Making_predictions_with_Scikit-Learn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#SciKit Learn Series\n",
    "# Video URL: https://www.youtube.com/watch?v=hd1W4CyPX58&feature=youtu.be\n",
    "# Jupyter Botebook 4.3.1 Anaconda 1.3.1\n",
    "# Reference: https://youtu.be/RlQuVL6-qe8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#https://plot.ly/python/ipython-notebook-tutorial/\n",
    "from IPython.display import YouTubeVideo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#http://nbviewer.jupyter.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAUDBAoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoJCgoK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\nFFERRRRREUUUURFFFFERRRRREUUUURf/2Q==\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/RlQuVL6-qe8\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x271fdfc3c50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"RlQuVL6-qe8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-Learn 4-Step Modeling Patterns\n",
    "\n",
    "- Step 1: Import the class\n",
    "- Step 2: Instantiate the Estimator\n",
    "    a. Estimator is a scikit-learn's term for model\n",
    "    b. Instantiate means make an instance of\n",
    "- Step 3: Fit the model with data\n",
    "    a. Model learns relationships between features and responses (X and y)\n",
    "    b. Occurs in place"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Step 1\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import Load_iris function from datasets module\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.datasets.base.Bunch"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sace \"bunch\" object containing iris dataset and its attributes\n",
    "iris = load_iris()\n",
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 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",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 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",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 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",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 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]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the iris data\n",
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the names of the four features\n",
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print integers representing the species of each object\n",
    "iris.target #target represents what we are going to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the encoding scheme for species: 0 = setosa, 1 = versicolor, 2 = virginica\n",
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the types of the features and response\n",
    "type(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of the features (first dimension = # of observations, secon dimensions = number of features)\n",
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of the response (single dimension matching the number of observations)\n",
    "iris.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# store feature matrix in \"X\"\n",
    "X = iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# store feature matrix in \"y\"\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Step 2\n",
    "knn = KNeighborsClassifier(n_neighbors=1) #Knn object is looking for 1 nearest neighbor\n",
    "knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Step 3\n",
    "knn.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Valued Customer\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise 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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict([3,5,4,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new = [[3,5,4,2],[5,4,3,2]]\n",
    "knn.predict(X_new) #Prediction for the first unknown iris = 2, and the first = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Model tuning\n",
    "# instantiate the model (using K = 5)\n",
    "knn = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit the model with data\n",
    "knn.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict the response for new observations\n",
    "knn.predict(X_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a different classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Step 1: Import the class\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Step 2: instantiate the model with default parameters\n",
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 3: fit the model with data\n",
    "logreg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 4: predict the response for new observations\n",
    "logreg.predict(X_new) #Model predicts a value of 2 for the first unknown iris and 0 for the second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "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": 1
}