random_forests_breast_cancer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69702e29",
   "metadata": {},
   "source": [
    "# Breast Cancer Prediction Using Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f653469",
   "metadata": {},
   "source": [
    "This code builds a Random Forest Classifier using the Scikit-Learn library.<br>\n",
    "\n",
    "This is a supervised Machine learning algorithm. It creates a set of decision trees from a randomly selected subset of the training set, based on which it will reach a final prediction.<br>\n",
    "\n",
    "We use the  <a href=\"https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29\">Breast Cancer Wisconsin Diagnostic Data Set</a> to train and test the model that classifies whether a tumor with certain characteristics is a malignant or a benign tumor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d87dc7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5aeb4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upload the dataset\n",
    "\n",
    "df = pd.read_csv('breastCancer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "003b67b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 33)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the data\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b344be24",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>diagnosis</th>\n",
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       "      <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",
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       "    <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",
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       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
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       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
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      "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]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f46606cd",
   "metadata": {},
   "outputs": [
    {
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       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
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       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
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       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "564  926424         M        21.56         22.39          142.00     1479.0   \n",
       "565  926682         M        20.13         28.25          131.20     1261.0   \n",
       "566  926954         M        16.60         28.08          108.30      858.1   \n",
       "567  927241         M        20.60         29.33          140.10     1265.0   \n",
       "568   92751         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "564  ...          26.40           166.10      2027.0           0.14100   \n",
       "565  ...          38.25           155.00      1731.0           0.11660   \n",
       "566  ...          34.12           126.70      1124.0           0.11390   \n",
       "567  ...          39.42           184.60      1821.0           0.16500   \n",
       "568  ...          30.37            59.16       268.6           0.08996   \n",
       "\n",
       "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "564            0.21130           0.4107                0.2216          0.2060   \n",
       "565            0.19220           0.3215                0.1628          0.2572   \n",
       "566            0.30940           0.3403                0.1418          0.2218   \n",
       "567            0.86810           0.9387                0.2650          0.4087   \n",
       "568            0.06444           0.0000                0.0000          0.2871   \n",
       "\n",
       "     fractal_dimension_worst  Unnamed: 32  \n",
       "564                  0.07115          NaN  \n",
       "565                  0.06637          NaN  \n",
       "566                  0.07820          NaN  \n",
       "567                  0.12400          NaN  \n",
       "568                  0.07039          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d4d1282f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "741ac540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           0\n",
       "diagnosis                    0\n",
       "radius_mean                  0\n",
       "texture_mean                 0\n",
       "perimeter_mean               0\n",
       "area_mean                    0\n",
       "smoothness_mean              0\n",
       "compactness_mean             0\n",
       "concavity_mean               0\n",
       "concave points_mean          0\n",
       "symmetry_mean                0\n",
       "fractal_dimension_mean       0\n",
       "radius_se                    0\n",
       "texture_se                   0\n",
       "perimeter_se                 0\n",
       "area_se                      0\n",
       "smoothness_se                0\n",
       "compactness_se               0\n",
       "concavity_se                 0\n",
       "concave points_se            0\n",
       "symmetry_se                  0\n",
       "fractal_dimension_se         0\n",
       "radius_worst                 0\n",
       "texture_worst                0\n",
       "perimeter_worst              0\n",
       "area_worst                   0\n",
       "smoothness_worst             0\n",
       "compactness_worst            0\n",
       "concavity_worst              0\n",
       "concave points_worst         0\n",
       "symmetry_worst               0\n",
       "fractal_dimension_worst      0\n",
       "Unnamed: 32                569\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34ef6c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diagnosis.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75408e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot data of malign and benign tumors\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.countplot(x='diagnosis',data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca984748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    357\n",
       "1    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert diagnosis tags to numeric values\n",
    "\n",
    "df.diagnosis = LabelEncoder().fit_transform(df.diagnosis)\n",
    "df.diagnosis.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28d53142",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose specific traits of the data to train and test the model\n",
    "\n",
    "cols = ['radius_mean', 'texture_mean', 'perimeter_mean',\n",
    "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
    "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6271a77e",
   "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_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>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\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",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\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",
       "   fractal_dimension_mean  \n",
       "0                 0.07871  \n",
       "1                 0.05667  \n",
       "2                 0.05999  \n",
       "3                 0.09744  \n",
       "4                 0.05883  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize this data\n",
    "\n",
    "X = df[cols]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7fb5448c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: diagnosis, Length: 569, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.diagnosis\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a393d7c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing set is 33% of the data, training set is 67%.\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78a04bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aee8ec67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 1 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 1 1 0 1 1 0 0 0\n",
      " 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 0 1 1 0 0 0 1 0 0 0 1 0\n",
      " 1 1 0 1 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 1\n",
      " 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0 1 1 0 1 1 1 1 1\n",
      " 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X=X_test)\n",
    "precision = accuracy_score(predictions, y_test)\n",
    "\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f7642e95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:93.09%  \n"
     ]
    }
   ],
   "source": [
    "# Check accuracy of the model\n",
    "\n",
    "print(f'Accuracy:{precision:.2%}  ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2dd6325e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function that will convert numeric value into predection of the type of tumor\n",
    "\n",
    "def prediction(value):\n",
    "    if(value == 0):\n",
    "        result = 'Bening tumor'\n",
    "    elif(value == 1):\n",
    "        result = 'Malignant tumor'\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "14e77d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try model with data known to be associated to a malignant tumor\n",
    "\n",
    "# Malign\n",
    "radius = 16.110\n",
    "texture = 18.05\n",
    "perimeter = 105.1\n",
    "area = 813\n",
    "smoothness = 0.09721\n",
    "compactness = 0.1137\n",
    "concavity = 0.09447\n",
    "concave_points = 0.05943\n",
    "symmetry = 0.1861\n",
    "fractal_dimension = 0.06248"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da455d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "traits = pd.DataFrame([[radius,texture,perimeter,area,smoothness,compactness,\n",
    "                        concavity,concave_points,symmetry,fractal_dimension]],columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "50965c26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: \n",
      "Malignant tumor\n"
     ]
    }
   ],
   "source": [
    "print('Prediction: ')\n",
    "value = model.predict(X=traits)\n",
    "\n",
    "print(prediction(value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b07bd66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try model with data known to be associated to a benign tumor\n",
    "\n",
    "#Benign\n",
    "radius = 1.66\n",
    "texture = 7.07\n",
    "perimeter = 73.7\n",
    "area = 42\n",
    "smoothness = 0.07561\n",
    "compactness = 0.0363\n",
    "concavity = 0.008306\n",
    "concave_points = 0.01162\n",
    "symmetry = 0.1671\n",
    "fractal_dimension = 0.05731"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8b07feac",
   "metadata": {},
   "outputs": [],
   "source": [
    "traits = pd.DataFrame([[radius,texture,perimeter,area,smoothness,compactness,\n",
    "                        concavity,concave_points,symmetry,fractal_dimension]],columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0887209a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: \n",
      "Bening tumor\n"
     ]
    }
   ],
   "source": [
    "print('Prediction: ')\n",
    "value = model.predict(X=traits)\n",
    "\n",
    "print(prediction(value))"
   ]
  }
 ],
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