SVM-Iris-Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data=iris.data,columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "      <td>4.7</td>\n",
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       "      <td>2.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target']=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
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       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
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       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>53</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
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       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
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      "text/plain": [
       "    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "50                7.0               3.2                4.7               1.4   \n",
       "51                6.4               3.2                4.5               1.5   \n",
       "52                6.9               3.1                4.9               1.5   \n",
       "53                5.5               2.3                4.0               1.3   \n",
       "54                6.5               2.8                4.6               1.5   \n",
       "\n",
       "    target  \n",
       "50       1  \n",
       "51       1  \n",
       "52       1  \n",
       "53       1  \n",
       "54       1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['flower_names']=df.target.apply(lambda x:iris.target_names[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
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       "      <td>setosa</td>\n",
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       "      <th>1</th>\n",
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       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target flower_names  \n",
       "0         0       setosa  \n",
       "1         0       setosa  \n",
       "2         0       setosa  \n",
       "3         0       setosa  \n",
       "4         0       setosa  \n",
       "..      ...          ...  \n",
       "145       2    virginica  \n",
       "146       2    virginica  \n",
       "147       2    virginica  \n",
       "148       2    virginica  \n",
       "149       2    virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0=df[df.target==0]\n",
    "df1=df[df.target==1]\n",
    "df2=df[df.target==2]\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x289758a0970>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(df0['sepal length (cm)'],df0['sepal width (cm)'],color='green',marker='o')\n",
    "plt.scatter(df1['sepal length (cm)'],df1['sepal width (cm)'],color='red',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x289758fc6d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(df0['petal length (cm)'],df0['petal width (cm)'],color='green',marker='o')\n",
    "plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='red',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(['target','flower_names'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x289787e5880>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}