11 Classification_Iris_PCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification_Iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "li=load_iris()"
   ]
  },
  {
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 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",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(li.data,columns=li.feature_names)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src= iris-machinelearning.png style= width:650px>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150, 4), (150,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=li.data\n",
    "y=li.target\n",
    "\n",
    "X.shape   ,       y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['class']=li.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50\n",
       "1    50\n",
       "2    50\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0\n",
       "sepal width (cm)     0\n",
       "petal length (cm)    0\n",
       "petal width (cm)     0\n",
       "class                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using only one feature for visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x257b86c9508>"
      ]
     },
     "execution_count": 14,
     "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(df.loc[df['class']==0,'petal length (cm)'].index, df.loc[df['class']==0,'petal length (cm)'], label= 'setosa')\n",
    "plt.scatter(df.loc[df['class']==1,'petal length (cm)'].index, df.loc[df['class']==1,'petal length (cm)'], label= 'versicolor')\n",
    "plt.scatter(df.loc[df['class']==2,'petal length (cm)'].index, df.loc[df['class']==2,'petal length (cm)'], label= 'virginica')\n",
    "plt.xlabel('Sample') \n",
    "plt.ylabel('Petal Length')\n",
    "plt.title('Petal Length Scatter Plot')\n",
    "plt.legend()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((105, 4), (45, 4), (105,), (45,))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape        , X_test.shape     ,                y_train.shape      , y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logR=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logR.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted=logR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiclass classsification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0, 13,  0],\n",
       "       [ 0,  0, 13]], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        13\n",
      "           2       1.00      1.00      1.00        13\n",
      "\n",
      "    accuracy                           1.00        45\n",
      "   macro avg       1.00      1.00      1.00        45\n",
      "weighted avg       1.00      1.00      1.00        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "The **recall** means \"how many of this class you find over the whole number of element of this class\"\n",
    "\n",
    "The **precision** will be \"how many are correctly classified among that class\"\n",
    "\n",
    "The **f1-score** is the harmonic mean between precision & recall\n",
    "\n",
    "The **support** is the number of occurence of the given class in your dataset (so you have 37.5K of class 0 and 37.5K of class 1, which is a really well balanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "0        1          1\n",
       "1        0          0\n",
       "2        2          2\n",
       "3        1          1\n",
       "4        1          1\n",
       "5        0          0\n",
       "6        1          1\n",
       "7        2          2\n",
       "8        1          1\n",
       "9        1          1\n",
       "10       2          2\n",
       "11       0          0\n",
       "12       0          0\n",
       "13       0          0\n",
       "14       0          0\n",
       "15       1          1\n",
       "16       2          2\n",
       "17       1          1\n",
       "18       1          1\n",
       "19       2          2\n",
       "20       0          0\n",
       "21       2          2\n",
       "22       0          0\n",
       "23       2          2\n",
       "24       2          2\n",
       "25       2          2\n",
       "26       2          2\n",
       "27       2          2\n",
       "28       0          0\n",
       "29       0          0\n",
       "30       0          0\n",
       "31       0          0\n",
       "32       1          1\n",
       "33       0          0\n",
       "34       0          0\n",
       "35       2          2\n",
       "36       1          1\n",
       "37       0          0\n",
       "38       0          0\n",
       "39       0          0\n",
       "40       2          2\n",
       "41       1          1\n",
       "42       1          1\n",
       "43       0          0\n",
       "44       0          0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'Actual':y_test, 'Predicted':predicted})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "sklearn_pca= PCA(n_components=.95) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 4)\n",
      "(105, 2)\n",
      "(45, 4)\n",
      "(45, 2)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "\n",
    "sklearn_pca.fit(X_train)\n",
    "X_train_transformed = sklearn_pca.transform (X_train)\n",
    "print(X_train_transformed.shape)\n",
    "\n",
    "print(X_test.shape)\n",
    "\n",
    "X_test_transformed=sklearn_pca.transform (X_test)\n",
    "print(X_test_transformed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr= LogisticRegression ()\n",
    "lr.fit(X_train_transformed,y_train)\n",
    "y_predict_pca= lr.predict (X_test_transformed)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score (y_predict_pca,y_test)\n",
    "print(accuracy)"
   ]
  }
 ],
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