Lab7-2(DecisionTree).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f4bfe000-c3fd-49bc-b2df-5fc69b8fe239",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "989c4700-2455-4b24-bf4c-4b9a261ee44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/baraa/PythonCodes/archive/data.csv\")      # Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fb83984d-b516-4348-9097-2d6d30e5bd5b",
   "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>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>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "02d1de52-816b-406a-be86-39eb6d1ad446",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8687788d-f393-49cc-b1c0-f2e514046d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"Unnamed: 32\",\"id\"],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2fe5f143-eb03-40b4-8198-34bed8897a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = data[data.diagnosis==\"M\"]\n",
    "B = data[data.diagnosis==\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0fcbbd9e-f360-4f81-ac49-c4152b117ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(M.radius_mean,M.texture_mean,color=\"red\",label=\"malignant\") \n",
    "plt.scatter(B.radius_mean,B.texture_mean,color=\"green\",label=\"benign\")\n",
    "plt.xlabel(\"radius_mean\")\n",
    "plt.ylabel(\"texture_mean\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4c0f1b31-c628-4588-b451-e64f456217a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.diagnosis = [1 if each==\"M\" else 0 for each in data.diagnosis] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e95e06db-65ae-42db-aa79-abca9c35c877",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.diagnosis.values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "eb9488bc-189b-49aa-b09e-ad03eaefe7f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data= data.iloc[:,1:3].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "65b13301-0888-4b33-b2d4-6fb5162e19a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c6d345f2-3c42-4ba5-b0f9-164cd8a42b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "st_x=StandardScaler()\n",
    "x_train=st_x.fit_transform(x_train)\n",
    "x_test=st_x.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "69a5054a-37d2-4127-b713-a6445a68f37c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=1)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree_classification=DecisionTreeClassifier(random_state=1,criterion='entropy')\n",
    "tree_classification.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "71bd5068-17d5-49df-9d57-583abee6219b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_head=tree_classification.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "166d79d6-8bbe-4b8b-9329-5968814e23a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.847953216374269"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy=accuracy_score(y_test,y_head)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2e62f2c8-de55-4235-8c96-668fb8eb54e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(y_test,y_head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e48d3767-9868-44f4-8047-bd8b4c90eb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,fmt='.0f',linewidths=0.5,linecolor=\"red\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_head\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3123d2bf-7437-4de1-9654-48a7000c4592",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}