Classification breast Cancer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de89a6af",
   "metadata": {},
   "source": [
    "The dataset for this project is the Breast cancer wisconsin (diagnostic) dataset. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.\n",
    "\n",
    "Main objective of the analysis is to focus on classification of the cell. In this project, I will be using different classification models to classify wether the cell is Malignant or Benign based on the features . best candidate model with the best results will be chosen. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bbaeaa",
   "metadata": {},
   "source": [
    "# Data exploration and preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f55fa1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4e0b3a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "data=load_breast_cancer(as_frame=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2247f78c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_X1=pd.DataFrame(data.data,columns=data.feature_names)\n",
    "df_y1=pd.DataFrame(data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "521d5eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_data = pd.concat([df_X1, df_y1], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "701146c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   mean radius              569 non-null    float64\n",
      " 1   mean texture             569 non-null    float64\n",
      " 2   mean perimeter           569 non-null    float64\n",
      " 3   mean area                569 non-null    float64\n",
      " 4   mean smoothness          569 non-null    float64\n",
      " 5   mean compactness         569 non-null    float64\n",
      " 6   mean concavity           569 non-null    float64\n",
      " 7   mean concave points      569 non-null    float64\n",
      " 8   mean symmetry            569 non-null    float64\n",
      " 9   mean fractal dimension   569 non-null    float64\n",
      " 10  radius error             569 non-null    float64\n",
      " 11  texture error            569 non-null    float64\n",
      " 12  perimeter error          569 non-null    float64\n",
      " 13  area error               569 non-null    float64\n",
      " 14  smoothness error         569 non-null    float64\n",
      " 15  compactness error        569 non-null    float64\n",
      " 16  concavity error          569 non-null    float64\n",
      " 17  concave points error     569 non-null    float64\n",
      " 18  symmetry error           569 non-null    float64\n",
      " 19  fractal dimension error  569 non-null    float64\n",
      " 20  worst radius             569 non-null    float64\n",
      " 21  worst texture            569 non-null    float64\n",
      " 22  worst perimeter          569 non-null    float64\n",
      " 23  worst area               569 non-null    float64\n",
      " 24  worst smoothness         569 non-null    float64\n",
      " 25  worst compactness        569 non-null    float64\n",
      " 26  worst concavity          569 non-null    float64\n",
      " 27  worst concave points     569 non-null    float64\n",
      " 28  worst symmetry           569 non-null    float64\n",
      " 29  worst fractal dimension  569 non-null    float64\n",
      " 30  target                   569 non-null    int32  \n",
      "dtypes: float64(30), int32(1)\n",
      "memory usage: 135.7 KB\n"
     ]
    }
   ],
   "source": [
    "cancer_data.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd79c30",
   "metadata": {},
   "source": [
    "as we can see above we have 28 features and all of them are numeric which requires no encoding to be used"
   ]
  },
  
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "205af350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "correlations=cancer_data.corr()\n",
    "sns.heatmap(data=correlations ,square=True,cmap='bwr')\n",
    "sns.set(rc = {'figure.figsize':(15,10)})\n",
    "plt.xticks(rotation=80,fontsize=14);\n",
    "plt.yticks(rotation=0);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d53932",
   "metadata": {},
   "source": [
    "we can see that we have a lot of +ve and  -ve correlations up there in our heat map.\n",
    "the target column is  -ve correlated with most of the features.\n",
    "Its hard to tell all of our correlation using the heatmap as we have a lot of features.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a5b6bb",
   "metadata": {},
   "source": [
    "Now lets check our features and their values. So we can tell that wether or not our data needs to be scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5d25602a",
   "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>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.338222</td>\n",
       "      <td>0.323965</td>\n",
       "      <td>0.332935</td>\n",
       "      <td>0.216920</td>\n",
       "      <td>0.394785</td>\n",
       "      <td>0.260601</td>\n",
       "      <td>0.208058</td>\n",
       "      <td>0.243137</td>\n",
       "      <td>0.379605</td>\n",
       "      <td>0.270379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363998</td>\n",
       "      <td>0.283138</td>\n",
       "      <td>0.170906</td>\n",
       "      <td>0.404138</td>\n",
       "      <td>0.220212</td>\n",
       "      <td>0.217403</td>\n",
       "      <td>0.393836</td>\n",
       "      <td>0.263307</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.627417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.166787</td>\n",
       "      <td>0.145453</td>\n",
       "      <td>0.167915</td>\n",
       "      <td>0.149274</td>\n",
       "      <td>0.126967</td>\n",
       "      <td>0.161992</td>\n",
       "      <td>0.186785</td>\n",
       "      <td>0.192857</td>\n",
       "      <td>0.138456</td>\n",
       "      <td>0.148702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.163813</td>\n",
       "      <td>0.167352</td>\n",
       "      <td>0.139932</td>\n",
       "      <td>0.150779</td>\n",
       "      <td>0.152649</td>\n",
       "      <td>0.166633</td>\n",
       "      <td>0.225884</td>\n",
       "      <td>0.121954</td>\n",
       "      <td>0.118466</td>\n",
       "      <td>0.483918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.223342</td>\n",
       "      <td>0.218465</td>\n",
       "      <td>0.216847</td>\n",
       "      <td>0.117413</td>\n",
       "      <td>0.304595</td>\n",
       "      <td>0.139685</td>\n",
       "      <td>0.069260</td>\n",
       "      <td>0.100944</td>\n",
       "      <td>0.282323</td>\n",
       "      <td>0.163016</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241471</td>\n",
       "      <td>0.167837</td>\n",
       "      <td>0.081130</td>\n",
       "      <td>0.300007</td>\n",
       "      <td>0.116337</td>\n",
       "      <td>0.091454</td>\n",
       "      <td>0.223127</td>\n",
       "      <td>0.185098</td>\n",
       "      <td>0.107700</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.302381</td>\n",
       "      <td>0.308759</td>\n",
       "      <td>0.293345</td>\n",
       "      <td>0.172895</td>\n",
       "      <td>0.390358</td>\n",
       "      <td>0.224679</td>\n",
       "      <td>0.144189</td>\n",
       "      <td>0.166501</td>\n",
       "      <td>0.369697</td>\n",
       "      <td>0.243892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.356876</td>\n",
       "      <td>0.235320</td>\n",
       "      <td>0.123206</td>\n",
       "      <td>0.397081</td>\n",
       "      <td>0.179110</td>\n",
       "      <td>0.181070</td>\n",
       "      <td>0.343402</td>\n",
       "      <td>0.247782</td>\n",
       "      <td>0.163977</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.416442</td>\n",
       "      <td>0.408860</td>\n",
       "      <td>0.416765</td>\n",
       "      <td>0.271135</td>\n",
       "      <td>0.475490</td>\n",
       "      <td>0.340531</td>\n",
       "      <td>0.306232</td>\n",
       "      <td>0.367793</td>\n",
       "      <td>0.453030</td>\n",
       "      <td>0.340354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.471748</td>\n",
       "      <td>0.373475</td>\n",
       "      <td>0.220901</td>\n",
       "      <td>0.494156</td>\n",
       "      <td>0.302520</td>\n",
       "      <td>0.305831</td>\n",
       "      <td>0.554639</td>\n",
       "      <td>0.318155</td>\n",
       "      <td>0.242949</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter   mean area  mean smoothness  \\\n",
       "count   569.000000    569.000000      569.000000  569.000000       569.000000   \n",
       "mean      0.338222      0.323965        0.332935    0.216920         0.394785   \n",
       "std       0.166787      0.145453        0.167915    0.149274         0.126967   \n",
       "min       0.000000      0.000000        0.000000    0.000000         0.000000   \n",
       "25%       0.223342      0.218465        0.216847    0.117413         0.304595   \n",
       "50%       0.302381      0.308759        0.293345    0.172895         0.390358   \n",
       "75%       0.416442      0.408860        0.416765    0.271135         0.475490   \n",
       "max       1.000000      1.000000        1.000000    1.000000         1.000000   \n",
       "\n",
       "       mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "count        569.000000      569.000000           569.000000     569.000000   \n",
       "mean           0.260601        0.208058             0.243137       0.379605   \n",
       "std            0.161992        0.186785             0.192857       0.138456   \n",
       "min            0.000000        0.000000             0.000000       0.000000   \n",
       "25%            0.139685        0.069260             0.100944       0.282323   \n",
       "50%            0.224679        0.144189             0.166501       0.369697   \n",
       "75%            0.340531        0.306232             0.367793       0.453030   \n",
       "max            1.000000        1.000000             1.000000       1.000000   \n",
       "\n",
       "       mean fractal dimension  ...  worst texture  worst perimeter  \\\n",
       "count              569.000000  ...     569.000000       569.000000   \n",
       "mean                 0.270379  ...       0.363998         0.283138   \n",
       "std                  0.148702  ...       0.163813         0.167352   \n",
       "min                  0.000000  ...       0.000000         0.000000   \n",
       "25%                  0.163016  ...       0.241471         0.167837   \n",
       "50%                  0.243892  ...       0.356876         0.235320   \n",
       "75%                  0.340354  ...       0.471748         0.373475   \n",
       "max                  1.000000  ...       1.000000         1.000000   \n",
       "\n",
       "       worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "count  569.000000        569.000000         569.000000       569.000000   \n",
       "mean     0.170906          0.404138           0.220212         0.217403   \n",
       "std      0.139932          0.150779           0.152649         0.166633   \n",
       "min      0.000000          0.000000           0.000000         0.000000   \n",
       "25%      0.081130          0.300007           0.116337         0.091454   \n",
       "50%      0.123206          0.397081           0.179110         0.181070   \n",
       "75%      0.220901          0.494156           0.302520         0.305831   \n",
       "max      1.000000          1.000000           1.000000         1.000000   \n",
       "\n",
       "       worst concave points  worst symmetry  worst fractal dimension  \\\n",
       "count            569.000000      569.000000               569.000000   \n",
       "mean               0.393836        0.263307                 0.189596   \n",
       "std                0.225884        0.121954                 0.118466   \n",
       "min                0.000000        0.000000                 0.000000   \n",
       "25%                0.223127        0.185098                 0.107700   \n",
       "50%                0.343402        0.247782                 0.163977   \n",
       "75%                0.554639        0.318155                 0.242949   \n",
       "max                1.000000        1.000000                 1.000000   \n",
       "\n",
       "           target  \n",
       "count  569.000000  \n",
       "mean     0.627417  \n",
       "std      0.483918  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      1.000000  \n",
       "75%      1.000000  \n",
       "max      1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8334da51",
   "metadata": {},
   "source": [
    "as we can see our features fall on  different ranges of value, hence we need to scale our data using minmax scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "582ea4d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mm = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "80e303be",
   "metadata": {},
   "outputs": [],
   "source": [
    "features=['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
    "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
    "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
    "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
    "       'smoothness error', 'compactness error', 'concavity error',\n",
    "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
    "       'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
    "       'worst smoothness', 'worst compactness', 'worst concavity',\n",
    "       'worst concave points', 'worst symmetry', 'worst fractal dimension',\n",
    "       'target']\n",
    "cancer_data[features] = mm.fit_transform(cancer_data[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9cb69b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean radius</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.416</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean texture</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.409</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean perimeter</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.417</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean area</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.271</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean smoothness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.475</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean compactness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concavity</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.306</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concave points</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.368</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean symmetry</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.453</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.340</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.133</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.246</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.123</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.072</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.219</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.227</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.106</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.279</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.219</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal dimension error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.127</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst radius</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.386</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst texture</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.472</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst perimeter</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.373</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst area</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.221</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst smoothness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.494</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst compactness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.303</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concavity</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.306</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concave points</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.555</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst symmetry</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.318</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.243</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count   mean    std  min    25%    50%    75%  max\n",
       "mean radius              569.0  0.338  0.167  0.0  0.223  0.302  0.416  1.0\n",
       "mean texture             569.0  0.324  0.145  0.0  0.218  0.309  0.409  1.0\n",
       "mean perimeter           569.0  0.333  0.168  0.0  0.217  0.293  0.417  1.0\n",
       "mean area                569.0  0.217  0.149  0.0  0.117  0.173  0.271  1.0\n",
       "mean smoothness          569.0  0.395  0.127  0.0  0.305  0.390  0.475  1.0\n",
       "mean compactness         569.0  0.261  0.162  0.0  0.140  0.225  0.341  1.0\n",
       "mean concavity           569.0  0.208  0.187  0.0  0.069  0.144  0.306  1.0\n",
       "mean concave points      569.0  0.243  0.193  0.0  0.101  0.167  0.368  1.0\n",
       "mean symmetry            569.0  0.380  0.138  0.0  0.282  0.370  0.453  1.0\n",
       "mean fractal dimension   569.0  0.270  0.149  0.0  0.163  0.244  0.340  1.0\n",
       "radius error             569.0  0.106  0.100  0.0  0.044  0.077  0.133  1.0\n",
       "texture error            569.0  0.189  0.122  0.0  0.105  0.165  0.246  1.0\n",
       "perimeter error          569.0  0.099  0.095  0.0  0.040  0.072  0.123  1.0\n",
       "area error               569.0  0.063  0.085  0.0  0.021  0.033  0.072  1.0\n",
       "smoothness error         569.0  0.181  0.102  0.0  0.117  0.159  0.219  1.0\n",
       "compactness error        569.0  0.174  0.134  0.0  0.081  0.137  0.227  1.0\n",
       "concavity error          569.0  0.081  0.076  0.0  0.038  0.065  0.106  1.0\n",
       "concave points error     569.0  0.223  0.117  0.0  0.145  0.207  0.279  1.0\n",
       "symmetry error           569.0  0.178  0.116  0.0  0.102  0.153  0.219  1.0\n",
       "fractal dimension error  569.0  0.100  0.091  0.0  0.047  0.079  0.127  1.0\n",
       "worst radius             569.0  0.297  0.172  0.0  0.181  0.250  0.386  1.0\n",
       "worst texture            569.0  0.364  0.164  0.0  0.241  0.357  0.472  1.0\n",
       "worst perimeter          569.0  0.283  0.167  0.0  0.168  0.235  0.373  1.0\n",
       "worst area               569.0  0.171  0.140  0.0  0.081  0.123  0.221  1.0\n",
       "worst smoothness         569.0  0.404  0.151  0.0  0.300  0.397  0.494  1.0\n",
       "worst compactness        569.0  0.220  0.153  0.0  0.116  0.179  0.303  1.0\n",
       "worst concavity          569.0  0.217  0.167  0.0  0.091  0.181  0.306  1.0\n",
       "worst concave points     569.0  0.394  0.226  0.0  0.223  0.343  0.555  1.0\n",
       "worst symmetry           569.0  0.263  0.122  0.0  0.185  0.248  0.318  1.0\n",
       "worst fractal dimension  569.0  0.190  0.118  0.0  0.108  0.164  0.243  1.0\n",
       "target                   569.0  0.627  0.484  0.0  0.000  1.000  1.000  1.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(cancer_data.describe().T, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee811545",
   "metadata": {},
   "source": [
    " As we can see all of our features  are now normalized and they all have values between 0 and 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f16b9c",
   "metadata": {},
   "source": [
    "Now lets check our target column and see if our data is balanced or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2b9b473a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    357\n",
       "0    212\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = 'target'\n",
    "data[target].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee078b9",
   "metadata": {},
   "source": [
    "we can see that our data is quietly not balanced, hence we are gonna be using stratified shuffle split to guarentee that our data will be splitted equally in the training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "13c69f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "strat_shuf_split = StratifiedShuffleSplit(n_splits=1, \n",
    "                                          test_size=0.3, \n",
    "                                          random_state=42)\n",
    "\n",
    "train_idx, test_idx = next(strat_shuf_split.split(cancer_data[feature_cols], cancer_data.target))\n",
    "\n",
    "# Create the dataframes\n",
    "X_train = cancer_data.loc[train_idx, feature_cols]\n",
    "y_train = cancer_data.loc[train_idx, 'target']\n",
    "\n",
    "X_test  = cancer_data.loc[test_idx, feature_cols]\n",
    "y_test  = cancer_data.loc[test_idx, 'target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b26b6249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    0.371859\n",
       "1.0    0.628141\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts(normalize=True).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6f1fd87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    0.374269\n",
       "1.0    0.625731\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts(normalize=True).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfcb4a53",
   "metadata": {},
   "source": [
    "we can see now that our data is well splitted in the train and test values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5baa6cdf",
   "metadata": {},
   "source": [
    "with our data being  normalized well splitted and ready, lets try our regression models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35e18c3",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3646346",
   "metadata": {},
   "source": [
    "we will be defining a fucntion here that we will be using to plot the confusion matrices of our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddf236b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusionMatrix(Matrix):\n",
    "    fig, ax = plt.subplots(figsize=(7.5, 7.5))\n",
    "    ax.matshow(Matrix, cmap=plt.cm.Blues, alpha=0.3)\n",
    "    for i in range(Matrix.shape[0]):\n",
    "        for j in range(Matrix.shape[1]):\n",
    "            ax.text(x=j, y=i,s=Matrix[i, j], va='center', ha='center', size='xx-large')\n",
    "\n",
    "    plt.xlabel('Predictions', fontsize=18)\n",
    "    plt.ylabel('Actuals', fontsize=18)\n",
    "    plt.title('Confusion Matrix', fontsize=18)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42f03de",
   "metadata": {},
   "source": [
    "we will try Logistic regression as our first model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "41fe9a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Standard logistic regression\n",
    "lr = LogisticRegression(solver='liblinear').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "92e1ffc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.88      0.93        64\n",
      "         1.0       0.93      0.99      0.96       107\n",
      "\n",
      "    accuracy                           0.95       171\n",
      "   macro avg       0.96      0.93      0.94       171\n",
      "weighted avg       0.95      0.95      0.95       171\n",
      "\n",
      "Accuracy score:  0.95\n",
      "F1 Score:  0.96\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, f1_score\n",
    "y_predLR = lr.predict(X_test)\n",
    "print(classification_report(y_test, y_predLR))\n",
    "print('Accuracy score: ', round(accuracy_score(y_test, y_predLR), 2))\n",
    "print('F1 Score: ', round(f1_score(y_test, y_predLR), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d08f1278",
   "metadata": {},
   "source": [
    "logistic regression did well with the accuracy,f1 score and precison, but it was decent at the recall score for class 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bb7d0e64",
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_matrixLR = confusion_matrix(y_true=y_test, y_pred=y_predLR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c31bf0bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusionMatrix(conf_matrixLR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "265354fd",
   "metadata": {},
   "source": [
    "from our confusion matrix here we miss classified 8 samples as 1 intead of 0 for the 0 class and we only missclassified 1 sample as 0 instead of 1. which exlpains why our precision was better than the recall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b07ac265",
   "metadata": {},
   "source": [
    "# K-Nearest Neighbor(KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea75d9f9",
   "metadata": {},
   "source": [
    "Now lets check our KNN model chosing only 3 neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b4193470",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn = knn.fit(X_train, y_train)\n",
    "y_predKNN = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1b7c1195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.92      0.95        64\n",
      "         1.0       0.95      0.99      0.97       107\n",
      "\n",
      "    accuracy                           0.96       171\n",
      "   macro avg       0.97      0.96      0.96       171\n",
      "weighted avg       0.97      0.96      0.96       171\n",
      "\n",
      "Accuracy score:  0.96\n",
      "F1 Score:  0.97\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predKNN))\n",
    "print('Accuracy score: ', round(accuracy_score(y_test, y_predKNN), 2))\n",
    "print('F1 Score: ', round(f1_score(y_test, y_predKNN), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f0e3d39",
   "metadata": {},
   "source": [
    "Our accuracy here is better than the logistic regresion, alao the recall score has improved a lot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f7ced578",
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_matrixKNN = confusion_matrix(y_true=y_test, y_pred=y_predKNN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "035a0b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusionMatrix(conf_matrixKNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4681042a",
   "metadata": {},
   "source": [
    "so here we only miss classified 5 samples as 1 instead of 0 (which explains why our recall has improved from that we had in the logistic regression) and again we missclassified only 1 sample as 0 instead of 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4e70e3",
   "metadata": {},
   "source": [
    "# Decisicon Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b11e52",
   "metadata": {},
   "source": [
    "Now lets try the decsion tree model. \n",
    "I know that we previouly scaled our data for the KNN and LR models above. \n",
    "and this wont affect the decision tree classifier since trees don't get affected at all with the scaling of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d3229ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier(random_state=42)\n",
    "dt = dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c8166ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 6)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.tree_.node_count, dt.tree_.max_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "24e700ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from io import StringIO\n",
    "from IPython.display import Image\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "dot_data = StringIO()\n",
    "\n",
    "export_graphviz(dt, out_file=dot_data, filled=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "\n",
    "filename = 'CancerTree.png'\n",
    "graph.write_png(filename)\n",
    "Image(filename=filename) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74711bda",
   "metadata": {},
   "source": [
    "so after fitting our tree to the data set we ended up with a tree that has 36 nodes and 6 levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cb533732",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "def measure_error(y_true, y_pred, label):\n",
    "    return pd.Series({'accuracy':accuracy_score(y_true, y_pred),\n",
    "                      'precision': precision_score(y_true, y_pred),\n",
    "                      'recall': recall_score(y_true, y_pred),\n",
    "                      'f1': f1_score(y_true, y_pred)},\n",
    "                      name=label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ff760b65",
   "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>train</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.918129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           train      test\n",
       "accuracy     1.0  0.918129\n",
       "precision    1.0  0.934579\n",
       "recall       1.0  0.934579\n",
       "f1           1.0  0.934579"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The error on the training and test data sets\n",
    "y_train_pred = dt.predict(X_train)\n",
    "y_test_pred = dt.predict(X_test)\n",
    "\n",
    "train_test_full_error = pd.concat([measure_error(y_train, y_train_pred, 'train'),\n",
    "                              measure_error(y_test, y_test_pred, 'test')],\n",
    "                              axis=1)\n",
    "\n",
    "train_test_full_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c085fc8",
   "metadata": {},
   "source": [
    "our decision tree did perect job in the training data and also a very good job at our testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f24cb14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_matrixDT = confusion_matrix(y_true=y_test, y_pred=y_test_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0980cc59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYOBq7wFwZ1iz5BOtWPhxvutKeHgqdtE6xcbGasaMGcZ9vftRnMqVr1jYIwIOq1loiHGbjp0i9eboccUwDW4FsYRNTh77WZLUqceTcnJ2yrXO1dVNktSkSRN17tkv39v/9ssR7d+zQxWrVJd3Gd8inRVwNAOeHpRnmaeHm9IzrujTRQuUnn5JDzRqbIfJYCtiCZucPPazfP389egTAwrcJjQ0VJdLV8+z/FLa73r7pf4q4eGpZ157R+4lShThpIDjefpvg/Ms8/XxUuyMfyo9/ZK6PNpNnTp1scNksBXnLGF0OSNd5xJPq3L1Wrd0+0Wzpik5KVHdogeqQqWqhTwdcGc6cuSI/vmPWFWoUEFDh71i73FgQCxhdOr4L7JYLKpSvfZN3/bowf36ZutGValRR607dCuC6YA704QJE5SVlaWhw16Rl5eXvceBAU/DwujksaOSpMzLlzXj3RE69t+Dysy8rOq1A9UhKlr1GjYp8LaL51674OexvoPk7MzfZoAkbd+2VTt27FBQcH21bvOwvceBDez622vVqlXq2LGj2rVrpwULFthzFNzAqeO/SJI2rV6inJwcNX+4g4IeaKpf//uT/j7uVW1ZtyLf2x3a/51O/HxY1WoF3DCowF/N/H/PlSQ92a/gawDgWOx2ZJmYmKhp06Zp+fLlcnd3V69evRQaGqratW/+qT4ULWdnZ/n6+euJgS+pfuNm1uUnj/2sSa8/q7h//V1BIaFSXf9ct9uwMk6S9MhjfYp1XsCRHTr0k/bt+0H33XefWrRoae9xYCO7HVnu2LFDTZs2lY+Pj7y8vNS+fXt98cUX9hoHN9Bn8Mt6b9aSXKGUpCo1auvhzlHKyrqqPdu/zLXuQvI5/fT9bnn7+CoklF8IwHWrVn4uSerVq5ecnJxuvDEcht2OLM+ePSs/Pz/r1+XLl9f+/fttvn3TOn7mjVDkrrZpqrVL58s1M1mSFPZ/R5cLF26SxWJRZKcOahN8rz1HBByGxWLRtq1b5OzsrPbt28vXhwt77hR2i2VOTk6uv6osFstN/ZW162iSMq/mFMVo+IOsrCydOnZU2dnZqhUYlGf99z+fkSSdS7/2vdhyMFGStHTltWcJKt4fal0G+wi+t7S9R8D/+enAj0pKSlKjRg+qXLlySr6Qbu+R8AfOzk7y8fbMf10xz2JVoUIFJSUlWb9OSkpS+fLl7TUOCpCddVXvjRii6W+9rKtXr+RZf/SnfZKkGnXuty6zWCw6duQnubm7q2ZA3sACf1X7f7z288K79dx57BbL5s2ba+fOnUpOTlZGRobWr1+vVq1a2WscFKCEh6eCGzdT5uWMPO8Ne3DfHm3buEa+fv65zkueTTil9EtpqlrzPrm68uok4LrDhw5KkoKC6tt5Etwsu/0m8/f314svvqi+ffvq6tWrioqKUv36/A/kiHoNeEEnfj6s9Z8v0i+HD6hWQD0lJpzS/j075O5eQgNfHis39/+9hd3ZhHhJkv+9Vew1MuCQTp06KUmqUpV3srrT2PXP/sjISEVGRtpzBNjgnvIVNGrybK2Om6f9e3bo+M+HVLKUt5o89LA69+yXJ4ppqRckSWXv4Wl14I8upKTI2dk518WNuDM4WSwWi72HuBVc4ON4wur6czGPA+ICH8fk6+PFBT4OxiEv8AEA4E5BLAEAMCCWAAAYEEsAAAyIJQAABsQSAAADYgkAgAGxBADAgFgCAGBALAEAMCCWAAAYEEsAAAyIJQAABsQSAAADYgkAgAGxBADAgFgCAGBALAEAMCCWAAAYEEsAAAyIJQAABsQSAAADYgkAgAGxBADAgFgCAGBALAEAMCCWAAAYEEsAAAyIJQAABsQSAAADYgkAgAGxBADAgFgCAGBALAEAMCCWAAAYEEsAAAyIJQAABsQSAAADYgkAgAGxBADAgFgCAGBALAEAMCCWAAAYEEsAAAyIJQAABsQSAAADYgkAgAGxBADAgFgCAGBALAEAMCCWAAAYEEsAAAyIJQAABsQSAAADYgkAgAGxBADAgFgCAGBALAEAMCCWAAAYEEsAAAyIJQAABsQSAAADYgkAgAGxBADAgFgCAGBALAEAMCCWAAAYuN7sDTIyMuTp6SlJSklJ0dq1a+Xs7KwOHTrIx8ensOcDAMDubI5lamqqXnzxRaWmpmrJkiVKS0tT9+7dlZCQIIvFog8//FALFy5UlSpVinJeAACKnc1Pw06fPl27d+9Wy5YtJUlLly7V6dOn9eqrr+rf//63nJ2dNX369KKaEwAAu7H5yHLz5s2Kjo7WCy+8IEnauHGj7rnnHvXv31+S1KdPH82dO7dopgQAwI5sPrI8f/686tSpI0n6/ffftXfvXrVo0cK6vmzZssrIyCj8CQEAsDObY+nv76+TJ09KunZUmZ2drdatW1vXf//996pYsWKhDwgAgL3Z/DRsmzZt9MknnygtLU1r1qxRmTJlFB4ersTERM2ePVsrVqzQkCFDinJWAADswuZYvvrqq8rIyNDSpUvl7++vsWPHysPDQ0eOHNGCBQvUpUsXDRw4sChnBQDALpwsFovldnZw5coVXbx4UX5+foU1k012HU1S5tWcYr1P3FhYXX9tOZho7zHwJ8H3lrb3CMiHr4+Xki+k23sM/IGzs5N8vD3zX3e7O3d3dy/2UAIAUJwKfBr24YcfvumdOTk5aePGjbc1EAAAjqbAWN57773FOQcAAA6rwFjOnz+/OOcAAMBhFeqnjhw8eLAwdwcAgEOw+aUjV69e1axZs7R+/Xqlp6crJ+d/V6JmZ2fr0qVLSktL06FDh4pkUAAA7OWm3kg9NjZWFy9elKenp+Lj41WxYkW5urrqzJkzunr1qkaNGlWUswIAYBc2x/KLL75QkyZNtHnzZs2ePVuSNHr0aK1bt04fffSRsrKy5ObmVmSDAgBgLzbHMjExUe3atZOzs7P8/f11zz336IcffpAkhYWFqVu3blq8eHGRDQoAgL3YHEsPD49cR45Vq1bVkSNHrF/Xr1/f+kbrAADcTWyO5f3336+vv/7a+nXNmjWtR5bStSNPJyenwp0OAAAHYHMs+/Tpo02bNumJJ55QWlqaOnXqpIMHD2rkyJGaPXu25s2bp+Dg4KKcFQAAu7D5pSOPPPKI3n77bc2dO1eenp5q3ry5/va3v1kv9rn33ns1cuTIIhsUAAB7ue1PHTl9+rQuXryoWrVqyd3dvbDmMuJTRxwPnzrimPjUEcfEp444nht96ojNR5YFuffee3kfWQDAXc3mWNr6KSSbNm265WEAAHBENscyv6PHnJwcnTt3TidOnFD16tXVokWLQh0OAABHYHMsb/QpJAcOHNDTTz+tJk2aFMpQAAA4kkL51JGgoCBFR0dr5syZhbE7AAAcym1f4HNduXLldPz48cLanVGgfynl5NzWhbwoAlx56XjWbT1g7xGQj96RTfjeOJiSnu7q0rZhvusK5cgyKSlJixYt4qpYAMBd6bavhr1y5YqSk5OVnZ2tMWPGFNpgAAA4itu6GlaSXFxcFBoaqs6dO6t169aFNRcAAA6jUK6GBQDgbmbzOcu+fftq586dBa7fvHmzOnXqVChDAQDgSAo8sszIyFBKSor162+++UYRERGqVq1anm1zcnL09ddf69SpU0UzJQAAdnTDWHbt2lW///67JMnJyUnvvvuu3n333Xy3t1gsvIMPAOCuVGAsfX199f777+vHH3+UxWLRzJkzFRERoYCAgDzbOjs7y9fXl6dhAQB3pRte4BMWFqawsDBJ1z6Kq1evXmrQoEGxDAYAgKOw+QKfCRMmyM/PT5MnT9bFixety2fPnq2JEyfq/PnzRTIgAAD2ZnMsjxw5om7dumnu3LlKSEiwLr948aIWLFigrl276uTJk0UyJAAA9mRzLKdMmaKSJUtqzZo1CgwMtC5/5ZVXtGbNGrm5uWny5MlFMiQAAPZkcyz37t2rJ598UtWrV8+zrkqVKoqOjta3335bmLMBAOAQbI6lxWJRZmbmDddfvny5UIYCAMCR2BzLBg0aKC4uTqmpqXnWXbp0SUuWLOFKWQDAXcnm94Z97rnnFB0drc6dOysyMlLVqlWTk5OTfvvtN61Zs0Znz57VhAkTinJWAADswuZYNmjQQHPnztXEiRM1Z84cWSz/++DlwMBAvffeewoJCSmSIQEAsCebYylJjRs31pIlS5ScnKz4+Hjl5OSoYsWKkqSVK1dq3LhxWr16dZEMCgCAvdxULK/z9fVV6dKltWnTJn344Yfavn27srKy5OLiUtjzAQBgdzcdywMHDuizzz7T6tWrlZqaKovFonLlyql79+7q2bNnUcwIAIBd2RTL8+fPa8WKFfrss8/0888/y2KxyMnJSZL0/PPPa9CgQXJ1vaWDVAAAHF6BhcvKytLmzZu1fPlybdu2TVlZWXJ3d1dYWJj100eioqIUGBhIKAEAd7UCK9eyZUtduHBBpUqVUkREhCIiIhQWFqaSJUtKkuLj44ttSAAA7KnAWKakpMjLy0uRkZEKDQ3Vgw8+aA0lAAB/JQXGct68eVq9erVWr16tRYsWycnJSQ0bNlS7du0UERFRnDMCAGBXBcayadOmatq0qUaPHq0tW7Zo1apV2rJli77//ntNnDhR1atXl5OTk9LT04tzXgAAip3xyhx3d3frOcu0tDStW7dOq1at0rfffiuLxaLXXntNy5cvV1RUlCIiIuTu7l4ccwMAUGxu6jLWUqVKqXv37urevbuSkpK0Zs0arVq1Sjt37tSuXbvk7e2t3bt3F9WsAADYhc2fOvJnfn5+6tevn5YtW6Z169ZpyJAh8vHxKcTRAABwDLccyz+qVq2ann/+ea1bt64wdgcAgEMplFgCAHA3I5YAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABgQSwAADIglAAAGxBIAAANiCQCAgau9B8CdqVloiHGbjp0i9eboccUwDWB/OTk5mjPzbbm4uOqpIaPyrE85f1abv1iq478c1uWMS1r2SYACGoQpqGFonm0tFot++GaLdm/foOSkMyrh4an76oYo/JEolSpdpjgeDv6EWOKWDHh6UJ5lnh5uSs+4ok8XLVB6+iU90KixHSYD7GPtZ5/o1ImfVa1mYJ51yefP6uMZ43Q5I13BIc3k6VVSJ47u09L/zFDqxWQ1D+uQa/sNqz/Vji1r5X9vVTV5qJ2Sz53R999s0a9Hf9LAoW/Jq2Tp4npY+D/EErfk6b8NzrPM18dLsTP+qfT0S+ryaDd16tTFDpMBxevy5QytiJutQz9+W+A2X3w+X5d+v6iYgcNV675gSVKnsHHq0OlRbfp/S1S3/oPyKVtOknT61HHt2LJW1WoGqu+gEXJxcZEk7dm5WauXzdXmL5apc/d+Rf64kBvnLFFojhw5on/+I1YVKlTQ0GGv2HscoMgd2LtLMyYN16Efv1XtwPr5bpN8/qyOHN6najUDrKGUJG9vb7Vq20XZWVe199ut1uW7t66TJLVu180aSklq1LSNfMtV0L7vtunq1StF9IhQEGKJQjNhwgRlZWVp6LBX5OXlZe9xgCL37Y5NcpKTuvcZok6P9ct3mxO/HJYsFtWoXS/PuuvLjv9yyLrs+C+H5OLqpqo17su1rZOTk2rUvl9Xr2Qq/rdfCu9BwCZ2jWVaWpo6d+6sU6dO2XMMFILt27Zqx44dCgqur9ZtHrb3OECxCIvoqhdGTlZwSLMCt0k+nyhJ8i3nn2ddaW8fubq66XzSGUlSVlaWLl44rzI+98jFJe9ZMh9fP0mybo/iY7dY7tu3T71799bx48ftNQIK0fx/z5UkPdlvgJ0nAYpPzTr15ObmfsNt0i+lSZI8vUrlu76Eh6cuX06XJGWkX9+2ZL7benh4SpJ1exQfu8Vy8eLFGjNmjMqXL2+vEVBIDh36Sfv2/aD77rtPLVq0tPc4gEPJzs6SJLm65n89pYurm7Kyrtq8rSTr9ig+drsadvz48fa6axSyVSs/lyT16tVLTk5O9h0GcDBubtcCdz2Ef5addVXu7iX+b1v3/9s2u8BtJVm3R/G5Y1864uPtae8RoGsvnt62dYucnZ3Vvn17+fpwYY+j6R3ZxN4j/CWcOnVKH7wr+ZfzzvVvnvjLDn27Y5Ma1b1XHTvm/l70jmyiCaMy5evrq96RTXTlyhVNGecsd5ecfL9vvyfs1RpJrULrKYrva7G6Y2N5ITVDOTkWe4/xl/fTgR+VlJSkRo0eVLly5ZR8gXMpjmbd1gP2HuEvISU5SZKUeC5Vi1Z9Y11+5sK131Or1+/Qxexy1uW9I5voo/+sU2ZmprxKl7PepkzZcvrt5CktWLFLzs65z5R9ue07SdKvCZm57gOFo6Snu7q0bZjvOl46gtuy/8d9ksS79QAFqFYjQJJ07A8vD7nu+C8HJUlVqtf+3/Y1A5V19YpO/fZzrm0tFouO/3JYrq5uqlCpWhFOjPwQS9yWw4eu/bAHBeX/gmzgr87Ht5xq1K6rX48c0NHD+63LU1NT9fXGlXJxcdUDoa2ty0MebCVJ2rR2ibKy/nee87tdXyr53BmFNGllvAIXhc/uT8Nu3rzZ3iPgNpw6dVKSVKVqVTtPAjiuDl1j9PGMcVo0d6qCGjZVyVLemjV1n86dPa32XfrIu4yvddtqNQMU0iRMP3yzRR9Ne0P33d9QyecTdejAd/It56/W7R6z4yP567J7LHFnu5CSImdnZ/n5+dl7FMBhla9QWQOeG6PNXyzVkYM/KCcnRwH31VHz8Mfy/dSRyKj+8vOvpO93f6Vd29arZClvPdAkTG3ad1fJUt52eARwslgsd+RVMlzg43h8fby4wMcBcYGPY+od2YSLdBwMF/gAAHAbiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBgQCwBADAglgAAGBBLAAAMiCUAAAbEEgAAA2IJAIABsQQAwIBYAgBg4GrvAW6Vs5MTqXdAzs5O9h4Bf1LS093eI6AAfG8ci6dHwd8PJ4vFYinGWQAAuONwbIZCkZCQoPDwcCUkJNh7FMDh8fNy5yGWKBTZ2dmKj49Xdna2vUcBHB4/L3ceYgkAgAGxBG7SiBEjFBAQkOu/+++/Xw888IB69Oihzz77rMhnCA8PV0xMjPXrmJgYhYeH3/R+0tLSlJycbP06NjZWAQEBOnXqVKHMCdwt7tirYQF7GzlypMqWLStJslgsSktL08qVKzVixAilpKSof//+xTbL4MGDlZGRcVO3OXDggJ555hlNnjxZoaGhkqSIiAhVrVpVvr6+RTEmcMciligU3t7eeu655+Tt7W3vUYpN27ZtVbly5VzLoqKi1LFjR82cOVPR0dFydy+elwa0aNHipm9z5MgRnT17NteywMBABQYGFtZYKMBf8eflTsfTsCgU3t7eev755//yP/weHh4KDw9XWlqajh49au9x4KD4ebnzEEugkDk5XXtjhuzsbIWHh+uNN97Q66+/ruDgYLVq1cp6jvCHH37QU089pZCQEIWEhKh///7av39/nv2tXbtWjz76qOrXr6/OnTtr165debbJ75zlL7/8oqFDhyo0NFSNGjVSTEyM9uzZI+naucmRI0dKkvr27Wu9bX7nLFNSUjR27Fi1bNlSQUFBat++vWbNmpXrSs7Y2FgFBwfr+PHjGjRokEJCQvTggw/qtddeU0pKSq65Fi1apMjISDVo0EChoaF69tln+cMCDo+nYYFClJOTo2+++Ubu7u6qVauWJGnNmjWqUaOGRo0apXPnzsnX11fbt2/XoEGDFBgYqKFDh+rKlStavny5+vTpo7lz56px48aSpOXLl2vkyJEKCQnRq6++qhMnTmjw4MHKyclRpUqVCpzj+PHjevzxx+Xq6qro6Gj5+vrq008/1VNPPaUFCxYoIiJCSUlJiouL0+DBgxUcHJzvfi5evKhevXopPj5evXr1Uo0aNbR9+3ZNmTJFBw8e1PTp03M99r59+6px48Z67bXX9OOPP2rp0qW6fPmyPvjgA0nSypUrNXbsWHXt2lUxMTFKTk7WJ598opiYGG3YsEGlS5cupO8EULiIJXCLUlNTrUeJ1183N2/ePB0+fFj9+vVTyZIlJUmXL1/W9OnTVbVqVUnXojJmzBgFBwfrP//5j1xcXCRJ0dHR6tq1q9555x19/vnnys7O1uTJkxUcHKz58+fLzc1NklS3bl3rUWFBpk+frqysLC1fvlzVqlWTJHXs2FERERH6+OOP9cEHH6hhw4aKi4tT8+bNrRf4/Nns2bN1/PhxzZw5U23btpUk9enTR2+99ZYWLlyobt26KSwsTJKUlZWljh07asSIEZKkXr16KTExURs3blRGRoY8PT21atUq1alTRxMnTrTex/33369JkybpyJEjatSo0c1/I4BiQCyBW9StW7c8y9zd3RUTE6OXX37Zuqxq1arWUErSwYMHdfLkSfXu3VsXL17Mdfs2bdpo3rx5OnPmjM6ePavz58/rueees4ZSkh599FG99957Bc6Vk5OjLVu2KCwszBpKSSpbtqwWLlxovYLXFps3b1atWrWsobxuyJAhWrhwoTZt2mSNpSR16NAh13b333+/tm7dqgsXLsjT01MVKlTQ9u3bNWPGDHXt2lWVK1dWWFhYrn0AjohYArfo/fffV7ly5SRJzs7O8vb2Vq1atVSiRIlc291zzz25vv7tt98kSZMmTdKkSZPy3XdCQoLOnDkjSblCK0kuLi65IvhnFy5cUHp6er7b3HfffYZHldupU6fUsmXLPMv9/Pzk7e2t+Pj4XMv//JKT61cDXz+/+eyzz2rv3r2KjY1VbGysateurfDwcPXo0SPP4wQcCbEEbtEDDzyQ56Uj+bn+NOt1OTk5kqShQ4eqYcOG+d6mZs2aSkxMlCRlZmbmWX99H/m5HiZn59u/fu9Gn7OQk5OT64hX+t/FTQWpUKGCVqxYod27d2vTpk3aunWrZs2apblz52rOnDlq0qTJbc8MFAViCRSz6xfmeHl5qXnz5rnW7d+/XxcvXpSHh4eqVKki6drFOn9ksVgUHx+vOnXq5Lv/smXLysPDQydOnMiz7uOPP9a5c+f02muv2Tzrr7/+mmd5UlKS0tLSVLFiRZv2c91///tfSVKzZs3UrFkzSdJ3332nJ598UvPnzyeWcFi8dAQoZkFBQfLz89P8+fN16dIl6/K0tDQNGzZMI0eOlIuLi+rWratKlSpp0aJFud6dZ82aNXlejvFHrq6uatGihbZs2ZLrUy0uXryojz/+2Po08PUjzxsdpbZp00a//vqrNm7cmGv5rFmzJEmtW7e2/YHr2tH08OHDc73spG7dunJzcyuUI2GgqHBkCRQzNzc3vfnmmxo2bJgee+wxRUVFqUSJElqyZIlOnz6tyZMny9X12o/mm2++qWeffVY9e/ZU9+7dlZiYqAULFsjHx+eG9/Hyyy+rR48e6tGjh/r06aNSpUpp8eLFSk9P17BhwyT97/ziokWLdO7cOUVGRubZz6BBg7R+/XoNGzZMvXv3VvXq1bVr1y6tX79e7dq1u+kLcwYMGKA33nhD/fr10yOPPCKLxaIVK1YoMzNTTzzxxE3tCyhOxBKwg/bt22vOnDn6xz/+oQ8//FDOzs6qU6eO/vGPf6hNmzbW7dq0aaOPPvpIsbGxmjp1qvz9/TV+/HgtWLDghvuvVauW4uLiNHXqVP3rX/+Ss7Oz6tevr4kTJ1qfvm3WrJk6dOigL7/8Urt27VK7du3y7MfHx0dxcXGaPn261q5dq9TUVFWpUkXDhw9Xv379bvpx9+jRQ25ubvr3v/+tqVOnKicnR0FBQZo9e3aBL18BHIGT5UZn8AEAAOcsAQAwIZYAABgQSwAADIglAAAGxBIAAANiCQCAAbEEAMCAWAIAYEAsAQAwIJYAABj8f9dtaX1gNW7GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 540x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusionMatrix(conf_matrixDT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "645d243a",
   "metadata": {},
   "source": [
    "so using our confusion matrix just on the test set \n",
    "we can see that in terms of recall our tree did a better job than the LR classifier but not as good as the KNN\n",
    "we can also see that it did a worse job at the recall  score than the two previous classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb96fd7",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebac7221",
   "metadata": {},
   "source": [
    "so Knn is the model recommended here for our problem as it has the best precison,recall,accuracy followed by the Logisitc Regression then the decision tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e9ea3f",
   "metadata": {},
   "source": [
    "# Next steps\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6562ba8e",
   "metadata": {},
   "source": [
    "Our models can be improved in the future if we used Grid search CV to try to tune these models on the most suitable hyper parameters so we can come up with better resutls"
   ]
  }
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