main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Asus\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import seaborn as sns  \n",
    "import matplotlib as plt\n",
    "import gradio as gr  \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,precision_score,recall_score,f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "data = pd.read_csv(\"../career-recommendation-system/Dataset/mldata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6901, 20)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6901 entries, 0 to 6900\n",
      "Data columns (total 20 columns):\n",
      " #   Column                               Non-Null Count  Dtype \n",
      "---  ------                               --------------  ----- \n",
      " 0   Logical quotient rating              6901 non-null   int64 \n",
      " 1   hackathons                           6901 non-null   int64 \n",
      " 2   coding skills rating                 6901 non-null   int64 \n",
      " 3   public speaking points               6901 non-null   int64 \n",
      " 4   self-learning capability?            6901 non-null   object\n",
      " 5   Extra-courses did                    6901 non-null   object\n",
      " 6   certifications                       6901 non-null   object\n",
      " 7   workshops                            6901 non-null   object\n",
      " 8   reading and writing skills           6901 non-null   object\n",
      " 9   memory capability score              6901 non-null   object\n",
      " 10  Interested subjects                  6901 non-null   object\n",
      " 11  interested career area               6901 non-null   object\n",
      " 12  Type of company want to settle in?   6901 non-null   object\n",
      " 13  Taken inputs from seniors or elders  6901 non-null   object\n",
      " 14  Interested Type of Books             6901 non-null   object\n",
      " 15  Management or Technical              6901 non-null   object\n",
      " 16  hard/smart worker                    6901 non-null   object\n",
      " 17  worked in teams ever?                6901 non-null   object\n",
      " 18  Introvert                            6901 non-null   object\n",
      " 19  Suggested Job Role                   6901 non-null   object\n",
      "dtypes: int64(4), object(16)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of numerical features: \n",
      " ['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points']\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\", data.select_dtypes(include=\"int\").columns.tolist())  \n",
    "print(\"List of categorical features: \\n\", data.select_dtypes(include=\"object\").columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self-learning capability?\n",
      "yes    3496\n",
      "no     3405\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Extra-courses did\n",
      "no     3529\n",
      "yes    3372\n",
      "Name: count, dtype: int64\n",
      "\n",
      "certifications\n",
      "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: count, dtype: int64\n",
      "\n",
      "workshops\n",
      "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: count, dtype: int64\n",
      "\n",
      "reading and writing skills\n",
      "excellent    2328\n",
      "medium       2315\n",
      "poor         2258\n",
      "Name: count, dtype: int64\n",
      "\n",
      "memory capability score\n",
      "medium       2317\n",
      "excellent    2303\n",
      "poor         2281\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Interested subjects\n",
      "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: count, dtype: int64\n",
      "\n",
      "interested career area \n",
      "system developer            1178\n",
      "security                    1177\n",
      "Business process analyst    1154\n",
      "developer                   1145\n",
      "testing                     1128\n",
      "cloud computing             1119\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Type of company want to settle in?\n",
      "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: count, dtype: int64\n",
      "\n",
      "Taken inputs from seniors or elders\n",
      "yes    3501\n",
      "no     3400\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Interested Type of Books\n",
      "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: count, dtype: int64\n",
      "\n",
      "Management or Technical\n",
      "Management    3461\n",
      "Technical     3440\n",
      "Name: count, dtype: int64\n",
      "\n",
      "hard/smart worker\n",
      "smart worker    3523\n",
      "hard worker     3378\n",
      "Name: count, dtype: int64\n",
      "\n",
      "worked in teams ever?\n",
      "no     3470\n",
      "yes    3431\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Introvert\n",
      "yes    3544\n",
      "no     3357\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Suggested Job Role\n",
      "Network Security Engineer                    630\n",
      "Software Engineer                            590\n",
      "UX Designer                                  589\n",
      "Software Developer                           587\n",
      "Database Developer                           581\n",
      "Software Quality Assurance (QA) / Testing    571\n",
      "Web Developer                                570\n",
      "CRM Technical Developer                      567\n",
      "Technical Support                            565\n",
      "Systems Security Administrator               562\n",
      "Applications Developer                       551\n",
      "Mobile Applications Developer                538\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "categorical_cols = data[['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",
    "numerical_cols = data[['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points']]\n",
    "\n",
    "for i in categorical_cols:\n",
    "    print(data[i].value_counts(), end=\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logical quotient rating\n",
      "6    799\n",
      "9    784\n",
      "2    782\n",
      "5    773\n",
      "3    772\n",
      "4    759\n",
      "1    756\n",
      "7    752\n",
      "8    724\n",
      "Name: count, dtype: int64\n",
      "\n",
      "hackathons\n",
      "5    1033\n",
      "2    1026\n",
      "0    1010\n",
      "6     989\n",
      "3     966\n",
      "1     952\n",
      "4     925\n",
      "Name: count, dtype: int64\n",
      "\n",
      "coding skills rating\n",
      "4    787\n",
      "5    777\n",
      "2    776\n",
      "6    774\n",
      "8    767\n",
      "7    766\n",
      "9    761\n",
      "3    755\n",
      "1    738\n",
      "Name: count, dtype: int64\n",
      "\n",
      "public speaking points\n",
      "7    807\n",
      "1    799\n",
      "8    777\n",
      "2    770\n",
      "3    766\n",
      "4    760\n",
      "9    758\n",
      "6    740\n",
      "5    724\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for j in numerical_cols:\n",
    "    print(data[j].value_counts(), end=\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "Palette = sns.color_palette(\"pastel\")\n",
    "Palette\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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     "name": "stderr",
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     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_21412\\4210140923.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot( data[\"Suggested Job Role\"], palette=sns.color_palette(\"pastel\"))\n",
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_21412\\4210140923.py:3: UserWarning: \n",
      "The palette list has fewer values (10) than needed (12) and will cycle, which may produce an uninterpretable plot.\n",
      "  sns.countplot( data[\"Suggested Job Role\"], palette=sns.color_palette(\"pastel\"))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='count', ylabel='Suggested Job Role'>"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We check for balancy for the label attribute, since this recommendation system \n",
    "# is similar classification model, therefore, it is a must to balance the label attribute\n",
    "sns.countplot( data[\"Suggested Job Role\"], palette=sns.color_palette(\"pastel\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data[['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points']].corr() \n",
    "sns.heatmap(corr,square=True,annot=True,linewidth = .2,center=2,cmap=Palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     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",
      "6896                       7          5                    6   \n",
      "6897                       6          5                    1   \n",
      "6898                       5          1                    6   \n",
      "6899                       1          6                    4   \n",
      "6900                       5          6                    2   \n",
      "\n",
      "     public speaking points  \n",
      "0                         2  \n",
      "1                         3  \n",
      "2                         1  \n",
      "3                         5  \n",
      "4                         4  \n",
      "...                     ...  \n",
      "6896                      2  \n",
      "6897                      8  \n",
      "6898                      7  \n",
      "6899                      6  \n",
      "6900                      5  \n",
      "\n",
      "[6901 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data.T.head(4).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['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",
      "     self-learning capability? Extra-courses did        certifications  \\\n",
      "0                          yes                no  information security   \n",
      "1                           no               yes     shell programming   \n",
      "2                           no               yes  information security   \n",
      "3                           no               yes         r programming   \n",
      "4                          yes                no         distro making   \n",
      "...                        ...               ...                   ...   \n",
      "6896                       yes                no     shell programming   \n",
      "6897                        no                no      machine learning   \n",
      "6898                       yes                no         distro making   \n",
      "6899                        no                no       app development   \n",
      "6900                        no               yes  information security   \n",
      "\n",
      "              workshops reading and writing skills memory capability score  \\\n",
      "0               testing                       poor                    poor   \n",
      "1               testing                  excellent                  medium   \n",
      "2               testing                  excellent                    poor   \n",
      "3     database security                  excellent                    poor   \n",
      "4      game development                  excellent                  medium   \n",
      "...                 ...                        ...                     ...   \n",
      "6896            hacking                       poor                    poor   \n",
      "6897            hacking                  excellent               excellent   \n",
      "6898       data science                       poor                    poor   \n",
      "6899   game development                       poor               excellent   \n",
      "6900  database security                  excellent                  medium   \n",
      "\n",
      "        Interested subjects   interested career area   \\\n",
      "0               programming                   testing   \n",
      "1                Management          system developer   \n",
      "2          data engineering  Business process analyst   \n",
      "3                  networks                   testing   \n",
      "4      Software Engineering          system developer   \n",
      "...                     ...                       ...   \n",
      "6896   Software Engineering                   testing   \n",
      "6897            programming                   testing   \n",
      "6898                    IOT          system developer   \n",
      "6899       data engineering                 developer   \n",
      "6900  Computer Architecture                  security   \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",
      "6896  Testing and Maintainance Services                                 yes   \n",
      "6897  Testing and Maintainance Services                                  no   \n",
      "6898                     Cloud Services                                 yes   \n",
      "6899                      SAaS services                                  no   \n",
      "6900                Sales and Marketing                                 yes   \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",
      "6896                  Trilogy              Management      smart worker   \n",
      "6897                  Science              Management       hard worker   \n",
      "6898                Self help               Technical       hard worker   \n",
      "6899                    Drama               Technical      smart worker   \n",
      "6900                    Drama              Management      smart 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  \n",
      "...                    ...       ...                     ...  \n",
      "6896                    no       yes           Web Developer  \n",
      "6897                    no        no           Web Developer  \n",
      "6898                   yes        no           Web Developer  \n",
      "6899                    no       yes           Web Developer  \n",
      "6900                   yes        no           Web Developer  \n",
      "\n",
      "[6901 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "# Now we understand there are 4 numerical values, 16 categorical values (nominal or binary[Yes/No])\n",
    "# Therefore, we need to split encoding as each of the question is different with answer option \n",
    "# For example, there are about 12 options (will be encoded 0-11) in suggested job role vs yes/no question that will be only (0/1)\n",
    "# Hence, we will proceed with Binary Encoding, then nominal encoding (Ordinal/ Label encoding)\n",
    "# Call Categorical variables for referring and columns name to copy and paste:\n",
    "print(categorical_cols.columns.tolist())\n",
    "print(categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "bicols = data[['self-learning capability?', 'Extra-courses did', 'Taken inputs from seniors or elders','worked in teams ever?', 'Introvert']]\n",
    "for i in bicols:\n",
    "    replace_nums = {i: {\"yes\": 1 ,\"no\": 0}}\n",
    "    data = data.replace(replace_nums )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6901 entries, 0 to 6900\n",
      "Data columns (total 20 columns):\n",
      " #   Column                               Non-Null Count  Dtype \n",
      "---  ------                               --------------  ----- \n",
      " 0   Logical quotient rating              6901 non-null   int64 \n",
      " 1   hackathons                           6901 non-null   int64 \n",
      " 2   coding skills rating                 6901 non-null   int64 \n",
      " 3   public speaking points               6901 non-null   int64 \n",
      " 4   self-learning capability?            6901 non-null   int64 \n",
      " 5   Extra-courses did                    6901 non-null   int64 \n",
      " 6   certifications                       6901 non-null   object\n",
      " 7   workshops                            6901 non-null   object\n",
      " 8   reading and writing skills           6901 non-null   int64 \n",
      " 9   memory capability score              6901 non-null   int64 \n",
      " 10  Interested subjects                  6901 non-null   object\n",
      " 11  interested career area               6901 non-null   object\n",
      " 12  Type of company want to settle in?   6901 non-null   object\n",
      " 13  Taken inputs from seniors or elders  6901 non-null   int64 \n",
      " 14  Interested Type of Books             6901 non-null   object\n",
      " 15  Management or Technical              6901 non-null   object\n",
      " 16  hard/smart worker                    6901 non-null   object\n",
      " 17  worked in teams ever?                6901 non-null   int64 \n",
      " 18  Introvert                            6901 non-null   int64 \n",
      " 19  Suggested Job Role                   6901 non-null   object\n",
      "dtypes: int64(11), object(9)\n",
      "memory usage: 1.1+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "orcols = data[[\"reading and writing skills\", \"memory capability score\"]]\n",
    "for i in orcols:\n",
    "    replace_nums = {i: {\"poor\": 0, \"medium\": 1, \"excellent\": 2}}\n",
    "    data = data.replace(replace_nums)\n",
    "\n",
    "print(data.info())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        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>...</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>worked in teams ever?</th>\n",
       "      <th>Introvert</th>\n",
       "      <th>Suggested Job Role</th>\n",
       "      <th>Management or Technical_Management</th>\n",
       "      <th>Management or Technical_Technical</th>\n",
       "      <th>hard/smart worker_hard worker</th>\n",
       "      <th>hard/smart worker_smart worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Applications Developer</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>Applications Developer</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
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       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>6</td>\n",
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       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Applications Developer</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Applications Developer</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 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               4          6                           0   \n",
       "1               8          6                           2   \n",
       "2               4          6                           2   \n",
       "3               7          2                           2   \n",
       "4               1          3                           2   \n",
       "\n",
       "   memory capability score  ...  Type of company want to settle in?  \\\n",
       "0                        0  ...                                   0   \n",
       "1                        1  ...                                   1   \n",
       "2                        0  ...                                   9   \n",
       "3                        0  ...                                   7   \n",
       "4                        1  ...                                   0   \n",
       "\n",
       "   Taken inputs from seniors or elders  Interested Type of Books  \\\n",
       "0                                    0                        28   \n",
       "1                                    1                         3   \n",
       "2                                    1                        29   \n",
       "3                                    1                        13   \n",
       "4                                    0                        14   \n",
       "\n",
       "   worked in teams ever?  Introvert      Suggested Job Role  \\\n",
       "0                      1          0  Applications Developer   \n",
       "1                      0          1  Applications Developer   \n",
       "2                      0          0  Applications Developer   \n",
       "3                      1          1  Applications Developer   \n",
       "4                      1          0  Applications Developer   \n",
       "\n",
       "   Management or Technical_Management Management or Technical_Technical  \\\n",
       "0                                True                             False   \n",
       "1                               False                              True   \n",
       "2                               False                              True   \n",
       "3                                True                             False   \n",
       "4                               False                              True   \n",
       "\n",
       "   hard/smart worker_hard worker  hard/smart worker_smart worker  \n",
       "0                          False                            True  \n",
       "1                           True                           False  \n",
       "2                          False                            True  \n",
       "3                          False                            True  \n",
       "4                           True                           False  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.select_dtypes(include=\"object\").columns.tolist()\n",
    "nominalcols = ['certifications',\n",
    " 'workshops',\n",
    " 'Interested subjects',\n",
    " 'interested career area ',\n",
    " 'Type of company want to settle in?',\n",
    " 'Interested Type of Books']\n",
    "#Left with these nominal attributes\n",
    "data.select_dtypes(include=\"object\").head()\n",
    "for i in nominalcols:\n",
    "    data[i] = data[i].astype('category')\n",
    "    data[i] = data[i].cat.codes\n",
    "\n",
    "data = pd.get_dummies(data, columns = ['Management or Technical','hard/smart worker'])\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#done converting all to numerical left with the label attribute\n",
    "#try with building machine learning model\n",
    "dataset = data\n",
    "dataset.head()\n",
    "#Separate all independant variables and targeted variable column\n",
    "df_train_x = dataset.drop('Suggested Job Role', axis = 1)\n",
    "df_train_y = dataset['Suggested Job Role']\n",
    "#Therefore, we split the dataset into train and test dataset\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_train_x,df_train_y,test_size=.20,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8 14 10  6 15  4 11 16 11  9 13 15]\n",
      " [ 9 14 13  9 10  8 10  6  4  5  9  8]\n",
      " [ 8 11 14  9 13  9 10  8 11 12  8 10]\n",
      " [ 7  5  7  9 11  7  9  8 10  6 10 11]\n",
      " [12 13  6  7 18 11  8 11  9  8  9  9]\n",
      " [ 7 13  9  6 14 12  9 13  9  8  4  9]\n",
      " [ 4 10 14  9 15 13 10  9  6  8 11  7]\n",
      " [ 7 11 15 10 12 13  6 10 10 11  7  6]\n",
      " [ 5 11 11  5 23 12  9  3  7  9 12  7]\n",
      " [ 5  8  8  7 14 15  8 10 13  6  6 11]\n",
      " [10 16 11  4 14 16 10  5 14  8  5  6]\n",
      " [ 7  8 10  8 10 16 12 12  6  8  5  7]]\n",
      "\n",
      "0.8689355539464156\n"
     ]
    }
   ],
   "source": [
    "#Proceed with random forest classifier\n",
    "rf = RandomForestClassifier(random_state=10)\n",
    "rf.fit(x_train,y_train)\n",
    "rf_predict_y = rf.predict(x_test)\n",
    "rfc_cm = confusion_matrix(y_test,rf_predict_y)\n",
    "rfc_acc = accuracy_score(y_test,rf_predict_y)\n",
    "print(rfc_cm, end=\"\\n\\n\")\n",
    "print(rfc_acc*10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "userdata = [['7','6','6','8','3','5','4', '4', '7', '3', '3', '6','8', \n",
    "                    '7','5','7','4','5','6','8','8']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Software Engineer']\n",
      "Probabilities of all classes:  [[0.07 0.11 0.07 0.05 0.05 0.06 0.12 0.11 0.11 0.09 0.06 0.1 ]]\n",
      "Probability of Predicted class :  0.12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Asus\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "c:\\Users\\Asus\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "pickle.dump(rf, open('rfweights.pkl','rb'))"
   ]
  }
 ],
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