_Feature Extraction Principal Componenet Analysis PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7d4ce18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc07e677",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd101498",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer=load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85586f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6d34f84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(cancer[\"DESCR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "314e9322",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets=pd.DataFrame(cancer[\"data\"], columns=cancer[\"feature_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b018005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=fd657638-0f0e-4be6-ba4d-e759dd4f79b4 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('fd657638-0f0e-4be6-ba4d-e759dd4f79b4').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4723664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "692ca48d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23a4ee39",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d4d9d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "975bfcb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data=scaler.transform(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "22756cf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  2.29607613,\n",
       "         2.75062224,  1.93701461],\n",
       "       [ 1.82982061, -0.35363241,  1.68595471, ...,  1.0870843 ,\n",
       "        -0.24388967,  0.28118999],\n",
       "       [ 1.57988811,  0.45618695,  1.56650313, ...,  1.95500035,\n",
       "         1.152255  ,  0.20139121],\n",
       "       ...,\n",
       "       [ 0.70228425,  2.0455738 ,  0.67267578, ...,  0.41406869,\n",
       "        -1.10454895, -0.31840916],\n",
       "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.28998549,\n",
       "         1.91908301,  2.21963528],\n",
       "       [-1.80840125,  1.22179204, -1.81438851, ..., -1.74506282,\n",
       "        -0.04813821, -0.75120669]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ffe9a189",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25c096aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "13bb1c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7d10d76e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca=pca.transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f1b59494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7ebbef6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f7fbe5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5f45dd1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second Principal Component')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x_pca[:,0],x_pca[:,1],c=cancer[\"target\"])\n",
    "plt.xlabel(\"First Principal Component\")\n",
    "plt.ylabel(\"Second Principal Component\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c864137d",
   "metadata": {},
   "source": [
    "### Lets have another example of PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "df91c006",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"50_Startups.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ca91e7ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=3c598d78-bdcd-47a4-a42a-b24cca7bdaaf style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('3c598d78-bdcd-47a4-a42a-b24cca7bdaaf').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f626383f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1]\n",
    "y=df.iloc[:,4]\n",
    "states=pd.get_dummies(X[\"State\"], drop_first=True)\n",
    "X=X.drop(\"State\", axis=1)\n",
    "X=pd.concat([X, states], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d90ae61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=bf8f7968-cc5e-4752-b792-eb11237510be style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('bf8f7968-cc5e-4752-b792-eb11237510be').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend  Florida  New York\n",
       "0  165349.20       136897.80        471784.10        0         1\n",
       "1  162597.70       151377.59        443898.53        0         0\n",
       "2  153441.51       101145.55        407934.54        1         0\n",
       "3  144372.41       118671.85        383199.62        0         1\n",
       "4  142107.34        91391.77        366168.42        1         0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13f05a44",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e5cd61c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9a831d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.26349203,  2.14843908, -1.28405778,  0.81649658, -0.60302269],\n",
       "       [ 0.68815421, -0.76784864,  0.16158446, -1.22474487, -0.60302269],\n",
       "       [ 0.7222701 , -0.16428861, -0.0645352 ,  0.81649658, -0.60302269],\n",
       "       [-1.31239516, -0.99509933, -0.77518518,  0.81649658, -0.60302269],\n",
       "       [ 2.13868296, -0.46757115,  1.89936921,  0.81649658, -0.60302269],\n",
       "       [-0.09702257,  0.39006283,  1.297909  , -1.22474487,  1.6583124 ],\n",
       "       [-1.52304468, -1.59735424, -0.5467499 , -1.22474487,  1.6583124 ],\n",
       "       [-0.39859934,  1.18729971, -1.61285131, -1.22474487,  1.6583124 ],\n",
       "       [-0.04515384,  0.22694578,  0.75012558,  0.81649658, -0.60302269],\n",
       "       [ 1.82712943, -0.78065072,  1.4405505 ,  0.81649658, -0.60302269],\n",
       "       [-0.55369838, -0.40620116, -0.22411107,  0.81649658, -0.60302269],\n",
       "       [-0.81428258, -0.9842928 , -0.32426708, -1.22474487,  1.6583124 ],\n",
       "       [-0.00847634,  0.91234776, -1.10936893,  0.81649658, -0.60302269],\n",
       "       [-0.80295616,  1.34753433, -0.26626369, -1.22474487, -0.60302269],\n",
       "       [ 0.44288439, -0.04932284,  0.65785139,  0.81649658, -0.60302269]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "sc.fit_transform(X_train)\n",
    "sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ff768bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(n_components=2)\n",
    "X_train=pca.fit_transform(X_train)\n",
    "X_test=pca.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a2738d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "explained_variance=pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "820bbb5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85923093, 0.09653941])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1aa5247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1686c5a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dc8d5dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "336c6cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0e084128",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e1689e20",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cde80590",
   "metadata": {},
   "outputs": [],
   "source": [
    "score=r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d73ba971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5856013608909427"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc4173a",
   "metadata": {},
   "source": [
    "#### Another example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "27e746a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Cancer_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5e3fb7d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=e42eded3-f098-43f6-8587-ce76e7136fdf style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('e42eded3-f098-43f6-8587-ce76e7136fdf').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e7d53d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 33)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9baa961a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,2:]\n",
    "y=df.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3dd44f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=X.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8ba90c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "51de4d4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X, y, train_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0ffbe087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.18451732,  0.29027332, -0.11194787, ...,  1.43674056,\n",
       "         1.14762553,  1.58422017],\n",
       "       [-0.23965237,  1.35938621, -0.30287827, ..., -0.80475167,\n",
       "        -0.75531247, -0.92105953],\n",
       "       [-0.00460295, -0.80822321, -0.07199547, ..., -0.46308057,\n",
       "        -1.26898899, -0.96648301],\n",
       "       ...,\n",
       "       [ 0.24205384, -0.57993484,  0.30860368, ...,  1.39965279,\n",
       "         1.14762553,  1.32798515],\n",
       "       [-0.10036382, -0.7155517 , -0.14559199, ..., -0.65300087,\n",
       "         0.71066694, -0.38937186],\n",
       "       [-0.34411877,  0.4823972 , -0.36469935, ..., -0.86718276,\n",
       "        -0.68193011, -0.12731331]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "sc.fit_transform(X_train)\n",
    "sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fdd90e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "explained_variance=pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7000d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f98ebc0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c262e474",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "21b4ba83",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "df0b69c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ae8327df",
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    "cm=confusion_matrix(y_test, y_pred)"
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       "array([[241,  12],\n",
       "       [ 13, 133]], dtype=int64)"
      ]
     },
     "execution_count": 51,
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