5O41ePb6Kja/jjJhxtaOW2f21k9pQsrjp31ihGaPwVeDKYPSWL1RsjNFnUNJGRkkh2GK8qy7zOiUosN6ENpBjqVP8uylX1GE7HgiaGqCq3r9rKuNRELjszuP0oJSXE84Xz57O/rpmH1g7vIrW7p4ebH36Dnh64/gNlJMRbdx7BtLIknz1HG9l1+Hi4QzmJr0+ocNZVTcpKZcL41Ji+OS+Q/7A4ERnveyEi2UBi8EIy4fDKjqNs2FvLlefMIiMl+B/v4hkTWDZ7In9+YQdH6ofeL+WfX9jJ5n3H+OJ7FjBliN2QmKE7Z/4U4uOE1RE2zoXTEqox7CMfigilXg8bKmvpidF6i0CSxW3AyyLybRH5NvAi8LPghmVCqavbuQGvICed94ZwFLn/fPd8EOG3T20Z0noVVXX8+YUdrCzJZ3lxn8O6m1E2Pi2JJTMmRFz3Hw0tHTS2doat2ay/Mq+HxtZO9hyJvKuv0RBIBfddOKPcNbiPj6jq3UGOy4TQ429Usb+2mU+tnBPS4pwJ41O54qwZvLjtCOt2Hg1oneOtHfzo4TeYlJ3GF85fEOQIjb8VJfnUNbVTvidy7ifYe6JyO/xjqsf6+Bb9/jKISIo7ngWquhH4J9ANFIUoNhMCzW2d/OG5HZQU5rB0Vujvtbzk9FMo8KTzqyc2D1rZrarc9s8K6pra+cYHFpKWbN15hNI7Zk4gIyWyuv+oClMHgn3Jy0wlPyc9Ziu5BzqNfBKYDiAi04FXgXnAl0XkByGIzYTA/S/uoqGlg8+cNy8sFYSJ8XFcc8ECDh1r4YGXdg+47ONv7OPFNw9z9fLZzJqSNeCyZvQlJcRz9rwpvLjtCC3tkdH9R2V1IxkpCeRkRMb9NaVeDxWVdSEfvyUUBkoWOaq63X1+Fc442Z8D3g3YeBYx4Eh9Cw+/socVxfnMnDx+8BWCZGFRLufMm8xfXtzJoWN9V3bvPdrI/z25mdNOyeWDp58S4giNz8qSfNo7u3nxzcPhDgWAqpompuWGp0+ovpR6PbR0dLHjUEO4Qxl1AyUL/1qs5TgdCKKq7TjjcZsod9cz2xCBTwTpBryh+Mx584iPE3795OaT7oLt6Orm5offIDUpga9eVEpchPwwjEXzCrKZnJ3GqghpFRXqoVQHU1oYu/UWAyWLzSJys4h8EZiF0/MsbjNa+2+Ncm8eqOeZTQe55B1FTBifGu5wyM1M4cqzZ/HqjqOs3f72yu47Vm1lz9FGvnZRKTkZKWGK0IDTRHRFcT4b9tSGfXS4+uZ2Glo6wtbNR1+yM5Lx5o2LyXqLgZLFp4EmYA5wvqo2u9MXALcEOzATPKrK7U9vISs9iY8sC+4NeENx8RIvhXkZ/N+Tm2nrdCq7X952hEfWVXLJO4pYPGNCmCM0AMuL81FgTcXBsMZR5XatURgBzWb9lRV52FxVN6LeCSJRv8lCVZtV9fuq+gVVfd1v+ovWdDa6vbTtCJv3HeNj58yKqBZFCW5l95GGVv7y753UHG/jlkc3MGNSJlcvD39RmXHk56QzryCb1RX7w9pxXmUENZv1V1roob2rh20HY6vewvpIGGM6u3u4c/VWpuVmcMHCqeEO5yQlhR5WFOfz15d3852/rqe9q4frP7BwVAZgMqNnRUk+ldVNYe3+o7K6ibTkBDzjIqMllE9xoQcBNkTQ/SijwZLFGPPP9ZUcrGvhP1bOJT4uMj/+T6+cQ2JCHNsPNvCF8+czNcKKGQycPW8yifFxrKoIX+eCTjcf4e0Tqi/jUhOZMXl8zNVbBNLr7CWBTDORr7G1kz+9sIOFRbksnpEX7nD6lZORwtcvLuPqd87mXaUF4Q7H9CEzNYklMyfwzKYDYbunoKqmicLcyCqC8in1enjzQP2JurdYEMip5Q19TPvmaAdigu++f++gqbWT/1g5uiPgBcPpsyZy2ZkzIj7OsWxlcT71zR28tiv0xS0NLR3UN3cwLYKazfor83ro7O5hy75j4Q5l1PRbuyki7wbOB/JFxL/1UyZ2n0XUOXSshUfWVXJeaQHTJ2WGOxwTAxbPnMC41ERWVxxgyczQtlSritDKbZ/5U3OIjxPK99Zw6imxMSjXQE1hjgKbgDZgs9/0RuD6YAZlRt/vVr9JXJxExA14JjYkxsdx7vwpPFm+j+b2TtKTQzdyQaXbbDYSepvtS1pyArOmjGdjDNVbDNR09g1V/R0wW1V/5z5/ENikqrFVzR/jNu+r44Wth7h06Sl4xtlNbWb0rCjOp6Orh39vDW33H5XVjaQlJZCXGbnf5zJvLtsONtDc3hnuUEZFIHUW/xKRTHfQowrgzyLyk0A2LiLni8g2EdkpIiddjYjIrSJS7j62i0i937xpIvKUiGwVkS0i4g3wmIwfVeWOp7eSk5HMpUutTyUzuubkZ5Gfkx7ynmirqpuYFoEtofyVeT30qLKpqi7coYyKQJJFjqoeBy4B7gEW4nQmOCC3e/NfARfg9FZ7uYjM819GVa9T1TJVLQN+ATzkN/te4CeqOhdYglMsZobo+S2H2HqgnqvOnUVKUuTcgGdig6/7j42VdcMa8XC4Iq1PqL7MLcgmMT4uZprQBpIsEkQkD7gUeFQDv2VzCbBTVXeragdwP3DRAMtfDtwH4CaVBFX1dV7YpKqh+ybGiI6ubn6/5k2KJozjvNLIuwHPxIYV7miFazaFpvuP4y0dHGtuZ1qENpv1SU6MZ25BVszUWwSSLH4APAdUqeqrInIKsCeA9fKBfX6v97vTTiIihTiDKq1xJ80C6kXkIRF5Q0R+4huIqdd6nxGR9SKyvrq6OoCQxpZ/rNvL4fpW/uO8ucTHRe7luoluk7LTWDAth9UbQ9P9x4k+oSL8ygKceotdh49zvKUj3KGMWCDDqt6vqvNU9TPu692qOtAVgk9fv079fZMuAx5UVd8dLAnAWcBXgcXAKcAn+ojtdlVdpKqL8vIi9yazcDje0sF9L+xk8Yw8TjvF3hsTXCuK89lX28z2EIzjEKl9QvWlrMiDAhsro//qIpA7uGeIyJMissF9XSIi3whg2/sB/7KPAqC/69TLcIug/NZ9w01MXcDfgVMD2Kdx/emFHbR2dPHpFXPDHYoZA3zdf6zeGPzuP6pqmkhNio/ollA+s6ZkkZwYz4axkCyAO4Hv8NaNeBXAlQGstw6YKSJFIpKEkxAe6b2QiMwGsoGXe62b7daVgDP40pYA9mmA/bVNPLq+kvMXTsM7IfLPvkz0y0hJ5PRZE3l280G6uoN7z+7e6saIGh1vIInxcSyYlkP5nrGRLNJV9SXfC7eCe9CGw+4VwTU4Y3lvBR5Q1c0i8l0R8R+W9XKcIVvVb91unCKo1SJSgVOkdUcgB2ScG/CSEuL4+Dmzwh2KGUNWluTT0NLB+l3BrT/0NZuNFmVeD1U1TdQ1tYU7lBEJpC1lrYgU4dY3iMjFQEB34KjqY8Bjvabd2Ov1Tf2s+zRQEsh+zFsqKmt5adsRrjp3FtkRMoi9GRsWTc9jfFoSqzYe4PRZE4Oyj8bWTuqa2qOictun1OsMtbpxbx3nLpgS5miGL5BkcQ3wO2COiFQCh3CKlGKCqsZMz5CqcPvTW8kdl8Ilp9sNeCa0EtzuPx57vYqmtk4yUka/+4+qGrdyO8KbzfqbMSmT9OQEyvfWxGayEJFLVPUhVd0JLPeNva2q9f2tE40aWjr4yC2rwh3GqPrqhaWkJNpgQSb0VpTk8491e3l+yyHec+q0Ud9+ZbXbJ1QUXVnEx8VRXOiJ+pvzBrqyuAG/O6pVNbbGCHSlJCXw6ZVzwh3GqMkdl8I586P37MVEt1mTxzPVk87qigNBShaNpCTGM2F86qhvO5jKvB7Wbj/C0YbWqIvdZ8z3/5CSGM+lS6eHOwxjYoKIsKKkgLuf2cbhYy1Myk4b1e1X1TQxLTeDuChoCeXPV2+xYW8t50XpgF4DtYaaIyIb+3hUiMjGkEVojIkqy91y+dVBGHLVGUo1euorfLwTxjE+LYnyvdHbYfdAVxZ7gPeHKhBjTGyYmJVGSWEOqysO8NGzRm+0w6a2Tmob26OqvsInToQSt95CVaPiHpHeBrqy6FDVyv4eIYvQGBN1VpYUcKCumTcPjF57GF83H5E64NFgSr0eao63cfBYdPaJOlCyeDFkURhjYsqZcyeRlBA3qkVRvg4EvVFYDAVOJTc49RbRaKCR8q4JZSDGmNiRnpzIGbMn8ezmg3SOUvcfVdVNJCfGMyErOlsTFXjS8YxLpnxPdNZbBNLdhzHGDNmK4nwaWztZt2N0xi2rrG6MypZQPiJCaaGHDZW1IenKfbRZsjDGBMVp03PJSk9i1SgVRVW6zWajWVlRLvXNHSduLowmAd1nISJnAF7/5VX13iDFZIyJAfFxcbxzQT6PrtvL8dYOMlOThr2t5rZOao63RWWzWX9v3W9RE3U9QgcynsUfgJ8CZ+IMRLQYWBTkuIwxMWBFcT5dPcrzWw6NaDvRNDreQCZlpTEpKzUqK7kDubJYBMwbwtjbxhgDOJ3oFeZlsHrjAd53WuGwtxPtzWb9lXo9vPjmEbp7NKqGOw6kzmITMCnYgRhjYo+IsKK4gC37j3GgrnnY26msaSI5IY6JWaPbfUg4lHlzaWrrZPeR4+EOZUgCSRa5wBZ3aNVHfI9gB2aMiQ3Li6cgwJoRVHRXVTcxNTcjqs7E++Ort4i2rj8CKYa6KdhBGGNiV15mKqVFHlZXHODKs2cOq6uLyupGSgo9QYgu9DzjUijwpLNxb21UdWI66JWFqj7X1yMUwRljYsPK4gIOHWthy/5jQ163ub2T6uNtMVFf4VPm9VBRVRf08cpHUyCtoU4XkXUi0iQiHSLSLSLRVdhmjAmrZXMmkZwYz6qNQy+K2neiJVR0NTUdSJk3l9aObrYfip5hggKps/glcDmwA0gFPu1OM8aYgKQlJ7Bs9kSe33KQjq6hDWMcjaPjDaYkCvuJCugObndo1XhV7VbVu4BzgxqVMSbmrCgpoKmti1eG2P1HVU0TSQlxTIqBllA+49OSKJowLqoquQNJFi0ikgSUi8iPReQ6ID3IcRljYszCIg85GclDLoqqrG5kqic2WkL5KyvKZcu+Y0O+0gqXQJLFx9zlrgGaganAB4MZlDEm9jjdf0xh3c6jNLR0BLxeVXVTTBVB+ZR5PXR09YzqmB/BFEhrqEpAgMmq+h1V/bJbLGWMMUOyoriA7h7luc0HA1q+taOLIw2tMVW57VM8LYc4gfI90VFvEUhrqPcD5cAT7usyuynPGDMc0ydlUjRhXMBFUSf6hIqhZrM+6SmJzJg8PmrqLQIphroJWALUA6hqOU4PtMYYM2QrSvLZdrD+RJPYgZzoEyoGi6HAaUK77UA9bR1d4Q5lUIEkiy5VjZ7GwMaYiLZ8QT5xElj3H1XVTSTGxzE5O3ZaQvkr9Xro6lE27xv6zYqhFlBHgiLyUSBeRGaKyC+Al4IclzEmRnnGpbCwKJfVFQfoGaQz68qaJgo86cTHxeY4bQumZhMfJ5RHwf0WgXwCXwTmA+3AfcBx4EvBDMoYE9tWFOdzpKGVzVV1Ay5XWd0Yk5XbPilJCczJz4qKeotAWkO1qOo3VXWxqi5yn7eFIjhjTGxaNmcSKYN0/9HW0cWR+taoH/BoMKVeDzsPNdDc1hnuUAbUb7Lw7468r0cogzTGxJaUpATOnDuJ57ceor2z75vSqmKwT6i+lHlz6VGoGOQqK9wG6qJ8KbAPp+jpFZx7LYZERM4Hfg7EA3eq6s295t8KvNN9mQZMUNUsd143UOHOq1LVC4e6f2NM5FpRXMCqjQdYu/0I58yfctL8E31CxWCzWX9zC7JISoijfG8tp8+aGO5w+jVQspgEnIfTieBHgX8B96nq5kA2LCLxwK/cbewH1onII6q6xbeMql7nt/wXgYV+m2hV1bJAD8QYE11KvR5yx6WwquJAP8mikcT4OKbkxGZLKJ+khHjmTc2O+E4F+y2GcjsNfEJVrwJOB3YCz7o/6oFYAuxU1d2q2gHcD1w0wPKX41zFGGPGgPg44Z0LprB+ZzX1ze0nza+K8ZZQ/koLPew+cnxI3aCE2oCfgogki8glwB+BLwD/CzwU4LbzcYqxfPa70/raTyFQBKzxm5wiIutFZK2IXNzPep9xl1lfXV0dYFjGmEixsqSAHlWe7aP7j6qappgvgvIpK8oFYGMEX10MVMF9D879FKcC33FbQ31PVQPtMrKvOo7+GlVfBjyoqv41XdNUdRFOEdhtInLS+IOqervbQmtRXl5egGEZYyKFd8I4ZkzKPKlVVFtnN4ePtcR85bbPrMnjSU2KZ0NlFCYLnN5mZwHXAi+JyHH30RjgSHn7cXqo9SkA+us97DJ6FUGp6kH3727gWd5en2GMiRErivPZcajhRNce4IyOp8RuNx+9JcTHsWBaDuV7Ivd+i4HqLOJUdZz7yPR7jFPVzAC2vQ6YKSJF7ngYlwEnNbkVkdlANvCy37RsEUl2n+cCy4Atvdc1xkS/cxdMIU6E1X5XF77EEYsdCPan1OthX20ztY2ReRtb0GqOVLULZwyMJ4GtwAOqullEvisi/s1gLwfuV33bff9zgfUisgF4BrjZvxWVMSZ25GSkcNr0XNZseqv7j6rqJhLihCk5Y2ectTKvU28Rqa2iBmo6O2Kq+hjwWK9pN/Z6fVMf670EFAczNmNM5FhRnM/ND5ezsbKWMm8ulTVN5HvSSYiP/ZZQPqdMzCQjJYENe2tZXtxnW6CwGjufhDEmYi2dPYm0pIQTFd2x3idUX+LjhJJCT8T2E2XJwhgTdimJ8Zw5dxL/3nqI4y0dTkuoMVRf4VPm9XC4vpXD9S3hDuUkliyMMRFhRUk+rR3dPPDSLpTY7xOqL6URXG9hycIYExFKCj3kZabwj3V7gbHTbNZfYV4GWelJliyMMaY/cSIsL86no6uH+DHWEspHRCh16y10kIGhQs2ShTEmYqx0WwHl56STOIZaQvkrK8qltrGdA3XN4Q7lbcbmp2GMiUjT8saxsCiXUq8n3KGETWmhc+yRNtRqUO+zMMaYofrhFUsQGfLwOTFjSk4auZkplO+p5X2nFYY7nBPsysIYE1HGcqIA5/jLvB42VtaeuKM9EliyMMaYCFPq9dDQ0kHl0cbBFw4RSxbGGBNhIrHewpKFMcZEmIlZaUzOTrNkYYwxZmBlXg8VlbV090RGvYUlC2OMiUClXg/N7V3sOtwQ7lAASxbGGBORfPeaREpRlCULY4yJQDkZKUzLzYiYfqIsWRhjTIQq9XrYVFVHZ3dPuEOxZGGMMZGqzOuhrbOb7Qfrwx2KJQtjjIlUJYUeBCjfE/6iKEsWxhgToTLTkjhlYiYbKi1ZGGOMGUBpkYct+47R3tkd1jgsWRhjTAQr83ro7O5h6/5jYY3DkoUxxkSwBdNyiBMJexNaSxbGGBPB0pMTmTVlfNhvzrNkYYwxEa7U62HbwXpaO7rCFoMlC2OMiXBl3ly6e5RNVXVhi8GShTHGRLh5U7NJiAtvvYUlC2OMiXApifHMLcgOa72FJQtjjIkCZV4Puw430NjaGZb9W7IwxpgoUOr10KNQURWeq4ugJgsROV9EtonIThG5vo/5t4pIufvYLiL1veZnisgBEfllMOM0xphINzs/i+SEuLDVWyQEa8MiEg/8CjgP2A+sE5FHVHWLbxlVvc5v+S8CC3tt5nvAc8GK0RhjokVSQjzzp+WErVPBYF5ZLAF2qupuVe0A7gcuGmD5y4H7fC9E5DRgIvBUEGM0xpioUeb1sLe6kfrm9pDvO5jJIh/Y5/d6vzvtJCJSCBQBa9zXccDPgK8NtAMR+YyIrBeR9dXV1aMStDHGRCrfUKvhKIoKZrKQPqZpP8teBjyoqr5uFT8PPKaq+/pZ3tmY6u2qukhVF+Xl5Y0gVGOMiXwzJ48nLSkhLE1og1ZngXMlMdXvdQFwsJ9lLwO+4Pd6KXCWiHweyACSRKRJVU+qJDfGmLEiPi6O4sIcNsbYlcU6YKaIFIlIEk5CeKT3QiIyG8gGXvZNU9UrVHWaqnqBrwL3WqIwxhinKGp/XTPVx1tDut+gJQtV7QKuAZ4EtgIPqOpmEfmuiFzot+jlwP2q2l8RlTHGGFdZmOotglkMhao+BjzWa9qNvV7fNMg27gbuHuXQjDEmKhVNzGRcaiIb9taysqQgZPu1O7iNMSaKxIlQUuihfG8toSyQsWRhjDFRpszr4WhDK4frQ1dvYcnCGGOijK/eonxvTcj2acnCGGOizNTcDHIykkNayW3Jwhhjooy49RYbQlhvYcnCGGOiUFmRh7qmdvbVNIVkf5YsjDEmCpV5cwHYUBmaoihLFsYYE4UmZaUyYXxqyLost2RhjDFRSEQo9XrYUFlLTwjqLSxZGGNMlCrzemhs7WTPkcag78uShTHGRKm3xrcI/v0WliyMMSZK5WWmkp+THpLxLYLakaAxxpjgOq+0gLaOrqDvx5KFMcZEscvPnBGS/VgxlDHGmEFZsjDGGDMoSxbGGGMGZcnCGGPMoCxZGGOMGZQlC2OMMYOyZGGMMWZQliyMMcYMSkI1ylKwiUg1UDmCTeQCoRvQNnhi5TjAjiVSxcqxxMpxwMiOpVBV8wZbKGaSxUiJyHpVXRTuOEYqVo4D7FgiVawcS6wcB4TmWKwYyhhjzKAsWRhjjBmUJYu33B7uAEZJrBwH2LFEqlg5llg5DgjBsVidhTHGmEHZlYUxxphBWbIwxhgzqDGdLERkqog8IyJbRWSziFwb7phGSkTiReQNEflnuGMZCRHJEpEHReRN9/NZGu6YhkNErnO/W5tE5D4RSQl3TIESkd+LyFER2eQ3LUdEnhaRHe7f7HDGGKh+juUn7vdro4g8LCJZ4YwxUH0di9+8r4qIikjuaO93TCcLoAv4iqrOBU4HviAi88Ic00hdC2wNdxCj4OfAE6o6ByglCo9JRPKB/wIWqeoCIB64LLxRDcndwPm9pl0PrFbVmcBq93U0uJuTj+VpYIGqlgDbgW+EOqhhupuTjwURmQqcB1QFY6djOlmo6iFVfd193ojzg5Qf3qiGT0QKgPcCd4Y7lpEQkUzgbOB3AKraoar14Y1q2BKAVBFJANKAg2GOJ2Cq+jxQ12vyRcA97vN7gItDGtQw9XUsqvqUqvoGr14LFIQ8sGHo53MBuBX4byAorZbGdLLwJyJeYCHwSngjGZHbcL4sPeEOZIROAaqBu9witTtFJD3cQQ2Vqh4AfopzpncIaFDVp8Ib1YhNVNVD4JxsARPCHM9o+STweLiDGC4RuRA4oKobgrUPSxaAiGQAfwO+pKrHwx3PcIjI+4CjqvpauGMZBQnAqcD/qepCoJnoKe44wS3PvwgoAqYA6SJyZXijMr2JyDdxiqT/FO5YhkNE0oBvAjcGcz9jPlmISCJOoviTqj4U7nhGYBlwoYjsBe4HlovIH8Mb0rDtB/arqu8q70Gc5BFtVgJ7VLVaVTuBh4AzwhzTSB0RkckA7t+jYY5nRETkKuB9wBUavTedTcc5Idng/v8XAK+LyKTR3MmYThYiIjjl4ltV9ZZwxzMSqvoNVS1QVS9OJeoaVY3Ks1hVPQzsE5HZ7qQVwJYwhjRcVcDpIpLmftdWEIUV9b08AlzlPr8K+EcYYxkRETkf+Dpwoaq2hDue4VLVClWdoKpe9/9/P3Cq+380asZ0ssA5G/8Yzll4uft4T7iDMgB8EfiTiGwEyoD/CXM8Q+ZeGT0IvA5U4Py/RU0XEyJyH/AyMFtE9ovIp4CbgfNEZAdOy5ubwxljoPo5ll8C44Cn3f/934Q1yAD1cyzB32/0XnkZY4wJlbF+ZWGMMSYAliyMMcYMypKFMcaYQVmyMMYYMyhLFsYYYwZlycJEFLfHzJ/5vf6qiNw0Stu+W0Q+NBrbGmQ/l7o95T7Ta7pXRFr9mmmXi8jHB9jOJ0Rkit/rO0ejo0s3jo+OdDtmbLFkYSJNO3BJMLpYHgkRiR/C4p8CPq+q7+xj3i5VLfN73DvAdj6B000IAKr6aVUdjZsTvcCQkoXbEaIZwyxZmEjThXPj2nW9Z/S+MhCRJvfvuSLynIg8ICLbReRmEblCRF4VkQoRme63mZUi8oK73Pvc9ePdsQ3WuWMbfNZvu8+IyJ9xbqrrHc/l7vY3iciP3Gk3AmcCvxGRnwRywO7+73a3UyHOGBgfAhbh3JhYLiKpIvKsiCzyHbuI/EhEXhORVSKyxJ2/2+1UzncF8YKIvO4+fF2N3Ayc5W73OhFJEZG73H2/ISLvdNf/hIj8VUQeBZ4Skcki8ry73iYROSuQ4zMxQlXtYY+IeQBNQCawFxgPfBW4yZ13N/Ah/2Xdv+cC9cBkIBk4AHzHnXctcJvf+k/gnCTNxOkWIQX4DHCDu0wysB6nr51zcToxLOojzik43Xnk4XR8uAa42J33LM4YFr3X8QKtQLnf4yzgNOBpv+Wy+tqO/2ucbqgvcJ8/DDwFJOKM/VHuTk8DUtznM4H1fu/XP/22+xXgLvf5HESy++IAAAJqSURBVPe4UnCubPYDOX7LfdN9Hg+MC/f3xR6he9ilpYk4qnpcRO7FGTioNcDV1qnbdbaI7ML58QTnisC/OOgBVe0BdojIbpwfx3cBJX5XLeNxflw7gFdVdU8f+1sMPKuq1e4+/4QzBsffB4lzl6qW+U8Qp3faU0TkF8C//GIfSAdO4vMdY7uqdopIBU5SAid5/FJEyoBuYFY/2zoT+AWAqr4pIpV+yz6tqr6xE9YBvxen882/q2p5AHGaGGHFUCZS3YZT9u8/jkUX7nfW7ZgvyW9eu9/zHr/XPfC2k6Le/dsoIMAX9a16hCJ9a9yJ5n7ik0APZDCqegzniuBZ4AsENnhVp6r6juXE8bqJ0He81wFH3G0v4u3vl7+BjuXE8asz6M7ZOFdufxioct7EHksWJiK5Z7MP4CQMn704RTbgjBOROIxNXyoicW49xinANuBJ4HPuGTMiMksGH2zpFeAcEcl1K78vB54bRjy4lflxqvo34Fu81R17I05Hd8M1HjjkJpCP4RQd9bXd54Er3FhmAdNw3pfecRbijJlyB05vzdHYbbwZJiuGMpHsZ8A1fq/vAP4hIq/ijP/c31n/QLbh/KhPBP5TVdtE5E6copvX3SuWagYZLlRVD4nIN4BncM7MH1PVQLrrni4i/sU3v3fjuUtEfCdvvrGg78apKG8FlgZycL38GvibiFzqxul7vzYCXSKywd3Hr939VOBcvX1CVdudt+JtzgW+JiKdOHVLdmUxhlivs8YYYwZlxVDGGGMGZcnCGGPMoCxZGGOMGZQlC2OMMYOyZGGMMWZQliyMMcYMypKFMcaYQf1/deBfbYs31aoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the scores\n",
    "plt.title('Mean Test Scores by Number of Estimators')\n",
    "plt.plot(estimators, dist_mean_scores, color='steelblue')\n",
    "plt.xlabel('Number of Estimators')\n",
    "plt.ylabel('Mean Test Scores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7856667406256934\n",
      "{'n_estimators': 9, 'max_features': 'auto', 'max_depth': 6, 'criterion': 'gini'}\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=6, 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=9, n_jobs=1,\n",
      "            oob_score=False, random_state=42, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "# Examine the best score\n",
    "print(rand.best_score_)\n",
    "print(rand.best_params_)\n",
    "print(rand.best_estimator_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use the Best Parameters to Make Predictions Using the Hold-Out Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 238 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "n_estimators = rand.best_params_['n_estimators']\n",
    "max_feature = rand.best_params_['max_features']\n",
    "max_depth = rand.best_params_['max_depth']\n",
    "criteria = rand.best_params_['criterion']\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators = n_estimators\n",
    "                             , max_features = max_feature\n",
    "                             , criterion = criteria\n",
    "                             , max_depth = max_depth\n",
    "                             , random_state = 42\n",
    "                            )\n",
    "rfc.fit(X, y)\n",
    "\n",
    "y_pred = rand.predict(X_eval)\n",
    "y_probs = rand.predict_proba(X_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the Model's Efficacy using Metrics, a Confusion Matrix and Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------\n",
      "Accuracy (The Percentage of Correct Predictions): 0.783\n",
      "----------------------------------------------------------------------------------\n",
      "Null Accuracy (Achieved by Always Predicting the Most Frequent Class): 0.735\n",
      "----------------------------------------------------------------------------------\n",
      "True: [1, 0, 0, 1, 0, 1, 0, 0, 1, 1]\n",
      "Pred: [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n",
      "----------------------------------------------------------------------------------\n",
      "\n",
      "-------------------------------------------------------\n",
      "Confusion Matrix\n",
      "-------------------------------------------------------\n",
      "Predictions    0    1\n",
      "Actuals              \n",
      "1            238  135\n",
      "0            968   68\n",
      "-------------------------------------------------------\n",
      "Area Under the Curve (AUC): 0.819\n",
      "-------------------------------------------------------\n",
      "\n",
      "-------------------------------------------------------\n",
      "Classification Report\n",
      "-------------------------------------------------------\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.93      0.86      1036\n",
      "          1       0.67      0.36      0.47       373\n",
      "\n",
      "avg / total       0.77      0.78      0.76      1409\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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TiIqIoG6tmIDu28ZsMqYyfnsJVr8PJ0yElie4HU3YWr51L//37QqcO86hYUJcwJMoWI3UmIqbeauTSJsOgKR73Y4mbC1Yt5vHPkwmr9BLp6Z1Oa13K9disURqTFlUD19kv/wN+P2/zi2eAKPfArFGnRtmLtvG3z5dgserjOrbmuE9W7oajyVSY8ry4x2Q/OKR69qc7lwvap2KuOLzhZt55atlKHDxkA5ce1rXar1vvjIskRpTVNpKmP2wbywfYMdc56x80n3OcssTnCa9CThVZcov63hz1hoArjm1K5ee0NHlqByWSE14O9gj06avnWb6wR6Z6nWA6NoQ3xg6ngP9q9AbvKkWBR4vv67aiQC3jOnFmf1rzvhtlkhN+Fo9FX6bBNt/dZYH3+9M67SF3te5F5cpUUxUJE+MS2LVtnSO79LU7XCOYInUhD5vIaQucQaUO8iTB19c6szH1ocLZ0DzJHfiM6XKL/Tw1aKtnD2w7aGu8GpaEgVLpCYcLHkVfijlHvh+N8OpkwIbjymX7LxCHpm6kCWb0kjLyOWa07q6HVKpLJGa0JSfCTl7IGPz4SR67qcQUeQjHxkDrU5yJz5Tpv3Z+Tz43nzW7NhPw4RYTunZwu2QymSJ1AS/jM2wbppz4uigWbcdWabDWdDpnMDGZSolNSOH+96Zx9a0AzTzdT7SIkCdj1SWJVITnFJ+hjmPOmfZt84suUzLYdDrWoipB53ODWx8plJS0rK479357N6fQ7vEOjw5PolGdQLX+UhlWSI1wSN9vXNdJ8CqD2DL906ybDkMjrsEul1+uKxEQKz/+6E01es/361i9/4curWsz8TLBlE3PvD3zVeGJVITPL67ETZ/c3i5dnMY+5N78Zhqd/s5vXlr1hquO62r38ZX8ofgidSEl7wM2DEPUCg4AD/eCfs3QvMhMPpNp0ytJq6GaKrH6u3pdGpWj8gIoW58DDeN7ul2SBVmidTUPCk/wwfFzqbHNYKBd0KHMTZ8cQj54Xen85GRfVvxlzG9XL9nvrIskZrA83ogbfmRF8gfNPcxWPeJM9+oO4z4tzPfoAvUahy4GI3ffbpgE//8ejlA0BwLLY0lUhM4e5bBmo/h99cga3vZZc98D7pdFpi4TECpKu/+vI63f3Q6H7nutK5cPLRmdD5SWZZITWDkZ8KbvY5cd84nJY9z1HQg1HG3f0njH15V/u+bFUybv4kIcTofGd2v5nQ+UlmWSE1g7Ep2pu1GwgVfOfNBejzMVN5HczYwbf4moiMjuOf8vgzr1tztkKqFJVITGNtnO9Okey2BhrEx/dswf+1uxg3rTP8OoXPM2xKp8b/09U6HyeA0201Yyc4rJCYqgqjICGrHRfO3K48P2rPzpbEBZ4z/fT0BVr7jdJQcGdxnZ03FpB/I46635vDC50vx+vpCCLUkClYjNf6SvQfSloGnALb9Ai1OgHP/Z4k0jOzen8N9784jJe0AB/IKycjOp37tWLfD8gtLpMY/vrrCGb7joMTedidSGNm6J4v73p1HakYu7Zs4nY+EahIFS6SmunkKYN9qJ4nWbQej3oCIaGhmvc+Hi7U79vPAe/PZn51Pj9YNmDh2EAlx0W6H5VeWSE312PYrpPwEsx86PAJn/79A6+GuhmUCa+2O/dz91lyy8wsZ1CmRBy8aQFx0pNth+Z0lUlM5ngL47ALI3u1czrRj3uHHomvD6Leh7Qj34jOuaNWoNm0SE2hWvxZ3ntuH6MjwOJ9tidRUzJaZzuVM3/7h8Lp2I6HtGdBtPBx3qXNCKQTPzJrSqSoiQnxMFE+OTyIuOorIiPD5DFgiNeWjCktfg+9uOLyuVhOYsBLiG7oXl3HdtPkbWbF1H/ec34/ICKF2bGgfDy2JJVJzbAd2wetdID/DWR79FrQ+BRJaOD3Rm7Ckqrzz01re+WktACP7tWZAh0SXo3KHJVJzbLNuc5KoRML4+dC0v9sRGZd5VfnXjOV8tmAzEQK3ntU7bJMoWCI1x5K1HVZNcYYxvuWAXVBvKPR4ef6zJfywbDvRkRHcd0E/TujazO2wXOXXdpmIjBKR1SKyTkTuLeHxNiIyU0QWichSETnTn/GYStg6y5n2ut6SqCGvwMOjHybzw7LtxMdE8thlg8I+iYIfa6QiEgm8AowAUoAFIvKZqq4oUuxBYKqq/ktEugPTgXb+islUwIFdkJcO08c7y/1vcTceU2Pk5BVSJz6axy9LomvL+m6HUyP4s2mfBKxT1Q0AIvI+cC5QNJEqcHDM3HrAMbpNNwGxKxneKdJLU70O0PA49+IxNUZsdCSPXjqQtKw82jROcDucGsOfTfuWwNYiyym+dUU9AlwuIik4tdGb/RiPKY/CPFg3zZnvcwOMeR+uXOxuTMZVu9Kz+efXy/F4vQDUjou2JFqMP2ukJV2Nq8WWLwMmq+rzIjIEeFtEeqqq94gNiVwPXA/Qpk3wD0tQY22fA1OGHl4++QWIjncvHuO6zamZ3P/ufPZk5lInPporTu7idkg1kj9rpClA6yLLrTi66X4tMBVAVecAccBR3War6muqOlBVByYmhu8lFn7lKTicRFsMdcZTsiQa1lZvT+fON+ewJzOXHq0bcP7g9m6HVGP5s0a6AOgsIu2BbcBYYFyxMluA04DJItINJ5Gm+jEmU5qV7zjTtmfARTPcjcW4bvGmPTzywUJy8j0kdUrkgTDpfKSy/JZIVbVQRG4CZgCRwOuqulxEJgILVfUz4A7g3yJyG06zf4KqFm/+m0AoOOBMT3vF3TiM62av2smT/1tEgcfLKT1bcOc5fYgKk85HKsuvF+Sr6nSck0hF1z1UZH4FcII/YzDlkLUDfvCd54tr4G4sxlWqyvRFWyjweDl7YFtuHNWDCOuA5pjsziYD+zc601YnQZx1QBLORIQHLuzPzGXbGd2vdUiOr+QPVl8Pd2unwddXOfODH7Du78KQqvLtkhQKPM7FMvExUZzZv40l0QqwRBrOtsyE5BcgYzN0u9yGSg5DXlVe/moZz322hOc/W+J2OEHLmvbh6odbYNE/nPnEvnDm2+7GYwKuwOPluU+XMGu50/nIyd1buB1S0LJEGi7UCxu/gjxfn6KL/gEJreCkp6HLJe7GZgIut8DD4x8ls2BdKrVionjk0oH0adfI7bCCliXScLF7EXxy1pHrBt3tDA9iwkpWbgEPvb+A5Vv3Ua9WDE+MS6Jz83puhxXULJGGi2WTnenIN6DFEKeT5vodXQ3JuOPdn9eyfOs+GteN46nxg+2++WpgiTQc7PoNFr/szB93CUTXcjce46oJw48jK6eAK07uQpN6dhtwdbBEGuo+Ggmbv3Hme15jSTRMbUs7QGK9OGKiIomNjuSOc/q4HVJIsUQayrbPOZxEr10P9Tu4G49xxapt6Tw4ZT49WjfkoYv7ExlhVz1WN0ukoWzLD870wq8tiYap3zbs4dGpC8kt8KCqeLyK3TZf/SyRhrKsbRBTB9qOcDsS44JfVu7g6U8WU+Dxclqvltx+dm/rfMRPLJGGgsJc+OYPkLPnyPWbvoZmg2zs+TA0Y/FW/v7FUrwK5w5qxw0ju1vnI35kiTTYrfkIPr/48HKzpCLzg6DrZYGPybhqzupdvPD5UgCuOKkz40/qbPfN+5kl0mCXvt6Znvgk9PkTxNmojuFuQMfGDOjQmMGdm3BukvVqHwiWSIOZqjPiJ0D/W21okDDm8SqFHi+x0ZHEREXy+Lgka8oHkB08C2azH4Y1HzrzEfabGK4KPF6e/mQRj36YfKgrPEuigWXfvmDlKYC5jznzF38PkdHuxmNckZtfyGMf/cbC9U7nIyl7smjftK7bYYUdS6TBKm+/Mz3uUmhzqruxGFdk5jidj6xIOdz5iCVRd1giDVb7fSeZWtiQV+EoLTOXB96bz8bdmST6Oh9pbZ2PuMYSaTBSham+WmiDTu7GYgIuLTOXO96cw4592bRuVJsnxw+2zkdcVq5EKiIxQBtVXefneEx57N8IhdnOfLtR7sZiAq5erRhaN06gTlw0j49Lol6tGLdDCnvHTKQiMgZ4AYgB2otIX+BhVT3f38GZEmRshZm3OvMjX7fB6sJQVGQED17Yn0Kvl9qxdpKxJijP5U8TgcFAOoCqLgasPRlomdvguxvh321gw+fOOquNho3kDak89P4C8gs9AMRGR1oSrUHK07QvUNX0YreYqZ/iMcWpwheXHr5eVCKg84Vw8vOQ0Nzd2ExA/LxiB09/sohCr/LVoq2cO6id2yGZYsqTSFeKyCVAhIi0B/4CzPVvWAaA1KVOM37rTGd56EQ4/kFrzoeR6b9t4R/Tf8ercP7g9pw9sK3bIZkSlCeR3gQ8BHiB/wEzgPv8GZTxef9EyM905sfPdzohMWHjg1/X8/oPqwC4angXLjuxk3U+UkOVJ5GOVNV7gHsOrhCRC3CSqvGXnL1OEk1oAX/c5nY0JoBUlf9+v4oP52xAgD+P7sHZA9u5HZYpQ3lONj1YwroHqjsQU8SC5+CfvjHGu1/pbiwm4LwKO/ZlExkh3H1eX0uiQaDUGqmIjARGAS1F5IUiD9XFaeYbf9jwJfx0lzPf5RI48Ql34zEBFxkh3HN+X9bu2E+P1g3dDseUQ1lN+93AMiAXWF5kfSZwrz+DCkurP4TVH8Daj53l87+ADmPcjckETE5+Ie/+tJbLT+pMXEwUMVGRlkSDSKmJVFUXAYtE5F1VzQ1gTOFnzmMw+yFnvlF3SOxjSTSMZOTk89CUBazcls7erDzuPq+v2yGZCirPyaaWIvIE0B2IO7hSVbv4Lapwkpfh9CvadCCc/By0PtntiEwApWXmcv+789mUmknTevGMH9bZ7ZBMJZTnZNNk4A1AgNHAVOB9P8YUXj4aASj0vNqSaJjZvvcAt0+ezabUTNo0TuCFCUNp2ai222GZSihPIq2lqjMAVHW9qj4InOLfsMLEuk9h53xnvsNZ7sZiAmrDrgzueHMOO9Nz6NKiHs9fNYTGdeOO/URTI5WnaZ8nzlXA60XkBmAb0MS/YYU49cJnF8G6T5zlC2dA3TbuxmQC6ovkzezNyqNvu0Y8fMlAasVaj5bBrDzv3m1AAnAL8ARQD7jGn0GFvJy9ThJt1B363wbtznA7IhNgN47sQdN6tTh/cDtioiLdDsdU0TETqarO881mAlcAiEgrfwYV8qaPc6Y9r4He17kbiwmYBet206N1Q2rFRhEVGcGlJ3R0OyRTTco8Rioig0TkPBFp7FvuISJvYZ2WVN6uRbB7sTPf+4/uxmIC5svkzfx1ygIembqQQo/dzxJqSk2kIvIU8C4wHvhaRB4AZgJLALv0qTLmTIR3+kNOKvS7GWJsjJ1Qp6q8/8s6Jk1fhgL92zcmMsI6Hgk1ZTXtzwX6qGqOiDQEtvuWVwcmtBDhKYDJ3SBjM3gLnXUnPA5J95T9PBP0VJV/f7eSj+duRICbzuzJWQOsG7xQVFYizVXVHABV3SsiqyyJVlBBDrzZwxljCWDQ3dDnBqjX3t24jN95vF5e+vJ3ZixOOdT5yPAeLdwOy/hJWYm0g4gc7CpPgHZFllHVC/waWSj4+IzDSfSmdIit5248JmC+WrSVGYtTiI2K4K8XD2BQJ7tiMJSVlUgvLLb8ckU3LiKjgJeASOA/qvp0CWUuAR7BGb5kiaqOq+h+aiRPPmz7xZm/OQNi6rgbjwmo0f1as2Z7OiP7trbOR8JAWZ2WfF+VDYtIJPAKMAJIARaIyGequqJImc44ve2foKr7RCR0frY9ec60/62WRMNERnY+ERFCQlw0kRER3H52H7dDMgFSnltEKysJWKeqG1Q1H+f+/HOLlfkD8Iqq7gNQ1d1+jCew5j/jTO14aFhIzcjhjjfn8NcpC8jNL3Q7HBNg/kykLYGtRZZTfOuK6gJ0EZFfRWSu71DAUUTkehFZKCILU1NT/RRuNfIWwjxfh8zWw33I25Z2gDsmz2HLniwO5BWQbYk07JQ7kYpIbAW3XdLFcsWHcY4COgPDgcuA/4hI/aOepPqaqg5U1YGJiYkVDMMFH/lu+WzYDeKO+ndMCFm/cz+3vzmbXftz6NqyPs9dNYSGCdb5SLg5ZiIVkSQR+R1Y61vuIyL/KMe2U4DWRZZb4VyLWrzMp6paoKoaB9s4AAAgAElEQVQbgdU4iTW4pfzoTC+b7W4cxq+WbdnLXW/NJf1APv3aN+bpywdTNz7G7bCMC8pTI50EnAWkAajqEsrXjd4CoLOItBeRGGAs8FmxMtMObst3G2oXYEP5Qq+Bkl+El+Kd3p16XG210RC2cVcG9787jwN5hZzYtRkTxw4kPsZ6cApX5XnnI1R1c7HxtD3HepKqForITcAMnMufXlfV5SIyEVioqp/5HjtDRFb4tnmXqqZV+L+oCXLSYNbtznz/v0Av64wklLVtUoehXZsRGxXJLWN62W2fYa48iXSriCQB6ruk6WZgTXk2rqrTgenF1j1UZF6B231/waswD5ZPdubbnwmn/N3VcIz/5Bd6iImKJEKEu87tQ4QIxSoZJgyVJ5H+Cad53wbYBXznW2cOeqsP7PPdPXv6q+7GYvxCVZnyyzrmrNnFM5cfT63YKCIj/HnRiwkm5Umkhao61u+RBBtVpzn/zbWHk+hF30Ld1mU/zwQdryqvfbuST+Y5nY8s3ZzG8V2auh2WqUHKk0gXiMhq4APgf6qa6eeYar7cdHi7r9Oj00GX/gytTnQvJuMXHq+XFz//nW+XphAVIdxzfj9LouYo5ekhv6OIDMU56/6oiCwG3lfV8B1J9M0ekOW7kuu0f8Jxl0K83U8davILPTz58SLmrNlFbHQkD188gAEdg+A6ZhNw5TrIo6qzVfUWoD+QgdPhc3jyeg4n0b/kQN8/WRINQbkFHh6csoA5a3aREBfN05cPtiRqSlWeC/ITRGS8iHwOzAdSgaF+j6ymSl3iTIdOhCi7gyVUxUZF0Kx+PA0TYnn+qiF0b9XA7ZBMDVaeY6TLgM+BZ1X1Zz/HU7MV5sE7A5z5pgPcjcX4lYjwlzG92ZeVZ+PNm2MqT9O+g6reHPZJFCAv3Zk2HwwdznQ3FlPtUtKyePj9BWTlFgAQGSGWRE25lFojFZHnVfUO4GMRKd7ZSHj2kJ/m60rVenQKOWt37OeB9+azPzuft2at4cZRPdwOyQSRspr2H/imFe4ZPySpwoenOvON7EsWSn7fnMZDHywkO6+QAR0ac82px7kdkgkyZfWQP983201Vj0imvnvoq9SDftDZt/bwfOuT3YvDVKu5a3bxxMe/kV/o5aTuzbn7vL5ER9odS6ZiyvOJuaaEdddWdyA13uZvnOmYKe7GYarND79v49GpyeQXejmzfxvuPb+fJVFTKWUdI70U5yL89kVHDwXqAOn+DqzGOTgmfauT3I3DVJuV2/bhVeXSEzpy9SnHWecjptLKOkY6H6cP0lY4g9gdlAks8mdQNVpULbcjMNXkTyN7MKBDot3yaaqsrGOkG4GNOL09mR1z3Y7AVJFXlQ9nr2dUvzbUqxVDhIglUVMtSj0gJCI/+qb7RGRvkb99IrI3cCHWAOs+g9W+ixhiEtyNxVRKocfLc58u4fUfVvPo1IU4XeEaUz3KatofHE6kcSACqbH2robk5535sz+CCBtOItjkFXh48uPfmLt2N3HRkVx+Uhc7HmqqVak1UlX1+mZbA5Gq6gGGAH8Eagcgtpph7uOQ8hM07gldLnQ7GlNBB3ILeHDKfOau3U2d+GieuWIw/TuEd93AVL/yXOsxDWeYkY7AW0A34D2/RlUTqBd++wesfAeia8NVv7sdkamg9AN53P32XJZu3kujOrE8d+UQura0zkdM9StPIvWqagFwAfB3Vb0ZaOnfsGqA1KUw8xZnPuled2MxlfLNkhTW7cygeYNavDBhKO2a1HE7JBOiyjXUiIhcDFwBnOdbF+2/kGqIH+9ypud8Ap3PK7usqZEuHtKBgkIvo/u3pmGCdT5i/Ke8dzadgtON3gYRaQ+E/u09W3xXfbUb6W4cpkLW7djPvqw8wOkKb/xJnS2JGr87ZiJV1WXALcBCEekKbFXVJ/wemZvSVjrTxL4QHe9uLKbclmxK46635nL/e/M54OsKz5hAOGbTXkSGAW8D2wABmonIFar6q7+Dc83BQe0G3e1uHKbc5qx2Oh8p8Hhp0ziBmOhIt0MyYaQ8x0hfBM5U1RUAItINJ7EO9Gdgrpr3pDOt197dOEy5fLskhRc+X4pXlbMGtOHGUT2JjLDrRE3glCeRxhxMogCqulJEYvwYk/vy9jnTxD7uxmGO6ZN5G3n1G+fjOe7ETlw53C62N4FXnkT6m4j8H04tFGA8odxpSUE27FkGnc6z46M13OKNew4l0T+O6MYFx3dwOSITrsqTSG/AOdl0N84x0p+Af/gzKFe93c+ZRts99TVdn3aNOC+pHR2b1eWMPq3dDseEsTITqYj0AjoCn6jqs4EJyUWFubBvjXMn0+n/dDsaU4JCj5es3ALq145FRPjTSBv2xbivrN6f7se5PXQ88K2IlNRTfmhZ9oYz7X4VxNhdMDVNboGHRz9M5q635pKRne92OMYcUlaNdDzQW1UPiEgiMB14PTBhucTjXMhtt4TWPFm5BTz0/gKWb91H3fhoUjNyqFsrtM95muBRViLNU9UDAKqaKiKhP5hN3n5narXRGmVfVh4PvDef9bsyaFwnjifHJ9E20d4jU3OUlUg7FBmrSYCORcduCslx7TdOd6aRVtOpKXalZ3Pfu/PZtvcALRvW5qnxSTStb8O9mJqlrERavPPN0B/fPiIa4hMh2r6oNcH+7HxunzyHPZm5dGxalyfGJdEgIdbtsIw5SlljNoXXuPWeAtj+KzQ4zu1IjE/d+GhO6tGcNdv3M/HSgdSOC/1Ox0xwsnEzDto535k26u5uHAaP10tkRAQiwvWnd6PA4yUmyu6dNzVX6J9AKo8DO2HDF858v5vdjSXMzV61kxtf++WIrvAsiZqartyJVERC9+DUvKdg/tOAQLyN5+OWGYu38thHyWxKzeS7pSluh2NMuR0zkYpIkoj8Dqz1LfcRkdC5RXT/Rlg0ybkl9E+7ILGX2xGFpY/nbvD14ATjh3XmoiF237wJHuWpkU4CzgLSAFR1CYeHag5+P97pTI+7BGoluhtLGFJVJs9czWvfOp1p33BGd+vByQSd8pxsilDVzcU+2B4/xRN4EuFcN3rGf9yOJOyoKv/4ahlfJm8hQoTbz+7NiD6t3A7LmAorTyLdKiJJOEMyRwI3A2v8G1aA1e8EVgMKOBGhbnwM0ZERPHBhf4Yc19TtkIyplPI07f8E3A60AXYBx/vWHZOIjBKR1SKyTkRKvYFdRC4SERWRwPe6v+YjZwx744qrhnfhX9cPsyRqglp5Br/brapjVbWx72+squ451vN8tddXgNFAd+AyETnqIk0RqYPT3+m8iodfRekbnGlBdsB3Ha6ycgt46n+LSM3IAZxaaevG1verCW7lGfzu34AWX6+q1x/jqUnAOlXd4NvO+8C5wIpi5R4DngXuLE/A1Son1ZkOfTTguw5He7NyeeC9BWzYlUFmbgFPjktyOyRjqkV5mvbfAd/7/n4FmgB55XheS2BrkeUU37pDRKQf0FpVvyhXtNVtV7IzbX68K7sPJzv3ZXP75Dls2JVBq4a1uXWMXWZmQscxa6Sq+kHRZRF5G/i2HNsu6ezNoZqtr1u+F4EJx9yQyPXA9QBt2rQpx67LKS/dmda3axb9adPuTO5/bx5pmXl0auZ0PlK/duje32HCT2VuEW0PtC1HuRSg6EA6rYDtRZbrAD2BWSKyCeck1mclnXBS1ddUdaCqDkxMrMZrPQ/sqr5tmRKt2raPO9+aQ1pmHr3bNuTZK4+3JGpCTnmOke7jcE0yAtgLlKcL+QVAZxFpD2wDxgLjDj6oqvuBQ/djisgs4E5VXVje4KskL8O5owmca0mNXyzdvJfMnAKO79KU+y/oR2y03TdvQs+xBr8ToA9OIgTwqupRJ55KoqqFInITMAOIBF5X1eUiMhFYqKqfVSHuqts+25k2S4II6wTLXy4e0oEmdeMZ1r0ZkRH2g2VCU5kZRFVVRD5R1QGV2biqTscZ66nouodKKTu8MvuoPN/vwamTArvbMPD90hR6tGlIs/q1EBGG92zhdkjG+FV5qgjzRaS/3yMxIeHD2et59tMl3PfuPHLzC90Ox5iAKLVGKiJRqloInAj8QUTWAwdwzsarqgZ3cs3LcKZix+yqg6ryxg+r+WD2egDOG9SOuBg7ZGLCQ1mf9PlAf+C8AMUSWDvnOWM0JfZ2O5Kg5/EqL3+1jOm/OZ2P3HlOb07rbZ2PmPBRViIVAFVdH6BYAic3HZJfdGqjNmJolRR4vDw7bTE/rdhBTJTT+cjxXey+eRNeykqkiSJye2kPquoLfojH/+Y/A7Mfdubbn+luLCFg7ppd/LRiB7Vio5h46UB6tW3kdkjGBFxZiTQSSKDkO5SCkyr8fC/E1IW67eCs992OKOgN69aca07tSv8OjencvJ7b4RjjirIS6Q5VnRiwSAJh9yJn2mMCnPqSq6EEs7TMXHILPLRsWBuAS0/o6HJExrirrMufQqcmetBs3yWs7Ue7G0cQ27EvmzvenMN978xjT0au2+EYUyOUlUhPC1gUgZK925m2PMHdOILUxl0Z3D55Njv2ZVOvVgzRUXankjFQRtNeVfcGMpDAEGg3CmLquB1I0FmRso+/TplPVm4hfdo14pFLBlIr1q4TNQYq1/tT8Epd7HYEQSl5fSr3vjOPrNxChnRpyuOXDbIkakwR4fNtKMgGTz5kW9d5FbFjXzYPvb+AQq8yoncrbju7l3U+Ykwx4ZNIvb77vrte5m4cQaZ5g1qMP6kzGTkFXD+iGxE22qoxRwmfRHqQ3VtfLvuz86lXy7nr67ITOwHOQHXGmKNZG80cQVX5z3crufG1n9mV7oyuKiKWRI0pQ/jVSE2pPF5l0pe/8/XirURGCOt3ZtC0fi23wzKmxrNEagDIL/TwzCeL+WXVTmKjInjwogEkdW7idljGBAVLpIac/EIenZrMoo17qB0bxcSxg+jZpqHbYRkTNMInkXqtt/aSFHq83PfOPFZuS6dB7VieGJdEx2Z13Q7LmKASHolUFaYMdeYjot2NpYaJioxgWPfm7D2Qx1PjBx/qiMQYU37hkUi/vxH2rYYOZ0O38W5HUyOo6qEz8Rce34FR/VpTO9Z+ZIypjNC//EkVlr3hzJ81BeLt2N+GXRnc+O9f2JZ24NA6S6LGVF7oJ9IDO8CTB6e8BNHWbF2+dS93vTWHDbsymPLrOrfDMSYkhH7TPmOzM63Xwd04aoAF63bz2IfJ5BV6OaFrM245s6fbIRkTEkI/kW6c7kyjw/vC8lnLt/O3aYsp9Coj+7biL2Os8xFjqkvoJ9LIOGfaInw7c/4yeTP/mL4MBS4a0oHrTutqt3waU41CO5FmboMdc515Cd/al1dBgWtOPY5Lhna0JGpMNQvtRLpoEmz4Amo3h4jw7fXp7IFt6dayPp1slE9j/CK0q2l56c7Qy9dtDKsaqcfr5dVvVrAlNfPQOkuixvhP6GYX9cLS16DZQIiKdTuagMkv9PDER7/xybyNPDo1GY/X63ZIxoS80G3ab5rhTKPi3Y0jgLLzCnl06kIWb0ojIS6K28/pbWfmjQmA0E2kOXucadL97sYRIBnZ+TwwZT5rtu+nQe1YnhyfRIem1vmIMYEQuol041fOtHZTd+MIgNSMHO5/dz5b9mTRrH48T40fTAvrfMSYgAndRHpwbKa67VwNIxCWbdnLlj1ZtE1M4Knxg2lUJ87tkIwJK6GbSNPXQtMBYXHZ0yk9W6IKAzslUjc+xu1wjAk7oXkmQhV2zIMm/dyOxG+WbdnL+p0Zh5ZP7dXSkqgxLgnNRLp3tTPNzyy7XJCat3YX9707jwfem09qRo7b4RgT9kKvaa9emDvRmR94p7ux+MEPv2/juc+W4PEqp/ZqQsMEOx5qjNtCL5HumA+rpjjzjUOrm7jPFmzin18vR4FLhnbkmlOPs/vmjakBQi+RbvzSmY79BaJCo7amqrz38zre+nENANee1pVLhnZ0OSpjzEGhl0jnPu5MW4ZOt3nrdmbw9o9riBC4ZUwvRvdr43ZIxpgiQiuR5vnOYtcJrUTTuXk9/jy6J/VrxTCse3O3wzHGFBNaiRR1JgNudTeMapBf6GHnvmzaJNYBnK7wjDE1k18vfxKRUSKyWkTWici9JTx+u4isEJGlIvK9iFi2AA7kFfDglAXc8eacI7rCM8bUTH5LpCISCbwCjAa6A5eJSPdixRYBA1W1N/AR8Ky/4gkW6QfyuOfteSzZlEZUZARedTsiY8yx+LNGmgSsU9UNqpoPvA+cW7SAqs5U1Wzf4lygVZX2qMHd9+bu/Tnc+eYc1u7YT/MGtXhxwlDaNanjdljGmGPwZyJtCWwtspziW1eaa4GvqrTH3Yudad3gO0KwdU8Wt0+ezda0A7RvUofnrxpCswbhPfKpMcHCnyebSrpSvMSGqohcDgwETi7l8euB6wHatCnjjHxumjOt37kicbouJ7+Qu9+ey96sPLq1qs9jY5OoEx/tdljGmHLyZ400BWhdZLkVsL14IRE5HXgAOEdV80rakKq+pqoDVXVgYmJi6Xuc6TtbH2TjM8XHRDHhlOMY2DGRp8cPtiRqTJDxZ410AdBZRNoD24CxwLiiBUSkH/B/wChV3V2lva39H2Rtg5YnQsPjqrSpQMnOK6RWrPMWjOzbmhF9WhFht3waE3T8VnVT1ULgJmAGsBKYqqrLRWSiiJzjK/Y3IAH4UEQWi8hnldrZ5u/gswud+XOnQUTNvzz2+6UpTHh55hFd4VkSNSY4+TXjqOp0YHqxdQ8VmT+9Wna0/nNnesZ/IL5RtWzSn6bN38i/ZqwAYOH63XRsZmMrGRPMan7VrTzS10LjXtDrWrcjKZOq8s5Pa3nnp7UAXHd6Vy4eYp2PGBPsguusTEnU6wx0F12zLxXyqvKvGSt456e1RAjcdlYvS6LGhIjgr5Hu3+RMG/dyNYxj+fsXS5mxOIXoyAjuPb8vJ3azzkeMCRXBXyPdu8qZ9rja3TiOoW+7xtSKiWLi2EGWRI0JMcFfIz2oBp6pV9VDPdif2qslAzomUq+WDVBnTKgJ/hrplu+daQ27dCj9QB53vTWXNdvTD62zJGpMaAr+RJqT6kwb93Y3jiJ2pWdzx+Q5/L5lL/+csRxV68LJmFBW89rDFbV7EbQ/E6Ji3Y4EgC17srjv3XnsycilQ9O6PHzxQBugzpgQF/yJVBWi4t2OAoA129N5cMoC9mfn06N1AyaOHURCnN03b0yoC/5EWkMs3rSHRz5YSE6+h0GdEnnwogHERUe6HZYxJgAskVaTrJwC8go8DO/RgjvP7UN0ZPAffjbGlE8IJNKacSLnxG7Nee6qIXRr1cA6HzEmzAR3tWneU5C2Auq0PnZZP/h0/kaWb917aLlH64aWRI0JQ8FdI133iTMddFdAd6uqvPXjGt77eR0JcdG8cdNw6sbbNaLGhKvgTaSeAkhdCgPugIQWAdutV5V/fr2czxduJkKEG87obknUmDAXvIl0/wbw5EFi4C7EL/R4ee6zJcxctp3oyAjuv7AfQ49rFrD9G2NqpuBNpJ58ZxpdOyC7yy3w8MTHvzF/7W7iYyJ55NKB9G3XOCD7NsbUbMGbSLd850wDdHJn7fZ0Fq5LpW58NE+MS6JLi/oB2a8xpuYL3kR6sB/SNqcFZHe92jbi/gv60TYxgTaJdQKyT2NMcAjORKpe2PA5NB0IsfX8tptd6dmkZuTSs01DAIZ1t35EjTFHC87rSHcugP0b/Trs8ubUTG6fPIcHp8xn/c79ftuPMSb4BWciTV3iTAfd7ZfNr9qWzp1vzmFPZi4dm9WjWf2aPR6UMcZdwdm03/gV1Gnjl3GaFm10Oh/JLfAwuHMTHriwP7HW+YgxpgxBmEgVNn8L3a+s9jP2v67ayVP/W0SBx8upPVtwxzl9iLLOR4wxxxB8iVS9UHAAGnSu1s3uy8rjmU+cJHruoHbcMLK73TfvRwUFBaSkpJCbm+t2KCbMxMXF0apVK6Kjq6+v4OBLpH7SICGWu87ry6bdmVx+Umfr1d7PUlJSqFOnDu3atbPX2gSMqpKWlkZKSgrt27evtu2GdbtVVUlJyzq0PKxbc644uYt9sQMgNzeXRo0a2WttAkpEaNSoUbW3hIIvkXoKqmczXmXS9GXc+O9fjugKzwSOJVHjBn987oKvaV+Y40zjK3+fe4HHy9+mLebHFTuIjowgM6d6krMxJjwFX420MNuZNulXqafnFnh45IOF/LhiB7VionhiXBLHd2lajQGaYBEZGUnfvn3p2bMnZ599Nunp6YceW758OaeeeipdunShc+fOPPbYY0cMq/3VV18xcOBAunXrRteuXbnzzjtL3Ed5y/mLqnLqqaeSkZER0P1WRHJyMr169aJTp07ccsstJQ5fvn//fs4++2z69OlDjx49eOONNwBYvHgxQ4YMoUePHvTu3ZsPPvjg0HPGjh3L2rVrA/NPqGpQ/Q3o3Fj1OVRz9mlFZWTn662v/6pnTPxCL37uG12zPb3C2zDVY8WKFW6HoLVr1z40f+WVV+rjjz+uqqrZ2dnaoUMHnTFjhqqqHjhwQEeNGqUvv/yyqqr+/vvv2qFDB125cqWqqhYUFOgrr7xy1PbLW640hYWFlfvHivjiiy/01ltvrdBzqmO/FTFo0CCdPXu2er1eHTVqlE6fPv2oMk888YTefffdqqq6e/dubdCggebl5enq1at1zZo1qqq6bds2bdasme7b5+SGWbNm6XXXXVfiPkv6/AELtZJ5Kfia9gVZEJ8IcRXrfUlV+euU+azclk5i3TieHD+YNo0T/BSkqZCZt8LuxdW7zSZ94ZS/l7v4kCFDWLp0KQDvvfceJ5xwAmeccQYAtWrV4uWXX2b48OH8+c9/5tlnn+WBBx6ga9euAERFRXHjjTcetc2yyk2YMIGzzjqLiy66CICEhASysrKYNWsWjz76KM2bN2fx4sWcffbZtG3b9tDzHnnkEerUqcMdd9zB3/72N6ZOnUpeXh7nn38+jz766FExvPvuu1x//fWHls877zy2bt1Kbm4uf/nLXw49lpCQwO23386MGTN4/vnniY+P5/bbbycrK4vGjRszefJkmjdvzr///W9ee+018vPz6dSpE2+//Ta1alX+zr8dO3aQkZHBkCFDALjyyiuZNm0ao0ePPqKciJCZmYmqkpWVRcOGDYmKiqJLly6HyrRo0YImTZqQmppK/fr1GTZsGBMmTKCwsJCoKP+muuBr2ns9kJNa4aeJCJcN60S7xDq8MGGoJVFziMfj4fvvv+ecc84BnGb9gAEDjijTsWNHsrKyyMjIYNmyZUc9XpLylitu/vz5PPHEE6xYsYKxY8ce0VydOnUqF198Md988w1r165l/vz5LF68mOTkZH766aejtvXrr78eEcPrr79OcnIyCxcuZNKkSaSlpQFw4MABevbsybx58xg8eDA333wzH330EcnJyVxzzTU88MADAFxwwQUsWLCAJUuW0K1bN/773/8etc+ZM2fSt2/fo/6GDh16VNlt27bRqlWrQ8utWrVi27ZtR5W76aabWLlyJS1atKBXr1689NJLREQcmb7mz59Pfn4+HTt2BCAiIoJOnTqxZMmSMl/v6hB8NVJvAXS/otzF8ws9xEQ5t3gO7tyUgR0TiYwIvt+PkFaBmmN1ysnJoW/fvmzatIkBAwYwYsQIwGm9lHZmNxBXGiQlJR26xrFfv37s3r2b7du3k5qaSoMGDWjTpg2TJk3im2++oV8/51xBVlYWa9eu5aSTTjpiW3v37qVOncPdPk6aNIlPPnHGOtu6dStr166lUaNGREZGcuGFFwKwevVqli1bduj18Hg8NG/u9Hy2bNkyHnzwQdLT08nKymLkyJFHxX/KKaeweHH5WhhawvHQkl7jGTNm0LdvX3744QfWr1/PiBEjGDZsGHXr1gWcmu0VV1zBm2++eUSCbdKkCdu3b6/UD1pFBF8iBSgs3zVgK1P28dhHydxzXj/6tGsEYEnUHBIfH8/ixYvZv38/Z511Fq+88gq33HILPXr0OKp2t2HDBhISEqhTpw49evQgOTmZPn36lLn9sspFRUXh9XoBJ5nk5+cfeqx27SNHfbjooov46KOP2LlzJ2PHjj30nPvuu48//vGPZcZwcD8RERHMmjWL7777jjlz5lCrVi2GDx9+6HrKuLg4IiMjD227R48ezJkz56jtTZgwgWnTptGnTx8mT57MrFmzjiozc+ZMbrvttqPW16pVi9mzZx+xrlWrVqSkpBxaTklJoUWLo8dge+ONN7j33nsRETp16kT79u1ZtWoVSUlJZGRkMGbMGB5//HGOP/74I56Xm5tLfHx8ma9RtajswVW3/ga0QnXj1yUeQC5q4frdes5TX+kZE7/Qp//32zHLm8CqaSebfvvtN23durXm5+drdna2tm/fXr/99ltVdU4+jRkzRidNmqSqqkuWLNGOHTvq6tWrVVXV4/Ho888/f9T2yyr32GOPHTp58sknn6jzVVSdOXOmjhkz5ojtLFu2TIcMGaKdO3fW7du3q6rqjBkzNCkpSTMzM1VVNSUlRXft2nVUDIMHD9a1a9eqquq0adP0rLPOUlXVlStXamxsrM6cOfOo1yIvL087duyos2fPVlXV/Px8XbZsmaqqNmrUSHft2qX5+fl6+umn61VXXXWMV/nYBg4cqHPmzDl0sunLL788qswNN9ygDz/8sKqq7ty5U1u0aKGpqamal5enp556qr744oslbrtnz56HXrOiqvtkU3BWz+ITy3z45xU7eGjKAnILPJzeuyV3nlt2zcGYfv360adPH95//33i4+P59NNPefzxxznuuOPo1asXgwYN4qabbgKgd+/e/P3vf+eyyy6jW7du9OzZkx07dhy1zbLK/eEPf+DHH38kKSmJefPmHVULLapHjx5kZmbSsmXLQ03sM844g3HjxjFkyBB69erFRRddRGZm5lHPHTNmzKFa46hRoy8z0vkAAApiSURBVCgsLKR379789a9/Par2dlBMTAwfffQR99xzD3369KFv376HapKPPfYYgwcPZsSIEYdOolXVv/71L6677jo6depEx44dD51oevXVV3n11VcB+Otf/8rs2bPp1asXp512Gs888wyNGzdm6tSp/PTTT0yePPnQsdiDhxV27dpFfHz8odfMn0RLOEZRkw1sLbpw+Sao27bEx79atIVJX/6OV+G8pHb88QzrfKQmWrlyJd26dXM7jJC3Y8cOrrzySr799lu3Qwm4F198kbp163Lttdce9VhJnz8RSVbVgZXZV3AeI42MLXH1/+Zt5P++WQHAlSd3YdywTnYboglrzZs35w9/+AMZGRmHTsyEi/r163PFFeU/MV0VwZlIS9E2MYHoyAj+MKIb5w5q53Y4xtQIl1xyidshuOLqq68O2L5CKpEO6JDIGzcNJ7FuAM7SmSrTMi4zMsZf/HE4MzhPNvkUeLw888kiFq4/fIG+JdHgEBcXR1paml8+1MaURtXpjzQuLq5atxu0NdLc/EImfphM8oY9LN6UxuSbTrGxlYLIwesHU1MrfpeaMVVxsIf86uTXRCoio4CXgEjgP6r6dLHHY4G3gAFAGnCpqm461nYzcgp5aOo8VqakU69WDI+NHWRJNMhER0dXaw/lxrjJb017EYkEXgFGA92By0Ske7Fi1wL7VLUT8CLwzLG2W0gkd324hpUp6TSpF88LE4bQqXm96g7fGGPKzZ/HSJOAdaq6QVXzgfeBc4uVORd40zf/EXCaHOPsQ4q3FZv25NC6UW1emDCEVo2s8xFjjLv8mUhbAluLLKf41pVYRlUL4f/bO9sYO6oyjv/+Wgut4iI2GAjo0tCipZZaq6kQxVogWAMoqd0lLbCmlLQKppJ+0NQYRD8QtCbWgmWDTcEAWUpANwhBxC0lTbcvkXa3bXippTGNjYWkNgYLKeXxwzkbx9u7e+feuS8z1+eX3GTmzJk5z3/n3mfPmTPPczgGfHSsi77LOKae08Hqnkt9YslxnFzQyGek5XqWpVO0aeog6VZgJKniO2uXfnHP2qUZrcsvk4A3W21EA2lnfe2sDdpf30W1nthIR3oIOD+xfx7w91HqHJI0DugATlmJzsx6gV4ASTtrDeMqAq6vuLSzNvj/0FfruY0c2u8Apki6QNJ4oBvoL6nTD9wctxcAfzZ/sdBxnILRsB6pmb0r6TbgWcLrT+vNbK+kuwjpqvqB3wC/lbSf0BPtbpQ9juM4jaKh75Ga2dPA0yVlP0psvw18s8rL9tbBtDzj+opLO2sD1zcqhUuj5ziOkzcKHWvvOI6TB3LrSCVdLekVSfslfb/M8dMk9cXj2yR1Nt/K2kmh7w5J+yQNSXpeUvlM1jmkkrZEvQWSTFKhZoLT6JO0MN6/vZIeabaNWUjx3fy4pAFJL8Xv5/xW2FkLktZLOiJpzyjHJWlN1D4kaVaqC9e6RkkjP4TJqb8Ck4HxwG5gWkmdbwPr4nY30Ndqu+usby4wMW4vL4q+NNpivTOAzcAgMLvVdtf53k0BXgI+EvfPbrXdddbXCyyP29OAg622uwp9XwJmAXtGOT4feIbwjvscYFua6+a1R9qQ8NIcUVGfmQ2Y2b/j7iDhPdwikObeAfwEuAdItyRsfkijbylwr5kdBTCzI022MQtp9Bkwkm6/g1PfD88tZraZMu+qJ7gOeMgCg8CZkiou+pRXR9qQ8NIckUZfkiWE/5JFoKI2SZ8Bzjezp5ppWJ1Ic++mAlMlbZE0GLOgFYU0+u4EFks6RHgr5/bmmNYUqv1tAvnNR1q38NKcktp2SYuB2cDlDbWofoypTdL7CJm+epplUJ1Jc+/GEYb3XyaMJF6UNN3M/tlg2+pBGn03ABvMbLWkLxDeBZ9uZu813ryGU5NfyWuPtJrwUsYKL80pafQh6QpgFXCtmb3TJNuyUknbGcB0YJOkg4TnUP0FmnBK+938vZmdMLPXgVcIjrUIpNG3BHgMwMy2AqcT4vDbgVS/zVLy6kjbPby0or44/L2f4ESL9IxtTG1mdszMJplZp5l1Ep7/XmtmNcc5N5k0383fESYLkTSJMNQ/0FQrayeNvr8B8wAkfYrgSNtlqYN+4KY4ez8HOGZmhyue1epZtDFm1+YDrxJmEFfFsrsIPzoIN28jsB/YDkxutc111vcn4B/Arvjpb7XN9dJWUncTBZq1T3nvBPwC2AcMA92ttrnO+qYBWwgz+ruAq1ptcxXaHgUOAycIvc8lwDJgWeLe3Ru1D6f9bnpkk+M4TkbyOrR3HMcpDO5IHcdxMuKO1HEcJyPuSB3HcTLijtRxHCcj7kidikg6KWlX4tM5Rt3O0TLrVNnmppiBaHcMtax6YTJJyyTdFLd7JJ2bOPaApGl1tnOHpJkpzlkhaWLWtp384I7UScNxM5uZ+BxsUruLzOwSQnKan1V7spmtM7OH4m4PcG7i2C1mtq8uVv7XzvtIZ+cKwB1pG+GO1KmJ2PN8UdJf4ufSMnUulrQ99mKHJE2J5YsT5fdLen+F5jYDF8Zz58U8mMMxt+RpsfzuRP7Wn8eyOyWtlLSAkK/g4djmhNiTnC1puaR7Ejb3SPpVjXZuJZHgQtKvJe1UyEn641j2XYJDH5A0EMuukrQ1/h03SvpQhXacnOGO1EnDhMSw/slYdgS40sxmAV3AmjLnLQN+aWYzCY7sUAwp7AIui+UngUUV2r8GGJZ0OrAB6DKzTxOSgyyXdBbwDeBiM5sB/DR5spk9Duwk9BxnmtnxxOHHgesT+11AX412Xk0IDx1hlYXli2cAl0uaYWZrCLHbc81sbgwh/SFwRfxb7gTuqNCOkzPymv3JyRfHozNJ8gFgbXwmeJIQT17KVmCVpPOAJ8zsNUnzgM8CO2L62AkEp1yOhyUdBw4SUrVdBLxuZq/G4w8C3wHWEvKaPiDpD0Dq9Hxm9oakAzGu+rXYxpZ43Wrs/CAhKXIyo/pCSbcSfmfnEEIrh0rOnRPLt8R2xhP+bk6BcEfq1Mr3CLkALiGMbE5J0Gxmj0jaBnwNeFbSLYRY5gfN7Acp2lhkiWQmksrmm7Ww9PfnCYk0uoHbgK9UoaUPWAi8DDxpZqbg1VLbSYg7v5sQp329pAuAlcDnzOyopA2E/BClCHjOzG6owl4nZ/jQ3qmVDuCwhRyUNxJ6Y/+DpMnAgTic7ScMcZ8HFkg6O9Y5S+nXo3oZ6JR0Ydy/EXghPlPssLD89wqg3Mz5vwgp/MrxBPB1Qp7NvlhWlZ1mdoIwRJ8THwt8GHgLOCbpY8BXR7FlELhsRJOkiZLK9e6dHOOO1KmV+4CbJQ0ShvVvlanTBeyRtAv4JGEJh30Eh/NHSUPAc4Rhb0XM7G3gW8BGScPAe8A6glN6Kl7vBUJvuZQNwLqRyaaS6x4lZGr6hJltj2VV2xmfva4GVprZbsK6TXuB9YTHBSP0As9IGjCzNwhvFDwa2xkk/K2cAuHZnxzHcTLiPVLHcZyMuCN1HMfJiDtSx3GcjLgjdRzHyYg7UsdxnIy4I3Ucx8mIO1LHcZyMuCN1HMfJyH8A58+7Oe7au58AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_accuracy(y_eval, y_pred, CLASSIFICATION_TYPE)\n",
    "show_confusion_matrix(y_eval, y_pred, y_probs, CLASSIFICATION_TYPE)\n",
    "show_classification_report(y_eval, y_pred)\n",
    "\n",
    "if CLASSIFICATION_TYPE == 'Binary':\n",
    "    show_roc_plot(y_eval, y_probs, 'Random Forests Classifier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare Model Accuracy Using K-Fold Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------\n",
      "Accuracy Score per Fold:  [0.76595745 0.81028369 0.78014184 0.79964539 0.79432624 0.7712766\n",
      " 0.78330373 0.79218472 0.77758007 0.77046263]\n",
      "-------------------------------------------------------------------\n",
      "Average Accuracy Score: 0.785\n",
      "-------------------------------------------------------------------\n",
      "Wall time: 1.91 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "scores = cross_val_score(rfc, X, y, cv=10, scoring='accuracy')\n",
    "\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Accuracy Score per Fold: \", scores)\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Average Accuracy Score: %0.3f\" % scores.mean())\n",
    "print('-------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a Classifier Using K-Nearest Neighbors and K-Fold Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------\n",
      "Accuracy Score per Fold:  [0.75177305 0.7606383  0.75531915 0.7535461  0.75       0.75177305\n",
      " 0.74777975 0.75843694 0.7366548  0.74911032]\n",
      "-------------------------------------------------------------------\n",
      "Average Accuracy Score: 0.752\n",
      "-------------------------------------------------------------------\n",
      "Wall time: 1.42 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
    "\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Accuracy Score per Fold: \", scores)\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Average Accuracy Score: %0.3f\" % scores.mean())\n",
    "print('-------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 39.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Define the parameter values to be searched\n",
    "k_range = list(range(2, 26))\n",
    "weight_options = ['uniform','distance']\n",
    "\n",
    "# Create a parameter grid\n",
    "param_grid = dict(n_neighbors=k_range, weights=weight_options)\n",
    "\n",
    "# Instantiate the grid\n",
    "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', return_train_score=False, n_jobs=-1)\n",
    "\n",
    "# Finally, fit the grid\n",
    "grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.744054</td>\n",
       "      <td>0.013930</td>\n",
       "      <td>{'n_neighbors': 2, 'weights': 'uniform'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.704118</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>{'n_neighbors': 2, 'weights': 'distance'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.739794</td>\n",
       "      <td>0.012697</td>\n",
       "      <td>{'n_neighbors': 3, 'weights': 'uniform'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.731984</td>\n",
       "      <td>0.015990</td>\n",
       "      <td>{'n_neighbors': 3, 'weights': 'distance'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.760028</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>{'n_neighbors': 4, 'weights': 'uniform'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_test_score  std_test_score                                     params\n",
       "0         0.744054        0.013930   {'n_neighbors': 2, 'weights': 'uniform'}\n",
       "1         0.704118        0.015469  {'n_neighbors': 2, 'weights': 'distance'}\n",
       "2         0.739794        0.012697   {'n_neighbors': 3, 'weights': 'uniform'}\n",
       "3         0.731984        0.015990  {'n_neighbors': 3, 'weights': 'distance'}\n",
       "4         0.760028        0.009250   {'n_neighbors': 4, 'weights': 'uniform'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the results\n",
    "pd.DataFrame(grid.cv_results_)[['mean_test_score', 'std_test_score', 'params']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------\n",
      "Mean Test Scores (Uniform): [0.744, 0.74, 0.76, 0.752, 0.761, 0.759, 0.762, 0.76, 0.768, 0.765, 0.773, 0.767, 0.774, 0.772, 0.773, 0.769, 0.772, 0.771, 0.772, 0.771, 0.774, 0.772, 0.775, 0.771]\n",
      "----------------------------------------------------------------------------------\n",
      "Mean Test Scores (Distance): [0.704, 0.732, 0.733, 0.742, 0.741, 0.746, 0.742, 0.748, 0.752, 0.753, 0.753, 0.756, 0.755, 0.758, 0.759, 0.759, 0.758, 0.76, 0.76, 0.761, 0.76, 0.761, 0.76, 0.762]\n",
      "----------------------------------------------------------------------------------\n",
      "K-Range Values: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]\n",
      "----------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mean_test_scores = grid.cv_results_['mean_test_score']\n",
    "params = grid.cv_results_['params']\n",
    "\n",
    "items = list(range(0, len(params)))\n",
    "weights = []\n",
    "\n",
    "for i in items:\n",
    "    weights.append(params[i]['weights'])\n",
    "\n",
    "results = pd.DataFrame({'scores':mean_test_scores, 'weights':weights})      \n",
    "uniform_scores = results[results.weights == 'uniform'].scores\n",
    "distance_scores = results[results.weights == 'distance'].scores\n",
    "\n",
    "print('----------------------------------------------------------------------------------')\n",
    "print('Mean Test Scores (Uniform):', round(uniform_scores, 3).tolist())\n",
    "print('----------------------------------------------------------------------------------')\n",
    "print('Mean Test Scores (Distance):', round(distance_scores, 3).tolist())\n",
    "print('----------------------------------------------------------------------------------')\n",
    "print('K-Range Values:', k_range)\n",
    "print('----------------------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Cross-validated Accuracy')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the scores\n",
    "plt.title('Mean Test Scores by Weight')\n",
    "plt.plot(k_range, uniform_scores, color='steelblue', label='Uniform')\n",
    "plt.plot(k_range, distance_scores, color='darkorange', label='Distance')\n",
    "plt.legend(loc='lower right', title='Weights')\n",
    "plt.xlabel('Value of K for KNN')\n",
    "plt.ylabel('Cross-validated Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7749378771742989\n",
      "{'n_neighbors': 24, 'weights': 'uniform'}\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=24, p=2,\n",
      "           weights='uniform')\n"
     ]
    }
   ],
   "source": [
    "# Examine the best score\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use the Best Parameters to Make Predictions Using Hold-Out Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------\n",
      "Accuracy Score per Fold:  [0.77304965 0.78546099 0.78014184 0.7748227  0.79787234 0.76595745\n",
      " 0.76554174 0.76021314 0.78825623 0.75800712]\n",
      "-------------------------------------------------------------------\n",
      "Average Accuracy Score: 0.775\n",
      "-------------------------------------------------------------------\n",
      "Wall time: 1.39 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "n_neighbors = grid.best_params_['n_neighbors']\n",
    "weights_option = grid.best_params_['weights']\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = n_neighbors, weights = weights_option)\n",
    "scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
    "\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Accuracy Score per Fold: \", scores)\n",
    "print('-------------------------------------------------------------------')\n",
    "print(\"Average Accuracy Score: %0.3f\" % scores.mean())\n",
    "print('-------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}