Iris dataset sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
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      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.4 3.7 1.5 0.2]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [4.3 3.  1.1 0.1]\n",
      " [5.8 4.  1.2 0.2]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [5.4 3.4 1.7 0.2]\n",
      " [5.1 3.7 1.5 0.4]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [5.1 3.3 1.7 0.5]\n",
      " [4.8 3.4 1.9 0.2]\n",
      " [5.  3.  1.6 0.2]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [5.2 3.4 1.4 0.2]\n",
      " [4.7 3.2 1.6 0.2]\n",
      " [4.8 3.1 1.6 0.2]\n",
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      " [5.2 4.1 1.5 0.1]\n",
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      " [4.4 3.  1.3 0.2]\n",
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      " [5.  3.5 1.3 0.3]\n",
      " [4.5 2.3 1.3 0.3]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [5.  3.5 1.6 0.6]\n",
      " [5.1 3.8 1.9 0.4]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [4.6 3.2 1.4 0.2]\n",
      " [5.3 3.7 1.5 0.2]\n",
      " [5.  3.3 1.4 0.2]\n",
      " [7.  3.2 4.7 1.4]\n",
      " [6.4 3.2 4.5 1.5]\n",
      " [6.9 3.1 4.9 1.5]\n",
      " [5.5 2.3 4.  1.3]\n",
      " [6.5 2.8 4.6 1.5]\n",
      " [5.7 2.8 4.5 1.3]\n",
      " [6.3 3.3 4.7 1.6]\n",
      " [4.9 2.4 3.3 1. ]\n",
      " [6.6 2.9 4.6 1.3]\n",
      " [5.2 2.7 3.9 1.4]\n",
      " [5.  2.  3.5 1. ]\n",
      " [5.9 3.  4.2 1.5]\n",
      " [6.  2.2 4.  1. ]\n",
      " [6.1 2.9 4.7 1.4]\n",
      " [5.6 2.9 3.6 1.3]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [5.6 3.  4.5 1.5]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.9 3.2 4.8 1.8]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [6.3 2.5 4.9 1.5]\n",
      " [6.1 2.8 4.7 1.2]\n",
      " [6.4 2.9 4.3 1.3]\n",
      " [6.6 3.  4.4 1.4]\n",
      " [6.8 2.8 4.8 1.4]\n",
      " [6.7 3.  5.  1.7]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [5.7 2.6 3.5 1. ]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [5.5 2.4 3.7 1. ]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [6.  2.7 5.1 1.6]\n",
      " [5.4 3.  4.5 1.5]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [6.3 2.3 4.4 1.3]\n",
      " [5.6 3.  4.1 1.3]\n",
      " [5.5 2.5 4.  1.3]\n",
      " [5.5 2.6 4.4 1.2]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [5.8 2.6 4.  1.2]\n",
      " [5.  2.3 3.3 1. ]\n",
      " [5.6 2.7 4.2 1.3]\n",
      " [5.7 3.  4.2 1.2]\n",
      " [5.7 2.9 4.2 1.3]\n",
      " [6.2 2.9 4.3 1.3]\n",
      " [5.1 2.5 3.  1.1]\n",
      " [5.7 2.8 4.1 1.3]\n",
      " [6.3 3.3 6.  2.5]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [7.1 3.  5.9 2.1]\n",
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      " [6.5 3.  5.8 2.2]\n",
      " [7.6 3.  6.6 2.1]\n",
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      " [6.  2.2 5.  1.5]\n",
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      " [5.6 2.8 4.9 2. ]\n",
      " [7.7 2.8 6.7 2. ]\n",
      " [6.3 2.7 4.9 1.8]\n",
      " [6.7 3.3 5.7 2.1]\n",
      " [7.2 3.2 6.  1.8]\n",
      " [6.2 2.8 4.8 1.8]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [6.4 2.8 5.6 2.1]\n",
      " [7.2 3.  5.8 1.6]\n",
      " [7.4 2.8 6.1 1.9]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [6.3 2.8 5.1 1.5]\n",
      " [6.1 2.6 5.6 1.4]\n",
      " [7.7 3.  6.1 2.3]\n",
      " [6.3 3.4 5.6 2.4]\n",
      " [6.4 3.1 5.5 1.8]\n",
      " [6.  3.  4.8 1.8]\n",
      " [6.9 3.1 5.4 2.1]\n",
      " [6.7 3.1 5.6 2.4]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [6.7 3.3 5.7 2.5]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [6.3 2.5 5.  1.9]\n",
      " [6.5 3.  5.2 2. ]\n",
      " [6.2 3.4 5.4 2.3]\n",
      " [5.9 3.  5.1 1.8]]\n"
     ]
    }
   ],
   "source": [
    "print(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "# print(iris.feature_names)\n",
    "# print(iris.target)\n",
    "print(iris.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data #features\n",
    "y = iris.target # response\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making an instance of the scikit-learn model(estimator)\n",
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Training\n",
    "knn.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek Singh\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting response for a new data\n",
    "a = [3,4,5,2]\n",
    "knn.predict(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek Singh\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X, y)\n",
    "\n",
    "logreg.predict(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluating the model\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "y_pred = logreg.predict(X_test)\n",
    "print(metrics.accuracy_score(y_test, y_pred))\n",
    "\n",
    "# using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95\n"
     ]
    }
   ],
   "source": [
    "# Using KNN with k = 5\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "print(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking the accuracy for different values of k\n",
    "\n",
    "k_range = range(1,26)\n",
    "score = []\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors = k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    y_pred = knn.predict(X_test)\n",
    "    score.append(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Testing accuracy')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28150519c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(k_range, score)\n",
    "plt.xlabel('Value of k for knn')\n",
    "plt.ylabel('Testing accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22360679774997896\n"
     ]
    }
   ],
   "source": [
    "# Printing RMS error\n",
    "import numpy as np\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         0.93333333 1.         1.         0.86666667 0.93333333\n",
      " 0.93333333 1.         1.         1.        ]\n"
     ]
    }
   ],
   "source": [
    "# K-Fold cross validation on iris dataset\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "score = cross_val_score(knn, X, y, cv=10, scoring = 'accuracy')\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666668\n"
     ]
    }
   ],
   "source": [
    "# mean accuracy\n",
    "print(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# searching for an optimal value of k for KNN \n",
    "k_range = range(1,31)\n",
    "k_scores = []\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    score = cross_val_score(knn, X, y, scoring = 'accuracy')\n",
    "    k_scores.append(score.mean())\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Cross validation accuracy')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x281519b9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(k_range, k_scores)\n",
    "plt.xlabel('value of k for KNN')\n",
    "plt.ylabel('Cross validation accuracy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: 0.96000, std: 0.05333, params: {'n_neighbors': 1},\n",
       " mean: 0.95333, std: 0.05207, params: {'n_neighbors': 2},\n",
       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 3},\n",
       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 4},\n",
       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 5},\n",
       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 6},\n",
       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 7},\n",
       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 8},\n",
       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 9},\n",
       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 10},\n",
       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 11},\n",
       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 12},\n",
       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 13},\n",
       " mean: 0.97333, std: 0.04422, params: {'n_neighbors': 14},\n",
       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 15},\n",
       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 16},\n",
       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 17},\n",
       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 18},\n",
       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 19},\n",
       " mean: 0.98000, std: 0.03055, params: {'n_neighbors': 20},\n",
       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 21},\n",
       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 22},\n",
       " mean: 0.97333, std: 0.03266, params: {'n_neighbors': 23},\n",
       " mean: 0.96000, std: 0.04422, params: {'n_neighbors': 24},\n",
       " mean: 0.96667, std: 0.03333, params: {'n_neighbors': 25},\n",
       " mean: 0.96000, std: 0.04422, params: {'n_neighbors': 26},\n",
       " mean: 0.96667, std: 0.04472, params: {'n_neighbors': 27},\n",
       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 28},\n",
       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 29},\n",
       " mean: 0.95333, std: 0.04269, params: {'n_neighbors': 30}]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can use gridsearchcv to automatically select the best parameter for our model by passing in a dictionary\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "k_range =list(range(1, 31))\n",
    "#print(kk_range)\n",
    "#create a dictionary which map the values which are to be searched\n",
    "param_grid = dict(n_neighbors = k_range)\n",
    "print(param_grid)\n",
    "\n",
    "grid = GridSearchCV(knn, param_grid, cv=10, scoring = 'accuracy')\n",
    "\n",
    "grid.fit(X, y) # fit the grid with data\n",
    "\n",
    "grid.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.96, 0.9533333333333334, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9733333333333334, 0.9666666666666667, 0.9666666666666667, 0.9733333333333334, 0.98, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.98, 0.9733333333333334, 0.98, 0.9666666666666667, 0.9666666666666667, 0.9733333333333334, 0.96, 0.9666666666666667, 0.96, 0.9666666666666667, 0.9533333333333334, 0.9533333333333334, 0.9533333333333334]\n"
     ]
    }
   ],
   "source": [
    "# Create a list of mean scores\n",
    "grid_mean_scores = [result.mean_validation_score for result in grid.grid_scores_]\n",
    "print(grid_mean_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Cross validation accuracy')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28154eddb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k_range, grid_mean_scores)\n",
    "plt.xlabel('value of k for KNN')\n",
    "plt.ylabel('Cross validation accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.98\n",
      "{'n_neighbors': 13}\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=13, p=2,\n",
      "           weights='uniform')\n"
     ]
    }
   ],
   "source": [
    "# Examine the best model\n",
    "\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "\n",
    "# hence we get the best model among values of k we provided\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}