helloworld.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "source": [
                "print(\"hello world\")"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "hello world\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "source": [
                "import numpy as np\r\n",
                "\r\n",
                "print(np.__version__)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "1.20.2\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "source": [
                "import tensorflow as tf\r\n",
                "\r\n",
                "print(tf.__version__)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "2.3.0\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "source": [
                "import pandas as pd\r\n",
                "\r\n",
                "# load the training dataset\r\n",
                "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/seeds.csv\r\n"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stderr",
                    "text": [
                        "--2021-08-06 15:12:26--  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/seeds.csv\n",
                        "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
                        "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
                        "HTTP request sent, awaiting response... 200 OK\n",
                        "Length: 9386 (9.2K) [text/plain]\n",
                        "Saving to: 'seeds.csv'\n",
                        "\n",
                        "     0K .........                                             100% 11.2M=0.001s\n",
                        "\n",
                        "2021-08-06 15:12:26 (11.2 MB/s) - 'seeds.csv' saved [9386/9386]\n",
                        "\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "source": [
                "data = pd.read_csv('seeds.csv')\r\n",
                "\r\n",
                "# Display a random sample of 10 observations (just the features)\r\n",
                "features = data[data.columns[0:6]]\r\n",
                "features.sample(10)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "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>area</th>\n",
                            "      <th>perimeter</th>\n",
                            "      <th>compactness</th>\n",
                            "      <th>kernel_length</th>\n",
                            "      <th>kernel_width</th>\n",
                            "      <th>asymmetry_coefficient</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>68</th>\n",
                            "      <td>14.37</td>\n",
                            "      <td>14.39</td>\n",
                            "      <td>0.8726</td>\n",
                            "      <td>5.569</td>\n",
                            "      <td>3.153</td>\n",
                            "      <td>1.4640</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>95</th>\n",
                            "      <td>16.87</td>\n",
                            "      <td>15.65</td>\n",
                            "      <td>0.8648</td>\n",
                            "      <td>6.139</td>\n",
                            "      <td>3.463</td>\n",
                            "      <td>3.6960</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>113</th>\n",
                            "      <td>19.14</td>\n",
                            "      <td>16.61</td>\n",
                            "      <td>0.8722</td>\n",
                            "      <td>6.259</td>\n",
                            "      <td>3.737</td>\n",
                            "      <td>6.6820</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>56</th>\n",
                            "      <td>14.46</td>\n",
                            "      <td>14.35</td>\n",
                            "      <td>0.8818</td>\n",
                            "      <td>5.388</td>\n",
                            "      <td>3.377</td>\n",
                            "      <td>2.8020</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>132</th>\n",
                            "      <td>15.38</td>\n",
                            "      <td>14.90</td>\n",
                            "      <td>0.8706</td>\n",
                            "      <td>5.884</td>\n",
                            "      <td>3.268</td>\n",
                            "      <td>4.4620</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>77</th>\n",
                            "      <td>20.71</td>\n",
                            "      <td>17.23</td>\n",
                            "      <td>0.8763</td>\n",
                            "      <td>6.579</td>\n",
                            "      <td>3.814</td>\n",
                            "      <td>4.4510</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>147</th>\n",
                            "      <td>12.49</td>\n",
                            "      <td>13.46</td>\n",
                            "      <td>0.8658</td>\n",
                            "      <td>5.267</td>\n",
                            "      <td>2.967</td>\n",
                            "      <td>4.4210</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>30</th>\n",
                            "      <td>13.16</td>\n",
                            "      <td>13.82</td>\n",
                            "      <td>0.8662</td>\n",
                            "      <td>5.454</td>\n",
                            "      <td>2.975</td>\n",
                            "      <td>0.8551</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>66</th>\n",
                            "      <td>14.34</td>\n",
                            "      <td>14.37</td>\n",
                            "      <td>0.8726</td>\n",
                            "      <td>5.630</td>\n",
                            "      <td>3.190</td>\n",
                            "      <td>1.3130</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>124</th>\n",
                            "      <td>15.99</td>\n",
                            "      <td>14.89</td>\n",
                            "      <td>0.9064</td>\n",
                            "      <td>5.363</td>\n",
                            "      <td>3.582</td>\n",
                            "      <td>3.3360</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "      area  perimeter  compactness  kernel_length  kernel_width  \\\n",
                            "68   14.37      14.39       0.8726          5.569         3.153   \n",
                            "95   16.87      15.65       0.8648          6.139         3.463   \n",
                            "113  19.14      16.61       0.8722          6.259         3.737   \n",
                            "56   14.46      14.35       0.8818          5.388         3.377   \n",
                            "132  15.38      14.90       0.8706          5.884         3.268   \n",
                            "77   20.71      17.23       0.8763          6.579         3.814   \n",
                            "147  12.49      13.46       0.8658          5.267         2.967   \n",
                            "30   13.16      13.82       0.8662          5.454         2.975   \n",
                            "66   14.34      14.37       0.8726          5.630         3.190   \n",
                            "124  15.99      14.89       0.9064          5.363         3.582   \n",
                            "\n",
                            "     asymmetry_coefficient  \n",
                            "68                  1.4640  \n",
                            "95                  3.6960  \n",
                            "113                 6.6820  \n",
                            "56                  2.8020  \n",
                            "132                 4.4620  \n",
                            "77                  4.4510  \n",
                            "147                 4.4210  \n",
                            "30                  0.8551  \n",
                            "66                  1.3130  \n",
                            "124                 3.3360  "
                        ]
                    },
                    "metadata": {},
                    "execution_count": 9
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "source": [
                "from sklearn.preprocessing import MinMaxScaler\r\n",
                "from sklearn.decomposition import PCA\r\n",
                "\r\n",
                "# Normalize the numeric features so they're on the same scale\r\n",
                "scaled_features = MinMaxScaler().fit_transform(features[data.columns[0:6]])\r\n",
                "\r\n",
                "# Get two principal components\r\n",
                "pca = PCA(n_components=2).fit(scaled_features)\r\n",
                "features_2d = pca.transform(scaled_features)\r\n",
                "features_2d[0:10]"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "array([[ 0.11883593, -0.09382469],\n",
                            "       [ 0.0696878 , -0.31077233],\n",
                            "       [-0.03499184, -0.37044705],\n",
                            "       [-0.06582089, -0.36365235],\n",
                            "       [ 0.32594892, -0.37695797],\n",
                            "       [-0.02455447, -0.31060184],\n",
                            "       [-0.00769646, -0.07594931],\n",
                            "       [-0.05646955, -0.26696284],\n",
                            "       [ 0.38196305, -0.05149471],\n",
                            "       [ 0.35701044, -0.17697998]])"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 6
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "source": [
                "import matplotlib.pyplot as plt\r\n",
                "\r\n",
                "%matplotlib inline\r\n",
                "\r\n",
                "plt.scatter(features_2d[:,0],features_2d[:,1])\r\n",
                "plt.xlabel('Dimension 1')\r\n",
                "plt.ylabel('Dimension 2')\r\n",
                "plt.title('Data')\r\n",
                "plt.show()"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "image/png": 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                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "source": [
                "def myfun(num1,num2):\r\n",
                "    res = num1 * num2\r\n",
                "    return res\r\n",
                "\r\n",
                "num1= int(input(\"Enter first num\"))\r\n",
                "num2= int(input(\"Enter secound num\"))\r\n",
                "\r\n",
                "res = myfun(num1 , num2)\r\n",
                "print(res)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "6\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "source": [
                " "
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "source": [
                "from sklearn import datasets\r\n",
                "iris = datasets.load_iris()\r\n",
                "digits = datasets.load_digits()"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "source": [
                "from numpy import array\r\n",
                "from sklearn.model_selection import KFold\r\n",
                "# data sample\r\n",
                "data = array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])\r\n",
                "# prepare cross validation\r\n",
                "kfold = KFold(3, True, 1)\r\n",
                "# enumerate splits\r\n",
                "for train, test in kfold.split(data):\r\n",
                "\tprint('train: %s, test: %s' % (data[train], data[test]))"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "train: [0.1 0.4 0.5 0.6], test: [0.2 0.3]\n",
                        "train: [0.2 0.3 0.4 0.6], test: [0.1 0.5]\n",
                        "train: [0.1 0.2 0.3 0.5], test: [0.4 0.6]\n"
                    ]
                },
                {
                    "output_type": "stream",
                    "name": "stderr",
                    "text": [
                        "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass shuffle=True, random_state=1 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
                        "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "source": [
                "import logging\r\n",
                "\r\n",
                "from azureml.core.run import Run\r\n",
                "\r\n",
                "run = Run.get_context()\r\n",
                "\r\n",
                "# Azure ML Scalar value logging\r\n",
                "run.log(\"scalar_value\", 0.95)\r\n",
                "\r\n",
                "# Python print statement\r\n",
                "print(\"I am a python print statement, I will be sent to the driver logs.\")\r\n",
                "\r\n",
                "# Initialize python logger\r\n",
                "logger = logging.getLogger(__name__)\r\n",
                "logger.setLevel(level=logging.DEBUG)\r\n",
                "\r\n",
                "# Plain python logging statements\r\n",
                "logger.debug(\"I am a plain debug statement, I will be sent to the driver logs.\")\r\n",
                "logger.info(\"I am a plain info statement, I will be sent to the driver logs.\")\r\n",
                "\r\n"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "Attempted to log scalar metric scalar_value:\n",
                        "0.95\n",
                        "I am a python print statement, I will be sent to the driver logs.\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "source": [
                "from argparse import ArgumentParser as AP\r\n",
                "\r\n",
                "parser = AP()\r\n",
                "parser.add_argument('--side',type=int)\r\n",
                "\r\n",
                "arg = parser.parse_args()\r\n",
                "\r\n",
                "def areasequare(side):\r\n",
                "    area = side ** 2\r\n",
                "    return area\r\n",
                "\r\n",
                "#side = int(input())\r\n",
                "area = areasequare(arg.side)\r\n",
                "print('Area of 20 is', area)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "Area of 20 is 4\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "source": [
                "\r\n",
                "import pandas as pd\r\n",
                "\r\n",
                "# Read dataset\r\n",
                "df = pd.read_csv('./adultincometrunc.csv')\r\n",
                "\r\n",
                "# Create Dummy variables\r\n",
                "data_prep = pd.get_dummies(df, drop_first=True)\r\n",
                "\r\n",
                "\r\n",
                "# Create X and Y Variables\r\n",
                "X = data_prep.iloc[:, :-1]\r\n",
                "Y = data_prep.iloc[:, -1]"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "source": [
                "df.head()"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "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>wc</th>\n",
                            "      <th>education</th>\n",
                            "      <th>marital status</th>\n",
                            "      <th>race</th>\n",
                            "      <th>gender</th>\n",
                            "      <th>hours per week</th>\n",
                            "      <th>IncomeClass</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>38</td>\n",
                            "      <td>Private</td>\n",
                            "      <td>HS-grad</td>\n",
                            "      <td>Divorced</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>40</td>\n",
                            "      <td>&lt;=50K</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>28</td>\n",
                            "      <td>Private</td>\n",
                            "      <td>Bachelors</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>Black</td>\n",
                            "      <td>Female</td>\n",
                            "      <td>40</td>\n",
                            "      <td>&lt;=50K</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>37</td>\n",
                            "      <td>Private</td>\n",
                            "      <td>Masters</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Female</td>\n",
                            "      <td>40</td>\n",
                            "      <td>&lt;=50K</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>31</td>\n",
                            "      <td>Private</td>\n",
                            "      <td>Masters</td>\n",
                            "      <td>Never-married</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Female</td>\n",
                            "      <td>50</td>\n",
                            "      <td>&gt;50K</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>42</td>\n",
                            "      <td>Private</td>\n",
                            "      <td>Bachelors</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>40</td>\n",
                            "      <td>&gt;50K</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   age        wc   education  marital status    race   gender  hours per week  \\\n",
                            "0   38   Private     HS-grad        Divorced   White     Male              40   \n",
                            "1   28   Private   Bachelors         Married   Black   Female              40   \n",
                            "2   37   Private     Masters         Married   White   Female              40   \n",
                            "3   31   Private     Masters   Never-married   White   Female              50   \n",
                            "4   42   Private   Bachelors         Married   White     Male              40   \n",
                            "\n",
                            "  IncomeClass  \n",
                            "0       <=50K  \n",
                            "1       <=50K  \n",
                            "2       <=50K  \n",
                            "3        >50K  \n",
                            "4        >50K  "
                        ]
                    },
                    "metadata": {},
                    "execution_count": 3
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "source": [
                "from sklearn.model_selection import train_test_split\r\n",
                "\r\n",
                "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3,random_state=1234,stratify=Y)\r\n",
                "\r\n",
                "from sklearn.ensemble import RandomForestClassifier\r\n",
                "rfc = RandomForestClassifier(random_state=1234)\r\n",
                "\r\n",
                "trained_model = rfc.fit(X_train,Y_train)\r\n",
                "\r\n",
                "Y_predict = rfc.predict(X_test)\r\n",
                "from sklearn.metrics import confusion_matrix\r\n",
                "cm = confusion_matrix(Y_test, Y_predict)\r\n",
                "score = rfc.score(X_test, Y_test)   "
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "source": [
                "\r\n",
                "# ---------------------------------------------------------------\r\n",
                "# Create explanations for the model\r\n",
                "# Make sure to install azureml-interpret azureml-explain-model\r\n",
                "# ---------------------------------------------------------------\r\n",
                "\r\n",
                "# Import the Tabular Explainer class\r\n",
                "from interpret.ext.blackbox import TabularExplainer\r\n",
                "\r\n",
                "\r\n",
                "# Define the Classes and features\r\n",
                "classes  = ['Not Greater than 50K', 'Greater than 50K']\r\n",
                "features = list(X.columns)\r\n",
                "\r\n",
                "\r\n",
                "# Create the Tabular Explainer object\r\n",
                "tab_explainer = TabularExplainer(trained_model,\r\n",
                "                                 X_train,\r\n",
                "                                 features=features,\r\n",
                "                                 classes=classes)\r\n",
                "\r\n",
                "\r\n",
                "# Get the global explanations\r\n",
                "global_explanation = tab_explainer.explain_global(X_train)\r\n",
                "\r\n",
                "\r\n",
                "# Get the feature importance data\r\n",
                "global_fi = global_explanation.get_feature_importance_dict()\r\n",
                "\r\n",
                "# Print the global feature importance values\r\n",
                "for feature, importance in global_fi.items():\r\n",
                "    print(feature,\":\", importance)\r\n",
                "\r\n",
                "\r\n",
                "# Get Local feature importances\r\n",
                "X_explain = X_test[0:5]\r\n",
                "\r\n",
                "\r\n",
                "# Create Local explanations object\r\n",
                "local_explanation = tab_explainer.explain_local(X_explain)\r\n",
                "\r\n",
                "# Extract the feature names and corresponding importance values\r\n",
                "local_features   = local_explanation.get_ranked_local_names()\r\n",
                "local_importance = local_explanation.get_ranked_local_values()\r\n",
                "\r\n",
                "\r\n",
                "# Print the local explanations\r\n",
                "for i in range(0, len(local_features)):\r\n",
                "    labels = local_features[i]\r\n",
                "    print(\"\\n Feature suppport values for : \", classes[i])\r\n",
                "    \r\n",
                "    for j in range(0, len(labels)):\r\n",
                "\r\n",
                "        if Y_predict[j] == i:\r\n",
                "            print(\"\\n\\tObservation number : \", j + 1)\r\n",
                "            feature_names = labels[j]\r\n",
                "            \r\n",
                "            print(\"\\t\\t\", \"Feature Name\".ljust(30), \"  Value\")\r\n",
                "            print(\"\\t\\t\", \"-\"*30, \"-\"*10)\r\n",
                "            \r\n",
                "            for k in range(0, len(feature_names)):\r\n",
                "                print(\"\\t\\t\", feature_names[k].ljust(30), round(local_importance[i][j][k], 6))"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "marital status_Married : 0.134457897553098\n",
                        "age : 0.08737999026945\n",
                        "hours per week : 0.054862878214208904\n",
                        "education_ HS-grad : 0.05459228226812601\n",
                        "marital status_ Never-married : 0.04201492036510847\n",
                        "education_ Some-college : 0.03902094175894924\n",
                        "gender_ Male : 0.026954924512638787\n",
                        "education_ Masters : 0.013130510698434426\n",
                        "race_ White : 0.013018742998495862\n",
                        "wc_ Private : 0.008260686989919535\n",
                        "wc_ Local-gov : 0.006514623134033063\n",
                        "education_ Prof-school : 0.006411507490632027\n",
                        "race_ Black : 0.00431141694330449\n",
                        "education_ Doctorate : 0.003320328208831908\n",
                        "race_ Asian-Pac-Islander : 0.0021244991478086886\n",
                        "marital status_ Widowed : 0.0016788032177812158\n",
                        "race_ Other : 0.0003622585777075659\n",
                        "education_ Preschool : 3.30258572723802e-05\n",
                        "\n",
                        " Feature suppport values for :  Not Greater than 50K\n",
                        "\n",
                        "\tObservation number :  1\n",
                        "\t\t Feature Name                     Value\n",
                        "\t\t ------------------------------ ----------\n",
                        "\t\t education_ HS-grad             0.157534\n",
                        "\t\t hours per week                 0.062715\n",
                        "\t\t wc_ Private                    0.0179\n",
                        "\t\t education_ Masters             0.012778\n",
                        "\t\t education_ Prof-school         0.004132\n",
                        "\t\t race_ Black                    0.003311\n",
                        "\t\t education_ Doctorate           0.001875\n",
                        "\t\t education_ Preschool           -1.1e-05\n",
                        "\t\t race_ Other                    -0.000325\n",
                        "\t\t wc_ Local-gov                  -0.000783\n",
                        "\t\t race_ Asian-Pac-Islander       -0.001856\n",
                        "\t\t marital status_ Widowed        -0.002629\n",
                        "\t\t gender_ Male                   -0.006227\n",
                        "\t\t race_ White                    -0.013401\n",
                        "\t\t education_ Some-college        -0.022706\n",
                        "\t\t marital status_ Never-married  -0.04377\n",
                        "\t\t age                            -0.064633\n",
                        "\t\t marital status_Married         -0.149913\n",
                        "\n",
                        "\tObservation number :  2\n",
                        "\t\t Feature Name                     Value\n",
                        "\t\t ------------------------------ ----------\n",
                        "\t\t marital status_Married         0.11369\n",
                        "\t\t hours per week                 0.058264\n",
                        "\t\t education_ HS-grad             0.051995\n",
                        "\t\t gender_ Male                   0.023164\n",
                        "\t\t race_ White                    0.017384\n",
                        "\t\t age                            0.010765\n",
                        "\t\t race_ Black                    0.005979\n",
                        "\t\t education_ Masters             0.005284\n",
                        "\t\t wc_ Private                    0.003024\n",
                        "\t\t race_ Asian-Pac-Islander       0.002518\n",
                        "\t\t education_ Prof-school         0.002331\n",
                        "\t\t education_ Doctorate           0.001872\n",
                        "\t\t marital status_ Widowed        0.000108\n",
                        "\t\t education_ Preschool           -9e-06\n",
                        "\t\t race_ Other                    -0.00012\n",
                        "\t\t wc_ Local-gov                  -0.000154\n",
                        "\t\t education_ Some-college        -0.011581\n",
                        "\t\t marital status_ Never-married  -0.017191\n",
                        "\n",
                        "\tObservation number :  3\n",
                        "\t\t Feature Name                     Value\n",
                        "\t\t ------------------------------ ----------\n",
                        "\t\t marital status_Married         0.134219\n",
                        "\t\t education_ HS-grad             0.042854\n",
                        "\t\t gender_ Male                   0.024328\n",
                        "\t\t education_ Masters             0.012574\n",
                        "\t\t hours per week                 0.00731\n",
                        "\t\t education_ Prof-school         0.003684\n",
                        "\t\t education_ Doctorate           0.002328\n",
                        "\t\t race_ Asian-Pac-Islander       0.001136\n",
                        "\t\t education_ Preschool           -1.6e-05\n",
                        "\t\t race_ Other                    -0.000133\n",
                        "\t\t wc_ Local-gov                  -0.00087\n",
                        "\t\t race_ Black                    -0.001906\n",
                        "\t\t wc_ Private                    -0.003067\n",
                        "\t\t race_ White                    -0.004392\n",
                        "\t\t marital status_ Widowed        -0.006525\n",
                        "\t\t education_ Some-college        -0.020963\n",
                        "\t\t marital status_ Never-married  -0.041202\n",
                        "\t\t age                            -0.062038\n",
                        "\n",
                        "\tObservation number :  5\n",
                        "\t\t Feature Name                     Value\n",
                        "\t\t ------------------------------ ----------\n",
                        "\t\t education_ Some-college        0.110241\n",
                        "\t\t age                            0.102257\n",
                        "\t\t hours per week                 0.063144\n",
                        "\t\t education_ Masters             0.014254\n",
                        "\t\t education_ Prof-school         0.00344\n",
                        "\t\t gender_ Male                   0.002959\n",
                        "\t\t education_ Doctorate           0.002483\n",
                        "\t\t education_ Preschool           -1.5e-05\n",
                        "\t\t race_ Other                    -0.00026\n",
                        "\t\t marital status_ Widowed        -0.000331\n",
                        "\t\t race_ Asian-Pac-Islander       -0.000612\n",
                        "\t\t wc_ Local-gov                  -0.000823\n",
                        "\t\t race_ Black                    -0.001552\n",
                        "\t\t wc_ Private                    -0.002132\n",
                        "\t\t race_ White                    -0.006911\n",
                        "\t\t education_ HS-grad             -0.021505\n",
                        "\t\t marital status_ Never-married  -0.028896\n",
                        "\t\t marital status_Married         -0.111086\n",
                        "\n",
                        " Feature suppport values for :  Greater than 50K\n",
                        "\n",
                        "\tObservation number :  4\n",
                        "\t\t Feature Name                     Value\n",
                        "\t\t ------------------------------ ----------\n",
                        "\t\t marital status_Married         0.146159\n",
                        "\t\t education_ Masters             0.113299\n",
                        "\t\t hours per week                 0.092064\n",
                        "\t\t education_ HS-grad             0.071788\n",
                        "\t\t age                            0.059597\n",
                        "\t\t marital status_ Never-married  0.048664\n",
                        "\t\t gender_ Male                   0.048334\n",
                        "\t\t wc_ Private                    0.020095\n",
                        "\t\t education_ Some-college        0.011597\n",
                        "\t\t race_ White                    0.010881\n",
                        "\t\t race_ Asian-Pac-Islander       0.001948\n",
                        "\t\t marital status_ Widowed        0.000716\n",
                        "\t\t race_ Black                    0.000622\n",
                        "\t\t race_ Other                    0.000392\n",
                        "\t\t education_ Preschool           1.9e-05\n",
                        "\t\t education_ Doctorate           -0.001695\n",
                        "\t\t education_ Prof-school         -0.002164\n",
                        "\t\t wc_ Local-gov                  -0.039636\n"
                    ]
                }
            ],
            "metadata": {}
        }
    ],
    "metadata": {
        "orig_nbformat": 4,
        "language_info": {
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