Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split a dataset into k folds\n",
    "def cross_validation_split(dataset, n_folds):\n",
    "    dataset_split = list()\n",
    "    dataset_copy = list(dataset)\n",
    "    fold_size = 1406\n",
    "    for i in range(n_folds):\n",
    "        fold = list()\n",
    "        while len(fold) < fold_size:\n",
    "            index = randrange(16)\n",
    "            fold.append(dataset_copy.pop(index))\n",
    "        dataset_split.append(fold)\n",
    "    return dataset_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest Algorithm on Sonar Dataset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import math\n",
    "from random import seed\n",
    "from random import randrange\n",
    "from csv import reader\n",
    "import scipy.stats as sps\n",
    "import matplotlib.pyplot as plt\n",
    "import io\n",
    "import plotly.offline as py#visualization\n",
    "py.init_notebook_mode(connected=True)#visualization\n",
    "import plotly.graph_objs as go#visualization\n",
    "import plotly.tools as tls#visualization\n",
    "import plotly.figure_factory as ff#visualization\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Load a CSV file\n",
    "def load_csv(filename):\n",
    "\tdataset = list()\n",
    "\twith open(filename, 'r') as file:\n",
    "\t\tcsv_reader = reader(file)\n",
    "\t\tfor row in csv_reader:\n",
    "\t\t\tif not row:\n",
    "\t\t\t\tcontinue\n",
    "\t\t\tdataset.append(row)\n",
    "\treturn dataset\n",
    "\n",
    "# Convert string column to float\n",
    "def str_column_to_float(dataset, column):\n",
    "\tfor row in dataset:\n",
    "\t\trow[column] = float(row[column].strip())\n",
    "\n",
    "# Convert string column to integer\n",
    "def str_column_to_int(dataset, column):\n",
    "\tclass_values = [row[column] for row in dataset]\n",
    "\tunique = set(class_values)\n",
    "\tlookup = dict()\n",
    "\tfor i, value in enumerate(unique):\n",
    "\t\tlookup[value] = i\n",
    "\tfor row in dataset:\n",
    "\t\trow[column] = lookup[row[column]]\n",
    "\treturn lookup\n",
    "\n",
    "\n",
    "\n",
    "# Calculate accuracy percentage\n",
    "def accuracy_metric(actual, predicted):\n",
    "\tcorrect = 0\n",
    "\tfor i in range(len(actual)):\n",
    "\t\tif actual[i] == predicted[i]:\n",
    "\t\t\tcorrect += 1\n",
    "\treturn correct / float(len(actual)) * 100.0\n",
    "\n",
    "# Evaluate an algorithm using a cross validation split\n",
    "def evaluate_algorithm(dataset, algorithm, n_folds, *args):\n",
    "\tfolds = cross_validation_split(dataset, n_folds)\n",
    "\tscores = list()\n",
    "\tfor fold in folds:\n",
    "\t\ttrain_set = list(folds)\n",
    "\t\ttrain_set.remove(fold)\n",
    "\t\ttrain_set = sum(train_set, [])\n",
    "\t\ttest_set = list()\n",
    "\t\tfor row in fold:\n",
    "\t\t\trow_copy = list(row)\n",
    "\t\t\ttest_set.append(row_copy)\n",
    "\t\t\trow_copy[-1] = None\n",
    "\t\tpredicted = algorithm(train_set, test_set, *args)\n",
    "\t\tactual = [row[-1] for row in fold]\n",
    "\t\taccuracy = accuracy_metric(actual, predicted)\n",
    "\t\tscores.append(accuracy)\n",
    "\treturn scores\n",
    "\n",
    "# Split a dataset based on an attribute and an attribute value\n",
    "def test_split(index, value, dataset):\n",
    "\tleft, right = list(), list()\n",
    "\tfor row in dataset:\n",
    "\t\tif row[index] < value:\n",
    "\t\t\tleft.append(row)\n",
    "\t\telse:\n",
    "\t\t\tright.append(row)\n",
    "\treturn left, right\n",
    "\n",
    "# Calculate the Gini index for a split dataset\n",
    "def gini_index(groups, classes):\n",
    "\t# count all samples at split point\n",
    "\tn_instances = float(sum([len(group) for group in groups]))\n",
    "\t# sum weighted Gini index for each group\n",
    "\tgini = 0.0\n",
    "\tfor group in groups:\n",
    "\t\tsize = float(len(group))\n",
    "\t\t# avoid divide by zero\n",
    "\t\tif size == 0:\n",
    "\t\t\tcontinue\n",
    "\t\tscore = 0.0\n",
    "\t\t# score the group based on the score for each class\n",
    "\t\tfor class_val in classes:\n",
    "\t\t\tp = [row[-1] for row in group].count(class_val) / size\n",
    "\t\t\tscore += p * p\n",
    "\t\t# weight the group score by its relative size\n",
    "\t\tgini += (1.0 - score) * (size / n_instances)\n",
    "\treturn gini\n",
    "\n",
    "# Select the best split point for a dataset\n",
    "def get_split(dataset, n_features):\n",
    "\tclass_values = list(set(row[-1] for row in dataset))\n",
    "\tb_index, b_value, b_score, b_groups = 999, 999, 999, None\n",
    "\tfeatures = list()\n",
    "\twhile len(features) < n_features:\n",
    "\t\tindex = randrange(len(dataset[0])-1)\n",
    "\t\tif index not in features:\n",
    "\t\t\tfeatures.append(index)\n",
    "\tfor index in features:\n",
    "\t\tfor row in dataset:\n",
    "\t\t\tgroups = test_split(index, row[index], dataset)\n",
    "\t\t\tgini = gini_index(groups, class_values)\n",
    "\t\t\tif gini < b_score:\n",
    "\t\t\t\tb_index, b_value, b_score, b_groups = index, row[index], gini, groups\n",
    "\treturn {'index':b_index, 'value':b_value, 'groups':b_groups}\n",
    "\n",
    "# Create a terminal node value\n",
    "def to_terminal(group):\n",
    "\toutcomes = [row[-1] for row in group]\n",
    "\treturn max(set(outcomes), key=outcomes.count)\n",
    "\n",
    "# Create child splits for a node or make terminal\n",
    "def split(node, max_depth, min_size, n_features, depth):\n",
    "\tleft, right = node['groups']\n",
    "\tdel(node['groups'])\n",
    "\t# check for a no split\n",
    "\tif not left or not right:\n",
    "\t\tnode['left'] = node['right'] = to_terminal(left + right)\n",
    "\t\treturn\n",
    "\t# check for max depth\n",
    "\tif depth >= max_depth:\n",
    "\t\tnode['left'], node['right'] = to_terminal(left), to_terminal(right)\n",
    "\t\treturn\n",
    "\t# process left child\n",
    "\tif len(left) <= min_size:\n",
    "\t\tnode['left'] = to_terminal(left)\n",
    "\telse:\n",
    "\t\tnode['left'] = get_split(left, n_features)\n",
    "\t\tsplit(node['left'], max_depth, min_size, n_features, depth+1)\n",
    "\t# process right child\n",
    "\tif len(right) <= min_size:\n",
    "\t\tnode['right'] = to_terminal(right)\n",
    "\telse:\n",
    "\t\tnode['right'] = get_split(right, n_features)\n",
    "\t\tsplit(node['right'], max_depth, min_size, n_features, depth+1)\n",
    "\n",
    "# Build a decision tree\n",
    "def build_tree(train, max_depth, min_size, n_features):\n",
    "\troot = get_split(train, n_features)\n",
    "\tsplit(root, max_depth, min_size, n_features, 1)\n",
    "\treturn root\n",
    "\n",
    "# Make a prediction with a decision tree\n",
    "def predict(node, row):\n",
    "\tif row[node['index']] < node['value']:\n",
    "\t\tif isinstance(node['left'], dict):\n",
    "\t\t\treturn predict(node['left'], row)\n",
    "\t\telse:\n",
    "\t\t\treturn node['left']\n",
    "\telse:\n",
    "\t\tif isinstance(node['right'], dict):\n",
    "\t\t\treturn predict(node['right'], row)\n",
    "\t\telse:\n",
    "\t\t\treturn node['right']\n",
    "\n",
    "# Create a random subsample from the dataset with replacement\n",
    "def subsample(dataset, ratio):\n",
    "\tsample = list()\n",
    "\tn_sample = round(len(dataset) * ratio)\n",
    "\twhile len(sample) < n_sample:\n",
    "\t\tindex = randrange(len(dataset))\n",
    "\t\tsample.append(dataset[index])\n",
    "\treturn sample\n",
    "\n",
    "# Make a prediction with a list of bagged trees\n",
    "def bagging_predict(trees, row):\n",
    "\tpredictions = [predict(tree, row) for tree in trees]\n",
    "\treturn max(set(predictions), key=predictions.count)\n",
    "\n",
    "# Random Forest Algorithm\n",
    "def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features):\n",
    "\ttrees = list()\n",
    "\tfor i in range(n_trees):\n",
    "\t\tsample = subsample(train, sample_size)\n",
    "\t\ttree = build_tree(sample, max_depth, min_size, n_features)\n",
    "\t\ttrees.append(tree)\n",
    "\tpredictions = [bagging_predict(trees, row) for row in test]\n",
    "\treturn(predictions)\n",
    "\n",
    "# Test the random forest algorithm\n",
    "seed(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "telcom= pd.read_csv(\"WA_Fn-UseC_-Telco-Customer-Churn.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and prepare data\n",
    "telcom= pd.read_csv(\"WA_Fn-UseC_-Telco-Customer-Churn.csv\")\n",
    "telcom.TotalCharges = pd.to_numeric(telcom.TotalCharges, errors='coerce')\n",
    "telcom = telcom[telcom[\"TotalCharges\"].notnull()]\n",
    "telcom = telcom.reset_index()[telcom.columns]\n",
    "telcom['Churn'].replace(to_replace='Yes', value=1, inplace=True)\n",
    "telcom['Churn'].replace(to_replace='No',  value=0, inplace=True)\n",
    "replace_cols = [ 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n",
    "                'TechSupport','StreamingTV', 'StreamingMovies']\n",
    "for i in replace_cols : \n",
    "    telcom[i]  = telcom[i].replace({'No internet service' : 'No'})\n",
    "#replace values\n",
    "telcom[\"SeniorCitizen\"] = telcom[\"SeniorCitizen\"].replace({1:\"Yes\",0:\"No\"})\n",
    "telcom = telcom.iloc[:,1:]\n",
    "data=telcom.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Partner'].replace(['Yes','No'],[1,0],inplace=True)\n",
    "data['SeniorCitizen'].replace(['Yes','No'],[1,0],inplace=True)\n",
    "data['Dependents'].replace(['Yes','No'],[1,0],inplace=True)\n",
    "data['PhoneService'].replace(['Yes','No'],[1,0],inplace=True)\n",
    "data['MultipleLines'].replace(['No phone service','No', 'Yes'],[0,0,1],inplace=True)\n",
    "data['InternetService'].replace(['No','DSL','Fiber optic'],[0,1,2],inplace=True)\n",
    "data['OnlineSecurity'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
    "data['OnlineBackup'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
    "data['DeviceProtection'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
    "data['TechSupport'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
    "data['StreamingTV'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
    "data['StreamingMovies'].replace(['No','Yes','No internet service'],[0,1,0],inplace=True)\n",
    "data['Contract'].replace(['Month-to-month', 'One year', 'Two year'],[0,1,2],inplace=True)\n",
    "data['PaperlessBilling'].replace(['Yes','No'],[1,0],inplace=True)\n",
    "data['PaymentMethod'].replace(['Electronic check', 'Mailed check', 'Bank transfer (automatic)','Credit card (automatic)'],[0,1,2,3],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "telcom1= data.copy()\n",
    "telcom1= telcom1.drop('gender', axis=1)\n",
    "telcom1= telcom1.drop('TotalCharges', axis=1)\n",
    "telcom1= telcom1.drop('StreamingTV', axis=1)\n",
    "telcom1 = telcom1.drop('StreamingMovies', axis=1)\n",
    "dataset= telcom1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SeniorCitizen',\n",
       " 'Partner',\n",
       " 'Dependents',\n",
       " 'tenure',\n",
       " 'PhoneService',\n",
       " 'MultipleLines',\n",
       " 'InternetService',\n",
       " 'OnlineSecurity',\n",
       " 'OnlineBackup',\n",
       " 'DeviceProtection',\n",
       " 'TechSupport',\n",
       " 'Contract',\n",
       " 'PaperlessBilling',\n",
       " 'PaymentMethod',\n",
       " 'MonthlyCharges',\n",
       " 'Churn']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "pop index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-86-1153714f9854>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mn_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn_trees\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate_algorithm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_forest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trees\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Trees: %d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mn_trees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Scores: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-80-b944b185e4f4>\u001b[0m in \u001b[0;36mevaluate_algorithm\u001b[0;34m(dataset, algorithm, n_folds, *args)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;31m# Evaluate an algorithm using a cross validation split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_algorithm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malgorithm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0mfolds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_validation_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfold\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfolds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-79-b94e1091713c>\u001b[0m in \u001b[0;36mcross_validation_split\u001b[0;34m(dataset, n_folds)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfold\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mfold_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m             \u001b[0mfold\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_copy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0mdataset_split\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdataset_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: pop index out of range"
     ]
    }
   ],
   "source": [
    "# evaluate algorithm\n",
    "n_folds = 5\n",
    "max_depth = 10\n",
    "min_size = 1\n",
    "sample_size = 1.0\n",
    "n_features = int(math.sqrt(dataset.shape[0]-1))\n",
    "for n_trees in [1, 5, 10]:\n",
    "\tscores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)\n",
    "\tprint('Trees: %d' % n_trees)\n",
    "\tprint('Scores: %s' % scores)\n",
    "\tprint('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))"
   ]
  },
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   "execution_count": null,
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   "source": []
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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