Improving Performance of KNN and SVM .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "source": [
    "The iris dataset contains measurements for 150 iris flowers from three different species.\n",
    "\n",
    "The three classes in the Iris dataset:\n",
    "\n",
    "    Iris-setosa (n=50)\n",
    "    Iris-versicolor (n=50)\n",
    "    Iris-virginica (n=50)\n",
    "\n",
    "The four features of the Iris dataset:\n",
    "\n",
    "    sepal length in cm\n",
    "    sepal width in cm\n",
    "    petal length in cm\n",
    "    petal width in cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I will try to apply KNN and SVM on the dataset and try to improve their performances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing important libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style = 'whitegrid'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('Iris.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check the head of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</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>5</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",
       "Id                                                                       \n",
       "1             5.1           3.5            1.4           0.2  Iris-setosa\n",
       "2             4.9           3.0            1.4           0.2  Iris-setosa\n",
       "3             4.7           3.2            1.3           0.2  Iris-setosa\n",
       "4             4.6           3.1            1.5           0.2  Iris-setosa\n",
       "5             5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 823.5x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris,hue='Species',palette='dark', markers='o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the pairplot we can identify that Setosa is the most seperable from other species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a kde plot of sepal_length versus sepal width for Setosa species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "setosa = iris[iris['Species']=='Iris-setosa']\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.kdeplot( setosa['SepalWidthCm'], setosa['SepalLengthCm'],cmap=\"inferno\", shade=True, shade_lowest=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(y='Species', x='SepalLengthCm', data=iris, inner='quartile')\n",
    "plt.show()\n",
    "sns.violinplot(y='Species', x='SepalWidthCm', data=iris, inner='quartile')\n",
    "plt.show()\n",
    "sns.violinplot(y='Species', x='PetalLengthCm', data=iris, inner='quartile')\n",
    "plt.show()\n",
    "sns.violinplot(y='Species', x='PetalWidthCm', data=iris, inner='quartile')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardizing the variables for applying KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(iris.drop('Species',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_features = scaler.transform(iris.drop('Species',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.032057</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.124958</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.337848</td>\n",
       "      <td>-1.398138</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.106445</td>\n",
       "      <td>-1.284407</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.263460</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0      -0.900681      1.032057      -1.341272     -1.312977\n",
       "1      -1.143017     -0.124958      -1.341272     -1.312977\n",
       "2      -1.385353      0.337848      -1.398138     -1.312977\n",
       "3      -1.506521      0.106445      -1.284407     -1.312977\n",
       "4      -1.021849      1.263460      -1.341272     -1.312977"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(scaled_features,columns=iris.columns[:-1])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(scaled_features,iris['Species'],test_size=0.3, random_state=55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Starting with k=1\n",
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14  1  0]\n",
      " [ 0 14  2]\n",
      " [ 0  0 14]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      0.93      0.97        15\n",
      "Iris-versicolor       0.93      0.88      0.90        16\n",
      " Iris-virginica       0.88      1.00      0.93        14\n",
      "\n",
      "      micro avg       0.93      0.93      0.93        45\n",
      "      macro avg       0.94      0.94      0.93        45\n",
      "   weighted avg       0.94      0.93      0.93        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "print(confusion_matrix(y_test,pred))\n",
    "\n",
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Accuracy: 93.33%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "accuracy = accuracy_score(y_test,pred)\n",
    "print(\"KNN Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The accuracy is not bad. Trying to explore whether a better K value can be chosen or not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing a k value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "for i in range(1,50):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(range(1,50),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='violet', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can see here in this case, higher the K value after 25, the error rate is getting higher.\n",
    "#### k = 1  was a good choice, still we will try to apply k = 5 to see the results.  We are not applying k=20 or k=22 as that may overfit the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITH K=5\n",
      "\n",
      "\n",
      "[[14  1  0]\n",
      " [ 0 15  1]\n",
      " [ 0  0 14]]\n",
      "\n",
      "\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      0.93      0.97        15\n",
      "Iris-versicolor       0.94      0.94      0.94        16\n",
      " Iris-virginica       0.93      1.00      0.97        14\n",
      "\n",
      "      micro avg       0.96      0.96      0.96        45\n",
      "      macro avg       0.96      0.96      0.96        45\n",
      "   weighted avg       0.96      0.96      0.96        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "knn.fit(X_train,y_train)\n",
    "pred = knn.predict(X_test)\n",
    "\n",
    "print('WITH K=5')\n",
    "print('\\n')\n",
    "print(confusion_matrix(y_test,pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can see from the confusion matrix, 2 points are there hard to get. Trying to get those 2 points the model can be overfitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Accuracy: 95.56%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "accuracy = accuracy_score(y_test,pred)\n",
    "print(\"KNN Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</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>5</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",
       "Id                                                                       \n",
       "1             5.1           3.5            1.4           0.2  Iris-setosa\n",
       "2             4.9           3.0            1.4           0.2  Iris-setosa\n",
       "3             4.7           3.2            1.3           0.2  Iris-setosa\n",
       "4             4.6           3.1            1.5           0.2  Iris-setosa\n",
       "5             5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train-test split for SVM, as we don't want the scaled features to put in our SVM model\n",
    "\n",
    "X_svm = iris.drop('Species',axis=1)\n",
    "y_svm = iris['Species']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_svm, y_svm, test_size=0.3,random_state=60)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trainng the SVM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc_model = SVC()\n",
    "svc_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = svc_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[18  0  0]\n",
      " [ 0 13  1]\n",
      " [ 0  1 12]]\n",
      "\n",
      "\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        18\n",
      "Iris-versicolor       0.93      0.93      0.93        14\n",
      " Iris-virginica       0.92      0.92      0.92        13\n",
      "\n",
      "      micro avg       0.96      0.96      0.96        45\n",
      "      macro avg       0.95      0.95      0.95        45\n",
      "   weighted avg       0.96      0.96      0.96        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 95.56%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "accuracy = accuracy_score(y_test,predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The model is pretty good, but still we couldn't classify those two points that couldn't be classified with the KNN model.\n",
    "#### But we can try to tune the parameters to get a even better result (just for practice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To tune the hyperparameters, we will use the GridSearch method.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a dictionary of parameters to feed Gridsearch. Gridsearch will choose the best parameters for SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.01, 0.1, 1, 10, 100], 'gamma': [1,0.1,0.01,0.001,0.0001]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 25 candidates, totalling 75 fits\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] .................................. C=0.01, gamma=1, total=   0.0s\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] .................................. C=0.01, gamma=1, total=   0.0s\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] .................................. C=0.01, gamma=1, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ................................ C=0.01, gamma=0.1, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ................................ C=0.01, gamma=0.1, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ................................ C=0.01, gamma=0.1, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] ............................... C=0.01, gamma=0.01, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] ............................... C=0.01, gamma=0.01, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] ............................... C=0.01, gamma=0.01, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] .............................. C=0.01, gamma=0.001, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] .............................. C=0.01, gamma=0.001, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] .............................. C=0.01, gamma=0.001, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] ............................. C=0.01, gamma=0.0001, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] ............................. C=0.01, gamma=0.0001, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] ............................. C=0.01, gamma=0.0001, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ................................... C=0.1, gamma=1, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ................................... C=0.1, gamma=1, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ................................... C=0.1, gamma=1, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ................................. C=0.1, gamma=0.1, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ................................. C=0.1, gamma=0.1, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ................................. C=0.1, gamma=0.1, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ................................ C=0.1, gamma=0.01, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ................................ C=0.1, gamma=0.01, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ................................ C=0.1, gamma=0.01, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ............................... C=0.1, gamma=0.001, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ............................... C=0.1, gamma=0.001, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ............................... C=0.1, gamma=0.001, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .............................. C=0.1, gamma=0.0001, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .............................. C=0.1, gamma=0.0001, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .............................. C=0.1, gamma=0.0001, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ..................................... C=1, gamma=1, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ..................................... C=1, gamma=1, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ..................................... C=1, gamma=1, total=   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ................................... C=1, gamma=0.1, total=   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ................................... C=1, gamma=0.1, total=   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ................................... C=1, gamma=0.1, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] .................................. C=1, gamma=0.01, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] .................................. C=1, gamma=0.01, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] .................................. C=1, gamma=0.01, total=   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ................................. C=1, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ................................. C=1, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ................................. C=1, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................................ C=1, gamma=0.0001, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................................ C=1, gamma=0.0001, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................................ C=1, gamma=0.0001, total=   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .................................... C=10, gamma=1, total=   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .................................... C=10, gamma=1, total=   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .................................... C=10, gamma=1, total=   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] .................................. C=10, gamma=0.1, total=   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] .................................. C=10, gamma=0.1, total=   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] .................................. C=10, gamma=0.1, total=   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ................................. C=10, gamma=0.01, total=   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ................................. C=10, gamma=0.01, total=   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ................................. C=10, gamma=0.01, total=   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................................ C=10, gamma=0.001, total=   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................................ C=10, gamma=0.001, total=   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................................ C=10, gamma=0.001, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ............................... C=10, gamma=0.0001, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ............................... C=10, gamma=0.0001, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ............................... C=10, gamma=0.0001, total=   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ................................... C=100, gamma=1, total=   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ................................... C=100, gamma=1, total=   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ................................... C=100, gamma=1, total=   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ................................. C=100, gamma=0.1, total=   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ................................. C=100, gamma=0.1, total=   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ................................. C=100, gamma=0.1, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................ C=100, gamma=0.01, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ................................ C=100, gamma=0.01, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ................................ C=100, gamma=0.01, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ............................... C=100, gamma=0.001, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ............................... C=100, gamma=0.001, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ............................... C=100, gamma=0.001, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .............................. C=100, gamma=0.0001, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .............................. C=100, gamma=0.0001, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .............................. C=100, gamma=0.0001, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  75 out of  75 | elapsed:    0.3s finished\n",
      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'C': [0.01, 0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001, 0.0001]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can inspect the best parameters by some of Gridsearch functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.1}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction and Evaluation of SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_predictions = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[18  0  0]\n",
      " [ 0 13  1]\n",
      " [ 0  0 13]]\n",
      "\n",
      "\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        18\n",
      "Iris-versicolor       1.00      0.93      0.96        14\n",
      " Iris-virginica       0.93      1.00      0.96        13\n",
      "\n",
      "      micro avg       0.98      0.98      0.98        45\n",
      "      macro avg       0.98      0.98      0.98        45\n",
      "   weighted avg       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,grid_predictions))\n",
    "print(\"\\n\")\n",
    "print(classification_report(y_test,grid_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here from the confusion matrix we can see that, we managed to classify one of those points, that we couldn't  classify in the previous stages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.78%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "accuracy = accuracy_score(y_test,grid_predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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