Iris_flower_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iris Flower Classification\n",
    "- Iris flower dataset contains 50 instances of 3 classes of Iris plants(Iris-setosa, Iris-versicolor & Iris-virginica)\n",
    "- Goal of Iris flower classification problem is to classify an unseen Iris plant.\n",
    "- Keras and Tensorflow is used to develop neural network model for this multi-class classification problem.\n",
    "- Finally, evaluating the model with kfold cross validation using scikit-learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Library imports\n",
    "- Using pandas for data processing\n",
    "- Keras to build the neural network\n",
    "- Sklearn to evaluate the build model using Kfold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "import numpy\n",
    "import keras\n",
    "from sklearn import datasets\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the Iris dataset into pandas dataframe\n",
    "\n",
    "Attribute Information:\n",
    "1. sepal length in cm \n",
    "2. sepal width in cm \n",
    "3. petal length in cm \n",
    "4. petal width in cm \n",
    "5. class: \n",
    "    - Iris Setosa \n",
    "    - Iris Versicolour \n",
    "    - Iris Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</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": [
       "     0    1    2    3            4\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pandas.read_csv(\"data\\iris_dataset.csv\", header = None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164e2d30978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.iloc[:,0:1][data[4]=='Iris-setosa'], data.iloc[:,1:2][data[4]=='Iris-setosa'], color ='green' , label='Iris-setosa')\n",
    "plt.scatter(data.iloc[:,0:1][data[4]=='Iris-versicolor'], data.iloc[:,1:2][data[4]=='Iris-versicolor'], color ='red' , label='Iris-versicolor')\n",
    "plt.scatter(data.iloc[:,0:1][data[4]=='Iris-virginica'], data.iloc[:,1:2][data[4]=='Iris-virginica'], color ='blue' , label='Iris-virginica')\n",
    "plt.title(\"Iris plant dataset\")\n",
    "plt.xlabel(\"Sepal length in cm\")\n",
    "plt.ylabel(\"Sepal width in cm\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164e2d1e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.iloc[:, 2:3][data[4]=='Iris-setosa'], data.iloc[:, 3:4][data[4]=='Iris-setosa'], color ='green' , label='Iris-setosa')\n",
    "plt.scatter(data.iloc[:, 2:3][data[4]=='Iris-versicolor'], data.iloc[:, 3:4][data[4]=='Iris-versicolor'], color ='red' , label='Iris-versicolor')\n",
    "plt.scatter(data.iloc[:, 2:3][data[4]=='Iris-virginica'], data.iloc[:, 3:4][data[4]=='Iris-virginica'], color ='blue' , label='Iris-virginica')\n",
    "plt.title(\"Iris plant dataset\")\n",
    "plt.xlabel(\"Petal length in cm\")\n",
    "plt.ylabel(\"Petal width in cm\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating data into input and output variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = data.values\n",
    "X = dataset[: , 0:4]\n",
    "y = dataset[: , 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using one hot encoding for multi-class classification problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "encoder.fit(y)\n",
    "encoder_y = encoder.transform(y)\n",
    "dummy_y = np_utils.to_categorical(encoder_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating baseline neural network model using keras\n",
    "- Built sequential model \n",
    "- Created simple fully connected network with one hidden layer that contains 8 neurons.\n",
    "- In the first layer, we are using dense layer with 8 neurons, input dimension as 4 (input features) and activation function\n",
    "    as relu\n",
    "- In the second layer i.e the output layer, 3 neurons are used to predict 3 output classes(3 flower categories) with activation function as softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def baseline_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(8, input_dim=4, activation='relu'))\n",
    "    model.add(Dense(3,activation='softmax'))\n",
    "    # compile the model\n",
    "    model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating KerasClassifier for use in scikit-learn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluation of the model created using neural networks on training data using kfold(10) validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility in evaluation\n",
    "seed = 7\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits = 10, shuffle = True, random_state = seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Prediction Accuracy: 97.33% (4.42%)\n"
     ]
    }
   ],
   "source": [
    "results = cross_val_score(estimator, X, dummy_y, cv=kfold)\n",
    "print(\"Baseline Prediction Accuracy: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.93333334,  1.        ,  1.        ,  1.        ,\n",
       "        1.        ,  1.        ,  0.93333334,  1.        ,  0.86666667])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}