Lung-Cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Author : Amir Shokri\n",
    "# github link : https://github.com/amirshnll/Lung-Cancer\n",
    "# dataset link : http://archive.ics.uci.edu/ml/datasets/Lung+Cancer\n",
    "# email : amirsh.nll@gmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import tree\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Read_Data(address, Name='*.csv', Sperator=';'):\n",
    "    Data = pd.read_csv(address+Name, sep=Sperator, header=None)\n",
    "#    Data = Data.dropna()\n",
    "    X = Data.drop([0], axis=1)\n",
    "    Y = Data.iloc[:,0]\n",
    "    return X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def KNN_Plot(X, Y, n1, n2, knn_title):\n",
    "    \n",
    "    '''\n",
    "    n1 and n2 are Neighbours\n",
    "    '''\n",
    "    \n",
    "    neighbors = np.arange(n1, n2)\n",
    "    train_acc = np.empty(len(neighbors))\n",
    "    test_acc = np.empty(len(neighbors))\n",
    "    \n",
    "    x_train, x_test, y_train, y_test = train_test_split(X,\n",
    "                                                        Y,\n",
    "                                                        test_size=.2,\n",
    "                                                        random_state=42,\n",
    "                                                        stratify=Y)\n",
    "    \n",
    "    for i, k in enumerate(neighbors):\n",
    "        knn_model = KNeighborsClassifier(n_neighbors=k, weights='distance',\n",
    "                                         algorithm='auto', p=2)\n",
    "        knn_model.fit(x_train, y_train)\n",
    "        TAcc = knn_model.score(x_train, y_train)\n",
    "        TAcc *= 100\n",
    "        TAcc = float(format(TAcc,'.2f'))\n",
    "        train_acc[i] = TAcc\n",
    "        \n",
    "        pred = knn_model.predict(x_test)\n",
    "        Test_acc = accuracy_score(y_test, pred)\n",
    "        Test_acc *= 100\n",
    "        Test_acc = float(format(Test_acc,'.2f'))\n",
    "        test_acc[i] = Test_acc\n",
    "        \n",
    "    plt.plot(neighbors, train_acc, label='Train Accuracy')\n",
    "    plt.plot(neighbors, test_acc, label='Test Accuracy')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title(knn_title)\n",
    "    plt.xlabel('Neighbors')\n",
    "    plt.ylabel('Accuracy (%)')\n",
    "    plt.xticks(neighbors)\n",
    "    plt.show()\n",
    "    \n",
    "    return knn_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NB(x, y):\n",
    "   \n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y,\n",
    "                                                        test_size=.2,\n",
    "                                                        random_state=42,\n",
    "                                                        stratify=y)\n",
    "    \n",
    "    \n",
    "    nb_clf = GaussianNB()\n",
    "    nb_clf.fit(x_train, y_train)\n",
    "    Predict_nb_clf = nb_clf.predict(x_test)\n",
    "    Accuracy = accuracy_score(y_test, Predict_nb_clf)\n",
    "    \n",
    "    Accuracy *= 100\n",
    "    print('GaussianNB Accuracy: ')\n",
    "    print(float(format(Accuracy,'.2f')))\n",
    "    print('---------------------------------\\n')\n",
    "    \n",
    "    return Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Tree(X, Y):\n",
    "    \n",
    "    x_train, x_test, y_train, y_test = train_test_split(X,\n",
    "                                                        Y,\n",
    "                                                        test_size=.2,\n",
    "                                                        random_state=42,\n",
    "                                                        stratify=Y)\n",
    "    \n",
    "    clf = tree.DecisionTreeClassifier(random_state=80)\n",
    "    clf.fit(x_train, y_train)\n",
    "    \n",
    "    Predict = clf.predict(x_test)\n",
    "    Accuracy = accuracy_score(y_test, Predict)\n",
    "\n",
    "    Accuracy *= 100\n",
    "    print('DecisionTree Accuracy: ')\n",
    "    print(float(format(Accuracy,'.2f')))\n",
    "    print('---------------------------------\\n')\n",
    "    \n",
    "    return Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MLP(X, Y):\n",
    "    \n",
    "    x_train, x_test, y_train, y_test = train_test_split(X,\n",
    "                                                        Y,\n",
    "                                                        test_size=.2,\n",
    "                                                        random_state=42,\n",
    "                                                        stratify=Y)\n",
    "    \n",
    "    mlp = MLPClassifier(hidden_layer_sizes=(800,), max_iter=1000, random_state=50)\n",
    "    mlp.fit(x_train, y_train)\n",
    "\n",
    "    Predict = mlp.predict(x_test)\n",
    "    Accuracy = accuracy_score(y_test, Predict)\n",
    "\n",
    "    Accuracy *= 100\n",
    "    print('MLP Accuracy: ')\n",
    "    print(float(format(Accuracy,'.2f')))\n",
    "    print('---------------------------------\\n')\n",
    "    \n",
    "    return Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LogisticRegressionClf(X, Y):\n",
    "    \n",
    "    x_train, x_test, y_train, y_test = train_test_split(X,\n",
    "                                                        Y,\n",
    "                                                        test_size=.2,\n",
    "                                                        random_state=50,\n",
    "                                                        stratify=Y)\n",
    "    \n",
    "    clf = LogisticRegression(random_state=50, solver='lbfgs', max_iter=200)\n",
    "    clf.fit(x_train, y_train)\n",
    "    \n",
    "    Predict = clf.predict(x_test)\n",
    "    Accuracy = accuracy_score(y_test, Predict)\n",
    "\n",
    "    Accuracy *= 100\n",
    "    print('LogisticRegression Accuracy: ')\n",
    "    print(float(format(Accuracy,'.2f')))\n",
    "    print('---------------------------------\\n')\n",
    "    \n",
    "    return Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1   2   3   4   5   6   7   8   9   10  ...  47  48  49  50  51  52  53  \\\n",
      "0    0   3   3   1   0   3   1   3   1   1  ...   2   2   2   2   2   2   2   \n",
      "1    0   3   3   2   0   3   3   3   1   1  ...   2   2   2   2   2   2   2   \n",
      "2    0   2   3   2   1   3   3   3   1   2  ...   2   2   2   2   2   2   2   \n",
      "3    0   3   2   1   1   3   3   3   2   2  ...   2   2   2   2   2   2   2   \n",
      "4    0   3   3   2   0   3   3   3   1   2  ...   2   2   2   2   2   2   2   \n",
      "5    0   3   2   1   0   3   3   3   1   2  ...   2   2   2   2   1   2   2   \n",
      "6    0   2   2   1   0   3   1   3   3   3  ...   2   2   1   2   2   2   2   \n",
      "7    0   3   1   1   0   3   1   3   1   1  ...   2   2   2   2   2   2   2   \n",
      "8    0   2   3   2   0   2   2   2   1   2  ...   2   2   2   1   3   2   1   \n",
      "9    0   2   2   0   0   3   2   3   1   1  ...   2   2   2   2   2   2   2   \n",
      "10   0   2   3   2   0   1   2   1   1   2  ...   2   2   2   2   2   1   1   \n",
      "11   0   2   1   1   0   1   2   2   1   2  ...   2   2   2   2   2   2   2   \n",
      "12   0   2   2   1   1   2   3   3   1   1  ...   2   2   2   2   2   1   1   \n",
      "13   0   3   2   2   1   2   2   2   1   1  ...   2   2   2   2   2   2   2   \n",
      "14   0   3   2   2   0   1   1   3   1   1  ...   2   2   2   2   2   2   2   \n",
      "15   0   2   1   1   0   2   1   3   1   1  ...   2   2   2   2   2   1   1   \n",
      "16   0   1   2   1   0   3   3   3   1   2  ...   2   2   2   2   2   1   1   \n",
      "17   0   3   3   2   0   2   1   3   1   1  ...   2   2   1   2   2   2   2   \n",
      "18   0   2   3   1   1   2   2   1   1   1  ...   3   3   3   3   1   3   3   \n",
      "19   0   2   3   1   1   1   2   1   1   1  ...   2   2   2   2   2   2   2   \n",
      "20   0   3   3   1   0   3   3   1   1   1  ...   2   2   2   2   3   2   2   \n",
      "21   0   2   2   2   0   2   1   2   1   1  ...   2   2   2   2   2   2   2   \n",
      "22   0   2   2   1   0   2   2   2   1   1  ...   3   3   2   2   3   2   2   \n",
      "23   0   3   2   2   0   2   2   2   1   1  ...   2   2   2   3   1   2   2   \n",
      "24   0   2   1   1   0   2   2   1   1   1  ...   2   2   3   2   2   2   2   \n",
      "25   0   2   3   2   1   2   2   3   1   1  ...   2   2   2   2   2   2   2   \n",
      "26   0   2   3   1   0   2   3   3   1   1  ...   2   2   2   2   2   2   2   \n",
      "\n",
      "    54  55  56  \n",
      "0    1   2   2  \n",
      "1    2   1   2  \n",
      "2    2   2   2  \n",
      "3    1   2   2  \n",
      "4    2   1   2  \n",
      "5    2   1   2  \n",
      "6    1   2   2  \n",
      "7    1   2   2  \n",
      "8    1   2   2  \n",
      "9    2   2   2  \n",
      "10   2   2   1  \n",
      "11   1   2   2  \n",
      "12   1   2   2  \n",
      "13   2   2   2  \n",
      "14   1   2   2  \n",
      "15   1   2   2  \n",
      "16   2   2   1  \n",
      "17   2   1   2  \n",
      "18   2   2   1  \n",
      "19   2   2   1  \n",
      "20   2   2   1  \n",
      "21   1   2   1  \n",
      "22   2   2   1  \n",
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      "24   2   2   1  \n",
      "25   1   2   2  \n",
      "26   2   2   2  \n",
      "\n",
      "[27 rows x 56 columns] 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     2\n",
      "9     2\n",
      "10    2\n",
      "11    2\n",
      "12    2\n",
      "13    2\n",
      "14    2\n",
      "15    2\n",
      "16    2\n",
      "17    2\n",
      "18    3\n",
      "19    3\n",
      "20    3\n",
      "21    3\n",
      "22    3\n",
      "23    3\n",
      "24    3\n",
      "25    3\n",
      "26    3\n",
      "Name: 0, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "address = 'C:/'\n",
    "X, Y = Read_Data(address, Name='lc.csv', Sperator=';')\n",
    "print(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=11, weights='distance')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1 = 1\n",
    "n2 = 12\n",
    "knn_title = 'lung cancer knn Classifier'\n",
    "KNN_Plot(X, Y, n1, n2, knn_title)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB Accuracy: \n",
      "66.67\n",
      "---------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Accuracy = NB(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree Accuracy: \n",
      "83.33\n",
      "---------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Accuracy = Tree(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP Accuracy: \n",
      "16.67\n",
      "---------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Accuracy = MLP(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression Accuracy: \n",
      "50.0\n",
      "---------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "LGR_Accuraccy = LogisticRegressionClf(X, Y)"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}