sahiti_kovvuri_logistic reg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6dc53c9d",
   "metadata": {},
   "source": [
    "# Homework # 1 - Logistic Regression\n",
    "Data file: breast_cancer_diagnosis.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f63b2d6",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f55476c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b3344a",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0cc1068",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--@ 1 sahitikovvuri  staff  51590 Sep 23 12:14 breast_cancer_diagnosis.csv\r\n"
     ]
    }
   ],
   "source": [
    "# Read data from file (breast_cancer_diagnosis.csv) into dataframe\n",
    "! ls -l 'breast_cancer_diagnosis.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe72540e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id,name,radius,texture,perimeter,area,smoothness,compactness,concavity,symmetry,fractal_dimension,age,diagnosis\r",
      "\r\n",
      "ID842302,Glynnis Munson,,10.38,122.8,1001,0.1184,0.2776,0.3001,0.2419,0.07871,35,1\r",
      "\r\n",
      "ID842517,Lana Behrer,20.57,17.77,132.9,1326,0.08474,0.07864,0.0869,0.1812,0.05667,27,1\r",
      "\r\n",
      "ID84300903,Devondra Vanvalkenburgh,19.69,21.25,130,1203,0.1096,0.1599,0.1974,0.2069,0.05999,31,1\r",
      "\r\n",
      "ID84348301,Glory Maravalle,,20.38,77.58,386.1,0.1425,0.2839,0.2414,0.2597,0.09744,49,1\r",
      "\r\n",
      "ID84358402,Mellie Mccurdy,20.29,14.34,135.1,1297,0.1003,0.1328,0.198,0.1809,0.05883,20,1\r",
      "\r\n",
      "ID843786,Merle Yelon,,15.7,82.57,477.1,0.1278,0.17,0.1578,0.2087,0.07613,39,1\r",
      "\r\n",
      "ID844359,Corrianne Banzett,18.25,19.98,119.6,1040,0.09463,0.109,0.1127,0.1794,0.05742,38,1\r",
      "\r\n",
      "ID84458202,Noni Marcellino,,20.83,90.2,577.9,0.1189,0.1645,0.09366,0.2196,0.07451,28,1\r",
      "\r\n",
      "ID844981,Kacy Meltzer,,21.82,87.5,519.8,0.1273,0.1932,0.1859,0.235,0.07389,50,1\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head 'breast_cancer_diagnosis.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf65319f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('breast_cancer_diagnosis.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82bfde7",
   "metadata": {},
   "source": [
    "### Examine data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c59f675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review dataframe shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "739b1218",
   "metadata": {
    "scrolled": true
   },
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>radius</th>\n",
       "      <th>texture</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>area</th>\n",
       "      <th>smoothness</th>\n",
       "      <th>compactness</th>\n",
       "      <th>concavity</th>\n",
       "      <th>symmetry</th>\n",
       "      <th>fractal_dimension</th>\n",
       "      <th>age</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID842302</td>\n",
       "      <td>Glynnis Munson</td>\n",
       "      <td>NaN</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>0.30010</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID842517</td>\n",
       "      <td>Lana Behrer</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>0.08690</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID84300903</td>\n",
       "      <td>Devondra Vanvalkenburgh</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>0.19740</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID84348301</td>\n",
       "      <td>Glory Maravalle</td>\n",
       "      <td>NaN</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>0.24140</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID84358402</td>\n",
       "      <td>Mellie Mccurdy</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>0.19800</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ID843786</td>\n",
       "      <td>Merle Yelon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.15780</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>0.07613</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ID844359</td>\n",
       "      <td>Corrianne Banzett</td>\n",
       "      <td>18.25</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.11270</td>\n",
       "      <td>0.1794</td>\n",
       "      <td>0.05742</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ID84458202</td>\n",
       "      <td>Noni Marcellino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.09366</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>0.07451</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ID844981</td>\n",
       "      <td>Kacy Meltzer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.18590</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>0.07389</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ID84501001</td>\n",
       "      <td>Elka Ortolani</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.22730</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.08243</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     name  radius  texture  perimeter    area  \\\n",
       "0    ID842302           Glynnis Munson     NaN    10.38     122.80  1001.0   \n",
       "1    ID842517              Lana Behrer   20.57    17.77     132.90  1326.0   \n",
       "2  ID84300903  Devondra Vanvalkenburgh   19.69    21.25     130.00  1203.0   \n",
       "3  ID84348301          Glory Maravalle     NaN    20.38      77.58   386.1   \n",
       "4  ID84358402           Mellie Mccurdy   20.29    14.34     135.10  1297.0   \n",
       "5    ID843786              Merle Yelon     NaN    15.70      82.57   477.1   \n",
       "6    ID844359        Corrianne Banzett   18.25    19.98     119.60  1040.0   \n",
       "7  ID84458202          Noni Marcellino     NaN    20.83      90.20   577.9   \n",
       "8    ID844981             Kacy Meltzer     NaN    21.82      87.50   519.8   \n",
       "9  ID84501001            Elka Ortolani     NaN    24.04      83.97   475.9   \n",
       "\n",
       "   smoothness  compactness  concavity  symmetry  fractal_dimension  age  \\\n",
       "0     0.11840      0.27760    0.30010    0.2419            0.07871   35   \n",
       "1     0.08474      0.07864    0.08690    0.1812            0.05667   27   \n",
       "2     0.10960      0.15990    0.19740    0.2069            0.05999   31   \n",
       "3     0.14250      0.28390    0.24140    0.2597            0.09744   49   \n",
       "4     0.10030      0.13280    0.19800    0.1809            0.05883   20   \n",
       "5     0.12780      0.17000    0.15780    0.2087            0.07613   39   \n",
       "6     0.09463      0.10900    0.11270    0.1794            0.05742   38   \n",
       "7     0.11890      0.16450    0.09366    0.2196            0.07451   28   \n",
       "8     0.12730      0.19320    0.18590    0.2350            0.07389   50   \n",
       "9     0.11860      0.23960    0.22730    0.2030            0.08243   60   \n",
       "\n",
       "   diagnosis  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "5          1  \n",
       "6          1  \n",
       "7          1  \n",
       "8          1  \n",
       "9          1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display first 10 rows\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1684dc3a",
   "metadata": {},
   "source": [
    "Note: NaN in radius column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8041c4",
   "metadata": {},
   "source": [
    "### Prepare data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4de4294",
   "metadata": {},
   "source": [
    "### Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa61de2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    0\n",
       "name                  0\n",
       "radius               71\n",
       "texture               0\n",
       "perimeter             0\n",
       "area                  0\n",
       "smoothness            0\n",
       "compactness           0\n",
       "concavity             0\n",
       "symmetry              0\n",
       "fractal_dimension     0\n",
       "age                   0\n",
       "diagnosis             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84f0115",
   "metadata": {},
   "source": [
    "### Handle missing values\n",
    "Remember to use \"inplace=True\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94edbfdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure out strategy to replace values where radius is NaN\n",
    "#df['radius'].replace(r'\\N', df.isnull, inplace = True)\n",
    "df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ecb246e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             id                     name  radius  texture  perimeter    area  \\\n",
      "0      ID842302           Glynnis Munson    0.00    10.38     122.80  1001.0   \n",
      "1      ID842517              Lana Behrer   20.57    17.77     132.90  1326.0   \n",
      "2    ID84300903  Devondra Vanvalkenburgh   19.69    21.25     130.00  1203.0   \n",
      "3    ID84348301          Glory Maravalle    0.00    20.38      77.58   386.1   \n",
      "4    ID84358402           Mellie Mccurdy   20.29    14.34     135.10  1297.0   \n",
      "..          ...                      ...     ...      ...        ...     ...   \n",
      "564    ID926424                 Liz Babb   21.56    22.39     142.00  1479.0   \n",
      "565    ID926682             Silvana Shen   20.13    28.25     131.20  1261.0   \n",
      "566    ID926954             Sunny Hiorns   16.60    28.08     108.30   858.1   \n",
      "567    ID927241            Colline Beade   20.60    29.33     140.10  1265.0   \n",
      "568     ID92751         Rita Ryan blanco    7.76    24.54      47.92   181.0   \n",
      "\n",
      "     smoothness  compactness  concavity  symmetry  fractal_dimension  age  \\\n",
      "0       0.11840      0.27760    0.30010    0.2419            0.07871   35   \n",
      "1       0.08474      0.07864    0.08690    0.1812            0.05667   27   \n",
      "2       0.10960      0.15990    0.19740    0.2069            0.05999   31   \n",
      "3       0.14250      0.28390    0.24140    0.2597            0.09744   49   \n",
      "4       0.10030      0.13280    0.19800    0.1809            0.05883   20   \n",
      "..          ...          ...        ...       ...                ...  ...   \n",
      "564     0.11100      0.11590    0.24390    0.1726            0.05623   18   \n",
      "565     0.09780      0.10340    0.14400    0.1752            0.05533   24   \n",
      "566     0.08455      0.10230    0.09251    0.1590            0.05648   21   \n",
      "567     0.11780      0.27700    0.35140    0.2397            0.07016   41   \n",
      "568     0.05263      0.04362    0.00000    0.1587            0.05884   37   \n",
      "\n",
      "     diagnosis  \n",
      "0            1  \n",
      "1            1  \n",
      "2            1  \n",
      "3            1  \n",
      "4            1  \n",
      "..         ...  \n",
      "564          1  \n",
      "565          1  \n",
      "566          1  \n",
      "567          1  \n",
      "568          0  \n",
      "\n",
      "[569 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "# Replace values where radius is NaN based on above strategy\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012f6fc9",
   "metadata": {},
   "source": [
    "### Check for missing values again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d27c985b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "name                 0\n",
       "radius               0\n",
       "texture              0\n",
       "perimeter            0\n",
       "area                 0\n",
       "smoothness           0\n",
       "compactness          0\n",
       "concavity            0\n",
       "symmetry             0\n",
       "fractal_dimension    0\n",
       "age                  0\n",
       "diagnosis            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb7f0cf4",
   "metadata": {},
   "source": [
    "### Drop non-numeric variables\n",
    "Remember to use \"inplace=True\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3c4c5e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = df.select_dtypes(exclude='number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fcd74a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(numeric_cols, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "98594b9c",
   "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>radius</th>\n",
       "      <th>texture</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>area</th>\n",
       "      <th>smoothness</th>\n",
       "      <th>compactness</th>\n",
       "      <th>concavity</th>\n",
       "      <th>symmetry</th>\n",
       "      <th>fractal_dimension</th>\n",
       "      <th>age</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</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>0.30010</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>35</td>\n",
       "      <td>1</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>0.08690</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>27</td>\n",
       "      <td>1</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>0.19740</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</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>0.24140</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>49</td>\n",
       "      <td>1</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>0.19800</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     radius  texture  perimeter    area  smoothness  compactness  concavity  \\\n",
       "0      0.00    10.38     122.80  1001.0     0.11840      0.27760    0.30010   \n",
       "1     20.57    17.77     132.90  1326.0     0.08474      0.07864    0.08690   \n",
       "2     19.69    21.25     130.00  1203.0     0.10960      0.15990    0.19740   \n",
       "3      0.00    20.38      77.58   386.1     0.14250      0.28390    0.24140   \n",
       "4     20.29    14.34     135.10  1297.0     0.10030      0.13280    0.19800   \n",
       "..      ...      ...        ...     ...         ...          ...        ...   \n",
       "564   21.56    22.39     142.00  1479.0     0.11100      0.11590    0.24390   \n",
       "565   20.13    28.25     131.20  1261.0     0.09780      0.10340    0.14400   \n",
       "566   16.60    28.08     108.30   858.1     0.08455      0.10230    0.09251   \n",
       "567   20.60    29.33     140.10  1265.0     0.11780      0.27700    0.35140   \n",
       "568    7.76    24.54      47.92   181.0     0.05263      0.04362    0.00000   \n",
       "\n",
       "     symmetry  fractal_dimension  age  diagnosis  \n",
       "0      0.2419            0.07871   35          1  \n",
       "1      0.1812            0.05667   27          1  \n",
       "2      0.2069            0.05999   31          1  \n",
       "3      0.2597            0.09744   49          1  \n",
       "4      0.1809            0.05883   20          1  \n",
       "..        ...                ...  ...        ...  \n",
       "564    0.1726            0.05623   18          1  \n",
       "565    0.1752            0.05533   24          1  \n",
       "566    0.1590            0.05648   21          1  \n",
       "567    0.2397            0.07016   41          1  \n",
       "568    0.1587            0.05884   37          0  \n",
       "\n",
       "[569 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b7da4f9",
   "metadata": {},
   "source": [
    "### Review updated dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c719eb4",
   "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>radius</th>\n",
       "      <th>texture</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>area</th>\n",
       "      <th>smoothness</th>\n",
       "      <th>compactness</th>\n",
       "      <th>concavity</th>\n",
       "      <th>symmetry</th>\n",
       "      <th>fractal_dimension</th>\n",
       "      <th>age</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</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>0.30010</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>35</td>\n",
       "      <td>1</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>0.08690</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>27</td>\n",
       "      <td>1</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>0.19740</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</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>0.24140</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>49</td>\n",
       "      <td>1</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>0.19800</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.15780</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>0.07613</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.25</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.11270</td>\n",
       "      <td>0.1794</td>\n",
       "      <td>0.05742</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.09366</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>0.07451</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.18590</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>0.07389</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.00</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.22730</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.08243</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius  texture  perimeter    area  smoothness  compactness  concavity  \\\n",
       "0    0.00    10.38     122.80  1001.0     0.11840      0.27760    0.30010   \n",
       "1   20.57    17.77     132.90  1326.0     0.08474      0.07864    0.08690   \n",
       "2   19.69    21.25     130.00  1203.0     0.10960      0.15990    0.19740   \n",
       "3    0.00    20.38      77.58   386.1     0.14250      0.28390    0.24140   \n",
       "4   20.29    14.34     135.10  1297.0     0.10030      0.13280    0.19800   \n",
       "5    0.00    15.70      82.57   477.1     0.12780      0.17000    0.15780   \n",
       "6   18.25    19.98     119.60  1040.0     0.09463      0.10900    0.11270   \n",
       "7    0.00    20.83      90.20   577.9     0.11890      0.16450    0.09366   \n",
       "8    0.00    21.82      87.50   519.8     0.12730      0.19320    0.18590   \n",
       "9    0.00    24.04      83.97   475.9     0.11860      0.23960    0.22730   \n",
       "\n",
       "   symmetry  fractal_dimension  age  diagnosis  \n",
       "0    0.2419            0.07871   35          1  \n",
       "1    0.1812            0.05667   27          1  \n",
       "2    0.2069            0.05999   31          1  \n",
       "3    0.2597            0.09744   49          1  \n",
       "4    0.1809            0.05883   20          1  \n",
       "5    0.2087            0.07613   39          1  \n",
       "6    0.1794            0.05742   38          1  \n",
       "7    0.2196            0.07451   28          1  \n",
       "8    0.2350            0.07389   50          1  \n",
       "9    0.2030            0.08243   60          1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display first 10 rows\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf90de1f",
   "metadata": {},
   "source": [
    "### Separate independent and dependent variables\n",
    "* Independent variables: All remaining variables except Diagnosis\n",
    "* Dependent variable: Diagnosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c41094db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare dataset for model training\n",
    "X = df.iloc[:, :-1]   \n",
    "y = df.iloc[:, -1] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36a85e3",
   "metadata": {},
   "source": [
    "### Split data into training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b42d94e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training data and test data\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8efaee",
   "metadata": {},
   "source": [
    "### Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5bb03ef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/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()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1ee74d",
   "metadata": {},
   "source": [
    "### If above results in error, review error message, look up documentation for LogisticRegression, and change model hyperparameter appropriately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "54584ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=200)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model again\n",
    "model = LogisticRegression(max_iter = 200)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ee096b",
   "metadata": {},
   "source": [
    "### Test model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "53a44275",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 0 0 1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1\n",
      " 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 1 1 1 0 0 1 1 0 0 0 1 0 1\n",
      " 0 0 0 0 0 0 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 0 0 1 0 0 1\n",
      " 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "# Generate predictions against the test set\n",
    "predictions = model.predict(x_test)\n",
    "\n",
    "# printing predictions\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f62cb5",
   "metadata": {},
   "source": [
    "### Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ca4ccd8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.94      0.94        71\n",
      "           1       0.91      0.91      0.91        43\n",
      "\n",
      "    accuracy                           0.93       114\n",
      "   macro avg       0.93      0.93      0.93       114\n",
      "weighted avg       0.93      0.93      0.93       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print classification report\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f45b33dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy =  92.98245614035088 %\n"
     ]
    }
   ],
   "source": [
    "# Print model accuracy\n",
    "accuracy = model.score(x_test, y_test)\n",
    "print(\"accuracy = \", accuracy * 100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adb8766e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}