my approach.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "existing-teach",
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
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       "      <th>Age</th>\n",
       "      <th>purpose_of_travel</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Type Of Booking</th>\n",
       "      <th>Hotel wifi service</th>\n",
       "      <th>Departure/Arrival  convenience</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>Hotel location</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>Stay comfort</th>\n",
       "      <th>Common Room entertainment</th>\n",
       "      <th>Checkin/Checkout service</th>\n",
       "      <th>Other service</th>\n",
       "      <th>Cleanliness</th>\n",
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       "      <td>academic</td>\n",
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       "      <td>good</td>\n",
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       "      <td>52532</td>\n",
       "      <td>Male</td>\n",
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       "      <td>business</td>\n",
       "      <td>Group Travel</td>\n",
       "      <td>Group bookings</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>62644</td>\n",
       "      <td>Male</td>\n",
       "      <td>64</td>\n",
       "      <td>tourism</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Individual/Couple</td>\n",
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       "      <td>3</td>\n",
       "      <td>57270</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>academic</td>\n",
       "      <td>Group Travel</td>\n",
       "      <td>Group bookings</td>\n",
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       "      <td>bad</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>18607</td>\n",
       "      <td>Female</td>\n",
       "      <td>80</td>\n",
       "      <td>tourism</td>\n",
       "      <td>Group Travel</td>\n",
       "      <td>Individual/Couple</td>\n",
       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0     id  Gender  Age purpose_of_travel   Type of Travel  \\\n",
       "0           0  32746  Female   32          academic     Group Travel   \n",
       "1           1  52532    Male   44          business     Group Travel   \n",
       "2           2  62644    Male   64           tourism  Personal Travel   \n",
       "3           3  57270  Female   36          academic     Group Travel   \n",
       "4           4  18607  Female   80           tourism     Group Travel   \n",
       "\n",
       "     Type Of Booking  Hotel wifi service  Departure/Arrival  convenience  \\\n",
       "0  Individual/Couple                   5                               5   \n",
       "1     Group bookings                   3                               3   \n",
       "2  Individual/Couple                   3                               5   \n",
       "3     Group bookings                   2                               4   \n",
       "4  Individual/Couple                   2                               4   \n",
       "\n",
       "   Ease of Online booking  Hotel location  Food and drink  Stay comfort  \\\n",
       "0                       2               5               5             5   \n",
       "1                       3               3               5             3   \n",
       "2                       3               3               1             1   \n",
       "3                       4               4               4             3   \n",
       "4                       2               5               4             4   \n",
       "\n",
       "   Common Room entertainment  Checkin/Checkout service  Other service  \\\n",
       "0                          5                         1              2   \n",
       "1                          4                         2              4   \n",
       "2                          1                         3              5   \n",
       "3                          2                         1              2   \n",
       "4                          4                         2              2   \n",
       "\n",
       "   Cleanliness overall experience  \n",
       "0            5               good  \n",
       "1            1               good  \n",
       "2            1                bad  \n",
       "3            2                bad  \n",
       "4            4                bad  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as n\n",
    "import pandas as p\n",
    "import matplotlib.pyplot as m\n",
    "d= p.read_csv(\"hotel_experience.csv\")\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "alpine-earth",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Hotel wifi service</th>\n",
       "      <th>Departure/Arrival  convenience</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>Hotel location</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>Stay comfort</th>\n",
       "      <th>Common Room entertainment</th>\n",
       "      <th>Checkin/Checkout service</th>\n",
       "      <th>Other service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Gender_n</th>\n",
       "      <th>purpose_of_travel_n</th>\n",
       "      <th>Type of Travel_n</th>\n",
       "      <th>Type Of Booking_n</th>\n",
       "      <th>overall experience_n</th>\n",
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       "      <td>62644</td>\n",
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       "      <td>57270</td>\n",
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       "      <td>2</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0     id  Age  Hotel wifi service  Departure/Arrival  convenience  \\\n",
       "0           0  32746   32                   5                               5   \n",
       "1           1  52532   44                   3                               3   \n",
       "2           2  62644   64                   3                               5   \n",
       "3           3  57270   36                   2                               4   \n",
       "4           4  18607   80                   2                               4   \n",
       "\n",
       "   Ease of Online booking  Hotel location  Food and drink  Stay comfort  \\\n",
       "0                       2               5               5             5   \n",
       "1                       3               3               5             3   \n",
       "2                       3               3               1             1   \n",
       "3                       4               4               4             3   \n",
       "4                       2               5               4             4   \n",
       "\n",
       "   Common Room entertainment  Checkin/Checkout service  Other service  \\\n",
       "0                          5                         1              2   \n",
       "1                          4                         2              4   \n",
       "2                          1                         3              5   \n",
       "3                          2                         1              2   \n",
       "4                          4                         2              2   \n",
       "\n",
       "   Cleanliness  Gender_n  purpose_of_travel_n  Type of Travel_n  \\\n",
       "0            5         0                    0                 0   \n",
       "1            1         1                    2                 0   \n",
       "2            1         1                    4                 1   \n",
       "3            2         0                    0                 0   \n",
       "4            4         0                    4                 0   \n",
       "\n",
       "   Type Of Booking_n  overall experience_n  \n",
       "0                  1                     1  \n",
       "1                  0                     1  \n",
       "2                  1                     0  \n",
       "3                  0                     0  \n",
       "4                  1                     0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "l= preprocessing.LabelEncoder()\n",
    "d[\"Gender_n\"]=l.fit_transform(d[\"Gender\"])                           #Female:0 male: 1 \n",
    "d[\"purpose_of_travel_n\"]=l.fit_transform(d[\"purpose_of_travel\"])     #academic:0 aviation:1 business:2 personal:3 tourism:4 \n",
    "d[\"Type of Travel_n\"]=l.fit_transform(d[\"Type of Travel\"])           #Group Travel:0 Personal Travel:1\n",
    "d[\"Type Of Booking_n\"]=l.fit_transform(d[\"Type Of Booking\"])         #Group bookings:0 Individual/Couple:1 \n",
    "d[\"overall experience_n\"]=l.fit_transform(d[\"overall experience\"])   #bad:0 good:1\n",
    "d1=d.drop([\"Gender\",\"purpose_of_travel\",\"Type of Travel\",\"Type Of Booking\",\"overall experience\"], axis='columns')\n",
    "d1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "productive-sensitivity",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Departure/Arrival  convenience</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>Hotel location</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>Stay comfort</th>\n",
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      "text/plain": [
       "   Age  Hotel wifi service  Departure/Arrival  convenience  \\\n",
       "0   32                   5                               5   \n",
       "1   44                   3                               3   \n",
       "2   64                   3                               5   \n",
       "3   36                   2                               4   \n",
       "4   80                   2                               4   \n",
       "\n",
       "   Ease of Online booking  Hotel location  Food and drink  Stay comfort  \\\n",
       "0                       2               5               5             5   \n",
       "1                       3               3               5             3   \n",
       "2                       3               3               1             1   \n",
       "3                       4               4               4             3   \n",
       "4                       2               5               4             4   \n",
       "\n",
       "   Common Room entertainment  Checkin/Checkout service  Other service  \\\n",
       "0                          5                         1              2   \n",
       "1                          4                         2              4   \n",
       "2                          1                         3              5   \n",
       "3                          2                         1              2   \n",
       "4                          4                         2              2   \n",
       "\n",
       "   Cleanliness  Gender_n  purpose_of_travel_n  Type of Travel_n  \\\n",
       "0            5         0                    0                 0   \n",
       "1            1         1                    2                 0   \n",
       "2            1         1                    4                 1   \n",
       "3            2         0                    0                 0   \n",
       "4            4         0                    4                 0   \n",
       "\n",
       "   Type Of Booking_n  overall experience_n  \n",
       "0                  1                     1  \n",
       "1                  0                     1  \n",
       "2                  1                     0  \n",
       "3                  0                     0  \n",
       "4                  1                     0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_main=d1.drop(['Unnamed: 0','id'], axis='columns')\n",
    "d_main.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "lonely-million",
   "metadata": {},
   "outputs": [],
   "source": [
    "#training_data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X=d_main.drop(['Age'],axis='columns')\n",
    "X_train,X_test = train_test_split(X,test_size = 0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "verified-conspiracy",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gini(dataset,target):\n",
    "    groups,group_count=n.unique(dataset[target],return_counts=True)\n",
    "    gini=1\n",
    "    for i in range(len(groups)):\n",
    "        p=group_count[i]/sum(group_count)\n",
    "        gini-=p**2\n",
    "    return gini\n",
    "def ig(dataset,feature,target):\n",
    "    initial_gini=gini(dataset,target)\n",
    "    ig=initial_gini\n",
    "    value,value_count=n.unique(dataset[feature],return_counts=True)\n",
    "    weighted_feature_entropy = n.sum([(value_count[i]/n.sum(value_count))*gini(dataset.where(dataset[feature]\n",
    "                              ==value[i]).dropna(),target) for i in range(len(value))])    \n",
    "    gain = initial_gini - weighted_feature_entropy\n",
    "    return gain\n",
    "def tree(n_dataset,dataset,features,target,root):\n",
    "    value,value_count=n.unique(dataset[target],return_counts=True)\n",
    "    if len(value)<=1:\n",
    "        return value\n",
    "    elif len(dataset)==0:\n",
    "        return value[n.argmax(value_count)]\n",
    "    elif len(features)==0:\n",
    "        return root\n",
    "    elif gini(dataset,target)==0:\n",
    "        return n.unique(dataset[target],return_counts=False)\n",
    "    else:\n",
    "        root=value[n.argmax(value_count)]\n",
    "        item_values= [ig(dataset,feature,target) for feature in features]\n",
    "        optimum_feature=features[n.argmax(item_values)]\n",
    "        decision_tree={optimum_feature:{}}\n",
    "        features=[i for i in features if i!= optimum_feature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fallen-peripheral",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "completed-spread",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fundamental-andorra",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "angry-wireless",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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