notebook2-week-12-Support-Vector-Machines-Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Support Vector Machines Project \n",
    "\n",
    "Welcome to your Support Vector Machine Project! Just follow along with the notebook and instructions below. We will be analyzing the famous iris data set!\n",
    "\n",
    "## The Data\n",
    "We will be using the famous [Iris flower data set](http://en.wikipedia.org/wiki/Iris_flower_data_set). \n",
    "\n",
    "The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by Sir Ronald Fisher in the 1936 as an example of discriminant analysis. \n",
    "\n",
    "The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor), so 150 total samples. Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.\n",
    "\n",
    "Here's a picture of the three different Iris types:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The iris dataset contains measurements for 150 iris flowers from three different species.\n",
    "\n",
    "The three classes in the Iris dataset:\n",
    "\n",
    "    Iris-setosa (n=50)\n",
    "    Iris-versicolor (n=50)\n",
    "    Iris-virginica (n=50)\n",
    "\n",
    "The four features of the Iris dataset:\n",
    "\n",
    "    sepal length in cm\n",
    "    sepal width in cm\n",
    "    petal length in cm\n",
    "    petal width in cm\n",
    "\n",
    "## Get the data\n",
    "\n",
    "**Use seaborn to get the iris data by using: iris = sns.load_dataset('iris') **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the data and get you started!\n",
    "\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "Time to put your data viz skills to the test! Try to recreate the following plots, make sure to import the libraries you'll need!\n",
    "\n",
    "**Import some libraries you think you'll need.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a pairplot of the data set. Which flower species seems to be the most separable?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x12afb9cc0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SU7uIZ375Gi3+Wpqbdf1Ihlz9d+XyLY7dle0bc/K0vjSpdtva6x3fCW3t3kxOl+Iwm7Cr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BxgiVKqA2Nwcp7Weh2wWCl1VbACgZ9btdbnARcBWwLnADyttb4wmYEHyOBDCCGEEEIIeziKxnCE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\nj86sJYR4GMAOABkbH0ln5cmSy2XP6/yc03Bv1G13p3w9aZqa4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IaV2pjHfZ1gdfNFBEOu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12af83d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a kde plot of sepal_length versus sepal width for setosa species of flower.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f102080>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/4DMA7v59krXjfwZ4N/DcdK34D5D0CMZydweOShdNOxv4JHAu8EqSgIBnlyY+\nl+R5ELj7g8C3R+x2u7t/N/3zA8AxIW0RGUchIJ3k7n8L/BTwFZLF9T4B/D93f6m7vwR4KfCLQ79y\ncOjPy9Kf/5LkqV8PAO/N2YSvAK8hGYK6jaQn8kvpn+HZnsACh/49fHrE/obbt8DS69uL5KYQkE4y\nsw8Db3L3z5EMuZwFHG1mZ6dvuQy4eehXXpf+3suAVcD9wAXA+9PnNZ+bbg8tvl8mCY67097FTwEv\ndvfvpdsH+/kG8BtmNmVmzwd+IX39IIfO2anoSxSaGJau+gRwc/p0qIPAW4AfAB83syNJHnbypqH3\nv8DMvktylv3adBL3GpJn4u4BHNgGnBb4+d8kWS76m+nP/0AyGT0w6Al8Cvj3wD+RTAz/n/T1fwUe\nNrM7gEvR1UESiZaSlt5Lrw66xt2/VXdbRKqmnoBIwbPs9EEjq4ZeGjzQ53p331hGw0RiU09ARKTH\nNDEsItJjCgERkR5TCIiI9JhCQESkxxQCIiI9phAQEemx/w/cHHu5U3/V4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f138400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split\n",
    "\n",
    "** Split your data into a training set and a testing set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a Model\n",
    "\n",
    "Now its time to train a Support Vector Machine Classifier. \n",
    "\n",
    "**Call the SVC() model from sklearn and fit the model to the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Model Evaluation\n",
    "\n",
    "**Now get predictions from the model and create a confusion matrix and a classification report.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15  0  0]\n",
      " [ 0 13  1]\n",
      " [ 0  0 16]]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     setosa       1.00      1.00      1.00        15\n",
      " versicolor       1.00      0.93      0.96        14\n",
      "  virginica       0.94      1.00      0.97        16\n",
      "\n",
      "avg / total       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! You should have noticed that your model was pretty good! Let's see if we can tune the parameters to try to get even better (unlikely, and you probably would be satisfied with these results in real like because the data set is quite small, but I just want you to practice using GridSearch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gridsearch Practice\n",
    "\n",
    "** Import GridsearchCV from SciKit Learn.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a dictionary called param_grid and fill out some parameters for C and gamma.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a GridSearchCV object and fit it to the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n",
      "[CV] gamma=1, C=0.1 ..................................................\n",
      "[CV] ......................................... gamma=1, C=0.1 -   0.0s\n",
      "[CV] gamma=1, C=0.1 ..................................................\n",
      "[CV] ......................................... gamma=1, C=0.1 -   0.0s\n",
      "[CV] gamma=1, C=0.1 ..................................................\n",
      "[CV] ......................................... gamma=1, C=0.1 -   0.0s\n",
      "[CV] gamma=0.1, C=0.1 ................................................\n",
      "[CV] ....................................... gamma=0.1, C=0.1 -   0.0s\n",
      "[CV] gamma=0.1, C=0.1 ................................................\n",
      "[CV] ....................................... gamma=0.1, C=0.1 -   0.0s\n",
      "[CV] gamma=0.1, C=0.1 ................................................\n",
      "[CV] ....................................... gamma=0.1, C=0.1 -   0.0s\n",
      "[CV] gamma=0.01, C=0.1 ...............................................\n",
      "[CV] ...................................... gamma=0.01, C=0.1 -   0.0s\n",
      "[CV] gamma=0.01, C=0.1 ...............................................\n",
      "[CV] ...................................... gamma=0.01, C=0.1 -   0.0s\n",
      "[CV] gamma=0.01, C=0.1 ...............................................\n",
      "[CV] ...................................... gamma=0.01, C=0.1 -   0.0s\n",
      "[CV] gamma=0.001, C=0.1 ..............................................\n",
      "[CV] ..................................... gamma=0.001, C=0.1 -   0.0s\n",
      "[CV] gamma=0.001, C=0.1 ..............................................\n",
      "[CV] ..................................... gamma=0.001, C=0.1 -   0.0s\n",
      "[CV] gamma=0.001, C=0.1 ..............................................\n",
      "[CV] ..................................... gamma=0.001, C=0.1 -   0.0s\n",
      "[CV] gamma=1, C=1 ....................................................\n",
      "[CV] ........................................... gamma=1, C=1 -   0.0s\n",
      "[CV] gamma=1, C=1 ....................................................\n",
      "[CV] ........................................... gamma=1, C=1 -   0.0s\n",
      "[CV] gamma=1, C=1 ....................................................\n",
      "[CV] ........................................... gamma=1, C=1 -   0.0s\n",
      "[CV] gamma=0.1, C=1 ..................................................\n",
      "[CV] ......................................... gamma=0.1, C=1 -   0.0s\n",
      "[CV] gamma=0.1, C=1 ..................................................\n",
      "[CV] ......................................... gamma=0.1, C=1 -   0.0s\n",
      "[CV] gamma=0.1, C=1 ..................................................\n",
      "[CV] ......................................... gamma=0.1, C=1 -   0.0s\n",
      "[CV] gamma=0.01, C=1 .................................................\n",
      "[CV] ........................................ gamma=0.01, C=1 -   0.0s\n",
      "[CV] gamma=0.01, C=1 .................................................\n",
      "[CV] ........................................ gamma=0.01, C=1 -   0.0s\n",
      "[CV] gamma=0.01, C=1 .................................................\n",
      "[CV] ........................................ gamma=0.01, C=1 -   0.0s\n",
      "[CV] gamma=0.001, C=1 ................................................\n",
      "[CV] ....................................... gamma=0.001, C=1 -   0.0s\n",
      "[CV] gamma=0.001, C=1 ................................................\n",
      "[CV] ....................................... gamma=0.001, C=1 -   0.0s\n",
      "[CV] gamma=0.001, C=1 ................................................\n",
      "[CV] ....................................... gamma=0.001, C=1 -   0.0s\n",
      "[CV] gamma=1, C=10 ...................................................\n",
      "[CV] .......................................... gamma=1, C=10 -   0.0s\n",
      "[CV] gamma=1, C=10 ...................................................\n",
      "[CV] .......................................... gamma=1, C=10 -   0.0s\n",
      "[CV] gamma=1, C=10 ...................................................\n",
      "[CV] .......................................... gamma=1, C=10 -   0.0s\n",
      "[CV] gamma=0.1, C=10 .................................................\n",
      "[CV] ........................................ gamma=0.1, C=10 -   0.0s\n",
      "[CV] gamma=0.1, C=10 .................................................\n",
      "[CV] ........................................ gamma=0.1, C=10 -   0.0s\n",
      "[CV] gamma=0.1, C=10 .................................................\n",
      "[CV] ........................................ gamma=0.1, C=10 -   0.0s\n",
      "[CV] gamma=0.01, C=10 ................................................\n",
      "[CV] ....................................... gamma=0.01, C=10 -   0.0s\n",
      "[CV] gamma=0.01, C=10 ................................................\n",
      "[CV] ....................................... gamma=0.01, C=10 -   0.0s\n",
      "[CV] gamma=0.01, C=10 ................................................\n",
      "[CV] ....................................... gamma=0.01, C=10 -   0.0s\n",
      "[CV] gamma=0.001, C=10 ...............................................\n",
      "[CV] ...................................... gamma=0.001, C=10 -   0.0s\n",
      "[CV] gamma=0.001, C=10 ...............................................\n",
      "[CV] ...................................... gamma=0.001, C=10 -   0.0s\n",
      "[CV] gamma=0.001, C=10 ...............................................\n",
      "[CV] ...................................... gamma=0.001, C=10 -   0.0s\n",
      "[CV] gamma=1, C=100 ..................................................\n",
      "[CV] ......................................... gamma=1, C=100 -   0.0s\n",
      "[CV] gamma=1, C=100 ..................................................\n",
      "[CV] ......................................... gamma=1, C=100 -   0.0s\n",
      "[CV] gamma=1, C=100 ..................................................\n",
      "[CV] ......................................... gamma=1, C=100 -   0.0s\n",
      "[CV] gamma=0.1, C=100 ................................................\n",
      "[CV] ....................................... gamma=0.1, C=100 -   0.0s\n",
      "[CV] gamma=0.1, C=100 ................................................\n",
      "[CV] ....................................... gamma=0.1, C=100 -   0.0s\n",
      "[CV] gamma=0.1, C=100 ................................................\n",
      "[CV] ....................................... gamma=0.1, C=100 -   0.0s\n",
      "[CV] gamma=0.01, C=100 ...............................................\n",
      "[CV] ...................................... gamma=0.01, C=100 -   0.0s\n",
      "[CV] gamma=0.01, C=100 ...............................................\n",
      "[CV] ...................................... gamma=0.01, C=100 -   0.0s\n",
      "[CV] gamma=0.01, C=100 ...............................................\n",
      "[CV] ...................................... gamma=0.01, C=100 -   0.0s\n",
      "[CV] gamma=0.001, C=100 ..............................................\n",
      "[CV] ..................................... gamma=0.001, C=100 -   0.0s\n",
      "[CV] gamma=0.001, C=100 ..............................................\n",
      "[CV] ..................................... gamma=0.001, C=100 -   0.0s\n",
      "[CV] gamma=0.001, C=100 ..............................................\n",
      "[CV] ..................................... gamma=0.001, C=100 -   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  40 tasks       | elapsed:    0.2s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'gamma': [1, 0.1, 0.01, 0.001], 'C': [0.1, 1, 10, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now take that grid model and create some predictions using the test set and create classification reports and confusion matrices for them. Were you able to improve?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15  0  0]\n",
      " [ 0 13  1]\n",
      " [ 0  0 16]]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     setosa       1.00      1.00      1.00        15\n",
      " versicolor       1.00      0.93      0.96        14\n",
      "  virginica       0.94      1.00      0.97        16\n",
      "\n",
      "avg / total       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should have done about the same or exactly the same, this makes sense, there is basically just one point that is too noisey to grab, which makes sense, we don't want to have an overfit model that would be able to grab that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Great Job!"
   ]
  }
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