AashishTiwari · February 8, 2017 06:13 · AashishTiwari · Feb 8, 2017
diff --git a/IRIS data classification using SVM, KNN & Decision Trees.ipynb b/IRIS data classification using SVM, KNN & Decision Trees.ipynb
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   "cell_type": "markdown",
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   "source": [
    "# Classifying IRIS Data Set using SVM, KNN, Decision Trees & Naive Bayes.\n",
    "\n",
    "### Notebook by [Aashish K Tiwari](https://gist.github.com/AashishTiwari)\n",
    "#### You can see all my public gists @ https://gist.github.com/AashishTiwari\n",
    "\n",
    "#### [Persistent Systems Ltd]\n",
    "#### Data Source: IRIS dataset from Github"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "\n",
    "\n",
    "1. [Step 1: Checking the data](#Step-1:-Checking-the-data)\n",
    "\n",
    "2. [Step 2: Tidying the data](#Step-2:-Tidying-the-data)\n",
    "\n",
    "3. [Step 3: Exploratory analysis](#Step-3:-Exploratory-analysis)\n",
    "\n",
    "4. [Step 4: Classification](#Step-4:-Classification)\n",
    "    - [Cross-validation](#Cross-validation)\n",
    "\n",
    "    - [Parameter tuning](#Parameter-tuning)\n",
    "\n",
    "5. [Step 5: Features](#Step-5:-Features)\n",
    "\n",
    "6. [Step 6: Conclusion](#Step-6:-Conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required libraries\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "\n",
    "* **NumPy**: Provides a fast numerical array structure and helper functions.\n",
    "* **pandas**: Provides a DataFrame structure to store data in memory and work with it easily and efficiently.\n",
    "* **scikit-learn**: The essential Machine Learning package in Python.\n",
    "* **matplotlib**: Basic plotting library in Python; most other Python plotting libraries are built on top of it.\n",
    "* **Seaborn**: Advanced statistical plotting library.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The problem domain\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone.\n",
    "\n",
    "<img src=\"images/petal_sepal.jpg\" />\n",
    "\n",
    "We've been given a [data set](iris.csv) from our field researchers to develop the demo, which only includes measurements for three types of *Iris* flowers:\n",
    "\n",
    "### *Iris setosa*\n",
    "\n",
    "<img src=\"images/iris_setosa.jpg\" />\n",
    "\n",
    "### *Iris versicolor*\n",
    "<img src=\"images/iris_versicolor.jpg\" />\n",
    "\n",
    "### *Iris virginica*\n",
    "<img src=\"images/iris_virginica.jpg\" />\n",
    "\n",
    "The four measurements we're using currently come from hand-measurements by the field researchers, but they will be automatically measured by an image processing model in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Checking the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "iris_data = pd.read_csv('IRIS.csv')\n",
    "iris_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "iris_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris_data = pd.read_csv('IRIS.csv', na_values=['NA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Another Pandas beautiful feature to summarize your dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.species.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This line tells the notebook to show plots inside of the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's create a **scatterplot matrix**. Scatterplot matrices plot the distribution of each column along the diagonal, and then plot a scatterplot matrix for the combination of each variable. They make for an efficient tool to look for errors in our data.\n",
    "\n",
    "We can even have the plotting package color each entry by its class to look for trends within the classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f5475d6f650>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mbb04pD3oMMa8xvVQaKDRYIx5TYxF/oqaVvWW1STZqrS+Tj+Rfxr5XLe+k90a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ZUylVJvB9q11j8C/jf+OI1rA/vWKqU+ETh0HijSWo8A80qpxsDrlwKHTOV8BOjAf6/9cuC4t+Af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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5475ccb210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We have to temporarily drop the rows with 'NA' values\n",
    "# because the Seaborn plotting function does not know\n",
    "# what to do with them\n",
    "sb.pairplot(iris_data.dropna(), hue='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Tidying the data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.species.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sepal_length, sepal_width, petal_length, petal_width, species]\n",
       "Index: []"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['sepal_length'].isnull()) |\n",
    "              (iris_data['sepal_width'].isnull()) |\n",
    "              (iris_data['petal_length'].isnull()) |\n",
    "              (iris_data['petal_width'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5475e21510>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zD4DHAM+LiJ9tv6SR+r/+IuIs4HLg3az+me0xx/fwu8Dr6f5o8ryI+Pn2SxqpieMubwTe\nRbe/n46I5/V78KiDfR///1P0R4G7F21bPEPf2LttNenX32rXt7eImKI7S/+dzLy95dpG4aT9RcR0\nRGwGyMz7gduAp7Ve4fL023/PBB4JfAH4GN1guK7d8pat7/szM/88M+/NzKN036fntVzfcvXr715g\nd2bu7vV3O/CEfk826mA/6TlkMnMPMBURj46ItcALevdfTYY5R85qnREN6u164PrM/Mw4ihuBfv2t\nBf60t74O8GQg2y9xWfr97X00M8/rHZh7Md1vVbxhfKU2ctL+IuLMiPiHiHh4774XAXeMp8zG+u2/\neeDOiNjUu+/5DHh/jvyUAsefQwZ4EvDdzPzbiLgQuJbuEeyPZOa7Rjp4Cwb09xngUcCjgV3AuzLz\nprEVu0Qn643um24W+Ge6H1oLwIcz8wNjKrWRAfvul4GtwBG6X3fs+3WyU1G//hbd58eAmzLzmeOp\nsrkB+++1dL+uOgf8R2a+bnyVNjOgv03Ah+j+/X0tM6/s91yeK0aSivGXp5JUjMEuScUY7JJUjMEu\nScUY7JJUjMEuScUY7JJUjMEuScX8LxvwoBh4K5MGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f547445f150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.loc[iris_data['species'] == 'setosa', 'petal_width'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the petal widths for `Iris-setosa` fall within the 0.2-0.3 range, so let's fill in these entries with the average measured petal width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sepal_length, sepal_width, petal_length, petal_width, species]\n",
       "Index: []"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_petal_width = iris_data.loc[iris_data['species'] == 'setosa', 'petal_width'].mean()\n",
    "\n",
    "iris_data.loc[(iris_data['species'] == 'setosa') &\n",
    "              (iris_data['petal_width'].isnull()),\n",
    "              'petal_width'] = average_petal_width\n",
    "\n",
    "iris_data.loc[(iris_data['species'] == 'setosa') &\n",
    "              (iris_data['petal_width'] == average_petal_width)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sepal_length, sepal_width, petal_length, petal_width, species]\n",
       "Index: []"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['sepal_length'].isnull()) |\n",
    "              (iris_data['sepal_width'].isnull()) |\n",
    "              (iris_data['petal_length'].isnull()) |\n",
    "              (iris_data['petal_width'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now we've recovered those rows and no longer have missing data in our data set.\n",
    "\n",
    "**Note:** If you don't feel comfortable imputing your data, you can drop all rows with missing data with the `dropna()` call:\n",
    "\n",
    "    iris_data.dropna(inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take a look at the scatterplot matrix now that we've tidied the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test a few things that we know about our data set now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Exploratory analysis\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f547444ad10>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mbb04pD3oMMa8xvVQaKDRYIx5TYxF/oqaVvWW1STZqrS+Tj+Rfxr5XLe+k90a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ZUylVJvB9q11j8C/jf+OI1rA/vWKqU+ETh0HijSWo8A80qpxsDrlwKHTOV8BOjAf6/9cuC4t+Af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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54744256d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data, hue='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets have a look at the violin plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Yt24NDz30qNshZU0g0E8o0T4gHA7R19dLcfH4+/1NZR0d7QAUDUuwtgXiW+fc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\ncuUKXnrpeVf3vA0Ggzz91Pfp7evlDn8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4Smt58cU/09mZuUrp2TR37ny8Ph+x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dIdSsYLhZwRqXil1MMLRKurq0fczx2dFBdvhEsIIVJh4CZx4ulH7AYzEMjsukdJsETaPfXU45w4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\nFtxFtVftZwW76DnzLHa4l1Wrr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76WQwBs3347zc2dY3xaCDGSmprRG/sKIUS2SmkNllJq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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f547321db50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "for column_index, column in enumerate(iris_data.columns):\n",
    "    if column == 'species':\n",
    "        continue\n",
    "    plt.subplot(2, 2, column_index + 1)\n",
    "    sb.violinplot(x='species', y=column, data=iris_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So far you would have guessed, Type 1 flower is clearly seperable, but same is not the case with Type 2 and Type3 flowers, lets confirm this by using various classification algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Classification\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "\n",
    "A **training set** is a random subset of the data that we use to train our models.\n",
    "\n",
    "A **testing set** is a random subset of the data (mutually exclusive from the training set) that we use to validate our models on unforseen data.\n",
    "\n",
    "Especially in sparse data sets like ours, it's easy for models to **overfit** the data: The model will learn the training set so well that it won't be able to handle most of the cases it's never seen before. This is why it's important for us to build the model with the training set, but score it with the testing set.\n",
    "\n",
    "Note that once we split the data into a training and testing set, we should treat the testing set like it no longer exists: We cannot use any information from the testing set to build our model or else we're cheating.\n",
    "\n",
    "Let's set up our data first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our data is ready to be split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# iris_data.species.values\n",
    "# iris_data.loc[:,['sepal_length', 'sepal_width', 'petal_length', 'petal_width']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data using 30% source data to test model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "iris_data = pd.read_csv('IRIS.csv', na_values=['NA'])\n",
    "\n",
    "X = iris_data.loc[:,['sepal_length', 'sepal_width', 'petal_length', 'petal_width']].values \n",
    "y = iris_data.species.values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using SVM classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97777777777777775"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "# Create the classifier\n",
    "svc_classifier = svm.SVC()\n",
    "\n",
    "# Train the classifier on the training set\n",
    "svc_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Validate the classifier on the testing set using classification accuracy\n",
    "standard_svc = svc_classifier.score(X_test, y_test)\n",
    "standard_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "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": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     setosa       1.00      1.00      1.00        50\n",
      " versicolor       1.00      0.94      0.97        50\n",
      "  virginica       0.94      1.00      0.97        50\n",
      "\n",
      "avg / total       0.98      0.98      0.98       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 47  3]\n",
      " [ 0  0 50]]\n"
     ]
    }
   ],
   "source": [
    "expected_svm = y\n",
    "predicted_svm = svc_classifier.predict(X)\n",
    "from sklearn import metrics\n",
    "\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected_svm, predicted_svm))\n",
    "print(metrics.confusion_matrix(expected_svm, predicted_svm))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 Flowers from Class2 were incorectly predicted as Class3, However the other classes were perfectly predicted the model score a 98% accuracy, and hence SVM stands as one of the best simple classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.956\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipe_lr = Pipeline([('scl', StandardScaler()),\n",
    "                    ('pca', PCA(n_components=3)),\n",
    "                    ('clf', svm.SVC())])\n",
    "pipe_lr.fit(X_train, y_train)\n",
    "print('Test Accuracy: %.3f' % pipe_lr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying PCA with components as 3 has brought down model accuracy to 95.6%, another learning that applying dimensionality reduction is not always beneficial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Nearest Neighbour Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97777777777777775"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_classifier = KNeighborsClassifier()\n",
    "knn_classifier.fit(X_train,y_train)\n",
    "knn_score = knn_classifier.score(X_test, y_test)\n",
    "knn_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     setosa       1.00      1.00      1.00        50\n",
      " versicolor       0.96      0.92      0.94        50\n",
      "  virginica       0.92      0.96      0.94        50\n",
      "\n",
      "avg / total       0.96      0.96      0.96       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 46  4]\n",
      " [ 0  2 48]]\n"
     ]
    }
   ],
   "source": [
    "expected_knn = y\n",
    "predicted_knn = knn_classifier.predict(X)\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(expected_knn, predicted_knn))\n",
    "print(metrics.confusion_matrix(expected_knn, predicted_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4 Flowers of class 2 incorrectly identified as class 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying PCA and using KNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.956\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipe_lr = Pipeline([('scl', StandardScaler()),\n",
    "                    ('pca', PCA(n_components=3)),\n",
    "                    ('clf', KNeighborsClassifier())])\n",
    "\n",
    "pipe_lr.fit(X_train, y_train)\n",
    "print('Test Accuracy: %.3f' % pipe_lr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Lets use Gaussian Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93333333333333335"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gaussian_nb_classifier = GaussianNB()\n",
    "gaussian_nb_classifier.fit(X_train,y_train)\n",
    "gaussian_nb_score = gaussian_nb_classifier.score(X_test, y_test)\n",
    "gaussian_nb_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussian_nb_classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     setosa       1.00      1.00      1.00        50\n",
      " versicolor       0.94      0.90      0.92        50\n",
      "  virginica       0.90      0.94      0.92        50\n",
      "\n",
      "avg / total       0.95      0.95      0.95       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 45  5]\n",
      " [ 0  3 47]]\n"
     ]
    }
   ],
   "source": [
    "expected_gnb = y\n",
    "predicted_gnb = gaussian_nb_classifier.predict(X)\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(expected_gnb, predicted_gnb))\n",
    "print(metrics.confusion_matrix(expected_gnb, predicted_gnb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now lets use Bernoulli NB Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "iris_data = pd.read_csv('IRIS.csv', na_values=['NA'])\n",
    "\n",
    "X = iris_data.loc[:,['sepal_length', 'sepal_width', 'petal_length', 'petal_width']].values \n",
    "y = iris_data.species.values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28888888888888886"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "bernoulli_nb_classifier = BernoulliNB()\n",
    "bernoulli_nb_classifier.fit(X_train,y_train)\n",
    "bernoulli_nb_classifier.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Need to check low accuracy score for bernoulli model of Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the model accuracies (using  KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5472e579d0>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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35oKnejJ2rDMR3hesyv4E4mJsT14Zue/4YFbPs6dHALhoTX7rrA9U0LrChz09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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5473185110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracies = []\n",
    "\n",
    "for repetition in range(1000):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
    "    \n",
    "    knn_classifier = KNeighborsClassifier()\n",
    "    knn_classifier.fit(X_train, y_train)\n",
    "    classifier_accuracy = knn_classifier.score(X_test, y_test)\n",
    "    model_accuracies.append(classifier_accuracy)\n",
    "    \n",
    "sb.distplot(model_accuracies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Cross-validation\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5472e74990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "\n",
    "def plot_cv(cv, n_samples):\n",
    "    masks = []\n",
    "    for train, test in cv:\n",
    "        mask = np.zeros(n_samples, dtype=bool)\n",
    "        mask[test] = 1\n",
    "        masks.append(mask)\n",
    "        \n",
    "    plt.figure(figsize=(15, 15))\n",
    "    plt.imshow(masks, interpolation='none')\n",
    "    plt.ylabel('Fold')\n",
    "    plt.xlabel('Row #')\n",
    "\n",
    "plot_cv(StratifiedKFold(y, n_folds=10), len(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that we used **Stratified *k*-fold cross-validation** in the code above. Stratified *k*-fold keeps the class proportions the same across all of the folds, which is vital for maintaining a representative subset of our data set. (e.g., so we don't have 100% `Iris setosa` entries in one of the folds.)\n",
    "\n",
    "We can perform 10-fold cross-validation on our model with the following code:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets see the cross validation score of KNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5472be3e10>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VE6yBHjW1NvvxehP/bFms8mKf8Izm8ycvL8CsydnsP1FLp8fL+NzBXYEr3p/7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\nqiJwIZNZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54732b35d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "knn_classifier = KNeighborsClassifier()\n",
    "\n",
    "# cross_val_score returns a list of the scores, which we can visualize\n",
    "# to get a reasonable estimate of our classifier's performance\n",
    "cv_scores = cross_val_score(knn_classifier, X, y, cv=10)\n",
    "sb.distplot(cv_scores)\n",
    "plt.title('Average score: {}'.format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Parameter tuning\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Getting max optimal accuracy score when number of neighbours considered is 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=9, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_classifier = KNeighborsClassifier(n_neighbors=9)\n",
    "knn_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5472b3aad0>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bw1ySKmCYS1IFDHNJqoBhLkkVMMwlqQKGuSRVwDCXpAoY5pJUAcNckipgmEtSBQxzSaqA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\nyL4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5473196410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "cv_scores = cross_val_score(knn_classifier, X, y, cv=10)\n",
    "sb.distplot(cv_scores, kde=False)\n",
    "plt.title('Average score: {}'.format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Grid Search\n",
    "\n",
    "#### Please Note: We are only applying Grid Search on the KNN classifier, we can do the same steps for other classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.973333333333\n",
      "Best parameters: {'n_neighbors': 9, 'metric': 'euclidean'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "param_range = [1,2,3,4,5,6,7,8,9,10]\n",
    "\n",
    "knn_classifier = KNeighborsClassifier()\n",
    "parameter_grid = [{'n_neighbors': param_range, \n",
    "                  'metric': ['euclidean']},\n",
    "                  {'n_neighbors': param_range,  \n",
    "                  'metric': ['manhattan']},\n",
    "                  {'n_neighbors': param_range,  \n",
    "                  'metric': ['minkowski']}]\n",
    "\n",
    "\n",
    "cross_validation = StratifiedKFold(y, n_folds=10)\n",
    "\n",
    "grid_search = GridSearchCV(knn_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "\n",
    "grid_search.fit(X, y)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the grid search to see how the parameters interact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'metric': ['euclidean'], 'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
       " {'metric': ['manhattan'], 'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
       " {'metric': ['minkowski'], 'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5472b41d10>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g3My8tGf7G4CrMnPMFnrrb1CSpF8aA+qxR8QsYPfMnAkcD3y8Z18HuBA4FJgN/E5EzOh5\n+HuB+0Yc7zk0U8hX9fM0DHZJKjob8N8Y5gALATJzOTAtIqaWfdsBD2Tm/Zk5DCwBDgGIiD2AAL4y\n4nhn0vwyeLyf52GwS1IxwOmOOwFrej6/t2wjM9cAW0fEbhGxOc1MwR3L/T4MnE7P3wQR8XJgz8z8\nV/rsFhnsktTV3nTHkY84DrgE+DywGuhExNHAksy8a8R9z6cJ+7754qkkFQOc7riKMkIvZtAEOACZ\nuQjYHyAi5gMrgN8DXhIRvw/sAjwaEQB7AJ8vvfmdI2JRZh40WnGDXZKKAU53vBo4B1gQEXsDKzPz\n4e7OiLgSOJpmxswc4IzM/ELP/rOB/y2zYnpnxvzvWKEOBrskPWVQuZ6ZSyPi5oi4AXgSODkijgEe\nLMurzKcJ/ynAvJ51tcbS1zpbBrskFYN8g1Jmnjli0609+xZSZs2s57HrXHYlM1/aT22DXZKeUsdb\nTw12SSom+6qN/TLYJalwrRhJqoyrO0pSberIdYNdkroqyXWDXZK67LFLUmU6lSS7wS5JRR2xbrBL\n0lMqGbAb7JLU5XRHSapMLSN2L7QhSZVxxC5JRS0jdoNdkoqhSpLdYJekoo5YN9gl6WmVJLvBLkmF\n0x0lqTKVtNgNdknqqiTXDXZJekolyW6wS1JRy3THzvDw8ESfgyRpgFxSQJIqY7BLUmUMdkmqjMEu\nSZUx2CWpMga7JFWmqnnsEXEBsC+wFjgtM5e1WOuVwL8CF2Tm37ZVp9T6ELA/MAX4YGb+Wws1tgQ+\nA+wIPAd4f2Z+ZdB1RtTcArgNODczL22pxmzgi6VOB7glM09to1apdxTwHuDnwFmZeVVLdY4FjgaG\naZ7XazLzeS3U2Qq4FNgG+BWa79XVg65TanWATwJ7AY8B78rM77VRq3bVBHtEzAJ2z8yZEbEH8Clg\nZku1ngt8BGjlB3xErQOBPcvzmg58Cxh4sAO/A/xXZp4fES8CrgFaDXbgvcB9LdcAWJyZb267SPn+\nnAW8GtgaeB/QSrBn5qdofsa7P/tvaqMO8HZgeWbOi4idgeuAX22p1lzgeZm5X0TsBvwNcFhLtapW\nUytmDrAQIDOXA9MiYmpLtR4F3gjc3dLxe13P0//TPgg8t4xsBiozv5CZ55dPXwT8cNA1ekVEAEH7\nvzxg/N4ofghwTWY+kpl3Z+a7xqnuWcB5LR37HmDb8vF0YE1LdQBeBvwnQGb+AHhpGz/rm4Kagn0n\nnvlDd2/ZNnCZuTYzH2/j2Oup9Uj59Hjgysxs7e3CEXED8I/AaW3VKM4HTmd8QvcVEbEwIq6PiENa\nrLMrsFVEXBERSyLi4BZrARAR+wB3ZeY9bRw/M78IvDAivg8sovmeteU24A0RMVR+8b8Q2K7FetWq\nKdhHquo3fUTMBd4BnNJmnczcj+ZP4n9qq0ZEHA0sycy7yqY2v1ffB87JzCNo2goXR0RbLcgOzaj2\nCJrv1adbqtPreJrXRlpRXjP4YWa+jOYvkovaqlVej/gmzV+pxwGrqez/4/FSU7Cv4pkj9Bk0Pxi/\n9CLiDcBfAIdm5kMt1XhNRLwQIDO/A2wWEW2Nlt4IvCkiltIE01+2NbrNzFVl1Elm3gH8GHhBG7Vo\nWnM3ZuZwqfVQi1/DrgOBG1s8/n7AVwEy8xZglzbbI5l5ZmbuD5wJPL+tv0RqV1OwXw0cCRARewMr\nM/Phcajb6ogiIp4HfAg4LDN/0mKpAyh/ZkfEjsBWmXlvG4Uy8w8z83WZ+RvAPwDnZeZ1bdSKiLdE\nxNnl4x2A7YGVbdSi+Rk8OCI6EbEtLX4NAcqLmQ9l5hNt1QBup5lpRkS8GPhZW63AiHhlRCwon74J\nWNxGnU1BNbNiMnNpRNxcesRPAie3VSsiXkcTSNsDT0TECcDszHyghXJ/QPPi1RfKSGkYeFtm/mjA\ndT5J06a4HtgCOGnAx58oXwI+FxFfpxnInNhWEGbmqoi4HLiJ5vvUatsM2Jnmxc02/T3wqYhYTDPd\n9p0t1roVmBIRNwGPA3/UYq2quWyvJFWmplaMJAmDXZKqY7BLUmUMdkmqjMEuSZUx2CWpMga7Jq2I\nuCAiXj3K/hdHxDoXK4uItRHhz7c2SdW8QUn1ycx+Fpxa3xsxfIOGNlm+QUkbrVzE4gzgR8CeNO8W\nPDQzH13P/R8E3g/8Fs26Pm/OzP+OiF+jWd9+M2Bz4JTM/E5ELKJZjnYRMB94Fc1b3J+kefv+EuDr\nwOeA3wC2BA7PzNURsRY4BzgYmErzbt3/Ke8aPr+c63CptbzU+jawN/D6Uu9l5T7fysw/GcxXTWqf\nf6rq2doXOCMzZ9JcueoNo9z3eTRXMJoDXEazABg0K0mekJkH0ywFcfGIx70e2CszXwucSvOLoWtn\n4DOZOYsmmP+wZ99tmXkg8Lc0IQ9wCXBqOYePln1dD2XmbOAVwGszc7+yINUtEbH16F8GafKwFaNn\n67uZ2b0K0p00y9auzzBPL+x0J7BbRGxPc8GNi3tWDZw6YgXBXwNuAMjMeyKidzXDezLzu+XjHwHT\nevZdW/69EXh3RDwf2DEzv1m2Lwb+uef+3eN+F7g3Ir4MfBn4QlurakptMNj1bI1cUGus1S5779+h\nubblo2W0/gzNtRaA5i/LtRtRf23PtmF+se/eGbHtcYDMfAyYHRGvolwyMCJmZuZ4XDFLetZsxWg8\n/ULoZ+ZPgRUR8VsAEfHyiHjviLstB/Yp+3eg6aev95g95pR/9wduLbVWRcRry/bX06zE+Axlbfq3\nZea3M/M84Gbg5WM+O2mScMSuQRrrlfj17T8G+HhEnEHzM3n6iPtfBRxdlnO9naYt88SI+4z0BLBn\nRJxIs+zxW8v2twEfjYgnaF6E7V6XtPc4PwDOjoh30vxF0a0p/VJwVowmvdIbPzwzP1t6798Bjs3M\nZRN8atKk5IhdAxMRW9CMrntHC90+9gcz8+qNPPRDwEERcSrNSPzLhrq0fo7YJakyvngqSZUx2CWp\nMga7JFXGYJekyhjsklQZg12SKvP/tQQJv2yMEXoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5472b18550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_visualization = []\n",
    "\n",
    "for grid_pair in grid_search.grid_scores_:\n",
    "    grid_visualization.append(grid_pair.mean_validation_score)\n",
    "    \n",
    "grid_visualization = np.array(grid_visualization)\n",
    "grid_visualization.shape = (3,10)\n",
    "sb.heatmap(grid_visualization, cmap='Blues')\n",
    "# plt.xticks(np.arange(10) + 0.5, grid_search.param_grid['n_neighbors'])\n",
    "# plt.yticks(np.arange(3) + 0.5, grid_search.param_grid['metric'][0])\n",
    "plt.xlabel('n_neighbors')\n",
    "plt.ylabel('metric')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.966666666667\n",
      "Best parameters: {'max_features': 3, 'splitter': 'random', 'criterion': 'entropy', 'max_depth': 5}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "parameter_grid = {'criterion': ['gini', 'entropy'],\n",
    "                  'splitter': ['best', 'random'],\n",
    "                  'max_depth': [1, 2, 3, 4, 5],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "\n",
    "cross_validation = StratifiedKFold(y, n_folds=10)\n",
    "\n",
    "grid_search = GridSearchCV(decision_tree_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(X, y)\n",
    "dct_score = grid_search.best_score_\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can take the best classifier from the Grid Search and use that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=5,\n",
       "            max_features=3, max_leaf_nodes=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='random')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_classifier = grid_search.best_estimator_\n",
    "decision_tree_classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even visualize the decision tree with [GraphViz](http://www.graphviz.org/) to see how it's making the classifications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sklearn.tree as tree\n",
    "from sklearn.externals.six import StringIO\n",
    "\n",
    "with open('iris_dtc.dot', 'w') as out_file:\n",
    "    out_file = tree.export_graphviz(decision_tree_classifier, out_file=out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f547298d490>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD+CAYAAACZd9ZDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noJDoAhT6P6dwPyrA1yRmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5472b4c2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_scores = cross_val_score(decision_tree_classifier, X, y, cv=10)\n",
    "\n",
    "sb.boxplot(rf_scores)\n",
    "sb.stripplot(rf_scores, jitter=True, color='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.973333333333\n",
      "Best parameters: {'max_features': 2, 'n_estimators': 5, 'criterion': 'entropy', 'warm_start': True}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features=2, max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0, warm_start=True)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "random_forest_classifier = RandomForestClassifier()\n",
    "\n",
    "parameter_grid = {'n_estimators': [5, 10, 25, 50],\n",
    "                  'criterion': ['gini', 'entropy'],\n",
    "                  'max_features': [1, 2, 3, 4],\n",
    "                  'warm_start': [True, False]}\n",
    "\n",
    "cross_validation = StratifiedKFold(y, n_folds=10)\n",
    "\n",
    "grid_search = GridSearchCV(random_forest_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(X, y)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compare their performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5472845810>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M3gMsBnofzuPu3YOsNxnIPQ20Lp633N2bzKzWzA4GXiEaYuOBgdYpsFYREdkNhQbD54AL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\nGzSwlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5472d16950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_forest_classifier = grid_search.best_estimator_\n",
    "\n",
    "rf_df = pd.DataFrame({'accuracy': cross_val_score(random_forest_classifier, X, y, cv=10),\n",
    "                       'classifier': ['Random Forest'] * 10})\n",
    "dt_df = pd.DataFrame({'accuracy': cross_val_score(decision_tree_classifier, X, y, cv=10),\n",
    "                      'classifier': ['Decision Tree'] * 10})\n",
    "both_df = rf_df.append(dt_df)\n",
    "\n",
    "sb.boxplot(x='classifier', y='accuracy', data=both_df)\n",
    "sb.stripplot(x='classifier', y='accuracy', data=both_df, jitter=True, color='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Features\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the plots above, we see the correlation between petal length and petal width is very high, lets try to use these subset of features to predict the category of Flower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris_data = pd.read_csv('IRIS.csv')\n",
    "new_features = iris_data.loc[:,['petal_length', 'petal_width']].values \n",
    "new_target = iris_data.species.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "F_train, F_test, t_train, t_test = train_test_split(new_features, new_target, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9555555555555556"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "svc_classifier = svm.SVC()\n",
    "svc_classifier.fit(F_train, t_train)\n",
    "svc_classifier.score(F_test, t_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     setosa       1.00      1.00      1.00        50\n",
      " versicolor       0.96      0.94      0.95        50\n",
      "  virginica       0.94      0.96      0.95        50\n",
      "\n",
      "avg / total       0.97      0.97      0.97       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 47  3]\n",
      " [ 0  2 48]]\n"
     ]
    }
   ],
   "source": [
    "new_expected_svm = new_target\n",
    "new_predicted_svm = svc_classifier.predict(new_features)\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(new_expected_svm, new_predicted_svm))\n",
    "print(metrics.confusion_matrix(new_expected_svm, new_predicted_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.956\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipe_lr = Pipeline([('scl', StandardScaler()),\n",
    "                    ('pca', PCA(n_components=2)),\n",
    "                    ('clf', svm.SVC())])\n",
    "pipe_lr.fit(F_train, t_train)\n",
    "print('Test Accuracy: %.3f' % pipe_lr.score(F_test, t_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now Lets try petal length v/s Sepal Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris_data = pd.read_csv('IRIS.csv')\n",
    "X_svm = iris_data.loc[:,['petal_length', 'sepal_length']].values \n",
    "y_svm = iris_data.species.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "svm_train, svm_test, tgt_train, tgt_test = train_test_split(X_svm, y_svm, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97777777777777775"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "svc_classifier = svm.SVC()\n",
    "svc_classifier.fit(svm_train, tgt_train)\n",
    "svc_classifier.score(svm_test, tgt_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So we can achieve the same accuracy of SVM with just the use of Petal Length and Sepal Length, this optimizes algorithms runtime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Conclusion\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We draw conclusions from experiments in the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ListTable(list):\n",
    "    \"\"\" Overridden list class which takes a 2-dimensional list of \n",
    "        the form [[1,2,3],[4,5,6]], and renders an HTML Table in \n",
    "        IPython Notebook. \"\"\"\n",
    "    \n",
    "    def _repr_html_(self):\n",
    "        html = [\"<table>\"]\n",
    "        for row in self:\n",
    "            html.append(\"<tr>\")\n",
    "            \n",
    "            for col in row:\n",
    "                html.append(\"<td>{0}</td>\".format(col))\n",
    "            \n",
    "            html.append(\"</tr>\")\n",
    "        html.append(\"</table>\")\n",
    "        return ''.join(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "table = ListTable()\n",
    "table.append(['', 'Support Vector Machines', 'K-Nearest Neighbours', 'Decision Trees', 'Naive Bayes', 'Random Forest'])\n",
    "table.append(['Model Accuracies', standard_svc, knn_score, dct_score, gaussian_nb_score, '0.966666666667'])\n",
    "table.append(['Model Params', 'C=1.0 & kernel=rbf', 'minkowski, neighbors=5', 'entropy, max_depth 5', 'Gaussian', 'max_features: 2, n_estimators: 5, criterion: entropy'])\n",
    "table.append(['Features Used', '4 & 2', '4', '4', '4', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><td></td><td>Support Vector Machines</td><td>K-Nearest Neighbours</td><td>Decision Trees</td><td>Naive Bayes</td><td>Random Forest</td></tr><tr><td>Model Accuracies</td><td>0.977777777778</td><td>0.977777777778</td><td>0.966666666667</td><td>0.933333333333</td><td>0.966666666667</td></tr><tr><td>Model Params</td><td>C=1.0 & kernel=rbf</td><td>minkowski, neighbors=5</td><td>entropy, max_depth 5</td><td>Gaussian</td><td>max_features: 2, n_estimators: 5, criterion: entropy</td></tr><tr><td>Features Used</td><td>4 & 2</td><td>4</td><td>4</td><td>4</td><td>4</td></tr></table>"
      ],
      "text/plain": [
       "[['',\n",
       "  'Support Vector Machines',\n",
       "  'K-Nearest Neighbours',\n",
       "  'Decision Trees',\n",
       "  'Naive Bayes',\n",
       "  'Random Forest'],\n",
       " ['Model Accuracies',\n",
       "  0.97777777777777775,\n",
       "  0.97777777777777775,\n",
       "  0.96666666666666667,\n",
       "  0.93333333333333335,\n",
       "  '0.966666666667'],\n",
       " ['Model Params',\n",
       "  'C=1.0 & kernel=rbf',\n",
       "  'minkowski, neighbors=5',\n",
       "  'entropy, max_depth 5',\n",
       "  'Gaussian',\n",
       "  'max_features: 2, n_estimators: 5, criterion: entropy'],\n",
       " ['Features Used', '4 & 2', '4', '4', '4', '4']]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol>\n",
    "  <li>The <b>IRIS</b> dataset utilized above, had Type2 and Type3 flowers pretty much overlapping in features and we were able to confirm this with different classification algorithms, Type1 flower was predicted with 100% test accuracy score in almost all algorithms.</li>\n",
    "  <li>Utilization of feature set in our experiment, we used only <b>2 features</b> and got the same test accuracy score as we get using all the 4 features.</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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