BREAST CANCER TUMOR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f2ef59f1",
   "metadata": {},
   "source": [
    "# BREAST CANCER TUMOR DATASET"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde5d11e",
   "metadata": {},
   "source": [
    "#### importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "072a00f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634a4b92",
   "metadata": {},
   "source": [
    "#### load data from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "acf423ce",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Clump</th>\n",
       "      <th>UnifSize</th>\n",
       "      <th>UnifShape</th>\n",
       "      <th>MargAdh</th>\n",
       "      <th>SingEpiSize</th>\n",
       "      <th>BareNuc</th>\n",
       "      <th>BlandChrom</th>\n",
       "      <th>NormNucl</th>\n",
       "      <th>Mit</th>\n",
       "      <th>Class</th>\n",
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      "text/plain": [
       "        ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
       "0  1000025      5         1          1        1            2       1   \n",
       "1  1002945      5         4          4        5            7      10   \n",
       "2  1015425      3         1          1        1            2       2   \n",
       "3  1016277      6         8          8        1            3       4   \n",
       "4  1017023      4         1          1        3            2       1   \n",
       "\n",
       "   BlandChrom  NormNucl  Mit  Class  \n",
       "0           3         1    1      2  \n",
       "1           3         2    1      2  \n",
       "2           3         1    1      2  \n",
       "3           3         7    1      2  \n",
       "4           3         1    1      2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df = pd.read_csv(\"cell_samples.csv\")\n",
    "cell_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89546ea9",
   "metadata": {},
   "outputs": [
    {
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       "         ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
       "694  776715      3         1          1        1            3       2   \n",
       "695  841769      2         1          1        1            2       1   \n",
       "696  888820      5        10         10        3            7       3   \n",
       "697  897471      4         8          6        4            3       4   \n",
       "698  897471      4         8          8        5            4       5   \n",
       "\n",
       "     BlandChrom  NormNucl  Mit  Class  \n",
       "694           1         1    1      2  \n",
       "695           1         1    1      2  \n",
       "696           8        10    2      4  \n",
       "697          10         6    1      4  \n",
       "698          10         4    1      4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec2cfbc5",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
       "0    1000025      5         1          1        1            2       1   \n",
       "1    1002945      5         4          4        5            7      10   \n",
       "2    1015425      3         1          1        1            2       2   \n",
       "3    1016277      6         8          8        1            3       4   \n",
       "4    1017023      4         1          1        3            2       1   \n",
       "..       ...    ...       ...        ...      ...          ...     ...   \n",
       "694   776715      3         1          1        1            3       2   \n",
       "695   841769      2         1          1        1            2       1   \n",
       "696   888820      5        10         10        3            7       3   \n",
       "697   897471      4         8          6        4            3       4   \n",
       "698   897471      4         8          8        5            4       5   \n",
       "\n",
       "     BlandChrom  NormNucl  Mit  Class  \n",
       "0             3         1    1      2  \n",
       "1             3         2    1      2  \n",
       "2             3         1    1      2  \n",
       "3             3         7    1      2  \n",
       "4             3         1    1      2  \n",
       "..          ...       ...  ...    ...  \n",
       "694           1         1    1      2  \n",
       "695           1         1    1      2  \n",
       "696           8        10    2      4  \n",
       "697          10         6    1      4  \n",
       "698          10         4    1      4  \n",
       "\n",
       "[699 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c621fbd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "744cf2ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7689"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b9b39fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    458\n",
       "4    241\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff310e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     145\n",
       "5     130\n",
       "3     108\n",
       "4      80\n",
       "10     69\n",
       "2      50\n",
       "8      46\n",
       "6      34\n",
       "7      23\n",
       "9      14\n",
       "Name: Clump, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df[\"Clump\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a7fd4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clump  Class\n",
       "1      2        142\n",
       "       4          3\n",
       "2      2         46\n",
       "       4          4\n",
       "3      2         96\n",
       "       4         12\n",
       "4      2         68\n",
       "       4         12\n",
       "5      2         85\n",
       "       4         45\n",
       "6      4         18\n",
       "       2         16\n",
       "7      4         22\n",
       "       2          1\n",
       "8      4         42\n",
       "       2          4\n",
       "9      4         14\n",
       "10     4         69\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.groupby(\"Clump\")[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc09db2e",
   "metadata": {},
   "source": [
    "#### mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0d60aa58",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x2880 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell_df.hist(figsize=(40,40))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "79d4661b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cell_df.hist(figsize=(20,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7762ef",
   "metadata": {},
   "source": [
    "#### Distribution of the classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "33c36eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Clump', ylabel='UnifSize'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "benign_df = cell_df[cell_df[\"Class\"]==2][0:200]\n",
    "malignant_df = cell_df[cell_df[\"Class\"]==4][0:200]\n",
    "\n",
    "axes = benign_df.plot(kind =\"scatter\", x =\"Clump\", y = \"UnifSize\", color =\"blue\", label =\"Benign\")\n",
    "malignant_df.plot(kind = \"scatter\", x = \"Clump\", y = \"UnifSize\", color = \"Red\", label = \"Benign\", ax = axes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5882c4c0",
   "metadata": {},
   "source": [
    "#### identifying Unwanted Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e08ff296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID              int64\n",
       "Clump           int64\n",
       "UnifSize        int64\n",
       "UnifShape       int64\n",
       "MargAdh         int64\n",
       "SingEpiSize     int64\n",
       "BareNuc        object\n",
       "BlandChrom      int64\n",
       "NormNucl        int64\n",
       "Mit             int64\n",
       "Class           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "223e41b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             int64\n",
       "Clump          int64\n",
       "UnifSize       int64\n",
       "UnifShape      int64\n",
       "MargAdh        int64\n",
       "SingEpiSize    int64\n",
       "BareNuc        int32\n",
       "BlandChrom     int64\n",
       "NormNucl       int64\n",
       "Mit            int64\n",
       "Class          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df = cell_df[pd.to_numeric(cell_df[\"BareNuc\"], errors = \"coerce\").notnull()]\n",
    "cell_df[\"BareNuc\"]=cell_df[\"BareNuc\"].astype(\"int\")\n",
    "cell_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6346f68a",
   "metadata": {},
   "source": [
    "#### Remove Unwanted Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "17e97f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell_df 100 rows and 11 Columns\n",
    "# picked Only 9 Columns out of 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "37e5f765",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_df.columns\n",
    "\n",
    "feature_df = cell_df[[\"Clump\", \"UnifSize\", \"UnifShape\", \"MargAdh\", \"SingEpiSize\", \"BareNuc\", \"BlandChrom\", \"NormNucl\", \"Mit\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "21f1bba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Independent Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d8915822",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.asarray(feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a0de3b0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a3f0ae4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.asarray(cell_df[\"Class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8be4a2f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0:5]\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "915f9e0b",
   "metadata": {},
   "source": [
    "#### Divide the data as Train/Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "07408e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cell_df(100)--> Train(80 rows) / Test(20 rows)\\nTrain(x,y) ## X itself is a 2D Array. ## Y is 1D\\nTest(x,y)'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"cell_df(100)--> Train(80 rows) / Test(20 rows)\n",
    "Train(x,y) ## X itself is a 2D Array. ## Y is 1D\n",
    "Test(x,y)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "637be3de",
   "metadata": {},
   "source": [
    "#### importing train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0511cce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "39f11fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size =0.2, random_state = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b64d7198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(546, 9)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2126b932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(546,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d0b1202f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(137, 9)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ba338fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(137,)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b659aa",
   "metadata": {},
   "source": [
    "#### Modelling(SVM With Scikit_learn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a93780cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8d6a3bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = svm.SVC(kernel = \"linear\", gamma = \"auto\", C=2)\n",
    "classifier.fit(x_train, y_train)\n",
    "\n",
    "y_predict = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0176fcc7",
   "metadata": {},
   "source": [
    "#### Evaluation (Result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f05a0df5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       1.00      0.94      0.97        90\n",
      "           4       0.90      1.00      0.95        47\n",
      "\n",
      "    accuracy                           0.96       137\n",
      "   macro avg       0.95      0.97      0.96       137\n",
      "weighted avg       0.97      0.96      0.96       137\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "98c11340",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the accuracy score is: 96.35036496350365\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "acc = accuracy_score(y_test, y_predict)*100\n",
    "print(f\"the accuracy score is: {acc}\")\n",
    "sns.set(font_scale=1.5)\n",
    "cm = confusion_matrix( y_predict, y_test)\n",
    "sns.heatmap(cm, annot=True, fmt=\"g\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9080b11d",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}