A.LC PROJECT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this model detects lung cancer data based on data\n",
    "#importing the libraries\n",
    "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the dataset\n",
    "LC=pd.read_csv('LC.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "\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>TP53</th>\n",
       "      <th>PTEN</th>\n",
       "      <th>EGFR</th>\n",
       "      <th>KRAS</th>\n",
       "      <th>FGFR1</th>\n",
       "      <th>ALK</th>\n",
       "      <th>METex14</th>\n",
       "      <th>MET</th>\n",
       "      <th>BRAF</th>\n",
       "      <th>PIK3CA</th>\n",
       "      <th>ROS1</th>\n",
       "      <th>HER2</th>\n",
       "      <th>RET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1006</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1007</td>\n",
       "      <td>B</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id diagnosis  TP53  PTEN  EGFR  KRAS  FGFR1  ALK  METex14  MET  BRAF  \\\n",
       "0  1001         B     0     0     0     0      0    0        0    1     0   \n",
       "1  1002         B     0     0     0     0      0    0        0    0     1   \n",
       "2  1003         B     0     0     0     0      0    0        0    0     0   \n",
       "3  1004         B     0     0     0     0      0    1        0    0     0   \n",
       "4  1005         M     1     0     0     0      1    0        0    0     0   \n",
       "5  1006         B     0     0     0     0      0    0        0    0     0   \n",
       "6  1007         B     0     0     0     0      0    0        0    0     0   \n",
       "\n",
       "   PIK3CA  ROS1  HER2  RET  \n",
       "0       0     0     0    0  \n",
       "1       0     0     0    0  \n",
       "2       0     1     0    0  \n",
       "3       0     0     0    0  \n",
       "4       0     0     0    0  \n",
       "5       0     0     1    0  \n",
       "6       0     1     0    0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the fist 7 rows of the dataset\n",
    "LC.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#counting how many rows and colums are there in our dataset\n",
    "LC.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id           0\n",
       "diagnosis    0\n",
       "TP53         0\n",
       "PTEN         0\n",
       "EGFR         0\n",
       "KRAS         0\n",
       "FGFR1        0\n",
       "ALK          0\n",
       "METex14      0\n",
       "MET          0\n",
       "BRAF         0\n",
       "PIK3CA       0\n",
       "ROS1         0\n",
       "HER2         0\n",
       "RET          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count the number of empty values(Nan,nan,NA) in each column to check if any value is not at that place\n",
    "LC.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if there is any column with missing values then we can drop that column by using the following command\n",
    "#dataset_name.dropna(axis=column_number_of_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 15)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the count of the number of rows and columns after updating the rows and columns\n",
    "LC.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    92\n",
       "B    36\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the count of number of Malignant(M) and Benign(B) Tumour\n",
    "LC['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117efbb8220>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the count(graphical representation of Malignant and Benign)\n",
    "sns.countplot(LC['diagnosis'],label=\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id            int64\n",
       "diagnosis    object\n",
       "TP53          int64\n",
       "PTEN          int64\n",
       "EGFR          int64\n",
       "KRAS          int64\n",
       "FGFR1         int64\n",
       "ALK           int64\n",
       "METex14       int64\n",
       "MET           int64\n",
       "BRAF          int64\n",
       "PIK3CA        int64\n",
       "ROS1          int64\n",
       "HER2          int64\n",
       "RET           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at the datatypes to see which columns needs to be encoded(tranformed into a number value) \n",
    "LC.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "123    1\n",
       "124    1\n",
       "125    1\n",
       "126    1\n",
       "127    1\n",
       "Name: diagnosis, Length: 128, dtype: int32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Encoding the categorical data values i.e. converting Malignant into 1 and Benign into 0\n",
    "#le is the object for the label encoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "LC.iloc[:,1] = le.fit_transform(LC.iloc[:,1].values)\n",
    "LC.iloc[:,1]          #it will show the updated values of the column\n",
    "#BC.iloc[:,1].values  #it will show the original value of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>diagnosis</th>\n",
       "      <th>TP53</th>\n",
       "      <th>PTEN</th>\n",
       "      <th>EGFR</th>\n",
       "      <th>KRAS</th>\n",
       "      <th>FGFR1</th>\n",
       "      <th>ALK</th>\n",
       "      <th>METex14</th>\n",
       "      <th>MET</th>\n",
       "      <th>BRAF</th>\n",
       "      <th>PIK3CA</th>\n",
       "      <th>ROS1</th>\n",
       "      <th>HER2</th>\n",
       "      <th>RET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  diagnosis  TP53  PTEN  EGFR  KRAS  FGFR1  ALK  METex14  MET  BRAF  \\\n",
       "0  1001          0     0     0     0     0      0    0        0    1     0   \n",
       "1  1002          0     0     0     0     0      0    0        0    0     1   \n",
       "2  1003          0     0     0     0     0      0    0        0    0     0   \n",
       "3  1004          0     0     0     0     0      0    1        0    0     0   \n",
       "4  1005          1     1     0     0     0      1    0        0    0     0   \n",
       "\n",
       "   PIK3CA  ROS1  HER2  RET  \n",
       "0       0     0     0    0  \n",
       "1       0     0     0    0  \n",
       "2       0     1     0    0  \n",
       "3       0     0     0    0  \n",
       "4       0     0     0    0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing the first 5 rows of  our dataset to see the cleaner data with diagnosis value changed \n",
    "LC.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>diagnosis</th>\n",
       "      <th>TP53</th>\n",
       "      <th>PTEN</th>\n",
       "      <th>EGFR</th>\n",
       "      <th>KRAS</th>\n",
       "      <th>FGFR1</th>\n",
       "      <th>ALK</th>\n",
       "      <th>METex14</th>\n",
       "      <th>MET</th>\n",
       "      <th>BRAF</th>\n",
       "      <th>PIK3CA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>diagnosis</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.451781</td>\n",
       "      <td>0.051555</td>\n",
       "      <td>0.205849</td>\n",
       "      <td>0.187579</td>\n",
       "      <td>0.246941</td>\n",
       "      <td>0.036105</td>\n",
       "      <td>-0.053838</td>\n",
       "      <td>0.074035</td>\n",
       "      <td>-0.053838</td>\n",
       "      <td>-0.190104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP53</th>\n",
       "      <td>0.451781</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.268291</td>\n",
       "      <td>0.114539</td>\n",
       "      <td>0.050921</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030557</td>\n",
       "      <td>0.129099</td>\n",
       "      <td>0.103081</td>\n",
       "      <td>-0.064550</td>\n",
       "      <td>-0.221766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTEN</th>\n",
       "      <td>0.051555</td>\n",
       "      <td>-0.268291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154165</td>\n",
       "      <td>0.042592</td>\n",
       "      <td>0.151510</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>-0.093722</td>\n",
       "      <td>-0.069953</td>\n",
       "      <td>-0.093722</td>\n",
       "      <td>-0.043165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>0.205849</td>\n",
       "      <td>0.114539</td>\n",
       "      <td>0.154165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.096373</td>\n",
       "      <td>0.045765</td>\n",
       "      <td>-0.010500</td>\n",
       "      <td>0.080272</td>\n",
       "      <td>-0.033171</td>\n",
       "      <td>-0.054923</td>\n",
       "      <td>-0.085879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>0.187579</td>\n",
       "      <td>0.050921</td>\n",
       "      <td>0.042592</td>\n",
       "      <td>-0.096373</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142422</td>\n",
       "      <td>0.083503</td>\n",
       "      <td>-0.100799</td>\n",
       "      <td>-0.009915</td>\n",
       "      <td>-0.100799</td>\n",
       "      <td>0.094104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGFR1</th>\n",
       "      <td>0.246941</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.151510</td>\n",
       "      <td>0.045765</td>\n",
       "      <td>0.142422</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076225</td>\n",
       "      <td>-0.058554</td>\n",
       "      <td>-0.127273</td>\n",
       "      <td>0.019518</td>\n",
       "      <td>0.061468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALK</th>\n",
       "      <td>0.036105</td>\n",
       "      <td>0.030557</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>-0.010500</td>\n",
       "      <td>0.083503</td>\n",
       "      <td>0.076225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.071007</td>\n",
       "      <td>-0.066146</td>\n",
       "      <td>-0.071007</td>\n",
       "      <td>-0.060988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>METex14</th>\n",
       "      <td>-0.053838</td>\n",
       "      <td>0.129099</td>\n",
       "      <td>-0.093722</td>\n",
       "      <td>0.080272</td>\n",
       "      <td>-0.100799</td>\n",
       "      <td>-0.058554</td>\n",
       "      <td>-0.071007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062103</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-0.057260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MET</th>\n",
       "      <td>0.074035</td>\n",
       "      <td>0.103081</td>\n",
       "      <td>-0.069953</td>\n",
       "      <td>-0.033171</td>\n",
       "      <td>-0.009915</td>\n",
       "      <td>-0.127273</td>\n",
       "      <td>-0.066146</td>\n",
       "      <td>-0.062103</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062103</td>\n",
       "      <td>-0.053340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRAF</th>\n",
       "      <td>-0.053838</td>\n",
       "      <td>-0.064550</td>\n",
       "      <td>-0.093722</td>\n",
       "      <td>-0.054923</td>\n",
       "      <td>-0.100799</td>\n",
       "      <td>0.019518</td>\n",
       "      <td>-0.071007</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-0.062103</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.095433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PIK3CA</th>\n",
       "      <td>-0.190104</td>\n",
       "      <td>-0.221766</td>\n",
       "      <td>-0.043165</td>\n",
       "      <td>-0.085879</td>\n",
       "      <td>0.094104</td>\n",
       "      <td>0.061468</td>\n",
       "      <td>-0.060988</td>\n",
       "      <td>-0.057260</td>\n",
       "      <td>-0.053340</td>\n",
       "      <td>0.095433</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           diagnosis      TP53      PTEN      EGFR      KRAS     FGFR1  \\\n",
       "diagnosis   1.000000  0.451781  0.051555  0.205849  0.187579  0.246941   \n",
       "TP53        0.451781  1.000000 -0.268291  0.114539  0.050921  0.000000   \n",
       "PTEN        0.051555 -0.268291  1.000000  0.154165  0.042592  0.151510   \n",
       "EGFR        0.205849  0.114539  0.154165  1.000000 -0.096373  0.045765   \n",
       "KRAS        0.187579  0.050921  0.042592 -0.096373  1.000000  0.142422   \n",
       "FGFR1       0.246941  0.000000  0.151510  0.045765  0.142422  1.000000   \n",
       "ALK         0.036105  0.030557  0.008198 -0.010500  0.083503  0.076225   \n",
       "METex14    -0.053838  0.129099 -0.093722  0.080272 -0.100799 -0.058554   \n",
       "MET         0.074035  0.103081 -0.069953 -0.033171 -0.009915 -0.127273   \n",
       "BRAF       -0.053838 -0.064550 -0.093722 -0.054923 -0.100799  0.019518   \n",
       "PIK3CA     -0.190104 -0.221766 -0.043165 -0.085879  0.094104  0.061468   \n",
       "\n",
       "                ALK   METex14       MET      BRAF    PIK3CA  \n",
       "diagnosis  0.036105 -0.053838  0.074035 -0.053838 -0.190104  \n",
       "TP53       0.030557  0.129099  0.103081 -0.064550 -0.221766  \n",
       "PTEN       0.008198 -0.093722 -0.069953 -0.093722 -0.043165  \n",
       "EGFR      -0.010500  0.080272 -0.033171 -0.054923 -0.085879  \n",
       "KRAS       0.083503 -0.100799 -0.009915 -0.100799  0.094104  \n",
       "FGFR1      0.076225 -0.058554 -0.127273  0.019518  0.061468  \n",
       "ALK        1.000000 -0.071007 -0.066146 -0.071007 -0.060988  \n",
       "METex14   -0.071007  1.000000 -0.062103  0.066667 -0.057260  \n",
       "MET       -0.066146 -0.062103  1.000000 -0.062103 -0.053340  \n",
       "BRAF      -0.071007  0.066667 -0.062103  1.000000  0.095433  \n",
       "PIK3CA    -0.060988 -0.057260 -0.053340  0.095433  1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the correlation of the columns to see how one column can make an influence on the other column\n",
    "LC.iloc[:,1:12].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117f05575e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing the correletaion between the different columns means how much of a column is making an influence on another column\n",
    "#just the visual representation of correlation of the above command \n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(LC.iloc[:,1:12].corr(), annot=True , fmt='.0%')\n",
    "\n",
    "#figsize is used to make the size of table bigger to see the values clearly\n",
    "#annot is for the values(in decimals) shown in each cell colour \n",
    "#fmt is used for to change the value of annot into percentages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split the dataset into independent(X) terms and dependent(Y) terms\n",
    "#X includes the independent columns which do not include the diagnosis column\n",
    "#Y includes the diagnosis column as it is dependent on other columns\n",
    "# type(LC) shows that our dataset is a panda dataframe\n",
    "\n",
    "X = LC.iloc[:,2:31].values # contain all of the rows of data\n",
    "Y = LC.iloc[:,1].values    #\n",
    "\n",
    "type(LC) # dataframe type\n",
    "#type(X) #check the datatype of the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data into 75% training and 25% testing\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature scaling(scaling the data)\n",
    "#Feature Scaling is used to make(scale) the complete data within a specific range(like 0 to 100 or 0 to 1)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)     # transforming our training data(features)\n",
    "X_test = sc.transform(X_test)           #transforming our testing data(independent data)\n",
    "\n",
    "#X_test\n",
    "#X_train\n",
    "#we can check our testing and training data by typing X_test OR X_training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function for the models to detect cancer\n",
    "def models(X_train,Y_train):\n",
    "  \n",
    "  #fit is used to train the data\n",
    "  #Using Logistic Regression\n",
    "  from sklearn.linear_model import LogisticRegression\n",
    "  log = LogisticRegression(random_state = 0)\n",
    "  log.fit(X_train, Y_train)   \n",
    "  \n",
    "  #Using KNeighborsClassifier \n",
    "  from sklearn.neighbors import KNeighborsClassifier\n",
    "  knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "  knn.fit(X_train, Y_train)\n",
    "\n",
    "  #Using SVC linear\n",
    "  from sklearn.svm import SVC\n",
    "  svc_lin = SVC(kernel = 'linear', random_state = 0)\n",
    "  svc_lin.fit(X_train, Y_train)\n",
    "\n",
    "  #Using SVC rbf\n",
    "  from sklearn.svm import SVC\n",
    "  svc_rbf = SVC(kernel = 'rbf', random_state = 0)\n",
    "  svc_rbf.fit(X_train, Y_train)\n",
    "\n",
    "  #Using GaussianNB \n",
    "  from sklearn.naive_bayes import GaussianNB\n",
    "  gauss = GaussianNB()\n",
    "  gauss.fit(X_train, Y_train)\n",
    "\n",
    "  #Using DecisionTreeClassifier \n",
    "  from sklearn.tree import DecisionTreeClassifier\n",
    "  tree = DecisionTreeClassifier(criterion = 'entropy', random_state = 0) \n",
    "  tree.fit(X_train, Y_train)\n",
    "\n",
    "  #Using RandomForestClassifier method of ensemble class to use Random Forest Classification algorithm\n",
    "  from sklearn.ensemble import RandomForestClassifier\n",
    "  forest = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "  forest.fit(X_train, Y_train)\n",
    "  \n",
    "  #print model accuracy on the training data.\n",
    "  #score is used to calculate the accuracy\n",
    "  print('[0]Logistic Regression Training Accuracy:', log.score(X_train, Y_train))\n",
    "  print('[1]K Nearest Neighbor Training Accuracy:', knn.score(X_train, Y_train))\n",
    "  print('[2]Support Vector Machine (Linear Classifier) Training Accuracy:', svc_lin.score(X_train, Y_train))\n",
    "  print('[3]Support Vector Machine (RBF Classifier) Training Accuracy:', svc_rbf.score(X_train, Y_train))\n",
    "  print('[4]Gaussian Naive Bayes Training Accuracy:', gauss.score(X_train, Y_train))\n",
    "  print('[5]Decision Tree Classifier Training Accuracy:', tree.score(X_train, Y_train))\n",
    "  print('[6]Random Forest Classifier Training Accuracy:', forest.score(X_train, Y_train))\n",
    "  \n",
    "  return log, knn, svc_lin, svc_rbf, gauss, tree, forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]Logistic Regression Training Accuracy: 0.8229166666666666\n",
      "[1]K Nearest Neighbor Training Accuracy: 0.8854166666666666\n",
      "[2]Support Vector Machine (Linear Classifier) Training Accuracy: 0.875\n",
      "[3]Support Vector Machine (RBF Classifier) Training Accuracy: 0.9166666666666666\n",
      "[4]Gaussian Naive Bayes Training Accuracy: 0.8125\n",
      "[5]Decision Tree Classifier Training Accuracy: 0.9270833333333334\n",
      "[6]Random Forest Classifier Training Accuracy: 0.9166666666666666\n"
     ]
    }
   ],
   "source": [
    "#Getting  the output from all of the models with their accuracy on TRAINING DATA\n",
    "model = models(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 0\n",
      "[[ 2  4]\n",
      " [ 4 22]]\n",
      "Model accuracy= 0.75\n",
      "Model[0] Testing Accuracy = \"0.75!\"\n",
      "\n",
      "Model 1\n",
      "[[ 4  2]\n",
      " [ 4 22]]\n",
      "Model accuracy= 0.8125\n",
      "Model[1] Testing Accuracy = \"0.8125!\"\n",
      "\n",
      "Model 2\n",
      "[[ 3  3]\n",
      " [ 5 21]]\n",
      "Model accuracy= 0.75\n",
      "Model[2] Testing Accuracy = \"0.75!\"\n",
      "\n",
      "Model 3\n",
      "[[ 3  3]\n",
      " [ 3 23]]\n",
      "Model accuracy= 0.8125\n",
      "Model[3] Testing Accuracy = \"0.8125!\"\n",
      "\n",
      "Model 4\n",
      "[[ 2  4]\n",
      " [ 5 21]]\n",
      "Model accuracy= 0.71875\n",
      "Model[4] Testing Accuracy = \"0.71875!\"\n",
      "\n",
      "Model 5\n",
      "[[ 5  1]\n",
      " [ 5 21]]\n",
      "Model accuracy= 0.8125\n",
      "Model[5] Testing Accuracy = \"0.8125!\"\n",
      "\n",
      "Model 6\n",
      "[[ 4  2]\n",
      " [ 3 23]]\n",
      "Model accuracy= 0.84375\n",
      "Model[6] Testing Accuracy = \"0.84375!\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Testing model accuracy on TEST DATA by CONFUSION MATRIX\n",
    "from sklearn.metrics import confusion_matrix\n",
    "for i in range(len(model)):\n",
    " print('Model', i)\n",
    " cm = confusion_matrix(Y_test, model[i].predict(X_test))\n",
    "  \n",
    " TN = cm[0][0]\n",
    " TP = cm[1][1]\n",
    " FN = cm[1][0]\n",
    " FP = cm[0][1]\n",
    " print(cm)\n",
    " print('Model accuracy=',(TP + TN) / (TP + TN + FN + FP))# testing testing data accuracy\n",
    " print('Model[{}] Testing Accuracy = \"{}!\"'.format(i,  (TP + TN) / (TP + TN + FN + FP)))\n",
    " print()# Print a new line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number 0\n",
      "\n",
      "Predicted data by the model\n",
      "[1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1]\n",
      "\n",
      "Real data\n",
      "[0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1]\n",
      "\n",
      "\n",
      "Model Number 1\n",
      "\n",
      "Predicted data by the model\n",
      "[0 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1]\n",
      "\n",
      "Real data\n",
      "[0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1]\n",
      "\n",
      "\n",
      "Model Number 2\n",
      "\n",
      "Predicted data by the model\n",
      "[0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 0 0 0 1 1]\n",
      "\n",
      "Real data\n",
      "[0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1]\n",
      "\n",
      "\n",
      "Model Number 3\n",
      "\n",
      "Predicted data by the model\n",
      "[0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1]\n",
      "\n",
      "Real data\n",
      "[0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1]\n",
      "\n",
      "\n",
      "Model Number 4\n",
      "\n",
      "Predicted data by the model\n",
      "[1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1]\n",
      "\n",
      "Real data\n",
      "[0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1]\n",
      "\n",
      "\n",
      "Model Number 5\n",
      "\n",
      "Predicted data by the model\n",
      "[0 1 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 0 0 1 1]\n",
      "\n",
      "Real data\n",
      "[0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1]\n",
      "\n",
      "\n",
      "Model Number 6\n",
      "\n",
      "Predicted data by the model\n",
      "[0 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1]\n",
      "\n",
      "Real data\n",
      "[0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Print Prediction of Random Forest Classifier model\n",
    "for i in range(len(model)):\n",
    " print('Model Number', i)\n",
    " print()\n",
    " print('Predicted data by the model')\n",
    " pred = model[i].predict(X_test)\n",
    " print(pred)\n",
    "\n",
    "#predict means that the algorithm will classify that the patient will have cancer or not\n",
    "#Print a space\n",
    " print()\n",
    "\n",
    "#Print the actual values\n",
    " print('Real data')\n",
    " print(Y_test)\n",
    " print()\n",
    " print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THANK YOU\n"
     ]
    }
   ],
   "source": [
    "print(\"THANK YOU\")"
   ]
  }
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