RandomForestClassifier.ipynb

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   "source": [
    "## Imports"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, validation_curve\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, plot_roc_curve"
   ]
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   "source": [
    "## Loading the data"
   ]
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>37</td>\n",
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       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
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   "source": [
    "df = pd.read_csv('../input/heart-disease-uci/heart.csv')\n",
    "df.head()"
   ]
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    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 3,
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   "source": [
    "df.shape"
   ]
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   },
   "source": [
    "#### as we can see this data this data is about 303 rows and 14 column"
   ]
  },
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   "source": [
    "## Exploring our dataset "
   ]
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    {
     "data": {
      "text/plain": [
       "1    0.683168\n",
       "0    0.316832\n",
       "Name: sex, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "df.sex.value_counts(normalize=True)"
   ]
  },
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   "source": [
    "#### this mean we have more female then male."
   ]
  },
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   "cell_type": "markdown",
   "id": "d23b8697",
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   "source": [
    "#### let's plot only people who got disease by sex"
   ]
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     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sex[df.target==1].value_counts().plot(kind=\"bar\")\n",
    "# commenting the plot \n",
    "plt.title(\"people who got disease by sex\")\n",
    "plt.xlabel(\"sex\")\n",
    "plt.ylabel(\"effected\"); \n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a989eef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:10.343753Z",
     "iopub.status.busy": "2021-11-02T09:47:10.343022Z",
     "iopub.status.idle": "2021-11-02T09:47:10.349361Z",
     "shell.execute_reply": "2021-11-02T09:47:10.350004Z",
     "shell.execute_reply.started": "2021-11-02T09:39:58.730282Z"
    },
    "papermill": {
     "duration": 0.043584,
     "end_time": "2021-11-02T09:47:10.350215",
     "exception": false,
     "start_time": "2021-11-02T09:47:10.306631",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.544554\n",
       "0    0.455446\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "319a4451",
   "metadata": {
    "papermill": {
     "duration": 0.032911,
     "end_time": "2021-11-02T09:47:10.416766",
     "exception": false,
     "start_time": "2021-11-02T09:47:10.383855",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### the two classes are almost equal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17471a46",
   "metadata": {
    "papermill": {
     "duration": 0.033265,
     "end_time": "2021-11-02T09:47:10.483665",
     "exception": false,
     "start_time": "2021-11-02T09:47:10.450400",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Ploting Heart Disease by Age / Max Heart Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c9a82ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:10.554188Z",
     "iopub.status.busy": "2021-11-02T09:47:10.553509Z",
     "iopub.status.idle": "2021-11-02T09:47:10.883586Z",
     "shell.execute_reply": "2021-11-02T09:47:10.884075Z",
     "shell.execute_reply.started": "2021-11-02T09:39:58.739415Z"
    },
    "papermill": {
     "duration": 0.367224,
     "end_time": "2021-11-02T09:47:10.884306",
     "exception": false,
     "start_time": "2021-11-02T09:47:10.517082",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df.age, y=df.thalach, hue = df.target);\n",
    "# commenting the plot \n",
    "plt.title(\"Heart Disease by Age / Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.legend([\"Disease\", \"No Disease\"])\n",
    "plt.ylabel(\"Max Heart Rate\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6898561f",
   "metadata": {
    "papermill": {
     "duration": 0.036427,
     "end_time": "2021-11-02T09:47:10.954878",
     "exception": false,
     "start_time": "2021-11-02T09:47:10.918451",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Correlation matrix  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec33419f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:11.032512Z",
     "iopub.status.busy": "2021-11-02T09:47:11.031721Z",
     "iopub.status.idle": "2021-11-02T09:47:12.447168Z",
     "shell.execute_reply": "2021-11-02T09:47:12.446488Z",
     "shell.execute_reply.started": "2021-11-02T09:39:59.029190Z"
    },
    "papermill": {
     "duration": 1.45772,
     "end_time": "2021-11-02T09:47:12.447362",
     "exception": false,
     "start_time": "2021-11-02T09:47:10.989642",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 10))\n",
    "sns.heatmap(corr, annot=True, fmt='.2f', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b680e70c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:12.530022Z",
     "iopub.status.busy": "2021-11-02T09:47:12.529265Z",
     "iopub.status.idle": "2021-11-02T09:47:12.541585Z",
     "shell.execute_reply": "2021-11-02T09:47:12.542184Z",
     "shell.execute_reply.started": "2021-11-02T09:40:00.378987Z"
    },
    "papermill": {
     "duration": 0.056724,
     "end_time": "2021-11-02T09:47:12.542396",
     "exception": false,
     "start_time": "2021-11-02T09:47:12.485672",
     "status": "completed"
    },
    "tags": []
   },
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e682e888",
   "metadata": {
    "papermill": {
     "duration": 0.038195,
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     "start_time": "2021-11-02T09:47:12.580326",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90fae2ef",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2021-11-02T09:47:12.656482",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
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       "      <th>oldpeak</th>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
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       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
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       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8fbb4c",
   "metadata": {
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     "duration": 0.038913,
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     "start_time": "2021-11-02T09:47:12.754659",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Features / Lable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b3b9f161",
   "metadata": {
    "execution": {
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     "start_time": "2021-11-02T09:47:12.832854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
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       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
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       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('target', axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc253611",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:12.979840Z",
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     "shell.execute_reply.started": "2021-11-02T09:40:00.439481Z"
    },
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     "duration": 0.049716,
     "end_time": "2021-11-02T09:47:12.982500",
     "exception": false,
     "start_time": "2021-11-02T09:47:12.932784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.target\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c493d9",
   "metadata": {
    "papermill": {
     "duration": 0.040532,
     "end_time": "2021-11-02T09:47:13.063111",
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     "start_time": "2021-11-02T09:47:13.022579",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Spliting our dataset with 20% for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ba14fde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:13.153568Z",
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     "shell.execute_reply.started": "2021-11-02T09:40:00.447290Z"
    },
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     "start_time": "2021-11-02T09:47:13.103463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59a31ad3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:13.242098Z",
     "iopub.status.busy": "2021-11-02T09:47:13.241014Z",
     "iopub.status.idle": "2021-11-02T09:47:13.248164Z",
     "shell.execute_reply": "2021-11-02T09:47:13.248807Z",
     "shell.execute_reply.started": "2021-11-02T09:40:00.460593Z"
    },
    "papermill": {
     "duration": 0.051578,
     "end_time": "2021-11-02T09:47:13.249015",
     "exception": false,
     "start_time": "2021-11-02T09:47:13.197437",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132    1\n",
       "202    0\n",
       "196    0\n",
       "75     1\n",
       "176    0\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c65c61",
   "metadata": {
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     "duration": 0.040457,
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     "start_time": "2021-11-02T09:47:13.289613",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be33687a",
   "metadata": {
    "papermill": {
     "duration": 0.040107,
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     "start_time": "2021-11-02T09:47:13.370791",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Function for geting score (f1 and acc) and ploting the confusion metrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eedd37cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:13.584326Z",
     "iopub.status.busy": "2021-11-02T09:47:13.583279Z",
     "iopub.status.idle": "2021-11-02T09:47:13.590513Z",
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     "shell.execute_reply.started": "2021-11-02T09:40:00.474467Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "def getScore(model, X_test, y_test):\n",
    "    y_pred = model.predict(X_test)\n",
    "    print('f1_score')\n",
    "    print(f1_score(y_test,y_pred,average='binary'))\n",
    "    print('accuracy')\n",
    "    acc = accuracy_score(y_test,y_pred, normalize=True)\n",
    "    print(acc)\n",
    "    print('Confusion Matrix :')\n",
    "    plot_confusion_matrix(model, X_test, y_test)\n",
    "    plt.show()\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e77e760",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:13.676167Z",
     "iopub.status.busy": "2021-11-02T09:47:13.675470Z",
     "iopub.status.idle": "2021-11-02T09:47:14.179275Z",
     "shell.execute_reply": "2021-11-02T09:47:14.178694Z",
     "shell.execute_reply.started": "2021-11-02T09:40:00.486183Z"
    },
    "papermill": {
     "duration": 0.547532,
     "end_time": "2021-11-02T09:47:14.179529",
     "exception": false,
     "start_time": "2021-11-02T09:47:13.631997",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score\n",
      "0.84375\n",
      "accuracy\n",
      "0.8360655737704918\n",
      "Confusion Matrix :\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "clf_accuracy = getScore(clf, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abaf7a0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T05:53:13.520136Z",
     "iopub.status.busy": "2021-11-02T05:53:13.519882Z",
     "iopub.status.idle": "2021-11-02T05:53:13.525584Z",
     "shell.execute_reply": "2021-11-02T05:53:13.524519Z",
     "shell.execute_reply.started": "2021-11-02T05:53:13.520105Z"
    },
    "papermill": {
     "duration": 0.040324,
     "end_time": "2021-11-02T09:47:14.260997",
     "exception": false,
     "start_time": "2021-11-02T09:47:14.220673",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "101fa669",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:14.353956Z",
     "iopub.status.busy": "2021-11-02T09:47:14.345516Z",
     "iopub.status.idle": "2021-11-02T09:47:14.369447Z",
     "shell.execute_reply": "2021-11-02T09:47:14.370340Z",
     "shell.execute_reply.started": "2021-11-02T09:40:00.960821Z"
    },
    "papermill": {
     "duration": 0.069066,
     "end_time": "2021-11-02T09:47:14.370567",
     "exception": false,
     "start_time": "2021-11-02T09:47:14.301501",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.83      0.83        29\n",
      "           1       0.84      0.84      0.84        32\n",
      "\n",
      "    accuracy                           0.84        61\n",
      "   macro avg       0.84      0.84      0.84        61\n",
      "weighted avg       0.84      0.84      0.84        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, clf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc11f1c8",
   "metadata": {
    "papermill": {
     "duration": 0.043453,
     "end_time": "2021-11-02T09:47:14.457140",
     "exception": false,
     "start_time": "2021-11-02T09:47:14.413687",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "24db04e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:14.546226Z",
     "iopub.status.busy": "2021-11-02T09:47:14.545163Z",
     "iopub.status.idle": "2021-11-02T09:47:14.804386Z",
     "shell.execute_reply": "2021-11-02T09:47:14.804921Z",
     "shell.execute_reply.started": "2021-11-02T09:40:00.983356Z"
    },
    "papermill": {
     "duration": 0.305756,
     "end_time": "2021-11-02T09:47:14.805132",
     "exception": false,
     "start_time": "2021-11-02T09:47:14.499376",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(clf, X_test, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3e9b09e",
   "metadata": {
    "papermill": {
     "duration": 0.044172,
     "end_time": "2021-11-02T09:47:14.893604",
     "exception": false,
     "start_time": "2021-11-02T09:47:14.849432",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e6e1769",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:14.987897Z",
     "iopub.status.busy": "2021-11-02T09:47:14.986768Z",
     "iopub.status.idle": "2021-11-02T09:47:15.290207Z",
     "shell.execute_reply": "2021-11-02T09:47:15.289329Z",
     "shell.execute_reply.started": "2021-11-02T09:40:01.226037Z"
    },
    "papermill": {
     "duration": 0.352969,
     "end_time": "2021-11-02T09:47:15.290409",
     "exception": false,
     "start_time": "2021-11-02T09:47:14.937440",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_dict = dict(zip(X.columns , list(clf.feature_importances_)))\n",
    "f_data = pd.DataFrame(f_dict, index=[0])\n",
    "f_data.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ad9618",
   "metadata": {
    "papermill": {
     "duration": 0.043571,
     "end_time": "2021-11-02T09:47:15.377975",
     "exception": false,
     "start_time": "2021-11-02T09:47:15.334404",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### from this plot we can see features who have importance or not"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1320293",
   "metadata": {
    "papermill": {
     "duration": 0.052192,
     "end_time": "2021-11-02T09:47:15.474435",
     "exception": false,
     "start_time": "2021-11-02T09:47:15.422243",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8ddc5c9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:15.585743Z",
     "iopub.status.busy": "2021-11-02T09:47:15.584712Z",
     "iopub.status.idle": "2021-11-02T09:47:16.652320Z",
     "shell.execute_reply": "2021-11-02T09:47:16.652830Z",
     "shell.execute_reply.started": "2021-11-02T09:40:01.510247Z"
    },
    "papermill": {
     "duration": 1.125562,
     "end_time": "2021-11-02T09:47:16.653054",
     "exception": false,
     "start_time": "2021-11-02T09:47:15.527492",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8241139165481272"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_precision = np.mean(cross_val_score(clf,\n",
    "                                       X,\n",
    "                                       y,\n",
    "                                       cv=5, \n",
    "                                       scoring=\"precision\")) \n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a14ff104",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:16.748456Z",
     "iopub.status.busy": "2021-11-02T09:47:16.747384Z",
     "iopub.status.idle": "2021-11-02T09:47:32.440726Z",
     "shell.execute_reply": "2021-11-02T09:47:32.438322Z",
     "shell.execute_reply.started": "2021-11-02T09:40:02.552751Z"
    },
    "papermill": {
     "duration": 15.742283,
     "end_time": "2021-11-02T09:47:32.440989",
     "exception": false,
     "start_time": "2021-11-02T09:47:16.698706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b2Mw0pBbqPbWrKM1N59H/p5b/dcvasP+Pn7qjhqKsNB7cezThZRM00MfA6/Nz7MIgJbnpPHzgVCAzYWjumQkL7VT3Jc5eHJrTsI1FRPjyvVsY8fr4218ej0PrFo61WGq6NQUrCzJJc8R//1hjDE1drgkrYkOlOlIoy8uYce/Yw+0DbC3PQ0R45zUVpDlS2HPwXNhzf/hKe6DnXRu+553mTOHd11YAl+vjRzLm89Pc5eLaYArlTGpXF9HeP7wk1gY0dkysJjqd2zaUcvALd3Dn5ul/p/IzU/ncXRtoONvH3tfPT3tePGigj0Fzt5uRMT9fuGcTX3nHVl481cO9D/922p7SUrE/uJJvpk1GYrGmJIf73ryGHx1qXzI9tXCsRS/TTcY6UoTVy7LiPnTT7fLQPzQ2ocbNZDOlWHq8Pho7XGwtzwcC1Tbv2rqcHx1qm5KpY4zh+/Wt1FUXsWaGvQesHqw1dBFJS88lxnxmxmuw1FVb4/SL/71yMvgBPJuEhXDeU7uKreV5/O2TJyJOmM+FBvoYHGkPBPSt5fn8wXWr2XPf9QyN+njHwy/y8zcS+4mcSPuOd7FxeW7EXkosPnHbOsoLMvmrnxxZckvcLVaPfrrJWAhk3sQ7l94KpjMND5QXZHJ+IHygb+pwM+YzXBEM9AC761YxOOLll0cmTsq+fKaXlotD7JphHN16vaw0x/jQRSSTy/jOZOPyXHLSnUsi0Dd2uGfMuImVI0V48O1b6Bgc4ZEDidtpVQN9DI60D5CV5hgfc7x2dRG/+ORNbF6Zx/3fe5Wv/mrp5ZAPDI3RcLYv5tWwkVgTs42dLv77pYWdmPX6/Lg9XnrcHtr6hmjucnOkfWC8vMF0rPsnr4oNVV2cw9mLl6adr5nNh9zlYmYz9+gv9I+EzT+3SlGEBvob1iyjalkWj708cfjm+/XnyM1w8patK2ZsU0qKUFOWG3Wgb+pwkSLMuEOZxelIYVtlAQ1xzryJd+2li25PsJpo5A+vWNRWFfH7V6/k0d+cnvO6helEFehF5G4RaRSRZhF5IMz9lSJyQEReFZE3ROSekPv+Ivi4RhG5K56Nn29H2gfYsjJvQr2O0rwMHvvY9bxzWzmPPHdqXmqUx9NzTV34/CYu4/OT3bWljJvWFfPNX59akBrdjR0ubv/ac6z7wi/Z+qWnqP2bZ7np7w5wxz/8mrf9y29527/8dsaFOl0uD/mZqTOWa768f+zEX1BjDA8faGbLl57idzHuOnSyy0VhVur4ZuThlBdkMOrzh12JfLh9gNwMJ5UhKzdFhPdtr+RgSy/NwWJpA0NjPHn4Ar9/dTmZaZFLUq8vzYk+0He6qSrOjqrUNQQ2sGnsdMVtzqu5y822h57mz594HY83Ptv7RfNNa7YeeMsmnCnC3/ziWNyfG6II9CLiAB4G3gJsBnaLyOZJp/0lgb1ktxHYPPyR4GM3B29vAe4GHgk+35Lj8xuOnh9ky8r8KfelOVN4/3WB/ONEVjNMhP0nuliWncZVFQVxf24R4Z4rVtDt8nA2QT2V6ew73sk7H3kB94iXz9yxns/fs5GHdm7hq++6kn/adTUfvamaLpeH5hkW6nQOjkybWmmxNp4InZAdGfPxme+/xt8/1ciYz8+3X2iJqe1NnYHNRmbaxnGmRVNH2gfYujJ/yuPffW0FzhRhT3Cl7E9fb8fj9c+Y/hhqw/JcetyjUS3yaep0zZiCOFltVRHGwCutcx++Mcbw0M+PMeY3PN7Qxgf+/eW4LExqGt+oPf6Bfnl+Bp/9vQ1sqyxMSKcomh59HdBsjDltjBkF9gA7J51jAGvdfD5gDVjvBPYYYzzGmDNAc/D5lpwzPW6Gx3wTvg6HsnpP53rnN6DNhdfn57nGbm7dUDrhW0o8zXfhKmMM//brU/zRfzewpiSHvfffxKfuqOG+N6/lgzdU8d7tq9h5dTkfuH51xHYFthCcuYrneHGz4IRs1+AIux79HT957Tyfu2sDH72xmmePd0adamqMoanTFXES08qlnxzoR71+TlxwcUXF1PdpSW46d24u44eH2vB4fTx28Bxby/PGJ20jqYlyQnZkzEfLxUvTZg2Fs62ygFSHxGXD8AONXTzf1M2f37WBf9m9jTfaBrj3Gy/MOWmiqdNFfmbqtOm2c/WRm6r5k1vDp2LOVTSBvhwIHdhrCx4L9SDwARFpA54EPhnDYxGR+0SkQUQauru7o2z6/LIWoEz3S1GSm066M2VJBfpDrf0MDI9FvcnIbKwrySE/MzXu46/heLw+PvuDN/jbX57gnq0rePyPb2B5fvhAPb5QZ4asoK7B6csfWIqy08jLcHK6JzDuv/PhF2jscPHND1zLJ25bx+7rKvH6DU+80hbVNXQMjuAa8UYcHphu0VRTp4tRn3/a9+muukr6hsb4h6ebOH5hcHwlbDSsHvrJrpmHb5q73PinKeM7nYxUB1eU58+5QzDq9fPXPz/OmpJsPnhDFW+/aiU/+F834PX7edcjL/LMsdnXire+pSQiECdavCZjdwPfNsZUAPcA3xGRqJ/bGPOoMabWGFNbUlIS+QEL4Ej7IBmpKawtCb/4Q0RYVZRF6xIK9PuOd5LqEG6qKU7Ya6SkCLWrC6k/m9gefY/bw/u/9TI/PNTGp++o4Rvv3zbjuLOIsL2qcNql98YYulzTlz8IfZ41JTnsP97Fu7/5IgI88Sc3cPfW5UBgMrKuuog99a1RFe6yess1YapWhsrLSCU3wzllAxJrleV03zxvXldMeUEm//b8aTJSU7j3qpUR22Qpy0snN8NJY8fMgf7yEEds2Snbq4p4o60/YrG2mfzXiy2c6bnEX71tM2nOQAi6sqKAn37iJtaW5nDfdxr41+dinzMyxtDY4ZpxgnwxiyYYtwOhg3gVwWOhPgo8DmCMeQnIAIqjfOyScLh9gE0r8sJWrLNUFmXR2jv3RR9drhH+8dmmhNeL2Xeii+uql5GbkZrQ16mtKuJ096WEFXDqcXvY+Y0XOHp+gIfffw2fvmN9VL2u7VXTL9TpGxpjzGcijtFDYEL2/MAIm1fk8dP7b5oyj7O7bhVnLw5FNSnb1GGlJUYOKOUFmVO2FDzcPkBuujNsCV0IfPBaqZT3XLGC/Mzo/+9FhA1lueN1eKbT1OkOlPFdNv2K2HC2VxUx5jOzrtXe7fLwz/tOctuGEm7bMPFb6vL8DL5/3w289YoV/N2vTvC/Y8yQ6xz0MDjiTcj4/HyIJtDXAzUiUi0iaQQmV/dOOqcV2AEgIpsIBPru4Hm7RCRdRKqBGuBgvBo/X/x+w7Hzg9P2kiyVRVmc6x2a82TKvz53in989iSvJnBzgs7BEZq73Ny6IfHfoLYHx+lfOZuY4ZvfnOymvX+Y//jQdt565cxpgqFmWqjT5QoulorQowf48I1V/Nmd68eLVk32lq2BgPpYffiVqZaRMR/fffksNaU5LJsh48YSbtHUkfODbCnPm3FBz/vqVrGtsoCP3lQd8TUmqynLpbHTNeN7vKnTxZqS7GkLmU1ne1URjhTht82z28Dma083Mjzm4y/fNjlXJCAzzcG/7N7GzqtX8l8vtsRUziHa0geLVcT/CWOMF7gfeAo4TiC75qiIPCQi9wZP+zPgYyLyOvAY8GETcJRAT/8Y8CvgE8aY+OQ6zaOWi5dwe7xsDZNxE6qiMBO3x0vfHFLERsZ8/DA4npvIYkfWilUr2CXSFRX5pDlTaEhQoG/scJPqkJivZePyXLLTHGEDfeegVf4gcsC9sqKAT+6omTaVMCPVwTu2lfPUkQ56Z6h0+ejzpzl7cYgvvj18oJps8qKpMZ+f4xcGI75PS3Mz+PHHbwybQRbJhrIcBobHZpxcbuxwzSog5melcu3qQp6dxebaR9oH+H7DOT78pqoZc/dFhI/dvIaRMT8/fS36wYWkD/QAxpgnjTHrjTFrjTFfCR77ojFmb/DnY8aYG40xVxljrjbGPB3y2K8EH7fBGPPLxFxGYh05f3lF7EzikXnzizcuMDjixZEi4xPAiVDf0kt2moPNK2LfZCRW6U4HV1XMfaJtOk2dLtaW5MTcg3Q6UrhmdSH1YTI9rMVS0fToo7G7rpJRn58fHQo/KXuud4iHDzTz1itWcHNNdN+yVhZk0j80xiVPYOn8yU43o15/2IybeLECXeM0+fSukTHa+4dnPcSxY2Mpxy8MxlT3xhjDQz87RlFWGp/cURPx/K3l+Wwtz+N7L7dG/e27scNFSW46RdlpUbdrMdGVsVE40j5AmjMl4kRM5bJAoJ/LhOxjB1tZU5zNzTXFHG1PXA2dg2d6uWZ14YxzDvFUW1XEkfYBhkfj/4Vutj1IuLxQp39oYk97vPxBFD36aGxYnss1lQU8djB8cPnyz47hSBH+8m2bon7OlQWBDyErKB6JkBkWD1Yd9ulSLE92zW1RUaQ9V8P5xeELHGzp5bN3bYh6zmHX9kpOdLh4vS26zlQ0Ka+LmQb6KBxpH2DT8tyIPcZVhXML9E2dLhrO9rG7rpIryvM52eWKOjCO+fxRrwAcGBqjsdM1vunDfNheVRiYaGvrj+vzuj1e2vuHZ/1LWBv8N5g8f9A1OEJehjPqlZ3R2FVXyanuS1MyffYd7+TZ45386Y4aVuRHX2+oYtKiqcPtA+SkO6mOcRI0FsU5gV5t0zSZN1Z1y1hSK0OtLclh9bKsqAP98KiPv33yBJtX5PHeaapvhrPz6pVkpjrGF4/NxO83gf17l+iwDWigj8gYEyh9EEUvKTvdSXFO2qyHbr73citpjhTedW0FW8vz8Rs4HuUijy/8+DDv+7ffRXVuw9lejGFeA/21lYHXivc2gyfnOHY6vlBnUrs6Bz3TlieerbdduYLcdOeE4DIy5uPBnx1lXWkOH7kxtsnRy7n0gWGmw+0DbF4580RsPKwvy6Fpmlz6xg43mamO8Q+hWIkIt28s5YXmnqg6Od/93Vna+4f50ts3x7ToLzcjlbdftYK9r5/H7Zm5auQvj3QwPObj6lUFUT//YqOBPoJzvcMMjngjZtxYKgqzONcXe6AfHvXxw0Nt3L11OUXZaeNfv6OZkDXGsP9EN6+d64/qQ+ZgSy+pDmFbZUHM7Zyt/KxUNpTlxn3LuLkuSx9fqDNp4VSXayRuwzaWrDQnO7et5BeHL4zXdPnmr09xrneYh+7dMp73Ha3S3AycKUJ7/xDe4ERstO/TuVgfTLEMNwTV1BnINZ/Lh82OjWV4vIE9V2fi9xv+5+Wz1FUVcd2a2HdG21VXydCoj72vTV95dmjUy9/84hibV+TxtiujX3Ow2Gigj2B8RWyUGQqVs1w09YvDF3CNeMdr5qzMz6AoOy2qQN9ycWi8uNWzxyOv/Ks/08uVFQVxHZaIRm1VIYfO9sV1Z67GDjcZqSnjw2azsb2qiMPtAxMW6nQOeiiL00RsqF3bK/F4/fz41TZaLw7xyHOnePtVK3nTutgXrTlShOX5GZzvH6G5243H62dreeIn19eX5eL2eDk/MLX6Z2Pn7OdLLHXVReSkO9kXYfjmpdMXabk4xO7roh+yCbVtVQEbl+fy2AzDN9/Y38yFgREe2rklYWVC5oMG+giOnB8g1SGsj3KVX2VRFuf7R2IuT/u9l8+ytiSb64IpgiLClpV5HI5iQtbqjeZlONkXITVtZMzH4faBeR22sWyvKsLl8UZcWRmLpmBgmUsP0lqo81pw3YIxhm6Xh5I49+ghMFF6ZUU+e+rP8eDPjpKaInzhnugnYCdbWZBJe/8wh9tmXhEbT+ObkEz6f+y7NEq3yzPr8XlLmjOFN68vZv+JzhmzYr73cisFWakRSyxPRySweOxw+0DYDtXpbjff+s1p3nlN+fhczlKlgT6CI+0DrC/LJd0ZXe+3sigLn99wYYb9PCc70THIodZ+dtdVTljReUV5Pic7XRGXhB9s6aUwK5VddZW8fOYirpHp8/hfbe1nzGeoq468xVu8WQXOGiKUQxjz+aNPe4tDD3K88FrwA3NgeIxRnz8hPXq4nPGx/0QXn75j/bT1eKJRXpBJe99wyF4Jic8MsSa+J5csjqaOfrRu31hG56CHo+fDd3S6XR6eOtrBu66pmNM303dsqyDdmcKeSZunG2P48s+OkeF08MBbNs76+RcLDfQzsCZiY+klrSqKPfPmey+3kuZM4V3XVEw4vrU8H6/fRKwBXt/SS21VEXdsKmPMZ3i+afqxzYNnehEJbJoy38oLMlmRnzHjOP3gyBi3fPUAjzwXebed3jj1IAuy0lhflkN9MPPGWiwV7zF6y71XryQrzcH6shw+fGPVnJ6rvCCTjsERXmubuldCohRkpVGamz4llz6eZXxv3VCCCNN+Q33ilTa8fsPuuuiLsoWTn5XKW69YwU9ePT9hK7+nj3Xy66ZuPn3n+ritpVhIGuhn0N4/TN/QWFQZN5ZVRYFsg2gnZIdHffz4UDv3bF1O4aTFGNYHzEwLp7oGRzh7cYi6qiKuqSygICuVfTOM09e39LKhLDemGifxIiJcu7qQ+jO90/bY/+nZk5wfGIlqrmF8tWIcAsv2qqLx+QOr/EG8s24sOelOvvtH1/EfH9oe8yKvyVYWZOLzG95o609o/vxkG5ZPrXnT2OkiN8PJ8jj8uxXnpLNtVQH7Tkx9H/j9hscOBva5XVc6928Pu+oqcXu8/PyNwDaLI2M+HvrZMTaU5fKhG1bP+fkXAw30M7D2iI2lR78iPxNnikTdo//ZG+dxeby8/7qpb6iKwkzyM1PH2xGOlRa4vboIpyOF2zaUcqCxK+yEp9fn51Br37yUPZjO9qoiOgZHwm6Y0dzl4r9ebCErzcHhtoGImyU3zTFnO1RddRFuj5fjFwYv9+gTVHcc4JrKwvFvf3NhLZoyZn7G5y01pbmc7HJNqMjZ1OGOaxnfHZvKeKNt6paPL5zqobV3iD+4bm69ecv2qkLWlmSPT8o+8twp2vuH+fLOLfO2oDDRkuMqEuRI+wCOFGFjDD1GR4pQUZgZdaD/3sutrCvNGS/8FUpE2FqeN2PmTUNLH5mpDrasDGRb7NhUSt/QGIdapw6PHD0/yNCob0EmYi3j4/SThm+sMdHMNAd/vXMrXr/htdb+GZ+rscNFXoYzqno0kdt1ucBZLAXNFlpovvr89uhzGBnzj39zNcYE5kviWN3x9o2BVbIHGicO3zx2sJXCrNTxUtBzJSLsrqvk1dZ+njnWyTd/fYp7r1rJ9bNI2VysNNDP4Mj5AWpKc2Ke7FkVrGIZybHzg7x2buokbKitK/Np7HBNu9HxwTO9wUU/gf/KN68vwZkiYYc+rFozC9mj37g8j5x055QJ2WePd/Gbkz38v3eu584tZYjAyzNsCgKB2i4blsenB1lekEl5QWYg0A96yM1wRrWP6kKzVtJmpjqi2og7Xqzdo6wMqm6Xh4HhMdbHYSjFsnF5LuUFmROKnHW5Rnj6aCfvvrYi6gSJaLzzmgrSHCl84n8OkZoifH4OmVCLkQb6aVgTsbPpJUW7Acn3661J2Cmbbo3bWp7PqM8fdkJ2cGSM4x2DE3roeRmpXLemKOwk1sEzvaxelpWwsedoOFKEa1YXTujRj4z5+OufH6OmNIcPXL+avIxUNq/IG6+wGc54DzKOy9Jrqwo5eKaPzsGRhA7bxFN2upOCrFQ2z9NErKUmGNCt2jaNcZwvsVirZH97smc88+wHDfGZhJ2sKDuNu7YuZ9Tn50931MwpE2ox0kA/jc5BDz3u0VmNe1YWZdE/NMbgDGmOAL9u6ubNNSUUZE1fEc/6oDl6furwzStn+zBmag99x8YymrvcnA3ZqNwYQ31L74IO21i2ry6ksdM1vjr0P357htbeIb709i3j30zqqot49VzftN9kuoI9yHhuBLG9qoget4eGs30L+mEYq4/cWM0H53nSMDcjlfKCzPEevfV3POZLQt2+qZThMR+/O30Rv9+wp76V69cUsSYB314+tWMdH7mxmj+MsRTFUqCBfhqX94iNfaVhNOWKuwZHaLk4NL5Aajqri7LITXeGzbypP9OLM2VqKYM7NpUBTPjK29zlpm9ojLpFEOhrq4owBg619tExMMLDB5q5a0vZhC0N66qKGBnzT5tx1NgR//rg1gdmt8uzZHr0AH+6o4adV0//rTBR1pfljH/TbOp0UZyTFtWGKbG4Yc0yMlMd7D/RxW+bezjXOxw2cSEe1pXm8sW3b465FMVSkHxXFCdH2gdIEdg0i3rt0QT6g1GOl6ekCJtX5oXNvKlv6WVLeT5Zac6Jr78si5rSnAlplqHZOQvt6lUFOFOE+pZe/vcvj+P1G/7yrRM327DaOd3wTSI2grA2MofEpVYmk/XLczndfQmvz09jpzviPrezkZHq4KaaYvYd7+J7L7cGhli2lMX9dZJdVIFeRO4WkUYRaRaRB8Lc/3UReS34p0lE+kPu+6qIHBWR4yLyz7JEtlA/0j7A2pKcKUE0GtEsmqo/00tW2uVsmZlcUZ7P8QuDE/aQHRnz8fq5AerCZOtAIDXt4Jne8e3S6s/0UpyTTtWyuaf0zVVmmoOt5fn88FAbP3ntPH/85jVTUg2Lc9JZW5I97WYljR2u8ZK58ZKSIuPZT+G2BFQTrS/NZdTn50zPJZo7XQnbT3XHxlLa+4f51dEO3hPnSVi7iBjoRcQBPAy8BdgM7BaRCd0vY8xngjtLXQ38C/Cj4GPfBNwIXAlsBbYDt8TzAhLlyPnYVsSGys9MJS/DOWOgf/lML9dGufHH1vJ8PF4/zd2XF6i80TbAqM8/7Zj7HZtK8foNv27qBqC+pY+66sK45TjP1faqQjoHPazIz+BPbl0b9py66iLqW3rDrglo6nSxIcr6Q7G1K/DvqT36yKzAfqCxi0ujvoTVa7fSLCGwuEnFLpoefR3QbIw5bYwZBfYAO2c4fzeBfWMBDIGNwtOAdCAViLzkcYE1d7npHPRw5Ry2ZKtclsW53vDbocW68Yc1IXs4ZDccq6c73XNsqyykKDuNfcc7ae8fpr1/eFGMz1tuWBvIUf78PZum/dZUV12Ea8TLiUk1+RO5EcRtG0vJTXfOasjObtaW5CDC+IrSRHzwApTmZVBXVcStG0qoLk7cpirJLJpxiXIgdPv6NuC6cCeKyGqgGtgPYIx5SUQOABcAAb5hjDk+pxbPg+/Xt+JMEd46h/rTlUVZnLgQvkZNrBt/VBdnk5Xm4Oj5Qd4TPHbwTC81pTlTyiZYHCnCrRtK2He8a3wP0sUwPm+5bUMpT336zTN+3a+rDnwY1J/pnbCRdVvfMMNjvrhneEBgzP/wl++K+/Mmo8w0B6uLsngj2AGpSeAOTN/+yHZSFsm30aUo3pOxu4AnjDE+ABFZB2wCKgh8YNwuIjdPfpCI3CciDSLS0N3dHecmxcbj9fHEK23cublsTuO0q4qyaOsbDjvsEOvGH44Uq2Rx4BfK5zccOtsXMXDfsamMgeExHn3+FLnpTjYuXzy9VBGJOKZrLWKavPtTPGvcqLmxgvuK/AzyMhJXPykrLb7bOtpNNIG+HQit7F8RPBbOLi4P2wC8A/idMcZtjHEDvwRumPwgY8yjxphaY0xtSUlJdC1PkKePdtI3NDbnBRmrCrMY9fnpHJxarng2G39sWZnPsfOD+PyGEx2DuDzeiEMxN9cUk+oQmjrdXFtVuCQ3TqirLuLgpCJo1uKcmjiuwlSzY32rWsr7qdpBNIG+HqgRkWoRSSMQzPdOPklENgKFwEshh1uBW0TEKSKpBCZiF/XQzZ76VioKM7lpFjv+hKqcJvNmeNTHG20DMZchuKI8n+ExH6e73eN10yP16HMzUsfrdSyGhVKzUVddRI97lDM9lxd/NXW6KC/IJDeBPUgVHav2fKIyblR8RAz0xhgvcD/wFIEg/bgx5qiIPCQi94acugvYYybWn30COAUcBl4HXjfG/CxurY+zsxcv8ULzRd5Xu2rOGyxPl0v/6rk+vH4T88To+B6y5weob+kbH9aIxFo8FWlh1mJlfUCF5tM3diQulU/Fxpo7iSZNWC2cqJLEjTFPAk9OOvbFSbcfDPM4H/DHc2jfvNpTfw5HivCe2tntQRlqZUEmKTI10I9v/DFN/vt01pZkk5GawuG2QQ629PKmtdFV1ttVt4qyvAyuXT3/O0rFw9qSbJZlp3GwpZdddZWM+fyc7r7ErRtKIz9YJdy60hx+dv9NbNZAv6jFvhooSY35/PygoY3bNpTGpaBRmjOFFflTyxXXt/SyaXlezBNXTkcKm1bk8asjF+h2eaIeikl3OuJWznUhiMj4OD0EvnWN+vwJS+VTsbtiDmnIan5oCYSgfcc76XF72F039968ZVXRxEA/5vNz6Gz/rMsEX1Gez/mBwOTuQpYanm/bq4po6xvmfP8wjR2BRWM6+adU9DTQBz128Bwr8jO4ZX38sn4qi7I413d50dSR9gGGx3yzDtJbg+OhBVmprJvH2uMLzfr3qm/ppbHTRYowr7XXlVrqNNATGEd//mQ376ldFdetwyqLsuh2eRgeDdTSjrSaNRJrQrZ2ddGcJ4uXkk0r8shNd/LymV6aOlxUFWdrTrVSMdBAD/ygIbDw9721FXF9XqtQl7Xd2sEzfawpzp71QqyashzWFGfzliU85j4bjhTh2qpCDp7ppanLxfoEVElUKpnZPtB7fX4eb2jjzTUlVBTGt7LjeC79xSH8/rlv/JHqSGH/Z2/lXdfG9wNpKairLqK5y01LzyVdEatUjGwf6J9r7KZjcCTuW5PBxHLFJ7vcDAyPLap6M0uJtQ7Ab+K/i5FSyc72gX5PfSvFOens2BT/vOxl2WlkpTk41zfEwTMXgaW7cGmhXVFeQHpw5x9NrVQqNrYO9B0DI+w/0cV7aivG9yqNJxEJZN70DnGwpY/leRlUFEZezaqmSnOmsK2ygDRHCquXaalapWJh6wVTBxq78Bt41zWJ229zVVEWLT2XcI14qasuWjQbfyxF9715DcfODybkQ1mpZGbrQG9ts7cyipoxs7WqMIt9xzvxm8VVD34pun1jGbdv1P1ClYqVrbtG7hEvKQKZCczJrizKxCpJv5h2eFJK2Ye9A73HS066M6HDKZXBzbgLslK1frpSakFooE9P7OiVlUtvt9WsSqnFw96BfsRLTkZiA31FYRaFWanckYD0TaWUioatJ2Pno0efkerg5c/fQapDe/NKqYVh6x69y+MlZx62o0tzpmhapVJqwUQV6EXkbhFpFJFmEXkgzP1fF5HXgn+aRKQ/5L5KEXlaRI6LyDERqYpf8+fGPTJGboJ79EoptdAiRjkRcQAPA3cCbUC9iOw1xhyzzjHGfCbk/E8C20Ke4r+BrxhjnhGRHMAfr8bP1SWPj+x0LXerlEpu0fTo64BmY8xpY8wosAfYOcP5u4HHAERkM+A0xjwDYIxxG2OGZnjsvAqM0Sd+6EYppRZSNIG+HDgXcrsteGwKEVkNVAP7g4fWA/0i8iMReVVE/j74DWHy4+4TkQYRaeju7o7tCmbJ7zeBQJ/grBullFpo8Z6M3QU8YYzxBW87gZuBzwLbgTXAhyc/yBjzqDGm1hhTW1ISv638ZnJp1AugY/RKqaQXTaBvB0J3zK4IHgtnF8Fhm6A24LXgsI8X+AlwzSzaGXduTyDQa49eKZXsogn09UCNiFSLSBqBYL538kkishEoBF6a9NgCEbG66bcDxyY/diG4R4KBXnv0SqkkFzHQB3vi9wNPAceBx40xR0XkIRG5N+TUXcAeY4wJeayPwLDNPhE5DAjwrXhewGyN9+g10CulklxUUc4Y8yTw5KRjX5x0+8FpHvsMcOUs25cwOnSjlLIL266M1aEbpZRd2DbQu3ToRillE7YN9FaPPleHbpRSSc6+gT7Yo8/WHr1SKsnZNtBf8nhJd6boRtNKqaRn2yjn8nh12EYpZQu2DfTukcRvOqKUUouBfQO9FjRTStmEfQO99uiVUjZh30A/D/vFKqXUYqCBXimlkpy9A72O0SulbMC+gX5EtxFUStmDLQO9x+tj1OfXPHqllC3YMtBr5UqllJ3YMtBf8gS2tNU6N0opO7BloHd5xgDt0Sul7CGqQC8id4tIo4g0i8gDYe7/uoi8FvzTJCL9k+7PE5E2EflGnNo9J1qiWCllJxEjnYg4gIeBO4E2oF5E9hpjxjf5NsZ8JuT8TwLbJj3NXwPPx6XFcaD7xSql7CSaHn0d0GyMOW2MGQX2ADtnOH838Jh1Q0SuBcqAp+fS0HjS/WKVUnYSTaAvB86F3G4LHptCRFYD1cD+4O0U4GvAZ2d6ARG5T0QaRKShu7s7mnbPifbolVJ2Eu/J2F3AE8YYX/D2x4EnjTFtMz3IGPOoMabWGFNbUlIS5yZNpemVSik7iSbStQOrQm5XBI+Fswv4RMjtG4CbReTjQA6QJiJuY8yUCd355PZ4EYGsNMdCNkMppeZFNIG+HqgRkWoCAX4X8P7JJ4nIRqAQeMk6Zoz5g5D7PwzULnSQB3AFSxSLyEI3RSmlEi7i0I0xxgvcDzwFHAceN8YcFZGHROTekFN3AXuMMSYxTY0ft8dLrg7bKKVsIqpoZ4x5Enhy0rEvTrr9YITn+Dbw7ZhalyDuEa1cqZSyD1uujL006tXyB0op27BloHfpNoJKKRuxZaB3e7xa/kApZRv2DPTao1dK2Yg9A71Hd5dSStmH7QK932+CgV4XSyml7MF2gX5oLFCdQdMrlVJ2YbtAf7nOjQ7dKKXswX6B3tpdSnv0SimbsF2gd1m7S2nWjVLKJmwX6HXTEaWU3dgu0F8KBvrsNA30Sil7sF2gd+nG4Eopm7FdoNdtBJVSdmO/QB/s0Wv1SqWUXdgv0Hu8pDtTSHPa7tKVUjZlu2jn0sqVSimbiSrQi8jdItIoIs0iMmXPVxH5uoi8FvzTJCL9weNXi8hLInJURN4QkffFuf0xu+TRTUeUUvYSMeKJiAN4GLgTaAPqRWSvMeaYdY4x5jMh538S2Ba8OQR80BhzUkRWAq+IyFPGmP44XkNMtESxUspuounR1wHNxpjTxphRYA+wc4bzdwOPARhjmowxJ4M/nwe6gJK5NXluXB4N9Eope4km0JcD50JutwWPTSEiq4FqYH+Y++qANOBUmPvuE5EGEWno7u6Opt2z5h7RMXqllL3EezJ2F/CEMcYXelBEVgDfAf7QGOOf/CBjzKPGmFpjTG1JSWI7/G7t0SulbCaaQN8OrAq5XRE8Fs4ugsM2FhHJA34BfMEY87vZNDKedDJWKWU30QT6eqBGRKpFJI1AMN87+SQR2QgUAi+FHEsDfgz8tzHmifg0eW5cHq8WNFNK2UrEQG+M8QL3A08Bx4HHjTFHReQhEbk35NRdwB5jjAk59l7gzcCHQ9Ivr45f82Pj8foY9fq1RLFSylaiinjGmCeBJycd++Kk2w+Gedx3ge/OoX1xdckT3EZQA71SykZstTJ2fBvBDN1GUCllH7YK9C5rG0Ht0SulbMRWgV6HbpRSdmSrQK8bgyul7ChpAv3ImI8Xm3to7x+e9hxrdynt0Sul7CRpAr1rxMv7//1lnj7aMe051u5SWgJBKWUnSRPoi3PSyEl30tJzadpz3NqjV0rZUNIEehGhqjiLMxeHpj3H7fEiAllpjnlsmVJKLaykCfQA1cU5nOlxT3u/2+MlJ82JiMxjq5RSamElV6BflkV73zCj3ikFMoHgpiM6Pq+UspnkCvQl2fgNtPaGH77REsVKKTtKqkBftSwbgDPTTMi6tXKlUsqGkirQVxcHAv10mTcu3S9WKWVDSRXoC7LSKMxK5czF8IH+kg7dKKVsKKkCPUBVcTZnumcYutFAr5SymaQL9NXLsmmZpkevWTdKKTtKvkBfnM2FgRGGRyfsT47fb3CPenV3KaWU7UQV6EXkbhFpFJFmEXkgzP1fD9kqsElE+kPu+5CInAz++VAc2x5WlTUhO6lXPzTmwxitXKmUsp+IUU9EHMDDwJ1AG1AvInuNMcesc4wxnwk5/5PAtuDPRcCXgFrAAK8EH9sX16sIEZp5s2lF3vhxq85NtvbolVI2E02Pvg5oNsacNsaMAnuAnTOcvxt4LPjzXcAzxpjeYHB/Brh7Lg2OxOrRn56UYmlVrtTJWKWU3UQT6MuBcyG324LHphCR1UA1sD+Wx4rIfSLSICIN3d3d0bR7WjnpTkpy06fk0muJYqWUXcV7MnYX8IQxxhfxzBDGmEeNMbXGmNqSkpI5N6K6eGrmzeUSxboxuFLKXqIJ9O3AqpDbFcFj4ezi8rBNrI+Nm+pl2VPKILh1Y3CllE1FE+jrgRoRqRaRNALBfO/kk0RkI1AIvBRy+Cng90SkUEQKgd8LHkuoquJsetyjDI6MjR+zthHUoRullN1EDPTGGC9wP4EAfRx43BhzVEQeEpF7Q07dBewxxpiQx/YCf03gw6IeeCh4LKHC1by55NGsG6WUPUUV9YwxTwJPTjr2xUm3H5zmsf8J/Ocs2zcrVqA/03OJKysKgMuTsdnpuruUUspekm5lLMDqZVkAtPRcrkvv8nhJc6aQ7tRAr5Syl6QM9BmpDsoLMidsK+ge0fIHSil7SspAD0zZKFw3HVFK2VXSBvrq4mzOdLux5obduumIUsqmkjbQVy3LZnDES99QIMXS7fFqxo1SypaSNtCHZt5AINDrGL1Syo6SPtC3hAR6HaNXStlR0gb6VUVZOFLkco9ex+iVUjaVtIE+1ZFCRWHm+EbhLu3RK6VsKmkDPViZN5cY9foZ9frJSdNAr5Syn6QO9FXBjcLHNx3RHr1SyoaSOtCvKclmaNQ3vkJWx+iVUnaU1IG+alkg8+aNtgFASxQrpewpqQO9lWJ5uD0Q6HV3KaWUHSV1oF9ZkEmaI4UjVqDXHr1SyoaSOtA7UoTKZVk0d1lj9FqiWCllP1EFehG5W0QaRaRZRB6Y5pz3isgxETkqIt8LOf7V4LHjIvLPIiLxanw0qpZl4w/ueaVDN0opO4o4liEiDuBh4E6gDagXkb3GmGMh59QAfwHcaIzpE5HS4PE3ATcCVwZP/S1wC/BcPC9iJmtKsgMbIKJDN0ope4qmR18HNBtjThtjRoE9wM5J53wMeNgY0wdgjOkKHjdABpAGpAOpQGc8Gh4tK/NGBLJSdehGKWU/0QT6cuBcyO224LFQ64H1IvKCiPxORO4GMMa8BBwALgT/PGWMOT73ZkfPyrzJSXOSkjKvo0ZKKbUoxGsswwnUALcCFcDzInIFUAxsCh4DeEZEbjbG/Cb0wSJyH3AfQGVlZZyaFGAFeq1Fr5Syq2h69O3AqpDbFcFjodqAvcaYMWPMGaCJQOB/B/A7Y4zbGOMGfgncMPkFjDGPGmNqjTG1JSUls7mOaZXlpZOZ6tDxeaWUbUUT6OuBGhGpFpE0YBewd9I5PyHQm0dEigkM5ZwGWoFbRMQpIqkEJmLndehGRKgqztYevVLKtiJGP2OMV0TuB54CHMB/GmOOishDQIMxZm/wvt8TkWOAD/icMeaiiDwB3A4cJjAx+ytjzM8SdTHT+dSOmuDLK6WU/Yi1efZiUVtbaxoaGha6GUoptaSIyCvGmNpw9yX1ylillFIa6JVSKulpoFdKqSSngV4ppZKcBnqllEpyGuiVUirJaaBXSqkkp4FeKaWS3KJbMCUi3cDZCKcVAz3z0JzFys7Xb+drB3tfv177zFYbY8IWC1t0gT4aItIw3QowO7Dz9dv52sHe16/XPvtr16EbpZRKchrolVIqyS3VQP/oQjdggdn5+u187WDv69drn6UlOUavlFIqeku1R6+UUipKGuiVUirJLblALyJ3i0ijiDSLyAML3Z5EE5H/FJEuETkScqxIRJ4RkZPBvwsXso2JIiKrROSAiBwTkaMi8qng8aS/fhHJEJGDIvJ68Nq/HDxeLSIvB9//3w9u75mURMQhIq+KyM+Dt+107S0iclhEXhORhuCxWb/vl1SgFxEH8DDwFmAzsFtENi9sqxLu28Ddk449AOwzxtQA+4K3k5EX+DNjzGbgeuATwf9vO1y/B7jdGHMVcDVwt4hcD/wd8HVjzDqgD/jowjUx4T7FxD2m7XTtALcZY64OyZ+f9ft+SQV6oA5oNsacNsaMAnuAnQvcpoQyxjwP9E46vBP4r+DP/wX8/ny2ab4YYy4YYw4Ff3YR+KUvxwbXbwLcwZupwT+GwB7MTwSPJ+W1A4hIBfBW4N+DtwWbXPsMZv2+X2qBvhw4F3K7LXjMbsqMMReCP3cAZQvZmPkgIlXANuBlbHL9waGL14Au4BngFNBvjPEGT0nm9/8/An8O+IO3l2Gfa4fAh/rTIvKKiNwXPDbr970z3q1T88sYY0QkqXNkRSQH+CHwaWPMYKBzF5DM12+M8QFXi0gB8GNg48K2aH6IyNuALmPMKyJy6wI3Z6HcZIxpF5FS4BkRORF6Z6zv+6XWo28HVoXcrgges5tOEVkBEPy7a4HbkzAikkogyP+PMeZHwcO2uX4AY0w/cAC4ASgQEauDlqzv/xuBe0WkhcDw7O3AP2GPawfAGNMe/LuLwId8HXN43y+1QF8P1ARn39OAXcDeBW7TQtgLfCj484eAny5gWxImOC77H8BxY8w/hNyV9NcvIiXBnjwikgncSWCO4gDw7uBpSXntxpi/MMZUGGOqCPyO7zfG/AE2uHYAEckWkVzrZ+D3gCPM4X2/5FbGisg9BMbvHMB/GmO+srAtSiwReQy4lUCZ0k7gS8BPgMeBSgIlnd9rjJk8YbvkichNwG+Aw1weq/08gXH6pL5+EbmSwISbg0CH7HFjzEMisoZAL7cIeBX4gDHGs3AtTazg0M1njTFvs8u1B6/zx8GbTuB7xpiviMgyZvm+X3KBXimlVGyW2tCNUkqpGGmgV0qpJKeBXimlkpwGeqWUSnIa6JVSKslpoFdKqSSngV4ppZLc/wURxuWhDGKqSwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "k = range(1, 50)\n",
    "train_score, val_score = validation_curve(model, X_train, y_train, param_name='n_estimators', param_range=k, cv=5)\n",
    "plt.plot(k, val_score.mean(axis=1));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "067683c9",
   "metadata": {
    "papermill": {
     "duration": 0.044613,
     "end_time": "2021-11-02T09:47:32.531868",
     "exception": false,
     "start_time": "2021-11-02T09:47:32.487255",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## GreadSearcheCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46c86178",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2021-11-02T09:47:32.627039Z",
     "iopub.status.busy": "2021-11-02T09:47:32.626347Z",
     "iopub.status.idle": "2021-11-02T09:50:24.594584Z",
     "shell.execute_reply": "2021-11-02T09:50:24.593932Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "papermill": {
     "duration": 172.017821,
     "end_time": "2021-11-02T09:50:24.594772",
     "exception": false,
     "start_time": "2021-11-02T09:47:32.576951",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 60 candidates, totalling 600 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   11.8s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   44.7s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=4, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=auto, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=4, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=auto, n_estimators=200, total=   0.7s\n",
      "[CV] criterion=gini, max_depth=4, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=auto, n_estimators=200, total=   0.8s\n",
      "[CV] criterion=gini, max_depth=4, max_features=auto, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=auto, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=gini, max_depth=4, max_features=auto, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=auto, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=4, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=4, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=4, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=4, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=sqrt, n_estimators=500, total=   1.7s\n",
      "[CV] criterion=gini, max_depth=4, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=sqrt, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=4, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=sqrt, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=4, max_features=log2, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=log2, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=4, max_features=log2, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=log2, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=4, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=log2, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=4, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=4, max_features=log2, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=5, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=auto, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=5, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=auto, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=5, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=auto, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=5, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=auto, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=5, max_features=auto, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=auto, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=5, max_features=auto, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=auto, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=5, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=5, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=5, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=5, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=sqrt, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=gini, max_depth=5, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=sqrt, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=5, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=sqrt, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=5, max_features=log2, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=5, max_features=log2, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=5, max_features=log2, n_estimators=200 \n",
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      "[CV] criterion=gini, max_depth=7, max_features=auto, n_estimators=500 [CV] criterion=gini, max_depth=4, max_features=auto, n_estimators=200 \n",
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      "[CV] criterion=gini, max_depth=7, max_features=sqrt, n_estimators=500 [CV] criterion=gini, max_depth=4, max_features=auto, n_estimators=200 \n",
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      "[CV] criterion=gini, max_depth=7, max_features=sqrt, n_estimators=500 [CV] criterion=gini, max_depth=4, max_features=auto, n_estimators=200 \n",
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      "[CV] criterion=gini, max_depth=7, max_features=sqrt, n_estimators=500 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  1.7min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV]  criterion=gini, max_depth=7, max_features=auto, n_estimators=500, total=   1.5s\n",
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      "[CV] criterion=gini, max_depth=8, max_features=log2, n_estimators=500 \n",
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      "[CV]  criterion=gini, max_depth=8, max_features=log2, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=entropy, max_depth=4, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=4, max_features=auto, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=4, max_features=auto, n_estimators=200 \n",
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      "[CV] criterion=entropy, max_depth=4, max_features=log2, n_estimators=200 \n",
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      "[CV] criterion=entropy, max_depth=4, max_features=log2, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=4, max_features=log2, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=4, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=4, max_features=log2, n_estimators=500, total=   1.6s\n",
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      "[CV]  criterion=entropy, max_depth=4, max_features=log2, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=auto, n_estimators=200, total=   0.8s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=auto, n_estimators=200 \n",
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      "[CV] criterion=entropy, max_depth=5, max_features=auto, n_estimators=500 \n",
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      "[CV] criterion=entropy, max_depth=5, max_features=auto, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=auto, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=500 \n",
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      "[CV]  criterion=entropy, max_depth=6, max_features=auto, n_estimators=200, total=   0.6s\n",
      "[CV]  criterion=gini, max_depth=7, max_features=sqrt, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=gini, max_depth=7, max_features=log2, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=7, max_features=log2, n_estimators=200, total=   0.6s\n",
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      "[CV] criterion=gini, max_depth=7, max_features=log2, n_estimators=500 \n",
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      "[CV] criterion=gini, max_depth=7, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=7, max_features=log2, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=gini, max_depth=7, max_features=log2, n_estimators=500 \n",
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      "[CV] criterion=gini, max_depth=8, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=8, max_features=auto, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=8, max_features=auto, n_estimators=200 \n",
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      "[CV] criterion=gini, max_depth=8, max_features=auto, n_estimators=500 \n",
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      "[CV] criterion=gini, max_depth=8, max_features=auto, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=8, max_features=auto, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=gini, max_depth=8, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=8, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=8, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=8, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=8, max_features=sqrt, n_estimators=200 \n",
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      "[CV] criterion=gini, max_depth=8, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=8, max_features=sqrt, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=gini, max_depth=8, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=8, max_features=sqrt, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=gini, max_depth=8, max_features=log2, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=8, max_features=log2, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=8, max_features=log2, n_estimators=200 \n",
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      "[CV] criterion=gini, max_depth=8, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=8, max_features=log2, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=gini, max_depth=8, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=8, max_features=log2, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=entropy, max_depth=4, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=4, max_features=auto, n_estimators=200, total=   0.6s\n",
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      "[CV] criterion=entropy, max_depth=4, max_features=log2, n_estimators=200 \n",
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      "[CV] criterion=entropy, max_depth=4, max_features=log2, n_estimators=500 \n",
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      "[CV] criterion=entropy, max_depth=4, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=4, max_features=log2, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=entropy, max_depth=4, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=4, max_features=log2, n_estimators=500, total=   1.9s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=auto, n_estimators=200, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=auto, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=auto, n_estimators=500, total=   1.6s\n",
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      "[CV]  criterion=entropy, max_depth=5, max_features=auto, n_estimators=500, total=   1.6s\n",
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      "[CV]  criterion=entropy, max_depth=5, max_features=auto, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=200, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=500, total=   1.5s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, n_estimators=200, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, n_estimators=200, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, n_estimators=500 \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, n_estimators=500, total=   1.6s\n",
      "[CV] criterion=entropy, max_depth=6, max_features=auto, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=6, max_features=auto, n_estimators=200, total=   0.6s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 600 out of 600 | elapsed:  2.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best grid =  {'criterion': 'entropy', 'max_depth': 7, 'max_features': 'sqrt', 'n_estimators': 200}\n",
      "Grid Score =  0.8688524590163934\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 500],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [4,5,6,7,8],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "grid_search = GridSearchCV(estimator = RandomForestClassifier(), param_grid = param_grid, \n",
    "                          cv = 10, n_jobs = -1, verbose = 2)\n",
    "grid_search.fit(X_train, y_train)\n",
    "best_grid = grid_search.best_params_\n",
    "print('best grid = ', best_grid)\n",
    "grid_accuracy = grid_search.score(X_test, y_test)\n",
    "print('Grid Score = ', grid_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "44fa4152",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:50:24.703000Z",
     "iopub.status.busy": "2021-11-02T09:50:24.701960Z",
     "iopub.status.idle": "2021-11-02T09:50:24.707526Z",
     "shell.execute_reply": "2021-11-02T09:50:24.706803Z"
    },
    "papermill": {
     "duration": 0.062668,
     "end_time": "2021-11-02T09:50:24.707691",
     "exception": false,
     "start_time": "2021-11-02T09:50:24.645023",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 7,\n",
       " 'max_features': 'sqrt',\n",
       " 'n_estimators': 200}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_grid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "89081dff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:50:24.817104Z",
     "iopub.status.busy": "2021-11-02T09:50:24.816399Z",
     "iopub.status.idle": "2021-11-02T09:50:24.821046Z",
     "shell.execute_reply": "2021-11-02T09:50:24.821709Z"
    },
    "papermill": {
     "duration": 0.061713,
     "end_time": "2021-11-02T09:50:24.821911",
     "exception": false,
     "start_time": "2021-11-02T09:50:24.760198",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33f4536",
   "metadata": {
    "papermill": {
     "duration": 0.054822,
     "end_time": "2021-11-02T09:50:24.931529",
     "exception": false,
     "start_time": "2021-11-02T09:50:24.876707",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Comparing results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "104ac201",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-02T09:50:25.043834Z",
     "iopub.status.busy": "2021-11-02T09:50:25.043041Z",
     "iopub.status.idle": "2021-11-02T09:50:25.258119Z",
     "shell.execute_reply": "2021-11-02T09:50:25.257559Z"
    },
    "papermill": {
     "duration": 0.272476,
     "end_time": "2021-11-02T09:50:25.258305",
     "exception": false,
     "start_time": "2021-11-02T09:50:24.985829",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_scores = {'clf': clf_accuracy, 'grid_searche': grid_accuracy}\n",
    "model_compare = pd.DataFrame(model_scores, index=['accuracy'])\n",
    "model_compare.T.plot.bar();\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfb6a28f",
   "metadata": {
    "papermill": {
     "duration": 0.053431,
     "end_time": "2021-11-02T09:50:25.365557",
     "exception": false,
     "start_time": "2021-11-02T09:50:25.312126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 210.909464,
   "end_time": "2021-11-02T09:50:26.737001",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-11-02T09:46:55.827537",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}