personality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Educational Background</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Openness to Experience</th>\n",
       "      <th>Emotional Stability</th>\n",
       "      <th>Values/Beliefs</th>\n",
       "      <th>Personality Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>12th Grade</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>dependable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>12th Grade</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>serious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>11th Grade</td>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>responsible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>Female</td>\n",
       "      <td>11th Grade</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>dependable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>12th Grade</td>\n",
       "      <td>65</td>\n",
       "      <td>75</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>serious</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender Educational Background  Extraversion  Agreeableness  \\\n",
       "0   17    Male             12th Grade            75             70   \n",
       "1   18  Female             12th Grade            70             80   \n",
       "2   17    Male             11th Grade            55             60   \n",
       "3   16  Female             11th Grade            80             70   \n",
       "4   18    Male             12th Grade            65             75   \n",
       "\n",
       "   Conscientiousness  Openness to Experience  Emotional Stability  \\\n",
       "0                 80                      85                   75   \n",
       "1                 85                      75                   70   \n",
       "2                 75                      80                   60   \n",
       "3                 85                      90                   80   \n",
       "4                 90                      70                   80   \n",
       "\n",
       "   Values/Beliefs Personality Class  \n",
       "0              70        dependable  \n",
       "1              80           serious  \n",
       "2              75       responsible  \n",
       "3              70        dependable  \n",
       "4              70           serious  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./Data/personality_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features = [feature for feature in df.columns if df[feature].dtype == 'O']\n",
    "len(categorical_features)\n",
    "3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_labels = df['Personality Class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "LabelEncoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['dependable'],\n",
       "       ['serious'],\n",
       "       ['responsible'],\n",
       "       ['extraverted'],\n",
       "       ['lively']], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_labels.reshape(5,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded = LabelEncoder.fit_transform(y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [4],\n",
       "       [3],\n",
       "       [1],\n",
       "       [2]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded.reshape(5,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_data = np.stack([y_labels,encoded],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['dependable', 0],\n",
       "       ['serious', 4],\n",
       "       ['responsible', 3],\n",
       "       ['extraverted', 1],\n",
       "       ['lively', 2]], dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>extraverted</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lively</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "             0  1\n",
       "0   dependable  0\n",
       "1      serious  4\n",
       "2  responsible  3\n",
       "3  extraverted  1\n",
       "4       lively  2"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data = pd.DataFrame(categorical_data)\n",
    "categorical_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Educational Background</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Openness to Experience</th>\n",
       "      <th>Emotional Stability</th>\n",
       "      <th>Values/Beliefs</th>\n",
       "      <th>Personality Class</th>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>75</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Educational Background  Extraversion  Agreeableness  \\\n",
       "0   17       1                       2            75             70   \n",
       "1   18       0                       2            70             80   \n",
       "2   17       1                       1            55             60   \n",
       "3   16       0                       1            80             70   \n",
       "4   18       1                       2            65             75   \n",
       "\n",
       "   Conscientiousness  Openness to Experience  Emotional Stability  \\\n",
       "0                 80                      85                   75   \n",
       "1                 85                      75                   70   \n",
       "2                 75                      80                   60   \n",
       "3                 85                      90                   80   \n",
       "4                 90                      70                   80   \n",
       "\n",
       "   Values/Beliefs  Personality Class  \n",
       "0              70                  0  \n",
       "1              80                  4  \n",
       "2              75                  3  \n",
       "3              70                  0  \n",
       "4              70                  4  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for feature in categorical_features:\n",
    "    dataset[feature] = LabelEncoder.fit_transform(dataset[feature])\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = dataset.iloc[:,:-1]\n",
    "y_data = dataset.iloc[:,-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_data,y_data, test_size=0.2, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\annar\\AppData\\Local\\Temp\\ipykernel_37932\\3543414343.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model.fit(X_train,y_train)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 2, 1, 0, 0, 3, 3, 4, 2, 4, 0, 2, 3, 0, 3])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "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>Personality Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Personality Class\n",
       "30                  3\n",
       "0                   0\n",
       "22                  2\n",
       "31                  1\n",
       "18                  0\n",
       "28                  0\n",
       "10                  3\n",
       "70                  3\n",
       "4                   4\n",
       "12                  2\n",
       "49                  4\n",
       "33                  0\n",
       "67                  2\n",
       "35                  3\n",
       "68                  0\n",
       "45                  3"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3, 0, 4, 4, 3, 2, 4, 2, 3, 4, 1, 4, 1, 4, 2, 3, 0, 4, 1, 3,\n",
       "       4, 0, 1, 0, 2, 0, 0, 3, 1, 1, 2, 0, 1, 0, 3, 2, 4, 1, 0, 4, 0, 2,\n",
       "       2, 1, 2, 3, 4, 0, 4, 2, 4, 4, 2, 1, 3, 0, 4, 3, 3, 1, 4, 1])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "Pkl_file = 'pickle_model'\n",
    "with open(Pkl_file, 'wb') as file:\n",
    "    pickle.dump(model,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pkl_file = 'pickle_model'\n",
    "with open(Pkl_file, 'rb') as file:\n",
    "    classifier=pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annar\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "input = np.array([18,  0,  2, 80, 75, 85, 90, 80, 70])\n",
    "input = input.reshape(1,-1)\n",
    "pred = classifier.predict(input)\n",
    "print(pred)"
   ]
  }
 ],
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