careerPredictionModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1. Import required Libraries** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2021-05-21T09:14:42.299138Z",
     "shell.execute_reply.started": "2021-05-21T09:14:42.293932Z"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "from sklearn import svm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2. Load the Data** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-05-21T09:14:43.702436Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Logical quotient rating</th>\n",
       "      <th>hackathons</th>\n",
       "      <th>coding skills rating</th>\n",
       "      <th>public speaking points</th>\n",
       "      <th>self-learning capability?</th>\n",
       "      <th>Extra-courses did</th>\n",
       "      <th>certifications</th>\n",
       "      <th>workshops</th>\n",
       "      <th>reading and writing skills</th>\n",
       "      <th>memory capability score</th>\n",
       "      <th>Interested subjects</th>\n",
       "      <th>interested career area</th>\n",
       "      <th>Type of company want to settle in?</th>\n",
       "      <th>Taken inputs from seniors or elders</th>\n",
       "      <th>Interested Type of Books</th>\n",
       "      <th>Management or Technical</th>\n",
       "      <th>hard/smart worker</th>\n",
       "      <th>worked in teams ever?</th>\n",
       "      <th>Introvert</th>\n",
       "      <th>Suggested Job Role</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>information security</td>\n",
       "      <td>testing</td>\n",
       "      <td>poor</td>\n",
       "      <td>poor</td>\n",
       "      <td>programming</td>\n",
       "      <td>testing</td>\n",
       "      <td>BPA</td>\n",
       "      <td>no</td>\n",
       "      <td>Series</td>\n",
       "      <td>Management</td>\n",
       "      <td>smart worker</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Applications Developer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>shell programming</td>\n",
       "      <td>testing</td>\n",
       "      <td>excellent</td>\n",
       "      <td>medium</td>\n",
       "      <td>Management</td>\n",
       "      <td>system developer</td>\n",
       "      <td>Cloud Services</td>\n",
       "      <td>yes</td>\n",
       "      <td>Autobiographies</td>\n",
       "      <td>Technical</td>\n",
       "      <td>hard worker</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Applications Developer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>information security</td>\n",
       "      <td>testing</td>\n",
       "      <td>excellent</td>\n",
       "      <td>poor</td>\n",
       "      <td>data engineering</td>\n",
       "      <td>Business process analyst</td>\n",
       "      <td>product development</td>\n",
       "      <td>yes</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Technical</td>\n",
       "      <td>smart worker</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Applications Developer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>r programming</td>\n",
       "      <td>database security</td>\n",
       "      <td>excellent</td>\n",
       "      <td>poor</td>\n",
       "      <td>networks</td>\n",
       "      <td>testing</td>\n",
       "      <td>Testing and Maintainance Services</td>\n",
       "      <td>yes</td>\n",
       "      <td>Guide</td>\n",
       "      <td>Management</td>\n",
       "      <td>smart worker</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Applications Developer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>distro making</td>\n",
       "      <td>game development</td>\n",
       "      <td>excellent</td>\n",
       "      <td>medium</td>\n",
       "      <td>Software Engineering</td>\n",
       "      <td>system developer</td>\n",
       "      <td>BPA</td>\n",
       "      <td>no</td>\n",
       "      <td>Health</td>\n",
       "      <td>Technical</td>\n",
       "      <td>hard worker</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Applications Developer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Logical quotient rating  hackathons  coding skills rating  \\\n",
       "0                        5           0                     6   \n",
       "1                        7           6                     4   \n",
       "2                        2           3                     9   \n",
       "3                        2           6                     3   \n",
       "4                        2           0                     3   \n",
       "\n",
       "   public speaking points self-learning capability? Extra-courses did  \\\n",
       "0                       2                       yes                no   \n",
       "1                       3                        no               yes   \n",
       "2                       1                        no               yes   \n",
       "3                       5                        no               yes   \n",
       "4                       4                       yes                no   \n",
       "\n",
       "         certifications          workshops reading and writing skills  \\\n",
       "0  information security            testing                       poor   \n",
       "1     shell programming            testing                  excellent   \n",
       "2  information security            testing                  excellent   \n",
       "3         r programming  database security                  excellent   \n",
       "4         distro making   game development                  excellent   \n",
       "\n",
       "  memory capability score   Interested subjects   interested career area   \\\n",
       "0                    poor           programming                   testing   \n",
       "1                  medium            Management          system developer   \n",
       "2                    poor      data engineering  Business process analyst   \n",
       "3                    poor              networks                   testing   \n",
       "4                  medium  Software Engineering          system developer   \n",
       "\n",
       "  Type of company want to settle in? Taken inputs from seniors or elders  \\\n",
       "0                                BPA                                  no   \n",
       "1                     Cloud Services                                 yes   \n",
       "2                product development                                 yes   \n",
       "3  Testing and Maintainance Services                                 yes   \n",
       "4                                BPA                                  no   \n",
       "\n",
       "  Interested Type of Books Management or Technical hard/smart worker  \\\n",
       "0                   Series              Management      smart worker   \n",
       "1          Autobiographies               Technical       hard worker   \n",
       "2                   Travel               Technical      smart worker   \n",
       "3                    Guide              Management      smart worker   \n",
       "4                   Health               Technical       hard worker   \n",
       "\n",
       "  worked in teams ever? Introvert      Suggested Job Role  \n",
       "0                   yes        no  Applications Developer  \n",
       "1                    no       yes  Applications Developer  \n",
       "2                    no        no  Applications Developer  \n",
       "3                   yes       yes  Applications Developer  \n",
       "4                   yes        no  Applications Developer  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/mldata.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:44.069016Z",
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     "shell.execute_reply.started": "2021-05-21T09:14:44.068982Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of our training set: 6901 professionals and 20 features\n"
     ]
    }
   ],
   "source": [
    "print('The shape of our training set: %s professionals and %s features'%(df.shape[0],df.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **3. Data Preprocessing** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in our dataset:  Index(['Logical quotient rating', 'hackathons', 'coding skills rating',\n",
      "       'public speaking points', 'self-learning capability?',\n",
      "       'Extra-courses did', 'certifications', 'workshops',\n",
      "       'reading and writing skills', 'memory capability score',\n",
      "       'Interested subjects', 'interested career area ',\n",
      "       'Type of company want to settle in?',\n",
      "       'Taken inputs from seniors or elders', 'Interested Type of Books',\n",
      "       'Management or Technical', 'hard/smart worker', 'worked in teams ever?',\n",
      "       'Introvert', 'Suggested Job Role'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"Columns in our dataset: \" , df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:46.244503Z",
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     "shell.execute_reply.started": "2021-05-21T09:14:46.244469Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of Numerical features: \n",
      " ['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points']\n",
      "\n",
      "\n",
      "List of Categorical features: \n",
      " ['self-learning capability?', 'Extra-courses did', 'certifications', 'workshops', 'reading and writing skills', 'memory capability score', 'Interested subjects', 'interested career area ', 'Type of company want to settle in?', 'Taken inputs from seniors or elders', 'Interested Type of Books', 'Management or Technical', 'hard/smart worker', 'worked in teams ever?', 'Introvert', 'Suggested Job Role']\n"
     ]
    }
   ],
   "source": [
    "print(\"List of Numerical features: \\n\" , df.select_dtypes(include=np.number).columns.tolist())\n",
    "print(\"\\n\\nList of Categorical features: \\n\" , df.select_dtypes(include=['object']).columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:47.285358Z",
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     "shell.execute_reply.started": "2021-05-21T09:14:47.285328Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Logical quotient rating                0\n",
       "hackathons                             0\n",
       "coding skills rating                   0\n",
       "public speaking points                 0\n",
       "self-learning capability?              0\n",
       "Extra-courses did                      0\n",
       "certifications                         0\n",
       "workshops                              0\n",
       "reading and writing skills             0\n",
       "memory capability score                0\n",
       "Interested subjects                    0\n",
       "interested career area                 0\n",
       "Type of company want to settle in?     0\n",
       "Taken inputs from seniors or elders    0\n",
       "Interested Type of Books               0\n",
       "Management or Technical                0\n",
       "hard/smart worker                      0\n",
       "worked in teams ever?                  0\n",
       "Introvert                              0\n",
       "Suggested Job Role                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation: No missing values.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) Checking Distinct Values for Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:48.679051Z",
     "iopub.status.busy": "2021-05-21T09:14:48.678627Z",
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     "shell.execute_reply.started": "2021-05-21T09:14:48.679014Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes    3496\n",
      "no     3405\n",
      "Name: self-learning capability?, dtype: int64\n",
      "\n",
      "no     3529\n",
      "yes    3372\n",
      "Name: Extra-courses did, dtype: int64\n",
      "\n",
      "excellent    2328\n",
      "medium       2315\n",
      "poor         2258\n",
      "Name: reading and writing skills, dtype: int64\n",
      "\n",
      "medium       2317\n",
      "excellent    2303\n",
      "poor         2281\n",
      "Name: memory capability score, dtype: int64\n",
      "\n",
      "yes    3501\n",
      "no     3400\n",
      "Name: Taken inputs from seniors or elders, dtype: int64\n",
      "\n",
      "Management    3461\n",
      "Technical     3440\n",
      "Name: Management or Technical, dtype: int64\n",
      "\n",
      "smart worker    3523\n",
      "hard worker     3378\n",
      "Name: hard/smart worker, dtype: int64\n",
      "\n",
      "no     3470\n",
      "yes    3431\n",
      "Name: worked in teams ever?, dtype: int64\n",
      "\n",
      "yes    3544\n",
      "no     3357\n",
      "Name: Introvert, dtype: int64\n",
      "\n",
      "system developer            1178\n",
      "security                    1177\n",
      "Business process analyst    1154\n",
      "developer                   1145\n",
      "testing                     1128\n",
      "cloud computing             1119\n",
      "Name: interested career area , dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "categorical_col = df[['self-learning capability?', 'Extra-courses did','reading and writing skills', 'memory capability score', \n",
    "                      'Taken inputs from seniors or elders', 'Management or Technical', 'hard/smart worker', 'worked in teams ever?', \n",
    "                      'Introvert', 'interested career area ']]\n",
    "for i in categorical_col:\n",
    "    print(df[i].value_counts(), end=\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "* The categorical features with yes or no can be made numerical easily using binary encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) Checking Data Balancing for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:49.890509Z",
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     "shell.execute_reply.started": "2021-05-21T09:14:49.890449Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Suggested Job Role', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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/ctBBB2XatGnZsGFDPv3pT3e8u+SFF15YfII+aNCgNDU15Zvf/Gbe//73Z8mSJfnc5z6X1atXZ7fddsull16a/fbbLyeeeGK+//3vp66uLk8++WQ+85nP5N57782cOXPyz//8z2lra8u73vWuXH755enbt2/H7b/yyiuZMmVKfvWrX6WmpibnnntuTjnllMyaNSv3339/1qxZk5UrV+bYY4/NxIkTU1NTk6985Su577770tramg984AOZMGFCli5dmo9//OPp169f+vbtm1tvvXXXf0Pwhnn44YezbNmyjB8/Pr16/f6N44866qhMnTo1bW1tW73O8uXL097enj333DObNm3KnnvumcGDB+fHP/5xjjrqqCTJj370o049KG/t+/3ggw/e6v7utddeWbduXS6++OI89dRTectb3pIvf/nL6devXz7wgQ9k+PDhWbx4cXr37p0vfelLGTRoUIYNG5bbbrst9fX1ueKKK7J48eLU1dXlggsuyMiRI3PfffdlxowZWbduXdavX5+rrroqf/EXf7FDs9fU1GTcuHE5+uij88QTT2Tw4MFb3Z2rr746DQ0NOffcc5P8/snMiSeemP/1v/5XLrvssvz3f/93ampq8pnPfGaLr933vve9fOlLX0pbW1sGDRqUK6+8Mvvuu+827yO39fWcOHFiVq9enSVLlmTChAmd/pvUdE9/+D36p3/6p/nc5z6Xp556KitWrMif/Mmf5Kabbsq0adOSJKeddlq+853v5Bvf+EbuvvvuvPLKK6mpqcmXvvSlvPOd70yS3HTTTXniiSfSt2/fXHHFFRk8eHCefPLJfP7zn8/LL7+cVatW5ZxzzklTU1MWLVrU8W4Pb33rW/MP//AP6d+/f/Fxa2s+9KEP5dBDD83cuXPT1NSUf//3f88NN9yQTZs25W1ve1s+//nP5z/+4z8yc+bM3HLLLUmSb3zjG3nmmWdyySWX5Nprr80jjzyS1tbWjB49OmefffZmt//000/nsssuy+rVq7PHHntk8uTJOfTQQzseF5988sm89NJLOf/883PKKadk7dq1ufLKK/PUU0+ltbU1Y8eOzYknnphZs2Zl9uzZWb16dY499th8+tOf3sV/qlTajjy2beu+Odn6jkycODFHHHHEZi/ArFixovgYkPz+L+hNnDgxV111VR566KEMGTIkSbb5nHhb37OvddBBB+VXv/pVVq9encmTJ+c3v/lN+vTpk4kTJ2bIkCFbvQ/4+c9/nsceeyxTpkzJTTfdlKuuuiqf/OQnc+SRR+b//J//k3vuuSe9e/fO0UcfnQkTJuT555/PJz/5yRx44IF5/PHHM2DAgFx//fXZc889M2nSpDz11FNJkjPOOCN/9Vd/td0/k/79++f888/PV7/61Zx22mlb/dodccQR+dCHPpQ5c+Zk3333zerVq3PiiSfme9/7XhYtWrTF/cVrX3Rva2vb5vP9G2+8MbW1tXn++edz6KGH5gtf+EL69Omzzfuxo446Ku9617uyYsWK3HXXXRWLR9m2hoaGvP/9788111yTz3/+81t8fmvP1V6NcU877bQceuiheec735kzzjgjM2fOzIwZM3Lfffdl48aN+cu//Mv827/9W374wx9u8/nZoYcemscff3yzdzuaP39+vvzlL+fWW2/d7BfMK1asyMaNG7Nx48b06dMn733ve/PFL36x47n41matqanJrbfemjvvvDO9e/fOsccemwkTJnTcz7z6C7NX9/zGG2/MT3/60zz//PM588wz09zcnE9+8pOZMWNGli1blgsvvDAHHnhg2traOh6zLrnkknzwgx/MyJEjd+hrPmvWrPzgBz/ImjVr8uyzz+boo4/O5z73uSTJP/zDP2T+/Pnp169f6uvrM2zYsBxxxBEdP7NPnDgxe+21V37xi1+kpaUlF154YcaMGbPNx9nW1tatPqY//PDDue6669LW1pYDDzww11xzTee/eah6L7zwQjZu3NjxDjN77rlnrr766o7nkT/96U/zhS98IevXr0+/fv1y5ZVX5plnnul4IahPnz751re+lSTZfffdc+utt+aHP/xhkuSDH/xgLrnkkowcOTJf+cpXOh4PJk2alBdffDHLly/PqFGj8nd/93dbPN9ramraocf019pzzz3z6U9/OmPHjs2nPvWpvPzyy1v9nv/IRz6SK6+8Mu9+97vT2tqaY489NrNnz87SpUszderUrFu3Lv369csVV1yxxS/et/X4fP7552fQoEFZsmRJ/viP/zjXXXdd9tlnn60+v+7Xr99m923f/OY3M2DAgF3658qb05tpX19rW78P7tOnT2677bZ84xvfyN577513vOMdefvb355x48Zt9pjc0tKSJUuWZOnSpTnttNNy/vnnb/OxLdn6cwG/n62s7vy9+61vfSt/8Rd/sdm7h5588slZsGBBvvWtb+X888/v+H5Mfv8c7pFHHsnVV19d/P3oww8/nJtuuinnn39+FixYkIceeihvectbMnny5DzwwAPZa6+98txzz+W8887reEOLV91777153/velz/7sz/Lt7/97Y648Iknnshll12WTZs2pW/fvpk6dWr233//rf68uKPPdQ888MBMnz4969aty5o1azJhwoSccMIJ23zOua2fibf1mPhad9xxR8eb6CxevDhPP/10vvKVr3T8DHjwwQfnE5/4RL785S93vIPjtr4WSdLY2Jivf/3rWzyPbWtry9KlS3foBfZtPYfe2tf6/vvv3+rrYY888kinntu/+mfyZrO9+4JFixbl+uuv79j12bNn56c//Wk+9rGPFb/Ozz777Fafw5111ln58z//8yxatCjr1q3LlClTcvvtt+fXv/51zj777Jx99tnb/D3rq1555ZW89NJLeeWVV5L8/v5o8v/f3n2HRXWmfwP/IkVxQUWwrL1iSxArICoOVrqKiBos+alRI6ivGiWxYMBYE41YN9EYS2KF1SiaqNgFFSsmGoIJtkisqESkzdzvH1xzwsAU2GRXMd/Pde21ZmbOmTOH8zz3/ZTznBkz8Pz5cwClH4fRttkAKDczhIWF6fR7TJs2DWvWrPlL64bc3FzMmDED33//PWrXro2MjAwABfXQmjVrICK4desWevfuDVtbWxw6dAhAQcx0cHAwGXcL130LFy7EqVOnYG5uju7du2PYsGGIjo5GVlYWVq9ejRo1aujUxb6+vsX6wPv27auzzZgxYwz2MbHN+vorS+OdpnIe7XfVqVNHyQM2bdpU6j5pY3MUtOWrfv36uHjxIgCgVq1ays01RK+SyMhIBAQEoFWrVoiOjsaGDRtK1PdfkrFNOzu7YjEpNDRUJ/7u27cP0dHRsLa2RsuWLaFWq7FgwQJ4enrC398fJ0+exIsXL7Bw4UK88cYbnC9AZY6rqyvmzZsHoODmGCsrK/Tp0wcnT56Em5sbzp07p7SBjc0zmDVrFpKTk2FnZ4d58+ahVq1aRr+Xc/VIIVRmHTx4UAIDA0VE5IMPPpCFCxeKiIhKpZLly5eLiEh8fLz4+vqKiEhISIiEh4eLiMjVq1fF3d1dcnJyZPr06RITEyO3b98WlUolIiKjR4+WzZs3i4jI0aNHZcKECXLjxg0JDQ0VtVotIiLvvfeerFu3TvnO27dv6/x78+bN8u6770p+fr5kZWVJYGCgHDlyRE6fPi3Ozs6Snp4uarVaAgMDJT4+XmJjY2XOnDkiInL9+nVZsGBBsd+sPdbCjh49Kl5eXiIiEhwcLD/88IOIiKSmpkqvXr1ERGTs2LFy+PBhERFZsmSJfPbZZ/LTTz/J4MGDJTs7W0REPv74Y1m5cqWIiDg6OoqIyMKFCyUqKkpERB49eiSenp5y7do1iYmJEXd3d3nw4IHk5ORIcHCwfPfdd3Ls2DEJCwuT/Px8UavVMnnyZNm1a5fcvn1bHB0dlXNEZdvatWtl4sSJBt+/ffu2tGrVSvz9/aV3797SsWNHGTlypBw/flx5X6VSyTfffKNc85cvX5bw8HC913jh/WrLqIjh611f+b19+7Y0a9ZMLl++LCIiYWFhymccHR3l4MGDIiIyf/58mT9/voj8UZY///xzmThxoqjVarl//754e3tLTk6ODBs2TB49eiQiIjt27JAxY8aISEFdc/r0aaPHrhUYGChxcXEGy84PP/wg/fr1ExGRzMxMpd6aNGmSHDp0SERE7t27J927d5fMzEyJjo6W6OhoefjwoXTu3Fkpc59//rmEhYUpv0tfHWnofE6fPl2mT5+u929Crw9T1+jZs2eV8qpWqyUkJES+/fZbEfkjZmRmZsrw4cPlxYsXIiLy6aefSmRkpIgUXHerVq0SkYJyGRAQICIic+fOlYSEBBERuXXrljg7O4tIQTnSltcNGzbIiRMnjMatwgrHZK2FCxdKRESEPHr0SPz9/eXJkyciIrJlyxb54IMPJDc3V9zd3ZXXg4OD5fLly/L111/LvHnzREQkJydHQkJCJCkpSU6fPi0hISHKOfruu+9EROTixYvSrVs3Jb94++23JTc3V9LT08XNzU3u378vixcvlg0bNijnzMfHR27duiUxMTHSs2dPycvLK9HfjF4tJYltpupmfWVEX55qKAYUdu3aNXF3d5f8/HxZuXKlTJgwQXnPWE6s75rVxhaRP8r7nDlzlFz1xx9/lIEDBxqtAwrHRu2/jx49KkFBQfLixQvJy8uTsWPHyubNm5WYrY1JoaGhsnHjRjlz5oyMHj1aREQeP36sNzYVPlat1NRUcXJyMnruoqKiZNOmTSIism3bNqP1hfbvZSrff/PNN+Xnn38WjUYjYWFh8sUXX5jMv4vmD/S/oy1jmZmZ0q1bNzl58qSI/PG3NpSrifxRLrSfESm41tzc3OTBgweSmJgoEyZMMFkHaHNg7bGcOHFC/Pz85MGDB3qP19vbW9q2bStjx46VDRs2yLNnz3SOo+ixXr58WXr27CnPnj2TvLw8GT58uFy5cqVY/q39PdHR0UqsE/mj7Bauj27duiUqlUo0Go08f/5cPDw8JCcnR+dYo6OjpVOnTuLv76/879133xURkZiYGPHw8JDMzEzJysqSrl27yo8//ijx8fEyePBgycnJkSdPnijnp/B3T58+XcaPHy8ajUZ+/PFH6dixo4iIwThrLKa3a9dOOX/0+po9e7a0bNlSAgMDZdGiRXLt2jURKbgeVCqVknvu27dP+vfvLyK6caXwv4OCgiQlJUWuX78ubm5uEhERISIib731lqSmpsratWslNjZWRESePXsmbdq0kUePHhXL90oS02NiYorFvKysLHF0dJSHDx8avObXr1+vxOqTJ0/KqFGjJCcnR/z8/OTXX38VEZHjx4/L8OHDRaRk8blwrJo/f75ERUWZjJeG2vdExpTl8tqhQwedmDdgwAAREYP9wdeuXZNevXpJZmamZGdnS1BQULHcOzo6WgYMGCA5OTny8OFDcXZ2lqdPnxqMbeyffXnK6rU7ZswYWb9+fbHfs3nzZqW/U3s9ivwRm9Rqtcn+0cL9J4XzzmnTpsmOHTtERGT58uXyr3/9q9j3BwQEyNGjR+Xu3bvi5OQkGRkZIiISHh4u+/btExGRuLg4+fe//22wvVjSXDcsLEyuX78uIiIJCQlKf6mhnFNfm9hYTCzM399fUlNTRUTkiy++kLFjxxb7zE8//SRt2rQxeS5ECuJ/hw4dRKPR6Ozj0qVLMnfu3GL71ircf2Yon9B3rotuq/13aXP715mhukCj0Yinp6fcvHlTRESGDh0qly5dMnmeTeVwH330kYgUlKUePXpIVlaW3LlzR9q3b698pmg/a1GrVq2SVq1aia+vr0RFRUlSUpLyXmnHYQr3MRftU9Lmkv+NumHt2rUydepUERFJS0uTN998U6mH2rRpI3fv3pWsrCxxdnaWLVu2iEhBffLll18qx6c9Zn1xV1v33blzR7y9vUVEJDs7W6ZMmSLZ2dk6eXvRuthQH3jhbYz1MbHN+vopy+OdIobrucLfJaJb1kvbJ21sjkLh8qWvP5jof8XQ2Grh3FmkoL/W0dFRtm/fbnBf/8nYpqGYpC0Xjx49End3d/ntt99ErVbL+PHjlbijUqmUNsDGjRslNDRURDhfgMqe/Px8cXNzk+zsbNm5c6csXbpUbt26Jf7+/iJSkO8dPHjQ5Pjc7t27ReSPnKwoztUjQ7jicRkWGxsLX19fAIC3tzemTp2qPDZKu/qZp6cnwsPD8fjxY53XW7RogWrVqil36ReVlJSEJUuWAAA8PDzg4eEBAJg+fTp27NiBtLQ0XLp0CfXq1TN4fGfOnEG/fv1gbm4Oa2tr+Pn5ITExEZ6enmjatClq1qwJAGjcuDGePn2KNm3aYMmSJbh37x66detWqkcoVahQAc+fP8f333+P999/X3k9KysLGRkZCAgIQFxcHFQqFfbv34+NGzfi0KFDuHnzpnJO8vLy0LJlS539nj59Wrk7pGrVqujevTvOnj0LGxsbeHp6Kiv0eXt74/Tp0yhfvjySk5OVu9Wzs7NRq1YttGvXDvb29v/RY7vo1VOuXDmIiNHPVK9eHbt374ZGo8GCBQuQkpKirP6opVKplBXf9u/fDy8vL+zbt69Ex2DsetdXfu/cuYPq1avDyckJANCkSRPljnegYOUQAGjatKmyErBWUlISBg4ciHLlyqFatWrKHfYrV67E4cOHkZaWhrNnzyqrP5eGmZkZKlSogMTERL1lJyAgALm5ubh58yYuXrwIlUoFKysrJCQk4JdffkF0dDSAghXgb9++rew3OTkZTk5OSpkLDg7GZ599prxftI787bffDJ5PAMp5o78f7TXaoUMHVKlSBV999RV++eUX3LhxA1lZWTqftbGxwSeffIK4uDjcuHEDJ06cQIsWLZT3g4KCABSUy/feew/Pnj1DeHg4Tpw4gX/9619ISUlR9qm9K7dHjx7o3r073N3dsXnzZpNxy9TvuHz5MtLT05XHBGo0GlSuXBmWlpbo1asXDhw4gE6dOuHJkydwcnLC2rVrce3aNZw+fRpAQblISUlBkyZNABTURbdu3UKvXr0AAM7OzqhcuTJ++eUXAED//v1haWmJmjVrom3btjh//jwSEhKQnZ2NmJgYZZ/aFXlatmwJCwump2WZsdhmqm7WV0b0MRQDCpe3mJgY9OnTB+bm5vD29saqVavw8OFDJXczlBPru2b1SUpKUlY8b9asGbZt2wYARuuAok6fPg0fHx9UqFABQMEjX3ft2gUPDw/Y29sr5btp06Z4+vQpmjZtirS0NIwcORJdu3bF1KlTDe67MG35N3buAgICMG/ePISEhGDv3r2YNGmSwfqiMGP5focOHdCoUSMABauFbd++HZaWlkbrsdatW5foN9F/j42NDaKiojBr1ix88803yuuGcrXCXFxcMHv2bKjVavzyyy/w9vZGUlISrly5ApVKZbIOKPz3z8jIQFhYGMLCwpRyW1idOnWwd+9eXLlyBQkJCdi1axe+/PJL7Nq1y+CxPnz4ECqVCra2tgBQojvbTeWAdevWRe3atZGUlIS7d+/Cw8MDVlZWxT43aNAghIWF6d1HmzZtYGNjo+zv6dOnSEhIgJeXF6ysrGBlZYUePXro3dbd3R1mZmZwdHTEkydPAMBgnE1MTDQY0xs2bKicF3p9ffjhh3j33Xdx8uRJnDx5EgMHDsTHH3+MBg0aoFKlSsr17uXlhdmzZyMzM9Pgvjw8PJCYmAgLCwsMGzYMcXFxyoqTTZo0QZMmTXD69GmsW7cOqampyMvLU1aQK5zvlSSm62NmZgYAKF++vMFr3sfHB4MGDcK0adOwd+9e+Pv748aNG7h9+zbGjRun7Ov333/X2bex+NygQQO4uLgAKHhc6NSpU+Hu7m40XjK20X+iLJdXT09PLFiwQO+x6OsPvnnzJlQqlRILfXx89LYDXFxcYGVlBXt7e1SpUgWZmZkGY9udO3fYP/uSlOVrV5/s7GyDT7cDCvqI/9P+0cDAQCxfvhwDBgzA3r17sWHDBp33r127hvT0dHTq1AmWlpZo0aIFdu3ahREjRsDDwwORkZE4ceIEVCoVevfujWfPnpW6vVg41128eDGOHDmCb7/9FpcvX1ZWegX055z62sRHjhwx2YYECla11NYFhhQ+98bOBVDQhhERZGRk6Kxie/z4cXTt2tXkeQAM59D6zrUxfya3f50Yqgt69eqFfv364ZtvvkH//v3x6NEjtG7dGunp6UbPs6kcTvt3rlWrFlq3bg1ra2vUrl1biSf6+lmLGjduHIKDg5GQkIBTp04pT9cICgoq9TiMMaZywz9TN5w9exbBwcEAgAYNGqBNmzbKdo6OjvjnP/8JALCzs1OeClarVq0Sx12tGjVqoHz58hg0aBBUKhUmTZqk94l8hetiQ33ghRnrY2Kb9fVWFsc7jdVzxpSmT9rYHAWOo9CrQl9ZERGl30br3LlzcHBwwIkTJ5RxoJIwNbZpKiadO3cObdq0QY0aNQAU9OVoV/wHdOuLAwcOGK2PAM4XoFeTubk5WrdujStXruDkyZN46623ULduXeVpNhcvXsQHH3yA3bt3Gxyfq1ChAvz9/QEUjOd9+umnJf5+ztUjZiRl1KNHj3D8+HF8//332LhxI0QEz549w4EDBwBAJ9nUaDQwNzcHAOX/ta8bSkoLvy4i+Pnnn5GdnY0pU6ZgxIgR6N27t8nJl0U75UQEarUaAHQCvpmZGUQEDRo0wP79+3HixAkcOXIEX3zxBfbv318sMSkqJSUFjRs3hkajgZWVFXbv3q2899tvv6FKlSrw9PTE/PnzkZSUhJo1a6JmzZpQq9Xw8vLCzJkzARQ0bLTHV/iYDf2GoufS3NwcarUaw4cPx9tvvw0AePbsGczNzZGRkaEMWlHZ98Ybb+Drr78uljgvWbIEnTp10gla5cqVw7Rp09C3b1988cUXGDNmjPKejY0NmjdvrjQgp0yZUuKJx8aud33lt0KFCjqva8udlrZMFn0dQLF64ubNm7C3t0dgYCACAgLQoUMHNGvWDF999VWJjl0rNzcXaWlpykCBvrIDAP7+/ti3bx8uXryI0aNHK79/w4YNqFKlCoCCx0Y4ODgojQV99U9+fr7e36TRaKBWqw2eTwAsv39Tha/R+Ph4REdHY9iwYejfvz8yMjKKlZX09HQMHToUISEh6Nq1KxwcHHDt2jXl/cJxAwAsLS0xadIkVKpUCSqVCt7e3kpH14gRI6BSqXDkyBEsXrwYycnJqFixosm4ZUhKSgp69+4NtVqNtm3bYs2aNQCAnJwcZTDJ398fy5Ytw9OnT5Ubm9RqNd577z2lw+zx48eoWLEiLl++DKCgbJUmVlpYWECj0WDx4sVo1aoVAODhw4eoXLky9uzZw7L2GjAW20zVzfrKiD6GYoBWXl4e9uzZAwsLC+XRlkDBZGRtHDaUE/8nuTIAJdYOHz7cYB2g73cUpT0f+nJlOzs7xMXF4dSpUzh27Bj69euHuLg4VKpUyeB3AH/kytrv1HfuzM3N8fTpUyQnJ+PevXto27YtDh06ZLC+MPQbDJV/EVFyZWP1GOuAV0Pnzp3RqVMnnceXGmrnFFa+fHk0b94ce/bsQaNGjeDi4oLExEScP38eo0aNUh79qFW0Dih63a9cuRJTp06Fj4+P0jmstWTJErz11ltwcnKCk5MTxo4di0GDBuHUqVMGj3Xnzp06+7h37x6sra118t+8vDydz5TkmgwMDMTevXtx9+5dg5OLjdFX3suVK2d0kkvRbQu3SQzF2ZiYGIMxnWXv9Xf06FFkZWXB29sbgYGBCAwMxPbt27Fz505Mnjy52OcL1+f6eHh4YMWKFbCyssLEiROxf/9+7NmzRxmwWbBgAW7fvg1fX1/06NEDCQkJSjkrfL2ZiumGpKSkoGbNmrCxsTF4zVtaWqJBgwY4c+YMEhMTMXv2bNy8eRN16tRR2n1qtRoPHz7U2bex+Fy0ra2Nbcbipb5JGETGvG7ltbC/IuYV3tZQe3XJkiXsn30JyvK1++abbyr9HEDB2Iu9vT0uX76MN954Q+eYzczMlLjw/Pnz/7h/tEOHDrh//z4OHDiAOnXqFMt3Y2JikJubq0zAfP78ObZu3YoRI0agT58+aNOmDY4cOYINGzbg2LFjmDt3rt72Yklz3SFDhsDFxQUuLi5wc3PTmbisL+fU1yY2FRO1zMzMlLbEm2++iQ0bNiAvLw+WlpZ4/Pgx7OzsdM69sXNR+HiKTnw5e/Ys3nnnHb3nvyhj+YS+c23In6nnXhfG6gLtxONRo0bBysoKAQEBAGDwmtbSaDRGc7jC/Uf6+nH09bMWnsR86dIl/PDDD3jrrbfg6+ur/G/evHkIDAws1TiMtbW1Tl9pfn6+zudMxaE/UzeYmZnpXGuFv7doH1vR9nxR+q7lwvvdsWMHzp49i+PHj2PQoEHYtGlTsX0U/q2G+sALM9bHxPj9eitr452m6jntsQLQ6fcCStcnbWzchWWCXhWVKlUqdjPho0ePdG4+S0xMxN69exEbG4thw4YhNjZWmRhoiqmxTVMxyVQeVjTPNVYfASx79Opyc3PDhQsXkJycjMWLFyuvxcfHo0qVKrC1tTU6Ple4LSUiJb65hXP1CABKvzwlvRK++eYbuLq64vjx4zh8+DCOHDmCsWPHKqutaRttBw8eROPGjZXgrp34ceXKFTx79gyOjo5699++fXtlHwkJCZg1axaSkpLQsWNHDB48GE2aNFEGdQEoBbkwV1dX7Nq1C2q1Gi9evMCePXuUlWH02bx5M5YvXw4vLy9ERETg8ePHRlc9AArudP76668xePBg2NraokGDBkpldurUKbz11lsAACsrK3Tp0gXz5s1T7tRwcXHBwYMH8ejRI4gI5syZU+zuYVdXV2WA+vHjx4iPj0fHjh0BFNwxn5mZiZycHMTFxaFr165wdXXF7t278fz5c+Tn52P8+PH47rvvjP4GKnvat28Pe3t7rFixQrnuT5w4gdjYWGUV0MIsLCwwbdo0rFmzBg8ePNB5z8vLC5988gneeOONUt2daux611d+/4wOHTpg//79EBE8evQIISEhuHr1KsqVK4exY8cqdVFJJ0ACBQnA8uXL0bp1a9SrV89o2fHz88O+fftw8+ZNtG/fHkBB2fz6668BANevX4e/v7+yqglQsHLA5cuXlRUGtm3bplP/FK0ja9eubfB80t9T0Ws0MTERXl5eCAwMhIODA5KSknRiYH5+Pq5cuYL69etjxIgRaN26dbFysWfPHgAF112jRo1gbW2NU6dOYcKECejRoweSkpIAFHSeBwUF4fnz5xgxYgRGjBiBq1evlihu6XP48GFcu3YNXl5eaN26NS5duoS0tDQAwKpVq7Bo0SIABasV379/H7t371Y6/l1dXbF9+3bk5eXh+fPnGDJkiM5gnI2NDerWravc+HTp0iU8fPgQTZs2BQCl7vj111+RnJyMdu3awdXVFVu2bAEA3L9/H/7+/khPT//P/1j0yjEU20zVzfrKiD6mYsCRI0dQtWpVnDx5EocPH8bhw4cRGRmJ7du3Kw1VQzmxvmtWn/bt2yv7+PnnnzF69Gh8//33BusAQ7lyXFwcsrOzkZ+fj5iYmGJPRygsPj4eU6dORbdu3TBz5kxUrFjRZNm5f/8+1qxZo8Q0Y+fOz88PERER8Pb2BgCj9UXh32Ao3z9//jzu3bsHjUaDXbt2oWvXrv9xPUb/e+Hh4Th58iTu378PAEZzNW0cBAomiKxcuRIdO3ZEx44dER8fD2tra1StWtVkHVBYlSpV4ObmhsGDB+sd2L937x5WrlyJ3NxcAMCTJ0+QkZEBR0dHg8favn17HD9+XHl9ypQp+P7771GlShVcv34dAHRWvDDEwsJCZ+CoT58+SExMxMOHD/+ylU3d3d1x4MAB5Obm4vfff8fRo0dN3pCrZSjOmorp9HqrUKECPvnkE6X8iQiuX7+OFi1aoFGjRnjy5AmSk5MBFMTIWrVqKYMqWoXLeqtWrZCWloYbN26gcePGcHFxwerVq9GtWzcABe2pkSNHwsvLC+np6Uo8KMpUTNcnMzMTy5Yt04lthnLLgIAALFy4EB07doS1tTUaNWqEp0+fKqtexcTEFFsR0lh8TktLU24qiomJQdeuXUsUL4lK43UqryXh5uaGY8eO4ffff0dubi4OHDhQqpinL7axf/blKMvX7uDBg3H+/HmlTRoREYGRI0fiwoULGDJkCICCFUJTU1MhIsoNrjdu3ChV/2jhdqGZmRn69u2LuXPnFpt4kZubiz179uDLL79U2rTx8fF48OABzpw5g0mTJiE5ORmDBg3CxIkTcfXqVYPtxZLkuk+ePMGNGzcwceJEeHh46Iz7GKKvTezk5FSimFivXj3cvXsXANCuXTs0atQICxcuRF5eHv79739j8ODBWLVqFcaPH2/yXAAFK9+KiM719PjxY/zjH/8o8Q1AhvIJfeca0N/GN+TP5PZlkbG6AABq166NmjVrYuvWrUr/o6nzXJIczhh9/ayFVa5cGStWrMCPP/6ovJaamooWLVqUehymUqVKePr0KR4/fozc3FycOHHC5PH9VXWDm5sb9u7dC41Gg19//RUXLlwo8TkqjatXryIkJAQdOnTA9OnT0bhxY6SlpenU4UUZ6gMvvE1px5Tp9VHWxjtN1XN2dnZK7I2Pj9fZtjR90sbmKBRmrOwR/bfZ2Nigfv36Ou2tbdu2KSvrP378GNOnT8fChQtRo0YNLF68WLmB0JSSjG0aiklabdu2xZUrV3D//n2ICPbt22c0DzNWHxG9yrT9II6Ojsq4rLu7O9avX6887cPY+FxWVpYSs2JiYtCpUyeT38m5eqTFFY/LqNjYWPy///f/dF4bMmQI1q5dCxsbG1y4cAE7d+6EtbW1zuPtbt++jX79+gEAli5davCu1tmzZ2PmzJn4+uuvYW1tjblz58LW1hahoaHw8/ODpaUlmjVrpiTV3bp1wzvvvIO1a9cq+wgODsaNGzcQEBCAvLw8+Pv7o2fPnkqHUFF9+/bF5MmT4efnBwsLC4SGhupdwS06OhobNmxQ7oyfPn062rZtC6DgkWBz5szB2rVrYWlpiaVLlyrJQ0BAAL755hv06dMHANC8eXOEhoZi+PDh0Gg0aNGiRbE74MePH485c+bAz88ParUaY8eORatWrZCSkgJ7e3uMHj1aWR5eu9rCjz/+iIEDB0KtVqNLly7o168ffv31VwN/SSqLzMzMsGrVKsyfPx++vr6wsLCAnZ0dPvvsMzg4OOh9nFbXrl3h7OyMTz/9VOduepVKhRkzZmDixImlPg5D17u+8vtnDBkyBHPnzlUSgVmzZqFdu3Zo0aIFvLy8UKFCBXTo0EHpMDbk/v37Smeitsx98sknAAoexamv7ADAP//5T9jZ2cHZ2VkpzzNnzsTs2bPh5+cHAFi0aJHyGDsAcHBwQGRkJEJDQ5GXl4datWrho48+Ut7XV0caqz/o78HYNRoUFISpU6fi22+/hZWVFZydnZWy3r17dwQEBGD79u3YsmULvL29YWVlBScnJ6Smpir718bEf/zjH8p1FxYWhiFDhqBSpUpo2LAhateujTt37mDy5MkIDw+HhYUFypcvjw8//BCOjo4m45bWO++8o6xkYWdnp+QHNjY2mDdvHiZNmgSNRqM09LW8vLxw8uRJ1K1bF0DBo+Fv3ryJfv36IT8/H/3794eLi4tOLNeWneXLl8PS0hLLly9XHjOfnZ2NwMBA5ObmIjIyEnZ2dggNDcWcOXPg6+urrFBVr169Yo89o7LLUGwzVTfrKyP6mIoBsbGxGDx4sM42vr6+WLJkiTLgYygn1nfN6jNhwgTMnDkT/v7+sLCwwKJFi9CiRQts3bpVbx3QpUsXRERE6Kwgq1KpcO3aNQQGBiI/Px9dunRBSEgIfvvtN73f2bVrV3z33Xfw8fFB+fLl0atXLzRr1qzY57Zu3YpDhw4pq3oEBwfDx8fH5LnTrnqufXxhtWrVjNYXgPF8v3r16pg2bRru3bsHd3d3BAUFwdzcvMT1GL1cNjY2iIqKwsiRIwEYz9W0cTA2NhbdunXDnDlz0LFjR1SuXBn29vbK5BBTdYA+77zzDvz9/REfH4/u3bsrr8+aNQsLFy5Enz59YG1tDUtLS0yZMgWNGzdG48aN9R6rmZkZQkJCMGjQIGg0GvTs2ROdOnVC3bp1MWnSJPj5+cHV1RXVqlUzekz29vaoVasWhg4dik2bNqFChQpwdnY2eFMv8Ee5LGz69OkGP+/h4YELFy6gX79+qFy5MqpXr17iSROG4mxJYjq9vlxdXREaGoqxY8cqqx126dIF48ePh5WVFZYuXYqoqCi8ePEClStXxtKlS4vtQzuI4+DggKFDh6Jdu3bKxC1XV1fs2LFD6fwdM2YMpk2bhkqVKsHe3h5vvPGG3nayqZiudfjwYQQEBMDMzAxqtRq9e/fGqFGjABi+5gGgZ8+eiIiIwJQpUwAUdHQvW7YMH330EXJycmBjY6MTmwHj8bly5cqIjo7GrVu30KxZM8ydOxcVK1Y0GS+JSuN1Ka+Fvf3228rj3YtydHTEsGHDEBwcjIoVK8LOzq7EMc9QbAPYP/sylOVrt2rVqvjqq6+wYMECrFixAgDQqFEj2Nvb4+jRoxgwYACmTJmCsWPHwsHBAe3atUNGRgaaN29eqv7RTp06YcmSJbC1tUWfPn3g4+OD9evXo0ePHjqfO3LkCGrXrq1zU5uNjQ2CgoKwdetWjB07FjNmzMCqVatgbm6O8PBwtGnTRm97cciQISZz3SpVqiAoKAg+Pj6wsbGBs7MzsrOzkZWVZfC36GsTV69evUQxUaVS4fTp02jcuLHypJNPPvkEPj4+sLS0RKVKlVCvXj0cP34cGRkZRs+Fi4sLkpKSoFKpdL7j5MmT6Ny5s8HjL8pQPqHvXAP6x8MM+TO5fVlkrC7Q8vb2xoEDB5TVfEtynk3lcMbo62ctrGHDhliwYAE++OAD/P777zAzM0Pr1q0xe/ZsAKUbh7G1tcXIkSMxYMAA1KxZE2+++abJ4/ur6oZFixYhNTUVXl5eqF27ttE26p/RsmVLODs7w9fXF9bW1mjRogW6du2K27dvY8WKFfj444/RqFEjnW0M9YE7OTkp20ycOLFUY8r0eilL452m6rkJEyYgKioKK1asKBaLStMnbWyOQmFF8yei/zVt+V25ciXy8vLQrFkzzJ49GyKC8PBw9O3bFx06dAAAODk5Yfjw4Xjvvffw1VdfFZurVNqxTe38gaIx6YcffgBQkOfPnDkT//d//wcrKyvUqVPH5BMkOV+AyiJHR0c8efJEuXEWKIhXkyZNUiYeG5sfV6lSJRw6dAjLli1DjRo1MH/+fL3fw7l6pI+ZFF2fmso8T09PbNy4EXXq1NF5fejQoQgNDeUdon+B2NhYnD171uikGCJ6NRmqI4norxUeHo6OHTuW+JFJRP8rhnJiXrN/nTNnzmDFihV6H7VJ9DoRETx//hzBwcH48ssvTU5aLqmLFy/ixo0b6NevH/Ly8hAcHIx58+ahefPmf8n+iaj07ty5g2HDhimrXBLRXyMtLQ3Hjh3DiBEjAADjxo1DUFAQPD09X+6BEaFgJd0rV64oK7b9lTQaDbZs2YK0tDTl8bJ/Fw8ePMCkSZOMPsZeo9Hg2LFjxSYU6xMWFobQ0FC9N+a+Cpjb68rPz8e0adPQp08f9OrV62Ufzivn71w3EL3u2CdN9L+XkZGBTZs2ITQ0FOXKlcPcuXNRv359TtInKsM4V+/VwxWPiYiIiIiIiIhK6cqVKxg1ahTGjx//l006BgpW3FqxYgXWr18PEUHfvn3/thMTiIjo9Va7dm1cuXIFvr6+MDMzQ+fOnUs00ZDof8HGxua/MukYKFhhNz09HevWrfuv7P9VVq1aNfTs2ROHDh0qtqKrVrly5UpUFyQnJ6NWrVqv7KRjgLl9YSKCLl26oFOnTgb/9n93f+e6gYiI6K9WpUoVPHv2DL6+vjA3N0erVq0wcODAl31YRESvFa54TERERERERERERERERERERERERERERCaVe9kHQERERERERERERERERERERERERERERK8+TjwmIiIiIiIiIiIiIiIiIiIiIiIiIiIikzjxmIiIiIiIiIiIiIiIiIiIiIiIiIiIiEzixGMiIiIiIiIiIiKiV9ClS5cwdOhQ+Pn5wdfXF6NGjUJqaurLPizF7du3ERYWVurt1q1bh/DwcKOfuXPnDtq0aWNyX0OHDsW3335r8nPLly+Hq6srAgICEBAQAH9/f3h6emL+/PkQEaPbxsbGYsyYMSa/g4iIiIiIiIiIiOjvwOJlHwARERERERERERER6crNzcWYMWPwxRdfoFWrVgCA3bt3Y/To0YiPj4e5uflLPkLg7t27SEtLe9mHUWLe3t6YPXu28t9Pnz6Fv78/OnfujC5durzEIyMiIiIiIiIiIiIqOzjxmIiIiIiIiIiIiOgV8+LFC2RmZiIrK0t5zd/fHzY2NlCr1Th37hyioqKwd+9eAMCZM2eU/37x4gUiIiJw+fJl2NraokmTJgCABQsWIDk5GXPmzEFeXh7q1auHu3fvIjw8HC4uLjh8+DBWr16NvLw8VKhQAdOnT0ebNm3w888/Y8aMGcjNzYWIYMCAARg0aBBmzpyJe/fuYeTIkVi3bh0uXLiAjz/+GC9evICZmRnCwsKgUqmQl5eHuXPnIiEhAfb29rC3t4etrW2pzsfKlSsRFxcHc3NzNGzYELNmzUK1atUAAAcPHsRnn32G7Oxs+Pn5Ydy4cSXa58OHD5GdnY3KlSsDAM6dO4dFixbhxYsXsLS0xKRJk9C1a1edbTIzM/HRRx/hp59+Ql5eHtzc3DBt2jRYWLCrnYiIiIiIiIiIiP4e2BtKRERERERERERE9IqpXLky3nvvPYwaNQoODg5o27YtXFxc4OPjAysrK6Pbrlq1Cmq1Gvv370dWVhaGDBmCli1bIj8/H2FhYYiMjISHhwdOnz6NESNGAABu3LiBpUuXYuPGjbCzs0NqairefvttHDhwAOvWrYOnpyfeeecdPHjwAPPmzcPgwYMxd+5cREVFYd26dXj69Cnef/99rFu3DnXq1MG9e/cwcOBANGvWDAcPHsSNGzcQFxeH/Px8hISElGricUxMDE6cOIGdO3eiYsWKWL58OcLDw7Fu3ToAwPPnz7F9+3ZkZ2cjKCgILVu2hIeHR7H97Nu3D+fPn0d2djaePHmCli1b4sMPP4STkxMyMjIwYcIErF69Gq1bt0ZqaipCQkKwc+dOnX3MmzcPrVq1woIFC6BWqxEeHo7169dj9OjRJf49RERERERERERERGUZJx4TERERERERERERvYLefvttBAUFISkpCUlJSfj888/x+eefF5sMW9SxY8fw/vvvo1y5crCxsUG/fv2QkpKCn376CQCUSbmurq5o2rQpAODUqVO4f/++MhEZAMzMzHDr1i307NkT06dPR3JyMtzc3DBz5kyUK1dO5zsvXbqEBw8eYPz48Trbp6SkIDExEb6+vrCysoKVlRX8/PyQkpJS4vNw/Phx9O/fHxUrVgQADBs2DGvWrEFubi4AYMCAAbCwsICNjQ169+6NhIQEvROPvb29MXv2bOTm5iIqKgqpqanKisbJycmoV68eWrduDQBo2rQp2rZti7Nnz8LMzEzZx9GjR3HlyhXlb5CdnV3i30FERERERERERET0OuDEYyIiIiIiIiIiIqJXzPnz53Hx4kWMGjUKKpUKKpUKkydPhp+fH06dOoWqVatCRJTP5+XlKf+2sLDQeU87Sdjc3Fznde1rAKDRaODm5oZPP/1UeS89PR3Vq1dH8+bN8d133yEhIQGJiYlYuXIltm7dqrMftVqNxo0bY8eOHcpr9+7dQ9WqVbFt2za931nUuXPnUKNGDdStWxciAguLgu7roses0WiQn5+vd3+FtzPEysoKs2bNQmBgIBYvXoyIiAhoNJpinxMR5Ofnw9LSUue7ly1bhsaNGwMAnj17pjMxmYiIiIiIiIiIiOh1V870R4iIiIiIiIiIiIjof6lq1apYvXo1zp07p7z24MEDvHjxAo6OjqhatSru3r2LR48eQURw6NAh5XMeHh6IiYmBRqPBixcvsHfvXpiZmaFx48awsrLC8ePHARSs8vvTTz/BzMwMrq6uOHXqFH7++WcABasm+/v7IycnB1OmTMG+ffvg4+ODiIgI2NjYID09Hebm5sqEZ2dnZ9y8eRNJSUkAgGvXrqF37964f/8+unTpgl27diEnJwc5OTnYt2+f3t8cGxuLmJgYAMDVq1dRt25dAEDnzp0RGxuLrKwsAMCmTZvQoUMHWFlZAQB27doFEcHTp0+xf/9+ZRVjY6ysrBAREYFt27bhhx9+QOvWrZGWlobk5GQAQGpqKpKSktCxY0ed7Tp37owvv/wSIoLc3FyMGzcOmzdvNvl9RERERERERERERK8LrnhMRERERERERERE9Ipp2LAhVq5ciaVLl+K3335D+fLlYWtri8jISDRq1AgAMGjQIAQGBqJatWro1q2bsu2YMWMQGRkJPz8/2Nrawt7eHhUqVICFhQWWL1+OiIgILFmyBA0aNICDgwMqVKiApk2bIjIyEpMnT1ZWDV69ejUqVqyId999FzNmzMC2bdtgbm6OHj16oGPHjnj27BnMzc0xYMAA7NixA9HR0Vi0aBFycnIgIli0aBFq166NQYMG4datW/D19UWVKlVQv359vb953LhxmDZtGnx9fWFmZobIyEgAwIABA5Ceno6goCBoNBrUr18fH3/8sbKdra0t+vfvj+zsbISEhMDFxaVE57h9+/bw8/NDVFQUtmzZgmXLliEqKgrZ2dkwMzPD/Pnz0bBhQ1y8eFHZZsaMGfjoo4/g5+eHvLw8dOrUCaNGjSrtn5eIiIiIiIiIiIiozDKTos+pIyIiIiIiIiIiIqIyKy4uDjY2NvDw8IBGo0FYWBjc3d0xZMgQLFy4ECNHjoSDgwPS09MREBCAQ4cOoVKlSi/7sImIiIiIiIiIiIioDOCKx0RERERERERERESvkaZNm2L27NlYsmQJ8vLy4OLigqCgIABA7dq1MWLECFhYWEBEMHfuXE46JiIiIiIiIiIiIqIS44rHREREREREREREREREREREREREREREZFK5l30ARERERERERERERERERERERERERERE9OrjxGMiIiIiIiIiIiIiIiIiIiIiIiIiIiIyiROPiYiIiIiIiIiIiIiIiIiIiIiIiIiIyCROPCYiIiIiIiIiIiIiIiIiIiIiIiIiIiKTOPGYiIiIiIiIiIiIiIiIiIiIiIiIiIiITOLEYyIiIiIiIiIiIiIiIiIiIiIiIiIiIjLp/wOpPv6DfR0gYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(50,10)})\n",
    "sns.countplot(x = df[\"Suggested Job Role\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **4. Exploratory Data Analysis** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) Correlation Between Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:51.054559Z",
     "iopub.status.busy": "2021-05-21T09:14:51.053970Z",
     "iopub.status.idle": "2021-05-21T09:14:51.424792Z",
     "shell.execute_reply": "2021-05-21T09:14:51.423212Z",
     "shell.execute_reply.started": "2021-05-21T09:14:51.054502Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df[['Logical quotient rating', 'hackathons', \n",
    "           'coding skills rating', 'public speaking points']].corr()\n",
    "f,axes = plt.subplots(1,1,figsize = (10,10))\n",
    "sns.heatmap(corr,square=True,annot = True,linewidth = .4,center = 2,ax = axes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No highly corelated numerical pair found.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (b) Visualization for Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:52.138482Z",
     "iopub.status.busy": "2021-05-21T09:14:52.138050Z",
     "iopub.status.idle": "2021-05-21T09:14:52.148135Z",
     "shell.execute_reply": "2021-05-21T09:14:52.146704Z",
     "shell.execute_reply.started": "2021-05-21T09:14:52.138446Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Software Engineering     731\n",
      "IOT                      722\n",
      "cloud computing          721\n",
      "programming              716\n",
      "networks                 713\n",
      "Computer Architecture    703\n",
      "data engineering         672\n",
      "hacking                  663\n",
      "Management               644\n",
      "parallel computing       616\n",
      "Name: Interested subjects, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Interested subjects\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:52.547914Z",
     "iopub.status.busy": "2021-05-21T09:14:52.547550Z",
     "iopub.status.idle": "2021-05-21T09:14:52.847939Z",
     "shell.execute_reply": "2021-05-21T09:14:52.846740Z",
     "shell.execute_reply.started": "2021-05-21T09:14:52.547884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure Size\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "# Horizontal Bar Plot\n",
    "title_cnt=df[\"Interested subjects\"].value_counts().sort_values(ascending=False).reset_index()\n",
    "mn= ax.barh(title_cnt.iloc[:,0], title_cnt.iloc[:,1],edgecolor='black', color=sns.color_palette('pastel',len(title_cnt)))\n",
    "\n",
    "# Remove axes splines\n",
    "for s in ['top','bottom','left','right']:\n",
    "    ax.spines[s].set_visible(False)\n",
    "\n",
    "# Remove x,y Ticks\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.yaxis.set_ticks_position('none')\n",
    "# Add padding between axes and labels\n",
    "ax.xaxis.set_tick_params(pad=5)\n",
    "ax.yaxis.set_tick_params(pad=10)\n",
    "\n",
    "\n",
    "# Show top values \n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Add Plot Title\n",
    "ax.set_title('Interested Subjects',weight='bold',fontsize=20)\n",
    "ax.set_xlabel('Count', weight='bold')\n",
    "\n",
    "# Add annotation to bars\n",
    "for i in ax.patches:\n",
    "    ax.text(i.get_width()+1, i.get_y()+0.5, str(round((i.get_width()), 2)),\n",
    "             fontsize=10, fontweight='bold', color='grey')\n",
    "plt.yticks(weight='bold')\n",
    "plt.xticks(weight='bold')\n",
    "\n",
    "# Show Plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:52.927093Z",
     "iopub.status.busy": "2021-05-21T09:14:52.926743Z",
     "iopub.status.idle": "2021-05-21T09:14:52.936450Z",
     "shell.execute_reply": "2021-05-21T09:14:52.935230Z",
     "shell.execute_reply.started": "2021-05-21T09:14:52.927062Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Guide                    405\n",
      "Health                   401\n",
      "Self help                377\n",
      "Horror                   377\n",
      "Biographies              219\n",
      "Science fiction          218\n",
      "Satire                   212\n",
      "Childrens                212\n",
      "Autobiographies          210\n",
      "Prayer books             207\n",
      "Fantasy                  205\n",
      "Journals                 203\n",
      "Trilogy                  203\n",
      "Anthology                202\n",
      "Encyclopedias            201\n",
      "Drama                    201\n",
      "Mystery                  200\n",
      "History                  199\n",
      "Science                  198\n",
      "Dictionaries             198\n",
      "Diaries                  197\n",
      "Religion-Spirituality    197\n",
      "Action and Adventure     193\n",
      "Poetry                   193\n",
      "Cookbooks                186\n",
      "Comics                   186\n",
      "Art                      186\n",
      "Travel                   186\n",
      "Series                   180\n",
      "Math                     176\n",
      "Romance                  173\n",
      "Name: Interested Type of Books, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Interested Type of Books\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:53.304220Z",
     "iopub.status.busy": "2021-05-21T09:14:53.303797Z",
     "iopub.status.idle": "2021-05-21T09:14:53.928148Z",
     "shell.execute_reply": "2021-05-21T09:14:53.926775Z",
     "shell.execute_reply.started": "2021-05-21T09:14:53.304142Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure Size\n",
    "fig, ax = plt.subplots(figsize=(12,15))\n",
    "\n",
    "# Horizontal Bar Plot\n",
    "title_cnt=df[\"Interested Type of Books\"].value_counts().sort_values(ascending=False).reset_index()\n",
    "mn= ax.barh(title_cnt.iloc[:,0], title_cnt.iloc[:,1],edgecolor='black', color=sns.color_palette('pastel',len(title_cnt)))\n",
    "\n",
    "\n",
    "\n",
    "# Remove axes splines\n",
    "for s in ['top','bottom','left','right']:\n",
    "    ax.spines[s].set_visible(False)\n",
    "\n",
    "# Remove x,y Ticks\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.yaxis.set_ticks_position('none')\n",
    "# Add padding between axes and labels\n",
    "ax.xaxis.set_tick_params(pad=5)\n",
    "ax.yaxis.set_tick_params(pad=10)\n",
    "\n",
    "\n",
    "# Show top values \n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Add Plot Title\n",
    "ax.set_title('Interested Books',weight='bold',fontsize=20)\n",
    "ax.set_xlabel('Count', weight='bold')\n",
    "\n",
    "# Add annotation to bars\n",
    "for i in ax.patches:\n",
    "    ax.text(i.get_width()+1, i.get_y()+0.5, str(round((i.get_width()), 2)),\n",
    "             fontsize=10, fontweight='bold', color='grey')\n",
    "plt.yticks(weight='bold')\n",
    "plt.xticks(weight='bold')\n",
    "\n",
    "# Show Plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:53.930671Z",
     "iopub.status.busy": "2021-05-21T09:14:53.930201Z",
     "iopub.status.idle": "2021-05-21T09:14:53.940055Z",
     "shell.execute_reply": "2021-05-21T09:14:53.939127Z",
     "shell.execute_reply.started": "2021-05-21T09:14:53.930625Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r programming           803\n",
      "information security    785\n",
      "shell programming       783\n",
      "machine learning        783\n",
      "full stack              768\n",
      "hadoop                  764\n",
      "python                  756\n",
      "distro making           740\n",
      "app development         719\n",
      "Name: certifications, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"certifications\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:54.083771Z",
     "iopub.status.busy": "2021-05-21T09:14:54.083157Z",
     "iopub.status.idle": "2021-05-21T09:14:54.383606Z",
     "shell.execute_reply": "2021-05-21T09:14:54.382132Z",
     "shell.execute_reply.started": "2021-05-21T09:14:54.083716Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure Size\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "# Horizontal Bar Plot\n",
    "title_cnt=df.certifications.value_counts().sort_values(ascending=False).reset_index()\n",
    "mn= ax.barh(title_cnt.iloc[:,0], title_cnt.iloc[:,1],edgecolor='black', color=sns.color_palette('pastel',len(title_cnt)))\n",
    "\n",
    "\n",
    "\n",
    "# Remove axes splines\n",
    "for s in ['top','bottom','left','right']:\n",
    "    ax.spines[s].set_visible(False)\n",
    "\n",
    "# Remove x,y Ticks\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.yaxis.set_ticks_position('none')\n",
    "# Add padding between axes and labels\n",
    "ax.xaxis.set_tick_params(pad=5)\n",
    "ax.yaxis.set_tick_params(pad=10)\n",
    "\n",
    "\n",
    "# Show top values \n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Add Plot Title\n",
    "ax.set_title('Certifications',weight='bold',fontsize=20)\n",
    "ax.set_xlabel('Count', weight='bold')\n",
    "\n",
    "# Add annotation to bars\n",
    "for i in ax.patches:\n",
    "    ax.text(i.get_width()+1, i.get_y()+0.5, str(round((i.get_width()), 2)),\n",
    "             fontsize=10, fontweight='bold', color='grey')\n",
    "plt.yticks(weight='bold')\n",
    "plt.xticks(weight='bold')\n",
    "\n",
    "# Show Plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:54.492267Z",
     "iopub.status.busy": "2021-05-21T09:14:54.491869Z",
     "iopub.status.idle": "2021-05-21T09:14:54.501132Z",
     "shell.execute_reply": "2021-05-21T09:14:54.500017Z",
     "shell.execute_reply.started": "2021-05-21T09:14:54.492224Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "database security    897\n",
      "system designing     891\n",
      "web technologies     891\n",
      "hacking              867\n",
      "testing              852\n",
      "data science         842\n",
      "game development     831\n",
      "cloud computing      830\n",
      "Name: workshops, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"workshops\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:54.934947Z",
     "iopub.status.busy": "2021-05-21T09:14:54.934600Z",
     "iopub.status.idle": "2021-05-21T09:14:55.351557Z",
     "shell.execute_reply": "2021-05-21T09:14:55.350212Z",
     "shell.execute_reply.started": "2021-05-21T09:14:54.934917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure Size\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "# Horizontal Bar Plot\n",
    "title_cnt=df.workshops.value_counts().sort_values(ascending=False).reset_index()\n",
    "mn= ax.barh(title_cnt.iloc[:,0], title_cnt.iloc[:,1],edgecolor='black', color=sns.color_palette('pastel',len(title_cnt)))\n",
    "\n",
    "\n",
    "\n",
    "# Remove axes splines\n",
    "for s in ['top','bottom','left','right']:\n",
    "    ax.spines[s].set_visible(False)\n",
    "\n",
    "# Remove x,y Ticks\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.yaxis.set_ticks_position('none')\n",
    "# Add padding between axes and labels\n",
    "ax.xaxis.set_tick_params(pad=5)\n",
    "ax.yaxis.set_tick_params(pad=10)\n",
    "\n",
    "\n",
    "# Show top values \n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Add Plot Title\n",
    "ax.set_title('Workshops Attended',weight='bold',fontsize=20)\n",
    "ax.set_xlabel('Count', weight='bold')\n",
    "\n",
    "# Add annotation to bars\n",
    "for i in ax.patches:\n",
    "    ax.text(i.get_width()+1, i.get_y()+0.5, str(round((i.get_width()), 2)),\n",
    "             fontsize=10, fontweight='bold', color='grey')\n",
    "plt.yticks(weight='bold')\n",
    "plt.xticks(weight='bold')\n",
    "\n",
    "# Show Plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:56.103870Z",
     "iopub.status.busy": "2021-05-21T09:14:56.103480Z",
     "iopub.status.idle": "2021-05-21T09:14:56.115234Z",
     "shell.execute_reply": "2021-05-21T09:14:56.113820Z",
     "shell.execute_reply.started": "2021-05-21T09:14:56.103825Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Service Based                        725\n",
      "Web Services                         719\n",
      "BPA                                  711\n",
      "Testing and Maintainance Services    698\n",
      "Product based                        695\n",
      "Finance                              694\n",
      "Cloud Services                       692\n",
      "product development                  669\n",
      "Sales and Marketing                  658\n",
      "SAaS services                        640\n",
      "Name: Type of company want to settle in?, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Type of company want to settle in?\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:56.462673Z",
     "iopub.status.busy": "2021-05-21T09:14:56.462316Z",
     "iopub.status.idle": "2021-05-21T09:14:56.756488Z",
     "shell.execute_reply": "2021-05-21T09:14:56.755426Z",
     "shell.execute_reply.started": "2021-05-21T09:14:56.462643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure Size\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "# Horizontal Bar Plot\n",
    "title_cnt=df[\"Type of company want to settle in?\"].value_counts().sort_values(ascending=False).reset_index()\n",
    "mn= ax.barh(title_cnt.iloc[:,0], title_cnt.iloc[:,1],edgecolor='black', color=sns.color_palette('pastel',len(title_cnt)))\n",
    "\n",
    "\n",
    "\n",
    "# Remove axes splines\n",
    "for s in ['top','bottom','left','right']:\n",
    "    ax.spines[s].set_visible(False)\n",
    "\n",
    "# Remove x,y Ticks\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.yaxis.set_ticks_position('none')\n",
    "# Add padding between axes and labels\n",
    "ax.xaxis.set_tick_params(pad=5)\n",
    "ax.yaxis.set_tick_params(pad=10)\n",
    "\n",
    "\n",
    "# Show top values \n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Add Plot Title\n",
    "ax.set_title('Type of Company you want to settle in?',weight='bold',fontsize=20)\n",
    "ax.set_xlabel('Count', weight='bold')\n",
    "\n",
    "# Add annotation to bars\n",
    "for i in ax.patches:\n",
    "    ax.text(i.get_width()+1, i.get_y()+0.5, str(round((i.get_width()), 2)),\n",
    "             fontsize=10, fontweight='bold', color='grey')\n",
    "plt.yticks(weight='bold')\n",
    "plt.xticks(weight='bold')\n",
    "\n",
    "# Show Plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:57.704002Z",
     "iopub.status.busy": "2021-05-21T09:14:57.703617Z",
     "iopub.status.idle": "2021-05-21T09:14:57.714005Z",
     "shell.execute_reply": "2021-05-21T09:14:57.712713Z",
     "shell.execute_reply.started": "2021-05-21T09:14:57.703967Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "system developer            1178\n",
      "security                    1177\n",
      "Business process analyst    1154\n",
      "developer                   1145\n",
      "testing                     1128\n",
      "cloud computing             1119\n",
      "Name: interested career area , dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"interested career area \"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:14:58.064643Z",
     "iopub.status.busy": "2021-05-21T09:14:58.064306Z",
     "iopub.status.idle": "2021-05-21T09:14:58.293301Z",
     "shell.execute_reply": "2021-05-21T09:14:58.292464Z",
     "shell.execute_reply.started": "2021-05-21T09:14:58.064614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure Size\n",
    "fig, ax = plt.subplots(figsize=(10,4)) #width,height\n",
    "\n",
    "# Horizontal Bar Plot\n",
    "title_cnt=df[\"interested career area \"].value_counts().sort_values(ascending=False).reset_index()\n",
    "mn= ax.barh(title_cnt.iloc[:,0], title_cnt.iloc[:,1],edgecolor='black', color=sns.color_palette('pastel',len(title_cnt)))\n",
    "\n",
    "\n",
    "\n",
    "# Remove axes splines\n",
    "for s in ['top','bottom','left','right']:\n",
    "    ax.spines[s].set_visible(False)\n",
    "\n",
    "# Remove x,y Ticks\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.yaxis.set_ticks_position('none')\n",
    "# Add padding between axes and labels\n",
    "ax.xaxis.set_tick_params(pad=5)\n",
    "ax.yaxis.set_tick_params(pad=10)\n",
    "\n",
    "\n",
    "# Show top values \n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Add Plot Title\n",
    "ax.set_title('Interested Career Area ',weight='bold',fontsize=20)\n",
    "ax.set_xlabel('Count', weight='bold')\n",
    "\n",
    "# Add annotation to bars\n",
    "for i in ax.patches:\n",
    "    ax.text(i.get_width()+1, i.get_y()+0.5, str(round((i.get_width()), 2)),\n",
    "             fontsize=10, fontweight='bold', color='grey')\n",
    "plt.yticks(weight='bold')\n",
    "plt.xticks(weight='bold')\n",
    "\n",
    "# Show Plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **5. Feature Engineering** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) Binary Encoding for Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:00.059997Z",
     "iopub.status.busy": "2021-05-21T09:15:00.059636Z",
     "iopub.status.idle": "2021-05-21T09:15:00.101071Z",
     "shell.execute_reply": "2021-05-21T09:15:00.100277Z",
     "shell.execute_reply.started": "2021-05-21T09:15:00.059968Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = df[[\"self-learning capability?\", \"Extra-courses did\",\"Taken inputs from seniors or elders\", \"worked in teams ever?\", \"Introvert\"]]\n",
    "for i in cols:\n",
    "    cleanup_nums = {i: {\"yes\": 1, \"no\": 0}}\n",
    "    df = df.replace(cleanup_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:00.401318Z",
     "iopub.status.busy": "2021-05-21T09:15:00.400761Z",
     "iopub.status.idle": "2021-05-21T09:15:00.409457Z",
     "shell.execute_reply": "2021-05-21T09:15:00.408574Z",
     "shell.execute_reply.started": "2021-05-21T09:15:00.401280Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "List of Categorical features: \n",
      " ['certifications', 'workshops', 'reading and writing skills', 'memory capability score', 'Interested subjects', 'interested career area ', 'Type of company want to settle in?', 'Interested Type of Books', 'Management or Technical', 'hard/smart worker', 'Suggested Job Role']\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\\nList of Categorical features: \\n\" , df.select_dtypes(include=['object']).columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) Number Encoding for Categorical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:02.630142Z",
     "iopub.status.busy": "2021-05-21T09:15:02.629617Z",
     "iopub.status.idle": "2021-05-21T09:15:02.680281Z",
     "shell.execute_reply": "2021-05-21T09:15:02.679472Z",
     "shell.execute_reply.started": "2021-05-21T09:15:02.630109Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "List of Categorical features: \n",
      " ['Management or Technical', 'hard/smart worker', 'Suggested Job Role']\n"
     ]
    }
   ],
   "source": [
    "mycol = df[[\"reading and writing skills\", \"memory capability score\"]]\n",
    "for i in mycol:\n",
    "    cleanup_nums = {i: {\"poor\": 0, \"medium\": 1, \"excellent\": 2}}\n",
    "    df = df.replace(cleanup_nums)\n",
    "\n",
    "category_cols = df[['certifications', 'workshops', 'Interested subjects', 'interested career area ', 'Type of company want to settle in?', \n",
    "                    'Interested Type of Books']]\n",
    "for i in category_cols:\n",
    "    df[i] = df[i].astype('category')\n",
    "    df[i + \"_code\"] = df[i].cat.codes\n",
    "\n",
    "print(\"\\n\\nList of Categorical features: \\n\" , df.select_dtypes(include=['object']).columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) Dummy Variable Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:04.145103Z",
     "iopub.status.busy": "2021-05-21T09:15:04.144613Z",
     "iopub.status.idle": "2021-05-21T09:15:04.152520Z",
     "shell.execute_reply": "2021-05-21T09:15:04.151594Z",
     "shell.execute_reply.started": "2021-05-21T09:15:04.145064Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Management' 'Technical']\n",
      "['smart worker' 'hard worker']\n"
     ]
    }
   ],
   "source": [
    "print(df['Management or Technical'].unique())\n",
    "print(df['hard/smart worker'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:05.552686Z",
     "iopub.status.busy": "2021-05-21T09:15:05.552121Z",
     "iopub.status.idle": "2021-05-21T09:15:05.585075Z",
     "shell.execute_reply": "2021-05-21T09:15:05.584283Z",
     "shell.execute_reply.started": "2021-05-21T09:15:05.552609Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Logical quotient rating</th>\n",
       "      <th>hackathons</th>\n",
       "      <th>coding skills rating</th>\n",
       "      <th>public speaking points</th>\n",
       "      <th>self-learning capability?</th>\n",
       "      <th>Extra-courses did</th>\n",
       "      <th>certifications</th>\n",
       "      <th>workshops</th>\n",
       "      <th>reading and writing skills</th>\n",
       "      <th>memory capability score</th>\n",
       "      <th>...</th>\n",
       "      <th>certifications_code</th>\n",
       "      <th>workshops_code</th>\n",
       "      <th>Interested subjects_code</th>\n",
       "      <th>interested career area _code</th>\n",
       "      <th>Type of company want to settle in?_code</th>\n",
       "      <th>Interested Type of Books_code</th>\n",
       "      <th>A_Management</th>\n",
       "      <th>A_Technical</th>\n",
       "      <th>B_hard worker</th>\n",
       "      <th>B_smart worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>information security</td>\n",
       "      <td>testing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>shell programming</td>\n",
       "      <td>testing</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>information security</td>\n",
       "      <td>testing</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>r programming</td>\n",
       "      <td>database security</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>distro making</td>\n",
       "      <td>game development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Logical quotient rating  hackathons  coding skills rating  \\\n",
       "0                        5           0                     6   \n",
       "1                        7           6                     4   \n",
       "2                        2           3                     9   \n",
       "3                        2           6                     3   \n",
       "4                        2           0                     3   \n",
       "\n",
       "   public speaking points  self-learning capability?  Extra-courses did  \\\n",
       "0                       2                          1                  0   \n",
       "1                       3                          0                  1   \n",
       "2                       1                          0                  1   \n",
       "3                       5                          0                  1   \n",
       "4                       4                          1                  0   \n",
       "\n",
       "         certifications          workshops  reading and writing skills  \\\n",
       "0  information security            testing                           0   \n",
       "1     shell programming            testing                           2   \n",
       "2  information security            testing                           2   \n",
       "3         r programming  database security                           2   \n",
       "4         distro making   game development                           2   \n",
       "\n",
       "   memory capability score  ... certifications_code workshops_code  \\\n",
       "0                        0  ...                   4              6   \n",
       "1                        1  ...                   8              6   \n",
       "2                        0  ...                   4              6   \n",
       "3                        0  ...                   7              2   \n",
       "4                        1  ...                   1              3   \n",
       "\n",
       "  Interested subjects_code  interested career area _code  \\\n",
       "0                        9                             5   \n",
       "1                        2                             4   \n",
       "2                        5                             0   \n",
       "3                        7                             5   \n",
       "4                        3                             4   \n",
       "\n",
       "  Type of company want to settle in?_code  Interested Type of Books_code  \\\n",
       "0                                       0                             28   \n",
       "1                                       1                              3   \n",
       "2                                       9                             29   \n",
       "3                                       7                             13   \n",
       "4                                       0                             14   \n",
       "\n",
       "   A_Management A_Technical  B_hard worker  B_smart worker  \n",
       "0             1           0              0               1  \n",
       "1             0           1              1               0  \n",
       "2             0           1              0               1  \n",
       "3             1           0              0               1  \n",
       "4             0           1              1               0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=[\"Management or Technical\", \"hard/smart worker\"], prefix=[\"A\", \"B\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:06.492745Z",
     "iopub.status.busy": "2021-05-21T09:15:06.492131Z",
     "iopub.status.idle": "2021-05-21T09:15:06.506687Z",
     "shell.execute_reply": "2021-05-21T09:15:06.505612Z",
     "shell.execute_reply.started": "2021-05-21T09:15:06.492706Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of Numerical features: \n",
      " ['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points', 'self-learning capability?', 'Extra-courses did', 'reading and writing skills', 'memory capability score', 'Taken inputs from seniors or elders', 'worked in teams ever?', 'Introvert', 'certifications_code', 'workshops_code', 'Interested subjects_code', 'interested career area _code', 'Type of company want to settle in?_code', 'Interested Type of Books_code', 'A_Management', 'A_Technical', 'B_hard worker', 'B_smart worker']\n"
     ]
    }
   ],
   "source": [
    "print(\"List of Numerical features: \\n\" , df.select_dtypes(include=np.number).columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **6. Building Machine Learning Model** 🤖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:08.496869Z",
     "iopub.status.busy": "2021-05-21T09:15:08.496508Z",
     "iopub.status.idle": "2021-05-21T09:15:08.512249Z",
     "shell.execute_reply": "2021-05-21T09:15:08.510942Z",
     "shell.execute_reply.started": "2021-05-21T09:15:08.496837Z"
    }
   },
   "outputs": [],
   "source": [
    "feed = df[['Logical quotient rating', 'coding skills rating', 'hackathons', 'public speaking points', 'self-learning capability?','Extra-courses did', \n",
    "           'Taken inputs from seniors or elders', 'worked in teams ever?', 'Introvert', 'reading and writing skills', 'memory capability score',  \n",
    "           'B_hard worker', 'B_smart worker', 'A_Management', 'A_Technical', 'Interested subjects_code', 'Interested Type of Books_code', 'certifications_code', \n",
    "           'workshops_code', 'Type of company want to settle in?_code',  'interested career area _code',\n",
    "             'Suggested Job Role']]\n",
    "\n",
    "# Taking all independent variable columns\n",
    "df_train_x = feed.drop('Suggested Job Role',axis = 1)\n",
    "\n",
    "# Target variable column\n",
    "df_train_y = feed['Suggested Job Role']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_train_x, df_train_y, test_size=0.20, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **(1) Decision Tree Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:13.994959Z",
     "iopub.status.busy": "2021-05-21T09:15:13.994602Z",
     "iopub.status.idle": "2021-05-21T09:15:14.071007Z",
     "shell.execute_reply": "2021-05-21T09:15:14.069396Z",
     "shell.execute_reply.started": "2021-05-21T09:15:13.994928Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrics= [[11 11 11  8 13 15 12 10  7 10 14 10]\n",
      " [10  4  6  8  5  7 11 13 17  7  8  9]\n",
      " [13  9 11  8 12 10 11 12  5 10  8 14]\n",
      " [ 5 10 12  7  5  8  6 10  4  7 14 12]\n",
      " [ 8 11  9 12 14 10  6  8  7  9 16 11]\n",
      " [12 13  8  9 13 12  4  7 11 10 11  3]\n",
      " [ 8 15 12  9  9  7 15  7  7 11 11  5]\n",
      " [10  9  7  3 14 11 18 11 15  8  6  6]\n",
      " [11  7 10 13  7 12 12  8  7  6  7 14]\n",
      " [ 9 10 10  8 18  8  7  6  9  9 11  6]\n",
      " [ 7 14  5 13  9 10 11  9  9 10 12 10]\n",
      " [ 9 10  9 15 10  9  7  5 14  7  6  8]]\n",
      "  \n",
      "accuracy= 0.8761766835626359\n"
     ]
    }
   ],
   "source": [
    "dtree = DecisionTreeClassifier(random_state=1)\n",
    "dtree = dtree.fit(x_train, y_train)\n",
    "\n",
    "y_pred = dtree.predict(x_test)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"confusion matrics=\",cm)\n",
    "print(\"  \")\n",
    "print(\"accuracy=\",accuracy*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Predicting class for one instance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:16.340785Z",
     "iopub.status.busy": "2021-05-21T09:15:16.340422Z",
     "iopub.status.idle": "2021-05-21T09:15:16.349794Z",
     "shell.execute_reply": "2021-05-21T09:15:16.348558Z",
     "shell.execute_reply.started": "2021-05-21T09:15:16.340753Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Technical Support']\n",
      "Probabilities of all classes:  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]]\n",
      "Probability of Predicted class :  1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hrugv\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\hrugv\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "userdata = [['7','6','6','8','3','5','4', '4', '7', '3', '3', '6','8', \n",
    "                    '7','5','7','4','5','6','8','8']]\n",
    "ynewclass = dtree.predict(userdata)\n",
    "ynew = dtree.predict_proba(userdata)\n",
    "print(ynewclass)\n",
    "print(\"Probabilities of all classes: \", ynew)\n",
    "print(\"Probability of Predicted class : \", np.max(ynew))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **(2) Supprt Vector Machine Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:17.589858Z",
     "iopub.status.busy": "2021-05-21T09:15:17.589485Z",
     "iopub.status.idle": "2021-05-21T09:15:22.119128Z",
     "shell.execute_reply": "2021-05-21T09:15:22.117845Z",
     "shell.execute_reply.started": "2021-05-21T09:15:17.589826Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrics= [[ 1  5  0  6 41 20 18  6 15 12  6  2]\n",
      " [ 1  7  0  3 44  5 15 13  3  9  3  2]\n",
      " [ 0  5  2  5 44 14 19  9  6 10  6  3]\n",
      " [ 2  7  1  3 41 13  9  6  3 10  2  3]\n",
      " [ 2  7  3  4 40 17 18  9  4 13  1  3]\n",
      " [ 1 10  0  4 44 14 12 13  4  8  2  1]\n",
      " [ 1  6  3  2 40  7 13 17 12  8  4  3]\n",
      " [ 0  7  1  6 37 12 16 13 11 11  4  0]\n",
      " [ 0  9  0  3 37 13 14 13  6 12  2  5]\n",
      " [ 0  8  0  3 43 13 15  8  5 11  3  2]\n",
      " [ 1  8  1  3 40 16 18 12  4 10  4  2]\n",
      " [ 2  9  6  2 33 13 10 12 10  9  2  1]]\n",
      "  \n",
      "accuracy= 0.8327299058653149\n"
     ]
    }
   ],
   "source": [
    "svm = svm.SVC()\n",
    "svm.fit(x_train, y_train)\n",
    "svm_y_pred = svm.predict(x_test)\n",
    "svm_cm = confusion_matrix(y_test,svm_y_pred)\n",
    "svm_accuracy = accuracy_score(y_test,svm_y_pred)\n",
    "print(\"confusion matrics=\",svm_cm)\n",
    "print(\"  \")\n",
    "print(\"accuracy=\",svm_accuracy*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:22.122725Z",
     "iopub.status.busy": "2021-05-21T09:15:22.122380Z",
     "iopub.status.idle": "2021-05-21T09:15:22.137421Z",
     "shell.execute_reply": "2021-05-21T09:15:22.135363Z",
     "shell.execute_reply.started": "2021-05-21T09:15:22.122694Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Software Engineer']\n",
      "Probabilities of all classes:  [[ 0.72317165  8.25769355 -0.2843185   7.17416306  3.83458056  4.87344345\n",
      "  10.27976749  9.2644453   1.73122697  7.04310678 10.27669569  2.77294512]]\n",
      "Probability of Predicted class :  10.279767490035844\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hrugv\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\hrugv\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "ynewclass = svm.predict(userdata)\n",
    "ynew = svm.decision_function(userdata)\n",
    "print(ynewclass)\n",
    "print(\"Probabilities of all classes: \", ynew)\n",
    "print(\"Probability of Predicted class : \", np.max(ynew))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **(3) Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:22.140049Z",
     "iopub.status.busy": "2021-05-21T09:15:22.139536Z",
     "iopub.status.idle": "2021-05-21T09:15:23.309802Z",
     "shell.execute_reply": "2021-05-21T09:15:23.308617Z",
     "shell.execute_reply.started": "2021-05-21T09:15:22.140011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrics= [[ 9 13 16  3 10 14 13 13 12  9 12  8]\n",
      " [ 8 13  9  7 19  8  5  7  4 10  8  7]\n",
      " [11 10  6 12 15  9 12 12 11  8  8  9]\n",
      " [ 9  8  5 13 25  4  6  6  7  6  4  7]\n",
      " [11 13 12  8 17  9  8  4 10  8 12  9]\n",
      " [ 6  8 12  6 18  6  4 12  9  7 14 11]\n",
      " [ 8  8 10 10 21  6 13  9 11  7  7  6]\n",
      " [11  8  9  8 21 11 13  8  8  7  6  8]\n",
      " [ 7 12 10  5 16 18 12  8 10  5  5  6]\n",
      " [ 6 10 14  6  9 12 12  9  9 10  5  9]\n",
      " [10 12 10  9 12  9 20  7  6 11  9  4]\n",
      " [10  9 11  8 14 11  9  8  7  7  8  7]]\n",
      "  \n",
      "accuracy= 0.8761766835626359\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(random_state = 10)\n",
    "rf.fit(x_train, y_train)\n",
    "rfc_y_pred = rf.predict(x_test)\n",
    "rfc_cm = confusion_matrix(y_test,rfc_y_pred)\n",
    "rfc_accuracy = accuracy_score(y_test,rfc_y_pred)\n",
    "print(\"confusion matrics=\",rfc_cm)\n",
    "print(\"  \")\n",
    "print(\"accuracy=\",rfc_accuracy*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Web Developer']\n",
      "Probabilities of all classes:  [[0.02 0.12 0.09 0.03 0.12 0.07 0.12 0.06 0.07 0.1  0.07 0.13]]\n",
      "Probability of Predicted class :  0.13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hrugv\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\hrugv\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "\n",
    "ynewclass = rf.predict(userdata)\n",
    "ynew = rf.predict_proba(userdata)\n",
    "print(ynewclass)\n",
    "print(\"Probabilities of all classes: \", ynew)\n",
    "print(\"Probability of Predicted class : \", np.max(ynew))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:17:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: 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",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrics= [[ 8  8 13  6 16 10 13  8 14 12 12 12]\n",
      " [ 6  9  6  5 14  9 11  6  9 12  8 10]\n",
      " [ 6  9 11 11 13 10 15 10  8  8 10 12]\n",
      " [ 7  9  5  6 12  7 10  9  7  4 13 11]\n",
      " [ 6  9  7  9 17  9 13 10 12 11  9  9]\n",
      " [ 5 11  6  8 17 10  6 15  9 10  9  7]\n",
      " [ 6  5 15  6 15 17  7  5 11 13  8  8]\n",
      " [10 11  8  7 17 13  8  5  6 12 14  7]\n",
      " [12 13  8  4  9 14 10  6  7 13  9  9]\n",
      " [ 6 10  6  3 15 12 10  7 11 11 12  8]\n",
      " [10 10 12  8 20 13  7  7  9  7 10  6]\n",
      " [ 6 11 13  6 11 11  9  6 11 11  6  8]]\n",
      "  \n",
      "accuracy= 0.7892831281679942\n"
     ]
    }
   ],
   "source": [
    "xgb = XGBClassifier(random_state = 42, learning_rate=0.02, n_estimators=300)\n",
    "xgb.fit(x_train, y_train)\n",
    "xgb_y_pred = xgb.predict(x_test)\n",
    "xgb_cm = confusion_matrix(y_test,xgb_y_pred)\n",
    "xgb_accuracy = accuracy_score(y_test,xgb_y_pred)\n",
    "print(\"confusion matrics=\",xgb_cm)\n",
    "print(\"  \")\n",
    "print(\"accuracy=\",xgb_accuracy*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Prediction for one instance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T09:15:25.598336Z",
     "iopub.status.busy": "2021-05-21T09:15:25.597837Z",
     "iopub.status.idle": "2021-05-21T09:15:25.635455Z",
     "shell.execute_reply": "2021-05-21T09:15:25.634610Z",
     "shell.execute_reply.started": "2021-05-21T09:15:25.598208Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Technical Support']\n",
      "Probabilities of all classes:  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]]\n",
      "Probability of Predicted class :  1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hrugv\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\hrugv\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "userdata = [['7','6','6','8','3','5','4', '4', '7', '3', '3', '6','8', \n",
    "                    '7','5','7','4','5','6','8','8']]\n",
    "ynewclass = dtree.predict(userdata)\n",
    "ynew = dtree.predict_proba(userdata)\n",
    "print(ynewclass)\n",
    "print(\"Probabilities of all classes: \", ynew)\n",
    "print(\"Probability of Predicted class : \", np.max(ynew))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "pickle.dump(dtree,open('weights.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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