jbanerje/iris.ipynb

## iris.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fisher's IRIS Dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.\n",
    "\n",
    "It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.\n",
    "\n",
    "The columns in this dataset are:\n",
    "SepalLength\n",
    "\n",
    "sepal_length\n",
    "\n",
    "sepal_width\n",
    "\n",
    "petal_length\n",
    "\n",
    "petal_width\n",
    "\n",
    "class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall Goal\n",
    "\n",
    "Overall objective of this exercise is to classify the Iris flower species (Iris-setosa,Iris-virginica and Iris-versicolor) using sepal_length, sepal_width, petal_length, petal_width"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Processing Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Data Vizuaization Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Machine Learning Library\n",
    "from sklearn.preprocessing import LabelEncoder # Encode Categorical Variable to Numerical Variable\n",
    "from sklearn.metrics import confusion_matrix # Library for model evaluation\n",
    "from sklearn.metrics import accuracy_score # Library for model evaluation\n",
    "from sklearn.model_selection import train_test_split # Library to split datset into test and train\n",
    "\n",
    "from sklearn.linear_model  import LogisticRegression # Logistic Regression Classifier\n",
    "from sklearn.linear_model import SGDClassifier # Stochastic Gradient Descent Classifier\n",
    "from sklearn.tree import DecisionTreeClassifier # Decision Tree Classifier\n",
    "from sklearn.ensemble  import RandomForestClassifier # Random Forest Classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier # K Nearest neighbors Classifier\n",
    "from sklearn.naive_bayes import GaussianNB #Naive Bayes Classifier\n",
    "from sklearn.svm import SVC #Support vector Machine Classifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_dataset = pd.read_csv(\"https://github.com/jbanerje/Data-Science-and-Machine-Learning/tree/master/data/iris_dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next step  - We will get the columns attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "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",
      "class           150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "iris_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the variable category\n",
    "#### Numerical Variables:\n",
    "sepal_length\n",
    "\n",
    "sepal_width\n",
    "\n",
    "petal_length\n",
    "\n",
    "petal_width\n",
    "\n",
    "#### Categorical Variables:\n",
    "class  (This is the species - Iris-setosa,Iris-virginica and Iris-versicolor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing Descriptive Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taking First Peek into the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset.shape  # This is prvide the no of rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
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       "<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>class</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>Iris-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>Iris-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>Iris-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>Iris-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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa\n",
       "5           5.4          3.9           1.7          0.4  Iris-setosa\n",
       "6           4.6          3.4           1.4          0.3  Iris-setosa\n",
       "7           5.0          3.4           1.5          0.2  Iris-setosa\n",
       "8           4.4          2.9           1.4          0.2  Iris-setosa\n",
       "9           4.9          3.1           1.5          0.1  Iris-setosa"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset.head(10) # Fetching top 10 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable Identification\n",
    "Based on the input/predictor variable , we have to classify the species.\n",
    "\n",
    "#### Input or Predictor Variables:\n",
    "\n",
    "sepal_length\n",
    "\n",
    "sepal_width\n",
    "\n",
    "petal_length\n",
    "\n",
    "petal_width\n",
    "\n",
    "#### Target Variables:\n",
    "\n",
    "class (This is the species - Iris-setosa,Iris-virginica and Iris-versicolor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Analysis\n",
    "### For the Numeric Variables - we will find out the central tendency (mean, median,mode,max,min and count) and dispersion(Interquartile range & standard deviation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inference:\n",
    "1. In Total, we have 150 records and all have columns have values filled. No data missing.\n",
    "2. Sepal Length& Sepal Width maximum length & width can vary almost twice.\n",
    "3. petal_length max is almost 7 times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Categorical Variables - we will find out the frequency distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              class\n",
       "count           150\n",
       "unique            3\n",
       "top     Iris-setosa\n",
       "freq             50"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset.describe(include=['O']) # This provides the details of the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-virginica     50\n",
       "Iris-versicolor    50\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frequency Distribution\n",
    "iris_dataset['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inference:\n",
    "1. In Total, we have 150 records.\n",
    "2. We have 3 different kind of categorical varibles (Iris-setosa,Iris-virginica and Iris-versicolor)\n",
    "3. Fequency is 50 rows for each kind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Analyzing the Kernel Density Estimation  - of Sepal / Petal Length/Width using Violin Plot\n",
    "\n",
    "Violin Plot shows the distribution of length & width across several levels of one (or more) categorical variables such that those distributions can be compared. Unlike a box plot, in which all of the plot components correspond to actual datapoints, the violin plot features a kernel density estimation of the underlying distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219a240fe48>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x219a22e7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(2,2,1)\n",
    "sns.violinplot(x='class',y='petal_length',data=iris_dataset,palette='husl')\n",
    "plt.subplot(2,2,2)\n",
    "sns.violinplot(x='class',y='petal_width',data=iris_dataset,palette='husl')\n",
    "plt.subplot(2,2,3)\n",
    "sns.violinplot(x='class',y='sepal_length',data=iris_dataset,palette='husl')\n",
    "plt.subplot(2,2,4)\n",
    "sns.violinplot(x='class',y='sepal_width',data=iris_dataset,palette='husl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bi-Variate Analysis by vizualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x219a1182978>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x219a0df99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "sns.pairplot(iris_dataset,hue='class',palette='husl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bivariate Analysis  - \n",
    "1. Sepal Length & Sepal Width (Row 1, Fig 2 ) : Setosa can be seperated easily but Versicolor & Virginica sepal length/width is mixed and cannot be seperated\n",
    "2. Petal length & Petal width (Row 3, Fig 4 ) :  Setosa can be seperated easily. Majority of Versicolor & Virginica can be separted as well with some errors in decision boundary.\n",
    "3. Sepal Length & Petal length  (Row 1, Fig 3 ) :  Setosa can be seperated easily. Majority of Versicolor & Virginica can be separted as well with some errors in decision boundary.\n",
    "3. Sepal width & Petal Width  (Row 2, Fig 4 ) :  Setosa can be seperated easily. Majority of Versicolor & Virginica can be separted as well with some errors in decision boundary.  \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing pairwise correlation of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x219a3edabe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#iris_dataset.corr(method='pearson')\n",
    "plt.figure(figsize=(7,4)) \n",
    "sns.heatmap(iris_dataset.corr(),annot=True,cmap='cubehelix_r') #draws  heatmap with input as the correlation matrix calculted by(iris.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inference :\n",
    "1. petal_length & petal_width shows strongest positive co-relation (0.96)\n",
    "2. sepal_length & width_width are not co-related\n",
    "3. sepal_length & petal_length shows strong positive co-relation (0.87)\n",
    "4. sepal_length & petal_width shows strong positive co-relation (0.81)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning Classification Begins Here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will encode the categorical variable column \"class\" to convert Setosa, Vericolor and Verginica into numerical data (0,1 and 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_dataset['species'] = iris_dataset['class']  #Taking a backup of column \"class\" into a new column \"iris_species\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 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",
      "class           150 non-null object\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(2)\n",
      "memory usage: 7.1+ KB\n"
     ]
    }
   ],
   "source": [
    "iris_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now converting the species into numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_species = LabelEncoder()\n",
    "labelencoder_X=LabelEncoder()\n",
    "iris_dataset['class'] = labelencoder_species.fit_transform(iris_dataset['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class      species\n",
       "0      0  Iris-setosa\n",
       "1      0  Iris-setosa\n",
       "2      0  Iris-setosa\n",
       "3      0  Iris-setosa\n",
       "4      0  Iris-setosa"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset[['class','species']].head(5) # Iris-Setosa - 0 ; Iris-virsicolor - 1 ; Iris-virginica - 2;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     class         species\n",
       "145      2  Iris-virginica\n",
       "146      2  Iris-virginica\n",
       "147      2  Iris-virginica\n",
       "148      2  Iris-virginica\n",
       "149      2  Iris-virginica"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset[['class','species']].tail(5) # Iris-Setosa - 0 ; Iris-virsicolor - 1 ; Iris-virginica - 2;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting columns into X (feature variable) and y(target variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris_dataset.iloc[:,0:4].values\n",
    "y = iris_dataset.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dividing the dataset into test & train\n",
    "#### test_size=0.25 will slice the data in such a way that 25% of data will be copied to the test variable and 75% to train variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 105 samples in the training set and 45 samples in the test set\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.30)\n",
    "print('There are {} samples in the training set and {} samples in the test set'.format(X_train.shape[0], X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calling the classifier\n",
    "#### We will run all classifiers to have an initial look at the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Model  Test Accuracy  Train Accuracy  Average_Accuracy\n",
      "3                     SVC         0.9778          0.9810           0.97940\n",
      "4  DecisionTreeClassifier         0.9556          1.0000           0.97780\n",
      "5  RandomForestClassifier         0.9556          1.0000           0.97780\n",
      "1    KNeighborsClassifier         0.9778          0.9714           0.97460\n",
      "2              GaussianNB         0.9556          0.9619           0.95875\n",
      "0      LogisticRegression         0.8667          0.9524           0.90955\n"
     ]
    }
   ],
   "source": [
    "classifier_model = [LogisticRegression(),KNeighborsClassifier(),GaussianNB(),SVC(),DecisionTreeClassifier(),RandomForestClassifier()]\n",
    "classifier_model_list= []\n",
    "classifier_accuracy_test = []\n",
    "classifier_accuracy_train = []\n",
    "\n",
    "for classifier_list in classifier_model:\n",
    "    classifier = classifier_list\n",
    "    classifier.fit(X_train,y_train)\n",
    "    \n",
    "    # Predicting the output on test datset\n",
    "    y_pred_test = classifier.predict(X_test) \n",
    "    score_test = accuracy_score(y_test, y_pred_test)\n",
    "    \n",
    "    # Predicting the output on training datset\n",
    "    y_pred_train = classifier.predict(X_train) \n",
    "    score_train = accuracy_score(y_train, y_pred_train)\n",
    "    \n",
    "    #Keeping the model and accuracy score into a list\n",
    "    classifier_model_list.append(classifier_list.__class__.__name__)\n",
    "    classifier_accuracy_test.append(round(score_test,4))\n",
    "    classifier_accuracy_train.append(round(score_train,4))\n",
    "    \n",
    "    #Creating pandas dataframe with Model and corresponding accuracy\n",
    "    accuracy_df = pd.DataFrame({'Model':classifier_model_list , 'Test Accuracy':classifier_accuracy_test, 'Train Accuracy' :classifier_accuracy_train },index=None)\n",
    "\n",
    "# Calculating Average Accuracy = (Test + Train)/2\n",
    "accuracy_df['Average_Accuracy'] =  (accuracy_df['Test Accuracy'] + accuracy_df['Train Accuracy'] )/ 2\n",
    "\n",
    "#Arranging the Columns\n",
    "accuracy_df = accuracy_df[['Model','Test Accuracy', 'Train Accuracy', 'Average_Accuracy']]  # This will arrange the columns in the order we want\n",
    "\n",
    "#Sorting the Columns based on Average Accuracy\n",
    "accuracy_df.sort_values('Average_Accuracy', axis=0, ascending=False, inplace=True) # Sorting the data with highest accuracy in the top\n",
    "print(accuracy_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree topped the list with 98.8% accuracy while KNeighborsClassifier (KNN) and Support Vector Classifier score 97.9% accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Improving the Model\n",
    "#### KNeighborsClassifier - We will try to change the nearest neighbor count and see if anything improves\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Neighbours  Accuracy\n",
      "2           5  0.977778\n",
      "4           7  0.977778\n",
      "0           3  0.955556\n",
      "1           4  0.955556\n",
      "3           6  0.955556\n",
      "5           8  0.955556\n"
     ]
    }
   ],
   "source": [
    "KNN_Neighbour= [3,4,5,6,7,8]  # Number of neighbors to use. default = 5.\n",
    "KNN_classifier_accuracy = []\n",
    "\n",
    "for neighbors in KNN_Neighbour :\n",
    "    classifier = KNeighborsClassifier(n_neighbors = neighbors)\n",
    "    classifier.fit(X_train,y_train)\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    score = accuracy_score(y_test, y_pred)\n",
    "    KNN_classifier_accuracy.append(score)\n",
    "\n",
    "#Creating pandas dataframe with Neighbors and corresponding accuracy\n",
    "knn_perf_df = pd.DataFrame({'Neighbours':KNN_Neighbour , 'Accuracy':KNN_classifier_accuracy})    \n",
    "knn_perf_df = knn_perf_df[['Neighbours','Accuracy']]  # This will arrange the columns in the order we want\n",
    "knn_perf_df.sort_values('Accuracy', axis=0, ascending=False, inplace=True) # Sorting the data with highest accuracy in the top\n",
    "print(knn_perf_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inference : KNN model has provided maximum accuracy in first place with default neighbour as 5. We will get similar result with leaf size 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVC Classifier - We will try to change the 'kernel ' and see if we can improve the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Kernel  Accuracy\n",
      "0   linear  0.977778\n",
      "2      rbf  0.977778\n",
      "1     poly  0.955556\n",
      "3  sigmoid  0.222222\n"
     ]
    }
   ],
   "source": [
    "SVC_Kernels= ['linear', 'poly', 'rbf', 'sigmoid']  # different avilable kernels. default is rbf.\n",
    "SVC_classifier_accuracy = []\n",
    "\n",
    "for kernel_type in SVC_Kernels :\n",
    "    classifier = SVC(kernel = kernel_type)\n",
    "    classifier.fit(X_train,y_train)\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    score = accuracy_score(y_test, y_pred)\n",
    "    SVC_classifier_accuracy.append(score)\n",
    "\n",
    "#Creating pandas dataframe with Neighbors and corresponding accuracy\n",
    "svc_perf_df = pd.DataFrame({'Kernel':SVC_Kernels , 'Accuracy':SVC_classifier_accuracy})    \n",
    "svc_perf_df = svc_perf_df[['Kernel','Accuracy']]  # This will arrange the columns in the order we want\n",
    "svc_perf_df.sort_values('Accuracy', axis=0, ascending=False, inplace=True) # Sorting the data with highest accuracy in the top\n",
    "print(svc_perf_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inference : SVC classifier model has provided maximum accuracy in first place with default 'rbf' kernel. We will get similar result with linear kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall - Decision Tree topped the list with 98.8% accuracy while KNeighborsClassifier (KNN) and Support Vector Classifier score 97.9% accuracy.\n",
    "If you liked the kernel, please feel free to Upvote.\n",
    "\n",
    "Thanks You!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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