neural-network-approach-to-iris-dataset.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "387eb544-e77d-45fe-b1a0-5fdce0aee8c8",
    "_uuid": "c3c657b1d685ccee6eb120aca64c61590f297646"
   },
   "source": [
    "**An approach of Neural Network to predict Iris Species**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import required libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "e97115b6-d0ee-44d7-b8cb-fbb28713e9c9",
    "_execution_state": "idle",
    "_uuid": "19970a38afe99ae032e4fe897a1cdadbc1445c5e"
   },
   "outputs": [],
   "source": [
    "import pandas as pd                                        #loading data in table form, data processing, CSV file I/O \n",
    "import seaborn as sns                                      #visualisation \n",
    "import matplotlib.pyplot as plt                            \n",
    "import numpy as np                                         # linear algebra\n",
    "from sklearn.preprocessing import normalize                #normalization\n",
    "import keras                                               #library for neural network\n",
    "from keras.models import Sequential \n",
    "from keras.layers import Dense,Activation,Dropout \n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Input the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "7baa3b65-788f-419b-8981-eaeb21e71e70",
    "_execution_state": "idle",
    "_uuid": "2514ae5b171fa72efc6e0ef4848d45abed4feec3"
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"Iris.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### first 5 records of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "6ebc83c0-54f5-4747-aaed-d1733d3eb1f9",
    "_uuid": "8a7ca38afc989effe0ca6662526412a33348f1c5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Describing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Info of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   SepalLengthCm  150 non-null    float64\n",
      " 1   SepalWidthCm   150 non-null    float64\n",
      " 2   PetalLengthCm  150 non-null    float64\n",
      " 3   PetalWidthCm   150 non-null    float64\n",
      " 4   Species        150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2e81130c-0732-46d5-86ae-c476d89a2ff7",
    "_uuid": "8beee9188c5790a1bab4d265c62e4b087fb91108"
   },
   "source": [
    "**Visualisation of the dataset**\n",
    "\n",
    "The coding below shows the visualisation of the dataset in order to understand the data more. It can be seen that every species of the Iris can be segregated into different regions to be predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "6befa766-9ee0-431e-b47d-5ed5e687e4d1",
    "_uuid": "497e20251bd73940e6ec4791ff01d447515ceb6d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='SepalLengthCm', y='SepalWidthCm',\n",
    "           data=data,\n",
    "           fit_reg=False,\n",
    "           hue=\"Species\",\n",
    "           scatter_kws={\"marker\": \"D\",\n",
    "                        \"s\": 50})\n",
    "plt.title('SepalLength vs SepalWidth')\n",
    "\n",
    "sns.lmplot(x='PetalLengthCm', y='PetalWidthCm',\n",
    "           data=data,\n",
    "           fit_reg=False,\n",
    "           hue=\"Species\",\n",
    "           scatter_kws={\"marker\": \"D\",\n",
    "                        \"s\": 50})\n",
    "plt.title('PetalLength vs PetalWidth')\n",
    "\n",
    "sns.lmplot(x='SepalLengthCm', y='PetalLengthCm',\n",
    "           data=data,\n",
    "           fit_reg=False,\n",
    "           hue=\"Species\",\n",
    "           scatter_kws={\"marker\": \"D\",\n",
    "                        \"s\": 50})\n",
    "plt.title('SepalLength vs PetalLength')\n",
    "\n",
    "sns.lmplot(x='SepalWidthCm', y='PetalWidthCm',\n",
    "           data=data,\n",
    "           fit_reg=False,\n",
    "           hue=\"Species\",\n",
    "           scatter_kws={\"marker\": \"D\",\n",
    "                        \"s\": 50})\n",
    "plt.title('SepalWidth vs PetalWidth')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9f1f98d3-016b-4d4d-bf4a-4815709a32ce",
    "_uuid": "40540e69f5dda9908c28d8adf5314e381d346099"
   },
   "source": [
    "Coding below convert the species into each respective category to be feed into the neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classes of Iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "365bb67b-6d95-4922-bddb-d4d3a43a0d0d",
    "_uuid": "ef7161477f4b6355166015e58db8d2704b093000"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "print(data[\"Species\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert the alphabetical names of classes into numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "77f8b61d-32b9-4fd9-9757-d9d44922d035",
    "_uuid": "2b63e0792aa5700a71fe9b7747fa4def17a0e4d3",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data.loc[data[\"Species\"]==\"Iris-setosa\",\"Species\"]=0\n",
    "data.loc[data[\"Species\"]==\"Iris-versicolor\",\"Species\"]=1\n",
    "data.loc[data[\"Species\"]==\"Iris-virginica\",\"Species\"]=2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fetch Data randomly from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "8c3f20d0-2e81-4960-80e2-279975a427e7",
    "_uuid": "1d6426dc4b82c1a0cdde44bdd39e36d5068b0a9d"
   },
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm Species\n",
       "110            6.5           3.2            5.1           2.0       2\n",
       "33             5.5           4.2            1.4           0.2       0\n",
       "12             4.8           3.0            1.4           0.1       0\n",
       "121            5.6           2.8            4.9           2.0       2\n",
       "139            6.9           3.1            5.4           2.1       2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.iloc[np.random.permutation(len(data))]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "96c0b24a-ef1d-44e7-872f-ce9065f4517f",
    "_uuid": "e9c7531857c15f4bcae71d1792e6ea2bddaf89c2"
   },
   "source": [
    "#### Converting data to numpy array in order for processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "b622856d-5fb1-458b-b395-bde6fbcc456a",
    "_uuid": "2bd511b4c77f6bcb831b620f237256ecbe966a29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of features column\t (150, 4)\n",
      "Shape of label column\t\t (150,)\n"
     ]
    }
   ],
   "source": [
    "X=data.iloc[:,0:4].values\n",
    "y=data.iloc[:,4].values\n",
    "\n",
    "print(\"Shape of features column\\t\",X.shape)\n",
    "print(\"Shape of label column\\t\\t\",y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "962d57b5-aa84-4170-a1a9-adb5e9eceb2a",
    "_uuid": "e6c5feb4fb4fefedcd03fd691ec6fe6370ef7dfb"
   },
   "source": [
    "**Normalization**\n",
    "\n",
    "It can be seen from above that the feature of the first dataset has 6cm in Sepal Length, 3.4cm in Sepal Width, 4.5cm in Petal Length and 1.6cm in Petal Width. However, the range of the dataset may be different. Therefore, in order to maintain a good accuracy, the feature of each dataset must be normalized to a range of 0-1 for processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "366e4a6e-82f1-4723-8e9f-d4c47e9a9cee",
    "_uuid": "c11d1eed202d7b2d79c256364de928777d982178"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples of X_normalised\n",
      " [[0.08993278 0.08470916 0.10034783 0.11502995]\n",
      " [0.07609697 0.11118077 0.02754646 0.01150299]\n",
      " [0.0664119  0.07941484 0.02754646 0.0057515 ]]\n"
     ]
    }
   ],
   "source": [
    "X_normalized=normalize(X,axis=0)\n",
    "print(\"Examples of X_normalised\\n\",X_normalized[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting Dataset into Train,test and validation data\n",
    "\n",
    "    80% -- train data\n",
    "    20% -- test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "9dced524-d0c2-486e-8654-dfaee41ce3f5",
    "_uuid": "0eb1b785254354dddab0642cac16ebff9bb824bd"
   },
   "outputs": [],
   "source": [
    "total_length=len(data)\n",
    "train_length=int(0.8*total_length)\n",
    "test_length=int(0.2*total_length)\n",
    "\n",
    "X_train=X_normalized[:train_length]\n",
    "X_test=X_normalized[train_length:]\n",
    "y_train=y[:train_length]\n",
    "y_test=y[train_length:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of train set x: (120, 4) y: (120,)\n",
      "Length of test set x: (30, 4) y: (30,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of train set x:\",X_train.shape,\"y:\",y_train.shape)\n",
    "print(\"Length of test set x:\",X_test.shape,\"y:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neural network module\n",
    "\n",
    "    Change the labels(classes) to one hot vector\n",
    "    [0]--->[1 0 0]\n",
    "    [1]--->[0 1 0]\n",
    "    [2]--->[0 0 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "82eae2e6-7369-4953-be41-725496a0c503",
    "_uuid": "4e43122514268433d653bf77e7b4ccbb8cd6e765",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of y_train (120, 3)\n",
      "Shape of y_test (30, 3)\n"
     ]
    }
   ],
   "source": [
    "y_train=np_utils.to_categorical(y_train,num_classes=3)\n",
    "y_test=np_utils.to_categorical(y_test,num_classes=3)\n",
    "print(\"Shape of y_train\",y_train.shape)\n",
    "print(\"Shape of y_test\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "b39b3da2-db3e-4fac-a9a2-625142f4c2dc",
    "_uuid": "5e05d8f7e756fb0fe25820ba0fe7c6b15fdc8d1e"
   },
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(1000,input_dim=4,activation='relu'))\n",
    "model.add(Dense(500,activation='relu'))\n",
    "model.add(Dense(300,activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(3,activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "3e90b8a7-842d-457e-8615-c732425c8e70",
    "_uuid": "a308c53350dbaab53abe34c77aa7bceac39358eb",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 1000)              5000      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 500)               500500    \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 300)               150300    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 300)               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 3)                 903       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 656,703\n",
      "Trainable params: 656,703\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "9f9d7219-7a2d-405c-8e6c-c3097e5e7e06",
    "_uuid": "0b5ea6209168703be0e88e9a4b4a119eb21e896d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "6/6 [==============================] - 3s 149ms/step - loss: 1.0827 - accuracy: 0.4833 - val_loss: 1.0489 - val_accuracy: 0.5667\n",
      "Epoch 2/10\n",
      "6/6 [==============================] - 0s 29ms/step - loss: 1.0042 - accuracy: 0.7250 - val_loss: 0.9221 - val_accuracy: 0.7667\n",
      "Epoch 3/10\n",
      "6/6 [==============================] - 0s 29ms/step - loss: 0.8532 - accuracy: 0.7750 - val_loss: 0.7825 - val_accuracy: 0.7333\n",
      "Epoch 4/10\n",
      "6/6 [==============================] - 0s 26ms/step - loss: 0.6661 - accuracy: 0.7500 - val_loss: 0.5520 - val_accuracy: 1.0000\n",
      "Epoch 5/10\n",
      "6/6 [==============================] - 0s 23ms/step - loss: 0.4931 - accuracy: 0.9167 - val_loss: 0.4095 - val_accuracy: 0.9667\n",
      "Epoch 6/10\n",
      "6/6 [==============================] - 0s 21ms/step - loss: 0.3542 - accuracy: 0.9167 - val_loss: 0.4473 - val_accuracy: 0.7333\n",
      "Epoch 7/10\n",
      "6/6 [==============================] - 0s 29ms/step - loss: 0.3013 - accuracy: 0.9083 - val_loss: 0.2470 - val_accuracy: 0.9667\n",
      "Epoch 8/10\n",
      "6/6 [==============================] - 0s 26ms/step - loss: 0.2208 - accuracy: 0.9250 - val_loss: 0.3341 - val_accuracy: 0.8333\n",
      "Epoch 9/10\n",
      "6/6 [==============================] - 0s 25ms/step - loss: 0.1930 - accuracy: 0.9500 - val_loss: 0.1428 - val_accuracy: 0.9667\n",
      "Epoch 10/10\n",
      "6/6 [==============================] - 0s 26ms/step - loss: 0.1568 - accuracy: 0.9417 - val_loss: 0.1463 - val_accuracy: 0.9333\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x18f5c4d1730>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,validation_data=(X_test,y_test),batch_size=20,epochs=10,verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "4af03573-a9a6-4cc8-afab-d098659a38b0",
    "_uuid": "7f296969c0072a6be7417c5d020de83e24bd0084"
   },
   "outputs": [],
   "source": [
    "prediction=model.predict(X_test)\n",
    "length=len(prediction)\n",
    "y_label=np.argmax(y_test,axis=1)\n",
    "predict_label=np.argmax(prediction,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### System Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the dataset 93.33333333333333\n"
     ]
    }
   ],
   "source": [
    "accuracy=np.sum(y_label==predict_label)/length * 100 \n",
    "print(\"Accuracy of the dataset\",accuracy )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "52532bef-70ad-4500-905d-a63ac20da4c7",
    "_uuid": "e55761033bce04b6026b94b1ffc8512b36ec3419"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  0,  0],\n",
       "       [ 0,  9,  0],\n",
       "       [ 0,  2, 11]], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(y_label,predict_label)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=data[\"Species\"].unique())\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}