cardiovascular_disease_prediction_notebook.ipynb

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   "source": [
    "# TASK #1 : UNDERSTAND THE PROBLEM STATEMENT"
   ]
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   "metadata": {
    "colab_type": "text",
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    "\n",
    "Aim of the problem is to detect the presence or absence of cardiovascular disease in person based on the given features.\n",
    "Features available are:\n",
    "\n",
    "\n",
    "- Age | Objective Feature | age | int (days)\n",
    "- Height | Objective Feature | height | int (cm) |\n",
    "- Weight | Objective Feature | weight | float (kg) |\n",
    "- Gender | Objective Feature | gender | categorical code |\n",
    "- Systolic blood pressure | Examination Feature | ap_hi | int |\n",
    "- Diastolic blood pressure | Examination Feature | ap_lo | int |\n",
    "- Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal |\n",
    "- Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal |\n",
    "- Smoking | Subjective Feature | smoke | binary |\n",
    "- Alcohol intake | Subjective Feature | alco | binary |\n",
    "- Physical activity | Subjective Feature | active | binary |\n",
    "- Presence or absence of cardiovascular disease | Target Variable | cardio | binary |\n",
    "\n",
    "Note that:\n",
    "- Objective: factual information;\n",
    "- Examination: results of medical examination;\n",
    "- Subjective: information given by the patient.\n",
    "\n",
    "Data Source:https://www.kaggle.com/sulianova/cardiovascular-disease-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK #2: IMPORT LIBRARIES AND DATASETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
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    "colab_type": "code",
    "id": "S0Cx3743urFY",
    "outputId": "b820039b-7ccb-4a68-8206-77bae97680dc",
    "scrolled": true
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   "outputs": [],
   "source": [
    "# import the necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tjIiJdM4u1IE"
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   "outputs": [],
   "source": [
    "# read the csv file \n",
    "cardio_df = pd.read_csv(\"cardio_train.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "height": 204
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    "id": "q4_wPDKCu5Uc",
    "outputId": "886d2aaf-0205-4f46-96a7-629d0f367d2f"
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   "outputs": [
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      "text/plain": [
       "   id    age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0   0  18393       2     168    62.0    110     80            1     1      0   \n",
       "1   1  20228       1     156    85.0    140     90            3     1      0   \n",
       "2   2  18857       1     165    64.0    130     70            3     1      0   \n",
       "3   3  17623       2     169    82.0    150    100            1     1      0   \n",
       "4   4  17474       1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  \n",
       "0     0       1       0  \n",
       "1     0       1       1  \n",
       "2     0       0       1  \n",
       "3     0       1       1  \n",
       "4     0       0       0  "
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "cardio_df.head()"
   ]
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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tMcr7xqMQre2"
   },
   "source": [
    "# TASK #3: PERFORM EXPLORATORY DATA ANALYSIS"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop id\n",
    "\n",
    "cardio_df = cardio_df.drop(columns = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# since the age is given in days, we convert it into years\n",
    "\n",
    "cardio_df['age'] = cardio_df['age']/365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>gender</th>\n",
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       "      <th>ap_hi</th>\n",
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      "text/plain": [
       "         age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0  50.391781       2     168    62.0    110     80            1     1      0   \n",
       "1  55.419178       1     156    85.0    140     90            3     1      0   \n",
       "2  51.663014       1     165    64.0    130     70            3     1      0   \n",
       "3  48.282192       2     169    82.0    150    100            1     1      0   \n",
       "4  47.873973       1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  \n",
       "0     0       1       0  \n",
       "1     0       1       1  \n",
       "2     0       0       1  \n",
       "3     0       1       1  \n",
       "4     0       0       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cardio_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "6JYkJo9pYaet",
    "outputId": "fcc89a98-b0a9-499d-acde-e480e78dccfc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            0\n",
       "gender         0\n",
       "height         0\n",
       "weight         0\n",
       "ap_hi          0\n",
       "ap_lo          0\n",
       "cholesterol    0\n",
       "gluc           0\n",
       "smoke          0\n",
       "alco           0\n",
       "active         0\n",
       "cardio         0\n",
       "dtype: int64"
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "# checking the null values\n",
    "cardio_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
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    "colab_type": "code",
    "id": "hMq3-KWOx0e1",
    "outputId": "22a5b184-1f07-46ef-dfc1-f8377fd7042f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 70000 entries, 0 to 69999\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   age          70000 non-null  float64\n",
      " 1   gender       70000 non-null  int64  \n",
      " 2   height       70000 non-null  int64  \n",
      " 3   weight       70000 non-null  float64\n",
      " 4   ap_hi        70000 non-null  int64  \n",
      " 5   ap_lo        70000 non-null  int64  \n",
      " 6   cholesterol  70000 non-null  int64  \n",
      " 7   gluc         70000 non-null  int64  \n",
      " 8   smoke        70000 non-null  int64  \n",
      " 9   alco         70000 non-null  int64  \n",
      " 10  active       70000 non-null  int64  \n",
      " 11  cardio       70000 non-null  int64  \n",
      "dtypes: float64(2), int64(10)\n",
      "memory usage: 6.4 MB\n"
     ]
    }
   ],
   "source": [
    "# Checking the dataframe information\n",
    "\n",
    "cardio_df.info()"
   ]
  },
  {
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       "      <td>1.349571</td>\n",
       "      <td>164.359229</td>\n",
       "      <td>74.205690</td>\n",
       "      <td>128.817286</td>\n",
       "      <td>96.630414</td>\n",
       "      <td>1.366871</td>\n",
       "      <td>1.226457</td>\n",
       "      <td>0.088129</td>\n",
       "      <td>0.053771</td>\n",
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       "      <td>0.499700</td>\n",
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       "      <th>std</th>\n",
       "      <td>6.759594</td>\n",
       "      <td>0.476838</td>\n",
       "      <td>8.210126</td>\n",
       "      <td>14.395757</td>\n",
       "      <td>154.011419</td>\n",
       "      <td>188.472530</td>\n",
       "      <td>0.680250</td>\n",
       "      <td>0.572270</td>\n",
       "      <td>0.283484</td>\n",
       "      <td>0.225568</td>\n",
       "      <td>0.397179</td>\n",
       "      <td>0.500003</td>\n",
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       "      <th>min</th>\n",
       "      <td>29.583562</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>-150.000000</td>\n",
       "      <td>-70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.394521</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>53.980822</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.430137</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.967123</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>16020.000000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</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",
       "</div>"
      ],
      "text/plain": [
       "                age        gender        height        weight         ap_hi  \\\n",
       "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
       "mean      53.339358      1.349571    164.359229     74.205690    128.817286   \n",
       "std        6.759594      0.476838      8.210126     14.395757    154.011419   \n",
       "min       29.583562      1.000000     55.000000     10.000000   -150.000000   \n",
       "25%       48.394521      1.000000    159.000000     65.000000    120.000000   \n",
       "50%       53.980822      1.000000    165.000000     72.000000    120.000000   \n",
       "75%       58.430137      2.000000    170.000000     82.000000    140.000000   \n",
       "max       64.967123      2.000000    250.000000    200.000000  16020.000000   \n",
       "\n",
       "              ap_lo   cholesterol          gluc         smoke          alco  \\\n",
       "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
       "mean      96.630414      1.366871      1.226457      0.088129      0.053771   \n",
       "std      188.472530      0.680250      0.572270      0.283484      0.225568   \n",
       "min      -70.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "25%       80.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "50%       80.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "75%       90.000000      2.000000      1.000000      0.000000      0.000000   \n",
       "max    11000.000000      3.000000      3.000000      1.000000      1.000000   \n",
       "\n",
       "             active        cardio  \n",
       "count  70000.000000  70000.000000  \n",
       "mean       0.803729      0.499700  \n",
       "std        0.397179      0.500003  \n",
       "min        0.000000      0.000000  \n",
       "25%        1.000000      0.000000  \n",
       "50%        1.000000      0.000000  \n",
       "75%        1.000000      1.000000  \n",
       "max        1.000000      1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical summary of the dataframe\n",
    "cardio_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MINI CHALLENGE\n",
    "- Obtain the features of the individuals who are older than 64.8 years old "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK #4: VISUALIZE DATASET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MINI CHALLENGE\n",
    "- plot the histogram for all features (use 20 bins) \n",
    "- plot the correlation matrix and indicate if there exists any correlations between features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Ka9uFRXSkWHw",
    "outputId": "f42a681e-93d4-4b1f-a29c-f58fc8a6f974"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f94685c6278>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 156 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(cardio_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "9q-tFxvskWDa",
    "outputId": "8834e9ec-7676-4e86-c5e7-20f4e9eccbcb"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 932
    },
    "colab_type": "code",
    "id": "clmblKfur8rm",
    "outputId": "760b0882-8993-4aed-9c38-866ef8a7c6cd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "53qDZFRn3-S1"
   },
   "source": [
    "# TASK #5: CREATE TRAINING AND TESTING DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the dataframe into target and features\n",
    "\n",
    "df_target = cardio_df['cardio']\n",
    "df_final = cardio_df.drop(columns =['cardio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "8QQTkOBL1yUR",
    "outputId": "e79771c7-a34d-430f-db8a-fa4c1253af78"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo', 'cholesterol',\n",
       "       'gluc', 'smoke', 'alco', 'active', 'cardio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cardio_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "XpGU63Ne1e9P",
    "outputId": "e16c74ca-dc1c-416c-dc44-7f927bb99bc6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 11)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "OjGj0RALA0qZ",
    "outputId": "26559a6c-880b-45b4-a1e8-3c4b92bea889"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GoReLFfnA6uF"
   },
   "outputs": [],
   "source": [
    "#spliting the data in to test and train sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_final, df_target, test_size = 0.2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56000, 11)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56000,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14000, 11)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14000, 11)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK #6: TRAIN AND TEST XGBOOST MODEL IN LOCAL MODE (NOTE THAT SAGEMAKER BUILT-IN ALGORITHMS ARE NOT USED HERE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (1.3.3)\r\n",
      "Requirement already satisfied: scipy in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from xgboost) (1.5.3)\r\n",
      "Requirement already satisfied: numpy in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from xgboost) (1.19.5)\r\n"
     ]
    }
   ],
   "source": [
    "# install xgboost\n",
    "\n",
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yHL-6mKwBURs",
    "outputId": "10d71b6d-9c2b-4bab-8b27-d3c5883e6a25"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:47:57] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=2, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use xgboost model in local mode\n",
    "\n",
    "# note that we have not performed any normalization or scaling since XGBoost is not sensitive to this.\n",
    "# XGboost is a type of ensemble algorithms and works by selecting thresholds or cut points on features to split a node. \n",
    "# It doesn't really matter if the features are scaled or not.\n",
    "\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# model = XGBClassifier(learning_rate=0.01, n_estimators=100, objective='binary:logistic')\n",
    "model = XGBClassifier()\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions on test data\n",
    "\n",
    "predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assess trained model performance on training dataset\n",
    "predict_train = model.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_train, predict_train)\n",
    "plt.figure()\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision = 0.7902157164869029\n",
      "Recall = 0.7298537730814388\n",
      "Accuracy = 0.7671607142857143\n"
     ]
    }
   ],
   "source": [
    "# print metrics for training dataset\n",
    "\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score\n",
    "\n",
    "print(\"Precision = {}\".format(precision_score(y_train, predict_train)))\n",
    "print(\"Recall = {}\".format(recall_score(y_train, predict_train)))\n",
    "print(\"Accuracy = {}\".format(accuracy_score(y_train, predict_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision = 0.7381722915703498\n",
      "Recall = 0.6970314318975553\n",
      "Accuracy = 0.7299285714285715\n"
     ]
    }
   ],
   "source": [
    "# print metrics for testing dataset\n",
    "\n",
    "print(\"Precision = {}\".format(precision_score(y_test, predict)))\n",
    "print(\"Recall = {}\".format(recall_score(y_test, predict)))\n",
    "print(\"Accuracy = {}\".format(accuracy_score(y_test, predict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWcAAAD4CAYAAAAw/yevAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAfGElEQVR4nO3deXwV1f3/8dcnYYuyiLKFBBUVq2ArKlJawboCigqoIKJCKzXWL671135FW1vb8nWpW3HBLyoFXIpRoCCKQkGrVGRRsQiIIlAJxARkBwvJzef3xx34XuBy740EMpm+nz7O4849c84sGj85+cyZGXN3REQkXLKq+wBERGRvCs4iIiGk4CwiEkIKziIiIaTgLCISQrUO9A7K1i7TdBDZS07LLtV9CBJC5TtW2f5uozIxp3aTY/Z7fweKRs4iIiF0wEfOIiIHVUWsuo+gSig4i0i0xMqr+wiqhIKziESKe0V1H0KVUHAWkWipUHAWEQkfjZxFREJIFwRFREJII2cRkfBxzdYQEQkhXRAUEQkhpTVEREJIFwRFREJII2cRkRDSBUERkRDSBUERkfBxV85ZRCR8lHMWEQkhpTVEREJII2cRkRCKlVX3EVQJBWcRiZaIpDX0glcRiRavyLykYWYrzGyBmc03s3lB3eFmNs3MPg8+Gye0H2JmS81siZl1S6g/LdjOUjMbZmZp3/qt4Cwi0VJRkXnJzNnu3t7dOwTf7wCmu3sbYHrwHTNrC/QD2gHdgSfNLDvoMxwoANoEpXu6nSo4i0i0VH1w3lNPYHSwPBrolVA/1t23u/tyYCnQ0cxygYbuPsvdHRiT0GefFJxFJFI8VpZxMbMCM5uXUAr23Bww1cw+SFjX3N2LAYLPZkF9HrAyoW9RUJcXLO9Zn5IuCIpItFRiKp27jwBGpGhyhruvNrNmwDQz+zRF22R5ZE9Rn5KCs4hESxXO1nD31cFnqZlNADoCJWaW6+7FQcqiNGheBLRK6J4PrA7q85PUp6S0hohESxXN1jCzQ82swc5loCvwCTAJGBg0GwhMDJYnAf3MrK6ZtSZ+4W9OkPrYbGadglkaAxL67JNGziISLVU3cm4OTAhmvdUCXnT3N8xsLlBoZoOAL4E+AO6+0MwKgUVAOTDY/+8pTDcAo4AcYEpQUlJwFpFoqaLbt919GXBykvqvgXP30WcoMDRJ/TzgpMrsX8FZRKKlXA/bFxEJHz34SEQkhCLybA0FZxGJFo2cRURCSCNnEZEQ0shZRCSENFtDRCSEPO1jK2oEBWcRiRblnEVEQkjBWUQkhHRBUEQkhGKx9G1qAAVnEYkWpTVEREJIwVlEJISUcxYRCR+v0DxnEZHwUVpDRCSENFtDRCSENHIWEQmhiATnrOo+gDDretlAel9zA5cNHEzfa2/eZ7sFi5fwvS49mPrWu/u9zx07dnD7r+/lgr7XcuV1t7KquASA1V+V0Pfam7hs4GB6XnU9L014bb/3Jd/O0yMeYnXRx8z/aHrS9bf//GfMmzuVeXOnMv+j6Wz/5ksaNz5sv/ZZp04dXnxhOJ8umsl7M1/lqKPyATjyyDxmvz+FeXOn8vH8GRRcd81+7ScS3DMvGTCzbDP7yMwmB99/a2arzGx+UC5MaDvEzJaa2RIz65ZQf5qZLQjWDbPgld6pKDinMfKx+xg3+gkKRw5Luj4Wi/HIk3/mjI6nVmq7q4pL+PGNv9yrfvzkqTRsUJ8phSO55opePPzkSACaHnE4zz/1EONGP8Ffnn6UZ58vpHTN15U/IdlvY8YU0uOiq/a5/qGHn6LD6V3pcHpXfvWr+3jnnfdZv35DRts+6qh8pk97ea/6a39yJevXb+SEtp15dNjT3Ps/dwFQXFxKlzN70uH0rvzwjIv45S8Gk5vb/FudV2RUVGReMnMLsHiPukfcvX1QXgcws7ZAP6Ad0B140syyg/bDgQKgTVC6p9tp2uBsZieY2X8H0f5PwfKJmZ5V1L34yiTOP+sMDt9jZPTqmzPo99NbuGzgYO55YBixDC9SzHh3Fj0vPA+Armd1YfYH83F3ateuTZ06dQDYUVZGRUQei1gTvTtzNusyDLZXXNGTsS/9ddf3/v0vZdY/JjNv7lSefOJ+srIyGx9dcnFXnnsuHrTHjXuNc87uDEBZWRk7duwAoG7duhlvL9IqPPOShpnlAz2AZzLYc09grLtvd/flwFKgo5nlAg3dfZa7OzAG6JVuYyn/S5rZfwNjAQPmAHOD5b+Y2R0ZHGyNZmYU3HYXfa+9iZcnvr7X+pI1a5n+znv07XXhbvVfrPiSN6b/neeCkW5WVhaTp76V0T5L13xNi2ZNAKhVK5v6hx7Cho2bACguWUPvATdwXu8BDLqqD82aHrGfZygHUk5OPbp1PYvxE+I/OyeccBx9+1xClx/1osPpXYnFYvTvf2lG22qZ14KVRauB+F9rGzdu4ogjGgOQn9+SDz+Yxoplc/njg09QHKTC/mPFYpmX9B4FfgnsOcy+0cz+aWYjzaxxUJcHrExoUxTU5QXLe9anlO6C4CCgnbuXJVaa2cPAQuC+ZJ3MrID4EJ4nH/oDPx1wZbrjCKXnhj9Es6ZH8PX6DVx36520PqoVHdp/d9f6+//0v9x2w7VkZ2fv1m/2vPks+nQp/QbdAsD27dt3jaxvHvI7Vq0uoay8jOKSNVw2cDAAV/ftSe8eXfEkI+Kd6anc5k2ZMGY4pWu+5uYhv+P8szvT5PDGe7WXcLjooq68N2verpTGOWd35tRTvsv7s+LBOienHmvWrAXglZef4eijj6ROndoc2SqPeXOnAvDYY88wekwhyVKUO39UiopWc+pp55Ob25zxrzzLuPGvUVq69sCfYEh5JS4IJsaqwAh3HxGsuwgodfcPzOyshDbDgd8DHnw+BFxLfOC61+GkqE8pXXCuAFoC/9qjPpe9f5P8317jJzcCoGztshr79/fOkekRjQ/j3DN/yIJFS3YLzgs//Zxf/Cb++2n9xk28O2su2dnZuDuXXHAet93wk722Oezeu4F4zvmuoQ8x6vEHdlvfvFkTvipdS4tmTSkvj7Fl6zYaNWyw13Ed1/ooPvz4E7qe3aVKz1mqzhV9L9ktpWFmPPf8y9z1q73HNJf3+SkQzzmPfOYRzj2/z27rVxUV0yq/JatWFZOdnU2jRg1Zt279bm2Ki0tYuOgzOnf+PuPH/wdfMK7EHYKJsSqJM4BLggt+9YCGZva8u1+9s4GZPQ1MDr4WAa0S+ucDq4P6/CT1KaVLUN0KTDezKWY2IihvANOJJ8kja9s3/2br1m27lt+b8yFtjjl6tzZvvjKKqeNGM3XcaLqe1Zlf/b/BnHvmD+nUoT3T3p7J18GIaeOmzaz+KrM/Nc/u3ImJr/8NgKlvv8v3TzsZM+Or0jX8e/v2Xdv7aMEijj4yP9WmpBo1bNiAM7t0YtKkN3fVzXhrJpf2voimwS/9xo0P48gj0/51C8Crk6dyzTXxgH3ZZT146+1/AJCXl0u9evUAOOywRvzwh6fz2WdfVOWp1DxekXlJtRn3Ie6e7+5HE7/QN8Pdrw5yyDv1Bj4JlicB/cysrpm1Jn7hb467FwObzaxTMEtjADAx3WmkHDm7+xtmdjzQkXiOxIj/Fpjr7tG4DWcfvl63nlvu/D0AsfIYF3Y9i86dOuyawnZF7x777Hts66O46boBFNx6FxVeQe1atbjr5/9Fyxbpr6JfelE3hvz+j1zQ91oaNWzAH++Jp/aXrVjJHx9/GjPD3fnxlZdy/LGtq+BMpbKef+4JfnTmD2jS5HBWLJvHPb97kNq1awMw4unnAOjV8wKm/e0dtm37Zle/xYs/5+7fPsCU1/9CVpZRVlbOzTffxZdfrkq7z5F/HsvoUcP4dNFM1q/fQP+r/wuAE084jgceuBt3MIOHH36KTz759ACcdQ1y4J+t8YCZtSeemlgBXA/g7gvNrBBYBJQDgxPi5A3AKCAHmBKUlCxZjrMq1eS0hhw4OS2VjpG9le9YlXb+bzpb7+6Xccw59Hdj93t/B4ruEBSRaNEjQ0VEQkiPDBURCZ/KTKULMwVnEYkWjZxFREJIwVlEJIT0sH0RkfDROwRFRMJIwVlEJIQ0W0NEJIQ0chYRCSEFZxGR8PGY0hoiIuGjkbOISPhoKp2ISBgpOIuIhFA0Us4KziISLV4ejeis4Cwi0RKN2KzgLCLRoguCIiJhpJGziEj4RGXknFXdByAiUqUqKlEyYGbZZvaRmU0Ovh9uZtPM7PPgs3FC2yFmttTMlphZt4T608xsQbBumJmlfeu3grOIRIqXZ14ydAuwOOH7HcB0d28DTA++Y2ZtgX5AO6A78KSZZQd9hgMFQJugdE+3UwVnEYkUr8i8pGNm+UAP4JmE6p7A6GB5NNAroX6su2939+XAUqCjmeUCDd19lrs7MCahzz4pOItItFQirWFmBWY2L6EU7LG1R4FfsnsSpLm7FwMEn82C+jxgZUK7oqAuL1jesz4lXRAUkUjJZES8q637CGBEsnVmdhFQ6u4fmNlZGWwuWR7ZU9SnpOAsIpFSmeCcxhnAJWZ2IVAPaGhmzwMlZpbr7sVByqI0aF8EtEronw+sDurzk9SnpLSGiESKxyzjknI77kPcPd/djyZ+oW+Gu18NTAIGBs0GAhOD5UlAPzOra2atiV/4mxOkPjabWadglsaAhD77pJGziERKFY6c9+U+oNDMBgFfAn0A3H2hmRUCi4ByYLC7x4I+NwCjgBxgSlBSsvjFwwOnbO2yaMwIlyqV07JLdR+ChFD5jlVp5/+mU9z57IxjTu7Mt/Z7fweKRs4iEikHYeR8UCg4i0ikuId2MFwpCs4iEikaOYuIhFBFmlkYNYWCs4hEilcoOIuIhI6Cs4hICB3g2cEHjYKziESKRs4iIiGkqXQiIiEU02wNEZHw0chZRCSElHMWEQkhzdYQEQkhjZxFREIoVhGNd4goOItIpCitISISQhWarSEiEj6aSiciEkJKa2Tou22vONC7kBpo8/PXV/chSERFJa0RjcuaIiKBWEVWxiUVM6tnZnPM7GMzW2hm9wT1vzWzVWY2PygXJvQZYmZLzWyJmXVLqD/NzBYE64aZWdrfIEpriEikVGFWYztwjrtvMbPawEwzmxKse8TdH0xsbGZtgX5AO6Al8DczO97dY8BwoAB4H3gd6A5MIQWNnEUkUircMi6peNyW4GvtoKSK/T2Bse6+3d2XA0uBjmaWCzR091nu7sAYoFe681BwFpFIcbeMi5kVmNm8hFKQuC0zyzaz+UApMM3dZwerbjSzf5rZSDNrHNTlASsTuhcFdXnB8p71KSk4i0ikVFSiuPsId++QUEYkbsvdY+7eHsgnPgo+iXiK4ligPVAMPBQ0TzYU9xT1KSk4i0ikOJZxyXib7huAt4Hu7l4SBO0K4GmgY9CsCGiV0C0fWB3U5yepT0nBWUQipdwt45KKmTU1s8OC5RzgPODTIIe8U2/gk2B5EtDPzOqaWWugDTDH3YuBzWbWKZilMQCYmO48NFtDRCKlMiPiNHKB0WaWTXwgW+juk83sOTNrTzw1sQK4HsDdF5pZIbAIKAcGBzM1AG4ARgE5xGdppJypAQrOIhIxFVW0HXf/J3BKkvprUvQZCgxNUj8POKky+1dwFpFIqcKRc7VScBaRSKmqkXN1U3AWkUiJaeQsIhI+EXlLlYKziERLhUbOIiLhE5HHOSs4i0i06IKgiEgIVaR/VHKNoOAsIpESS9+kRlBwFpFI0WwNEZEQ0mwNEZEQ0mwNEZEQUlpDRCSENJVORCSEYho5i4iEj0bOIiIhpOAsIhJCaV4NWGMoOItIpGjkLCISQlG5fTurug9ARKQqVVjmJRUzq2dmc8zsYzNbaGb3BPWHm9k0M/s8+Gyc0GeImS01syVm1i2h/jQzWxCsG2aW/ulMCs4iEikVlShpbAfOcfeTgfZAdzPrBNwBTHf3NsD04Dtm1hboB7QDugNPmll2sK3hQAHQJijd0+1cwVlEIqWqgrPHbQm+1g6KAz2B0UH9aKBXsNwTGOvu2919ObAU6GhmuUBDd5/l7g6MSeizTwrOIhIpXoliZgVmNi+hFCRuy8yyzWw+UApMc/fZQHN3LwYIPpsFzfOAlQndi4K6vGB5z/qUdEFQRCKlMs/WcPcRwIgU62NAezM7DJhgZiel2FyyPXuK+pQ0chaRSIlVomTK3TcAbxPPFZcEqQqCz9KgWRHQKqFbPrA6qM9PUp+SgrOIREoFnnFJxcyaBiNmzCwHOA/4FJgEDAyaDQQmBsuTgH5mVtfMWhO/8DcnSH1sNrNOwSyNAQl99klpDRGJlCq8CSUXGB3MuMgCCt19spnNAgrNbBDwJdAHwN0XmlkhsAgoBwYHaRGAG4BRQA4wJSgpKTiLSKRU1cP23f2fwClJ6r8Gzt1Hn6HA0CT184BU+eq9KDiLSKTo9m0RkRAqt2i8qErBWUQiJRqhWcFZRCJGaQ0RkRBKN0WuplBwFpFIiUZoVnAWkYhRWkNEJIRiERk7KziLSKRo5CwiEkKukbOISPho5BxxLVo25/7Hf0uTZkdQUeEUPjeB554eu1ub1scdxb3D7qbtd0/g0XuHM/LJ5/d7v7Xr1Ob+x++h3cknsGHdRn5ecCerVhbTMr8Fj/35AbKys6lVqxbPP/sSL40ev9/7k28nVlFB/+Fv0KxhDo9dc/Zu6zb/ewd3vfIeX23YSnmFM6DzifQ69dj92t+O8hi/Gvcei1evo9Ehdbm/b2fyGtdn9YYt3P6Xd4lVOOWxCq7sdDx9Oh6/X/uq6aIylU6PDN2HWHk59//mUXp07ku/C37CVddezrHHt96tzcYNm/jDnQ99q6Cc1yqXMROe2qv+8qt6smnjJrp9/1JG/++L3P7rmwBYU7KWfj0G0fucq7jigh9TcNNAmjVv8u1OTvbbi7OW0Lppw6TrXpr9Gcc0bUThjT14ZtB5PPzGh5SVZ/b04FXrtzDo2Wl71U/44Asa5tTh1dt6cvUPTuBPUz8CoGn9HEZf15XCwRfy/PXdGPnuIko3bfv2JxYBlXkTSpgpOO/DmtKvWbRgCQBbt27ji89W0Dy36W5t1q1dzyfzF1FeXr5X/4svv4DCN0YxYcYL3PPgELKyMvtXfW73M/nrS68B8OarM/hBl9MBKCsrp2xHGQB16tTBMtyeVL2Sjdt497NVXNrhuKTrDdi6owx355sd5TTKqUN28N/rtfnLueqpN+j7xOv8fuJsYhWZ/RH+9qdFXNz+GADOa3ckc5aV4O7UrpVNnVrxd4juiFUQf0Xdf7ZyPOMSZvo/PAN5rXI58bvf4eMPFmbU/pg2R3Nhz/Ppf1F8pBuLVXDx5WlftgtAsxbNKF5VAkAsFmPz5i0cdngjIJ5qmfj2i7z10WSeeXwMpSVrv90JyX754+vzuLXrKezr7fb9On2H5Ws2cf4D47n88df4xYUdyMoylpVu5M1P/sWoYKSblWW8/vGKjPZZumkbLRodCkCt7Czq163Nhm3bAfhq41b6PP4a3R+cwI+7tKVZw0Oq5DxrKq/EP2H2rXPOZvYTd//zPtYVEH8NOM3rH8VhOU2TNasRDjk0h2Ej7+feXz/M1i1bM+rzgy6n0+7kE3h56hgA6tWry7q16wB4bNQD5B+ZR+3atcjNb8GEGS8A8NyIsYwf+2ry/+GDn6GvVpfQ86z+NGvehMdHP8ibr07n6zXr9v8kJWPvLCmicf16tM07grnLS5K2ee/zYr7TojFP/+RcVq7bws9GTefUo5oxZ9lXLF69jqueegOA7WXlHH5oPQBue/HvrFq/lfJYjOKN2+j7xOsA9P/Bd+h16rEkGxDv/Flp0ehQXr6xB6WbtnHbi+9wfrsjOaJ+zgE4+5pBFwThHiBpcE58aeIJzU4P96+nFGrVymbYyPt5ddwbTHvtrYz7mRl/fek1Hh76xF7rbvrxL4H4aPzeYb9hQO+f7ba+pLiE3LzmlBSXkp2dTYMG9dmwfuNubUpL1rJ0yTI6fL89b06e8S3OTL6t+f9aw98/LWLmZ6vZUR5j6/Yy7nz5H/xPnzN2tZn40Rdc26UdZsaRRzQgr3F9lq/diAMXt2/NzV33en47j/T/ERDPOd89fhbPDjp/t/XNGx3CVxu30rzRIZTHKtiyvYxGOXV2a9Os4SEc26wRH65Yw/knHVn1J19DhH1EnKmUaQ0z++c+ygKg+UE6xmrzh0d/zRefrWDUUy9Wqt+sd+fS9eJzOLxJYwAaHdaQlvktMuo748136XVFDwC6XXwO78+cC0Dz3GbUrVcXgIaNGnBqx++x/It/Veq4ZP/d3PUUpv7iUqbc3ov7+nbm9NbNdwvMALmNDmX2sq8A+HrLN6xYu4n8xvXpeEwLpi1cybot/wZg47btrN6wJaP9/uiEPF6dvwyAvy38ktNbN8fMKNm4jX+Xxa95bPpmO/O/XMPRTRpU1enWSBWVKGGWbuTcHOgGrN+j3oD3DsgRhcSp3z+ZXn17sGTR57tSD48MfYLcIMi+NHo8TZodwStTR1O/waFUVDgDCvrRo/MVfPHZcv5071M8W/g4WVlGeVk5v7vjAVYXfZV2v6+8MJEHnriHN2ePZ+P6Tfz8+rsAOPb4o/nve27F3TEzRj75Ap8t/uLA/QuQSnl5zmcA9Ol4PNeddRJ3j5/F5Y9NxoFbu55C40Pr0fjQetx43vf42egZuDu1srMYctHptDysftrt9z71OO4a9x4XPzKRhjl1ub9v/BfCsjUbefiNDzHi2a8BZ5xImxaND9yJ1gCxiFwUtVRXd83sWeDP7j4zyboX3b1/uh3U5LSGHDgfPX5xdR+ChFBO37uTX2WthP5H9c445rz4rwn7vb8DJWVaw90HJQvMwbq0gVlE5GCrqtkaZtbKzN4ys8VmttDMbgnqf2tmq8xsflAuTOgzxMyWmtkSM+uWUH+amS0I1g2zfU31SaA7BEUkUqowl1wO3O7uH5pZA+ADM9t5h9Aj7v5gYmMzawv0A9oBLYG/mdnx7h4DhhOfwfY+8DrQHZiSauea5ywikVKBZ1xScfdid/8wWN4MLAbyUnTpCYx19+3uvhxYCnQ0s1ygobvP8ngeeQzQK915KDiLSKRUJq1hZgVmNi+hFCTbppkdDZwCzA6qbgxmro00s51XYPOAlQndioK6vGB5z/qUFJxFJFJi7hkXdx/h7h0Syog9t2dm9YFxwK3uvol4iuJYoD1QDDy0s2mSw/EU9Skp5ywikVKVT6Uzs9rEA/ML7j4ewN1LEtY/DUwOvhYBrRK65wOrg/r8JPUpaeQsIpFSVTehBDMqngUWu/vDCfW5Cc16A58Ey5OAfmZW18xaA22AOe5eDGw2s07BNgcAE9Odh0bOIhIpVXj79hnANcACM5sf1N0JXGlm7YmnJlYA1wO4+0IzKwQWEZ/pMTiYqQFwAzAKyCE+SyPlTA1QcBaRiKmqtEZwj0eyfPHrKfoMBYYmqZ8HnFSZ/Ss4i0ikROWZ1grOIhIpsYg8lU7BWUQiJSrvEFRwFpFIUVpDRCSENHIWEQmhqLwJRcFZRCIlKg/bV3AWkUhRWkNEJIQUnEVEQkizNUREQkgjZxGRENJsDRGREIp5Fb5FsBopOItIpCjnLCISQso5i4iEkHLOIiIhVKG0hohI+GjkLCISQpqtISISQlFJa2RV9wGIiFQlr8Q/qZhZKzN7y8wWm9lCM7slqD/czKaZ2efBZ+OEPkPMbKmZLTGzbgn1p5nZgmDdMDNL9uLY3Sg4i0ikVLhnXNIoB2539xOBTsBgM2sL3AFMd/c2wPTgO8G6fkA7oDvwpJllB9saDhQAbYLSPd3OFZxFJFKqauTs7sXu/mGwvBlYDOQBPYHRQbPRQK9guScw1t23u/tyYCnQ0cxygYbuPsvjd8iMSeizT8o5i0ikxDyWcVszKyA+ot1phLuPSNLuaOAUYDbQ3N2LIR7AzaxZ0CwPeD+hW1FQVxYs71mfkoKziERKZW7fDgLxXsE4kZnVB8YBt7r7phTp4mQrPEV9SgrOIhIpVXn7tpnVJh6YX3D38UF1iZnlBqPmXKA0qC8CWiV0zwdWB/X5SepTUs5ZRCLF3TMuqQQzKp4FFrv7wwmrJgEDg+WBwMSE+n5mVtfMWhO/8DcnSIFsNrNOwTYHJPTZJ42cRSRSqnCe8xnANcACM5sf1N0J3AcUmtkg4EugD4C7LzSzQmAR8Zkeg913JcBvAEYBOcCUoKSk4CwikVJVt2+7+0yS54sBzt1Hn6HA0CT184CTKrN/BWcRiRTdvi0iEkJ62L6ISAhF5dkaCs4iEikaOYuIhJBeUyUiEkIaOYuIhJBma4iIhJAuCIqIhJDSGiIiIaQXvIqIhJBGziIiIRSVnLNF5bdMTWBmBcnesiD/2fRzIcnoec4HV0H6JvIfSD8XshcFZxGREFJwFhEJIQXng0t5RUlGPxeyF10QFBEJIY2cRURCSMFZRCSEFJwPEjPrbmZLzGypmd1R3ccj1c/MRppZqZl9Ut3HIuGj4HwQmFk28ARwAdAWuNLM2lbvUUkIjAK6V/dBSDgpOB8cHYGl7r7M3XcAY4Ge1XxMUs3c/R1gXXUfh4STgvPBkQesTPheFNSJiCSl4HxwWJI6zWEUkX1ScD44ioBWCd/zgdXVdCwiUgMoOB8cc4E2ZtbazOoA/YBJ1XxMIhJiCs4HgbuXAzcCbwKLgUJ3X1i9RyXVzcz+AswCvmNmRWY2qLqPScJDt2+LiISQRs4iIiGk4CwiEkIKziIiIaTgLCISQgrOIiIhpOAsIhJCCs4iIiH0/wGGNnULuzKe/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the confusion matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, predict)\n",
    "plt.figure()\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MINI CHALLENGE\n",
    " - Retrain the XGBoost algorithm with large number of estimators (n_estimators = 500) and more depth (max_depth = 20)\n",
    " - Comment on the accuracy for both the training and testing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MINI CHALLENGE\n",
    "- Attempt to tune the XGBoost classifier using gridsearch, start with the param_grid listed below\n",
    "- Check out these great resources: https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "        'gamma': [0.5, 1, 5],   # regularization parameter \n",
    "        'subsample': [0.6, 0.8, 1.0], # % of rows taken to build each tree\n",
    "        'colsample_bytree': [0.6, 0.8, 1.0], # number of columns used by each tree\n",
    "        'max_depth': [3, 4, 5] # depth of each tree\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_optim = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print metrics for testing dataset\n",
    "\n",
    "print(\"Precision = {}\".format(precision_score(y_test, y_predict_optim)))\n",
    "print(\"Recall = {}\".format(recall_score(y_test, y_predict_optim)))\n",
    "print(\"Accuracy = {}\".format(accuracy_score(y_test, y_predict_optim)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK #7: PERFORM DIMENSIONALITY REDUCTION USING PCA ( USING SAGEMAKER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boto3 is the Amazon Web Services (AWS) Software Development Kit (SDK) for Python\n",
    "# Boto3 allows Python developer to write software that makes use of services like Amazon S3 and Amazon EC2\n",
    "\n",
    "\n",
    "\n",
    "import sagemaker\n",
    "import boto3\n",
    "from sagemaker import Session\n",
    "\n",
    "# Let's create a Sagemaker session\n",
    "sagemaker_session = sagemaker.Session()\n",
    "bucket = Session().default_bucket() \n",
    "prefix = 'pca'  # prefix is the subfolder within the bucket.\n",
    "\n",
    "#Let's get the execution role for the notebook instance. \n",
    "# This is the IAM role that you created when you created your notebook instance. You pass the role to the training job.\n",
    "# Note that AWS Identity and Access Management (IAM) role that Amazon SageMaker can assume to perform tasks on your behalf (for example, reading training results, called model artifacts, from the S3 bucket and writing training results to Amazon S3). \n",
    "\n",
    "role = sagemaker.get_execution_role()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import io # The io module allows for dealing with various types of I/O (text I/O, binary I/O and raw I/O). \n",
    "import numpy as np\n",
    "import sagemaker.amazon.common as smac # sagemaker common libary\n",
    "\n",
    "# Code below converts the data in numpy array format to RecordIO format\n",
    "# This is the format required by Sagemaker PCA\n",
    "\n",
    "buf = io.BytesIO() # create an in-memory byte array (buf is a buffer I will be writing to)\n",
    "df_matrix = df_final.to_numpy() # convert the dataframe into 2-dimensional array\n",
    "smac.write_numpy_to_dense_tensor(buf, df_matrix)\n",
    "buf.seek(0)\n",
    "\n",
    "# When you write to in-memory byte arrays, it increments 1 every time you write to it\n",
    "# Let's reset that back to zero "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uploaded training data location: s3://sagemaker-us-east-2-542063182511/pca/train/pca\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Code to upload RecordIO data to S3\n",
    " \n",
    "# Key refers to the name of the file \n",
    " \n",
    "key = 'pca'\n",
    "\n",
    "#following code uploads the data in record-io format to S3 bucket to be accessed later for training\n",
    "boto3.resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'train', key)).upload_fileobj(buf)\n",
    "\n",
    "# Let's print out the training data location in s3\n",
    "s3_train_data = 's3://{}/{}/train/{}'.format(bucket, prefix, key)\n",
    "\n",
    "\n",
    "print('uploaded training data location: {}'.format(s3_train_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training artifacts will be uploaded to: s3://sagemaker-us-east-2-542063182511/pca/output\n"
     ]
    }
   ],
   "source": [
    "# create output placeholder in S3 bucket to store the PCA output\n",
    "\n",
    "output_location = 's3://{}/{}/output'.format(bucket, prefix)\n",
    "print('training artifacts will be uploaded to: {}'.format(output_location))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The method get_image_uri has been renamed in sagemaker>=2.\n",
      "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n",
      "Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: 1.\n"
     ]
    }
   ],
   "source": [
    "# This code is used to get the training container of sagemaker built-in algorithms\n",
    "# all we have to do is to specify the name of the algorithm, that we want to use\n",
    "\n",
    "# Let's obtain a reference to the pca container image\n",
    "# Note that all  models are named estimators\n",
    "# You don't have to specify (hardcode) the region, get_image_uri will get the current region name using boto3.Session\n",
    "\n",
    "\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "\n",
    "\n",
    "container = get_image_uri(boto3.Session().region_name, 'pca')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train_instance_count has been renamed in sagemaker>=2.\n",
      "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n",
      "train_instance_type has been renamed in sagemaker>=2.\n",
      "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-25 18:48:42 Starting - Starting the training job...\n",
      "2021-03-25 18:49:06 Starting - Launching requested ML instancesProfilerReport-1616698122: InProgress\n",
      "......\n",
      "2021-03-25 18:50:06 Starting - Preparing the instances for training......\n",
      "2021-03-25 18:51:06 Downloading - Downloading input data...\n",
      "2021-03-25 18:51:44 Training - Training image download completed. Training in progress.\n",
      "2021-03-25 18:51:44 Uploading - Uploading generated training model.\u001b[34mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[34mRunning default environment configuration script\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Reading default configuration from /opt/amazon/lib/python3.7/site-packages/algorithm/resources/default-conf.json: {'algorithm_mode': 'regular', 'subtract_mean': 'true', 'extra_components': '-1', 'force_dense': 'true', 'epochs': 1, '_log_level': 'info', '_kvstore': 'dist_sync', '_num_kv_servers': 'auto', '_num_gpus': 'auto'}\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {'feature_dim': '11', 'algorithm_mode': 'regular', 'subtract_mean': 'False', 'num_components': '6', 'mini_batch_size': '100'}\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Final configuration: {'algorithm_mode': 'regular', 'subtract_mean': 'False', 'extra_components': '-1', 'force_dense': 'true', 'epochs': 1, '_log_level': 'info', '_kvstore': 'dist_sync', '_num_kv_servers': 'auto', '_num_gpus': 'auto', 'feature_dim': '11', 'num_components': '6', 'mini_batch_size': '100'}\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 WARNING 139888397997888] Loggers have already been setup.\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Launching parameter server for role scheduler\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] {'ENVROOT': '/opt/amazon', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'HOSTNAME': 'ip-10-0-109-145.us-east-2.compute.internal', 'TRAINING_JOB_NAME': 'pca-2021-03-25-18-48-42-568', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'TRAINING_JOB_ARN': 'arn:aws:sagemaker:us-east-2:542063182511:training-job/pca-2021-03-25-18-48-42-568', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/441fd4d8-f9a0-4fc0-bb08-a60241b569e7', 'CANONICAL_ENVROOT': '/opt/amazon', 'PYTHONUNBUFFERED': 'TRUE', 'NVIDIA_VISIBLE_DEVICES': 'void', 'LD_LIBRARY_PATH': '/opt/amazon/lib/python3.7/site-packages/cv2/../../../../lib:/usr/local/nvidia/lib64:/opt/amazon/lib', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'PWD': '/', 'LANG': 'en_US.utf8', 'AWS_REGION': 'us-east-2', 'HOME': '/root', 'SHLVL': '1', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'OMP_NUM_THREADS': '2', 'DMLC_INTERFACE': 'eth0', 'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'ECS_CONTAINER_METADATA_URI_V4': 'http://169.254.170.2/v4/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'SAGEMAKER_HTTP_PORT': '8080', 'SAGEMAKER_DATA_PATH': '/opt/ml'}\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] envs={'ENVROOT': '/opt/amazon', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'HOSTNAME': 'ip-10-0-109-145.us-east-2.compute.internal', 'TRAINING_JOB_NAME': 'pca-2021-03-25-18-48-42-568', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'TRAINING_JOB_ARN': 'arn:aws:sagemaker:us-east-2:542063182511:training-job/pca-2021-03-25-18-48-42-568', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/441fd4d8-f9a0-4fc0-bb08-a60241b569e7', 'CANONICAL_ENVROOT': '/opt/amazon', 'PYTHONUNBUFFERED': 'TRUE', 'NVIDIA_VISIBLE_DEVICES': 'void', 'LD_LIBRARY_PATH': '/opt/amazon/lib/python3.7/site-packages/cv2/../../../../lib:/usr/local/nvidia/lib64:/opt/amazon/lib', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'PWD': '/', 'LANG': 'en_US.utf8', 'AWS_REGION': 'us-east-2', 'HOME': '/root', 'SHLVL': '1', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'OMP_NUM_THREADS': '2', 'DMLC_INTERFACE': 'eth0', 'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'ECS_CONTAINER_METADATA_URI_V4': 'http://169.254.170.2/v4/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'SAGEMAKER_HTTP_PORT': '8080', 'SAGEMAKER_DATA_PATH': '/opt/ml', 'DMLC_ROLE': 'scheduler', 'DMLC_PS_ROOT_URI': '10.0.109.145', 'DMLC_PS_ROOT_PORT': '9000', 'DMLC_NUM_SERVER': '1', 'DMLC_NUM_WORKER': '1'}\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Launching parameter server for role server\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] {'ENVROOT': '/opt/amazon', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'HOSTNAME': 'ip-10-0-109-145.us-east-2.compute.internal', 'TRAINING_JOB_NAME': 'pca-2021-03-25-18-48-42-568', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'TRAINING_JOB_ARN': 'arn:aws:sagemaker:us-east-2:542063182511:training-job/pca-2021-03-25-18-48-42-568', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/441fd4d8-f9a0-4fc0-bb08-a60241b569e7', 'CANONICAL_ENVROOT': '/opt/amazon', 'PYTHONUNBUFFERED': 'TRUE', 'NVIDIA_VISIBLE_DEVICES': 'void', 'LD_LIBRARY_PATH': '/opt/amazon/lib/python3.7/site-packages/cv2/../../../../lib:/usr/local/nvidia/lib64:/opt/amazon/lib', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'PWD': '/', 'LANG': 'en_US.utf8', 'AWS_REGION': 'us-east-2', 'HOME': '/root', 'SHLVL': '1', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'OMP_NUM_THREADS': '2', 'DMLC_INTERFACE': 'eth0', 'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'ECS_CONTAINER_METADATA_URI_V4': 'http://169.254.170.2/v4/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'SAGEMAKER_HTTP_PORT': '8080', 'SAGEMAKER_DATA_PATH': '/opt/ml'}\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] envs={'ENVROOT': '/opt/amazon', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'HOSTNAME': 'ip-10-0-109-145.us-east-2.compute.internal', 'TRAINING_JOB_NAME': 'pca-2021-03-25-18-48-42-568', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'TRAINING_JOB_ARN': 'arn:aws:sagemaker:us-east-2:542063182511:training-job/pca-2021-03-25-18-48-42-568', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/441fd4d8-f9a0-4fc0-bb08-a60241b569e7', 'CANONICAL_ENVROOT': '/opt/amazon', 'PYTHONUNBUFFERED': 'TRUE', 'NVIDIA_VISIBLE_DEVICES': 'void', 'LD_LIBRARY_PATH': '/opt/amazon/lib/python3.7/site-packages/cv2/../../../../lib:/usr/local/nvidia/lib64:/opt/amazon/lib', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'PWD': '/', 'LANG': 'en_US.utf8', 'AWS_REGION': 'us-east-2', 'HOME': '/root', 'SHLVL': '1', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'OMP_NUM_THREADS': '2', 'DMLC_INTERFACE': 'eth0', 'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'ECS_CONTAINER_METADATA_URI_V4': 'http://169.254.170.2/v4/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'SAGEMAKER_HTTP_PORT': '8080', 'SAGEMAKER_DATA_PATH': '/opt/ml', 'DMLC_ROLE': 'server', 'DMLC_PS_ROOT_URI': '10.0.109.145', 'DMLC_PS_ROOT_PORT': '9000', 'DMLC_NUM_SERVER': '1', 'DMLC_NUM_WORKER': '1'}\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Environment: {'ENVROOT': '/opt/amazon', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'HOSTNAME': 'ip-10-0-109-145.us-east-2.compute.internal', 'TRAINING_JOB_NAME': 'pca-2021-03-25-18-48-42-568', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'TRAINING_JOB_ARN': 'arn:aws:sagemaker:us-east-2:542063182511:training-job/pca-2021-03-25-18-48-42-568', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/441fd4d8-f9a0-4fc0-bb08-a60241b569e7', 'CANONICAL_ENVROOT': '/opt/amazon', 'PYTHONUNBUFFERED': 'TRUE', 'NVIDIA_VISIBLE_DEVICES': 'void', 'LD_LIBRARY_PATH': '/opt/amazon/lib/python3.7/site-packages/cv2/../../../../lib:/usr/local/nvidia/lib64:/opt/amazon/lib', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'PWD': '/', 'LANG': 'en_US.utf8', 'AWS_REGION': 'us-east-2', 'HOME': '/root', 'SHLVL': '1', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'OMP_NUM_THREADS': '2', 'DMLC_INTERFACE': 'eth0', 'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'ECS_CONTAINER_METADATA_URI_V4': 'http://169.254.170.2/v4/f2322664-2a9c-4573-a889-9c9b83f0c4fb', 'SAGEMAKER_HTTP_PORT': '8080', 'SAGEMAKER_DATA_PATH': '/opt/ml', 'DMLC_ROLE': 'worker', 'DMLC_PS_ROOT_URI': '10.0.109.145', 'DMLC_PS_ROOT_PORT': '9000', 'DMLC_NUM_SERVER': '1', 'DMLC_NUM_WORKER': '1'}\u001b[0m\n",
      "\u001b[34mProcess 33 is a shell:scheduler.\u001b[0m\n",
      "\u001b[34mProcess 42 is a shell:server.\u001b[0m\n",
      "\u001b[34mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Using default worker.\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Loaded iterator creator application/x-labeled-vector-protobuf for content type ('application/x-labeled-vector-protobuf', '1.0')\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Loaded iterator creator application/x-recordio-protobuf for content type ('application/x-recordio-protobuf', '1.0')\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Loaded iterator creator protobuf for content type ('protobuf', '1.0')\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Checkpoint loading and saving are disabled.\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:42 INFO 139888397997888] Create Store: dist_sync\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:43 INFO 139888397997888] nvidia-smi took: 0.025273561477661133 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:43 INFO 139888397997888] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:43 INFO 139888397997888] The default executor is <PCAExecutor on cpu(0)>.\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:43 INFO 139888397997888] 11 feature(s) found in 'data'.\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:43 INFO 139888397997888] <PCAExecutor on cpu(0)> is assigned to batch slice from 0 to 99.\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1616698302.3992724, \"EndTime\": 1616698303.1574934, \"Dimensions\": {\"Algorithm\": \"PCA\", \"Host\": \"algo-1\", \"Operation\": \"training\"}, \"Metrics\": {\"initialize.time\": {\"sum\": 745.3489303588867, \"count\": 1, \"min\": 745.3489303588867, \"max\": 745.3489303588867}}}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1616698303.1576684, \"EndTime\": 1616698303.1577053, \"Dimensions\": {\"Algorithm\": \"PCA\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"Meta\": \"init_train_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}, \"Total Batches Seen\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}, \"Max Records Seen Between Resets\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}, \"Max Batches Seen Between Resets\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}, \"Reset Count\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}, \"Number of Records Since Last Reset\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}, \"Number of Batches Since Last Reset\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}}}\n",
      "\u001b[0m\n",
      "\u001b[34m[2021-03-25 18:51:43.158] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 0, \"duration\": 758, \"num_examples\": 1, \"num_bytes\": 11200}\u001b[0m\n",
      "\u001b[34m[2021-03-25 18:51:43.456] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 291, \"num_examples\": 700, \"num_bytes\": 7840000}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1616698303.1576095, \"EndTime\": 1616698303.4566295, \"Dimensions\": {\"Algorithm\": \"PCA\", \"Host\": \"algo-1\", \"Operation\": \"training\"}, \"Metrics\": {\"epochs\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}, \"update.time\": {\"sum\": 298.08592796325684, \"count\": 1, \"min\": 298.08592796325684, \"max\": 298.08592796325684}}}\n",
      "\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:43 INFO 139888397997888] #progress_metric: host=algo-1, completed 100.0 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1616698303.1585042, \"EndTime\": 1616698303.4570122, \"Dimensions\": {\"Algorithm\": \"PCA\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 70000.0, \"count\": 1, \"min\": 70000, \"max\": 70000}, \"Total Batches Seen\": {\"sum\": 700.0, \"count\": 1, \"min\": 700, \"max\": 700}, \"Max Records Seen Between Resets\": {\"sum\": 70000.0, \"count\": 1, \"min\": 70000, \"max\": 70000}, \"Max Batches Seen Between Resets\": {\"sum\": 700.0, \"count\": 1, \"min\": 700, \"max\": 700}, \"Reset Count\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}, \"Number of Records Since Last Reset\": {\"sum\": 70000.0, \"count\": 1, \"min\": 70000, \"max\": 70000}, \"Number of Batches Since Last Reset\": {\"sum\": 700.0, \"count\": 1, \"min\": 700, \"max\": 700}}}\n",
      "\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:43 INFO 139888397997888] #throughput_metric: host=algo-1, train throughput=234401.90650310766 records/second\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1616698303.4567368, \"EndTime\": 1616698303.4793773, \"Dimensions\": {\"Algorithm\": \"PCA\", \"Host\": \"algo-1\", \"Operation\": \"training\"}, \"Metrics\": {\"finalize.time\": {\"sum\": 22.00937271118164, \"count\": 1, \"min\": 22.00937271118164, \"max\": 22.00937271118164}}}\n",
      "\u001b[0m\n",
      "\u001b[34m[03/25/2021 18:51:43 INFO 139888397997888] Test data is not provided.\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1616698303.4794614, \"EndTime\": 1616698303.481241, \"Dimensions\": {\"Algorithm\": \"PCA\", \"Host\": \"algo-1\", \"Operation\": \"training\"}, \"Metrics\": {\"setuptime\": {\"sum\": 20.733118057250977, \"count\": 1, \"min\": 20.733118057250977, \"max\": 20.733118057250977}, \"totaltime\": {\"sum\": 1250.7221698760986, \"count\": 1, \"min\": 1250.7221698760986, \"max\": 1250.7221698760986}}}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2021-03-25 18:52:06 Completed - Training job completed\n",
      "Training seconds: 50\n",
      "Billable seconds: 50\n"
     ]
    }
   ],
   "source": [
    "# We have pass in the container, the type of instance that we would like to use for training \n",
    "# output path and sagemaker session into the Estimator. \n",
    "# We can also specify how many instances we would like to use for training\n",
    "\n",
    "\n",
    "pca = sagemaker.estimator.Estimator(container,\n",
    "                                       role, \n",
    "                                       train_instance_count=1, \n",
    "                                       train_instance_type='ml.c4.xlarge',\n",
    "                                       output_path=output_location,\n",
    "                                       sagemaker_session=sagemaker_session)\n",
    "\n",
    "# We can tune parameters like the number of features that we are passing in, mode of algorithm, mini batch size and number of pca components\n",
    "\n",
    "\n",
    "pca.set_hyperparameters(feature_dim=11,\n",
    "                        num_components=6,\n",
    "                        subtract_mean=False,\n",
    "                        algorithm_mode='regular',\n",
    "                        mini_batch_size=100)\n",
    "\n",
    "\n",
    "# Pass in the training data from S3 to train the pca model\n",
    "\n",
    "\n",
    "pca.fit({'train': s3_train_data})\n",
    "\n",
    "# Let's see the progress using cloudwatch logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MINI CHALLENGE:\n",
    " - Retrain the model with the following number of components 5, 4, and 7 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK #8: DEPLOY THE TRAINED PCA MODEL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------!"
     ]
    }
   ],
   "source": [
    "# Deploy the model to perform inference \n",
    "\n",
    "pca_reduction = pca.deploy(initial_instance_count = 1,\n",
    "                                          instance_type = 'ml.m4.xlarge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.predictor import csv_serializer, json_deserializer\n",
    "\n",
    "\n",
    "# Content type overrides the data that will be passed to the deployed model, since the deployed model expects data in text/csv format.\n",
    "\n",
    "# Serializer accepts a single argument, the input data, and returns a sequence of bytes in the specified content type\n",
    "\n",
    "# Deserializer accepts two arguments, the result data and the response content type, and return a sequence of bytes in the specified content type.\n",
    "\n",
    "# Reference: https://sagemaker.readthedocs.io/en/stable/predictors.html\n",
    "\n",
    "\n",
    "# pca_reduction.content_type = 'text/csv'\n",
    "pca_reduction.serializer = csv_serializer\n",
    "pca_reduction.deserializer = json_deserializer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The csv_serializer has been renamed in sagemaker>=2.\n",
      "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n",
      "The json_deserializer has been renamed in sagemaker>=2.\n",
      "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
     ]
    }
   ],
   "source": [
    "# make prediction on the test data\n",
    "\n",
    "result = pca_reduction.predict(np.array(df_final))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "  {'projection': [-0.2998508810997009,\n",
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       "    -242.9834442138672]},\n",
       "  {'projection': [0.9796647429466248,\n",
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       "  ...]}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result # results are in Json format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the results are in Json format, we access the scores by iterating through the scores in the predictions\n",
    "predictions = np.array([r['projection'] for r in result['projections']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.89103657e-01,  3.99437475e+00, -1.25495701e+01,\n",
       "         6.42405853e+01,  3.68210945e+01, -2.17848068e+02],\n",
       "       [ 1.09570193e+00, -4.77767706e+00,  1.23644390e+01,\n",
       "         4.45821114e+01,  4.56663399e+01, -2.41025101e+02],\n",
       "       [ 1.33668399e+00,  1.79976070e+00, -9.73582458e+00,\n",
       "         5.34248428e+01,  5.57539673e+01, -2.23016754e+02],\n",
       "       ...,\n",
       "       [ 9.36102509e-01,  6.50153160e+00,  2.10844498e+01,\n",
       "         4.50918350e+01,  7.45691071e+01, -2.82263916e+02],\n",
       "       [ 1.15454197e-03, -8.17430115e+00, -2.97180176e+00,\n",
       "         5.16770020e+01,  5.14335823e+01, -2.34024872e+02],\n",
       "       [ 3.68915290e-01, -1.09633231e+00, -4.88934517e+00,\n",
       "         6.41318741e+01,  4.39791260e+01, -2.28002136e+02]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 6)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete the end-point\n",
    "\n",
    "pca_reduction.delete_endpoint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK #9: TRAIN AND EVALUATE XGBOOST MODEL ON DATA AFTER DIMENSIONALITY REDUCTION (USING SAGEMAKER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the array into dataframe in a way that target variable is set as the first column and is followed by feature columns\n",
    "# This is because sagemaker built-in algorithm expects the data in this format\n",
    "\n",
    "train_data = pd.DataFrame({'Target':df_target})\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(predictions.shape[1]):\n",
    "    train_data[i] = predictions[:,i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_size = int(0.9 * train_data.shape[0])\n",
    "train_data_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle the data in dataframe and then split the dataframe into train, test and validation sets.\n",
    "\n",
    "import sklearn \n",
    "\n",
    "train_data = sklearn.utils.shuffle(train_data)\n",
    "train, test, valid = train_data[:train_data_size], train_data[train_data_size:train_data_size + 3500], train_data[train_data_size + 3500:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.shape, test.shape,valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, y_test = test.drop(columns = ['Target']), test['Target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save train_data and validation_data as csv files\n",
    "\n",
    "train.to_csv('train.csv',header = False, index = False)\n",
    "valid.to_csv('valid.csv',header = False, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = 'XGBoost-Classifier'\n",
    "key = 'XGBoost-Classifier'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data from csv file and then upload the data to s3 bucket\n",
    "with open('train.csv','rb') as f:\n",
    "    # The following code uploads the data into S3 bucket to be accessed later for training\n",
    "    boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'train', key)).upload_fileobj(f)\n",
    "\n",
    "# Let's print out the training data location in s3\n",
    "s3_train_data = 's3://{}/{}/train/{}'.format(bucket, prefix, key)\n",
    "print('uploaded training data location: {}'.format(s3_train_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the data from csv file and then upload the data to s3 bucket\n",
    "with open('valid.csv','rb') as f:\n",
    "    # The following code uploads the data into S3 bucket to be accessed later for training\n",
    "    boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'valid', key)).upload_fileobj(f)\n",
    "\n",
    "# Let's print out the validation data location in s3\n",
    "s3_valid_data = 's3://{}/{}/valid/{}'.format(bucket, prefix, key)\n",
    "print('uploaded validation data location: {}'.format(s3_valid_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creates output placeholder in S3 bucket to store the linear learner output\n",
    "\n",
    "output_location = 's3://{}/{}/output'.format(bucket, prefix)\n",
    "print('training artifacts will be uploaded to: {}'.format(output_location))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This code is used to get the training container of sagemaker built-in algorithms\n",
    "# all we have to do is to specify the name of the algorithm, that we want to use\n",
    "\n",
    "# Let's obtain a reference to the XGBoost container image\n",
    "# Note that all  models are named estimators\n",
    "# You don't have to specify (hardcode) the region, get_image_uri will get the current region name using boto3.Session\n",
    "\n",
    "\n",
    "container = get_image_uri(boto3.Session().region_name, 'xgboost','0.90-2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have pass in the container, the type of instance that we would like to use for training \n",
    "# output path and sagemaker session into the Estimator. \n",
    "# We can also specify how many instances we would like to use for training\n",
    "\n",
    "\n",
    "Xgboost_classifier = sagemaker.estimator.Estimator(container,\n",
    "                                       role, \n",
    "                                       train_instance_count=1, \n",
    "                                       train_instance_type='ml.m4.xlarge',\n",
    "                                       output_path=output_location,\n",
    "                                       sagemaker_session=sagemaker_session)\n",
    "\n",
    "# To improve the performance of the model, a hyperparameters tuning job need to be run \n",
    "\n",
    "Xgboost_classifier.set_hyperparameters(max_depth=3,\n",
    "                           objective='multi:softmax',\n",
    "                           num_class= 2,\n",
    "                           eta = 0.5,\n",
    "                           num_round = 150\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create \"train\", \"validation\" channels to feed in the model\n",
    "# Source: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html\n",
    "\n",
    "\n",
    "train_input = sagemaker.session.s3_input(s3_data = s3_train_data, content_type='csv',s3_data_type = 'S3Prefix')\n",
    "valid_input = sagemaker.session.s3_input(s3_data = s3_valid_data, content_type='csv',s3_data_type = 'S3Prefix')\n",
    "\n",
    "Xgboost_classifier.fit({'train': train_input, 'validation': valid_input})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MINI CHALLENGE\n",
    "- Retrain the XGBoost model with deeper trees (max_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK #10: DEPLOY AND TEST THE TRAINED XGBOOST MODEL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deploy the model to perfrom inference \n",
    "\n",
    "Xgboost_classifier = Xgboost_classifier.deploy(initial_instance_count = 1,\n",
    "                                          instance_type = 'ml.m4.xlarge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Content type over-rides the data that will be passed to the deployed model, since the deployed model expects data in text/csv format, we specify this as content -type.\n",
    "# Serializer accepts a single argument, the input data, and returns a sequence of bytes in the specified content type\n",
    "#Reference: https://sagemaker.readthedocs.io/en/stable/predictors.html\n",
    "\n",
    "from sagemaker.predictor import csv_serializer, json_deserializer\n",
    "\n",
    "Xgboost_classifier.serializer = csv_serializer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction\n",
    "\n",
    "XGB_prediction = Xgboost_classifier.predict(np.array(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "XGB_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom code to convert the values in bytes format to array\n",
    "\n",
    "def bytes_2_array(x):\n",
    "    \n",
    "    #makes entire prediction as string and splits based on ','\n",
    "    l = str(x).split(',')\n",
    "    \n",
    "    #Since the first element contains unwanted characters like (b,',') we remove them\n",
    "    l[0] = l[0][2:]\n",
    "    #same-thing as above remove the unwanted last character (')\n",
    "    l[-1] = l[-1][:-1]\n",
    "    \n",
    "    #iterating through the list of strings and converting them into float type\n",
    "    for i in range(len(l)):\n",
    "        l[i] = float(l[i])\n",
    "        \n",
    "    #converting the list to into array\n",
    "    l = np.array(l).astype('float32')\n",
    "    \n",
    "    #reshape one-dimensional array to two-dimentaional array\n",
    "    return l.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_values = bytes_2_array(XGB_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = np.array(y_test)\n",
    "y_test = y_test.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot metrics\n",
    "\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score\n",
    "\n",
    "print(\"Precision = {}\".format(precision_score(y_test, predicted_values, average='macro')))\n",
    "print(\"Recall = {}\".format(recall_score(y_test, predicted_values, average='macro')))\n",
    "print(\"Accuracy = {}\".format(accuracy_score(y_test, predicted_values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot confusion matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, predicted_values)\n",
    "plt.figure()\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete the end-point\n",
    "\n",
    "Xgboost_classifier.delete_endpoint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXCELLENT JOB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MINI CHALLENGE SOLUTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cardio_df [ cardio_df['ap_hi'] == 16020]\n",
    "cardio_df [ cardio_df['age'] > 64.8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cardio_df.hist(bins = 30, figsize = (20,20), color = 'r')\n",
    "# get the correlation matrix\n",
    "\n",
    "corr_matrix = cardio_df.corr()\n",
    "corr_matrix\n",
    "# plotting the correlation matrix\n",
    "plt.figure(figsize = (16,16))\n",
    "sns.heatmap(corr_matrix, annot = True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = XGBClassifier(learning_rate=0.01, n_estimators=100, objective='binary:logistic')\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "grid = GridSearchCV(xgb_model, param_grid, refit = True, verbose = 4)\n",
    "grid.fit(X_train, y_train)"
   ]
  }
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