lecture10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we have to download the iris flower data and put it in a data frome  to segregate which type of iris flower it is\n"
   ]
  },
  {
   "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": [
    "from sklearn.datasets import load_iris\n",
    "iris=load_iris()\n",
    "#df=pd.DataFrame(iris.data,columns=iris.feature_name)\n",
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(iris.data,columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we have to append a new column that is target\n",
    "df['target']=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</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>0</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>0</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>0</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>0</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>0</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   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
    "#for displaying the name of the flower we have to use a function that is lamba.\n",
    "df['flower_name']=df.target.apply(lambda x:iris.target_names[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (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",
       "      <th>flower_name</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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>setosa</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   \n",
       "\n",
       "   target flower_name  \n",
       "0       0      setosa  \n",
       "1       0      setosa  \n",
       "2       0      setosa  \n",
       "3       0      setosa  \n",
       "4       0      setosa  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we have to separate into 3 dataframe\n",
    "df0=df[df.target==0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df[df.target==1]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df2=df[df.target==2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7a53459e6c70>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df0['sepal length (cm)'],df0['sepal width (cm)'],color='red',marker='*')\n",
    "plt.scatter(df1['sepal length (cm)'],df1['sepal width (cm)'],color='blue',marker='*')\n",
    "plt.scatter(df2['sepal length (cm)'],df2['sepal width (cm)'],color='black',marker='*')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X=df.drop(['target','flower_name'],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 2, 0, 2, 1, 2, 1, 2, 1, 0, 2, 2, 1, 2, 0, 0, 0, 0, 0,\n",
       "       2, 1, 0, 1, 1, 2, 2, 1])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,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": "3.9.2"
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  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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 },
 "nbformat": 4,
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}