LAB 9-1 (Random Forest).ipynb

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   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e03f5a0c-ae76-4ec1-9c36-9b10b0e5cbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = r\"/Users/baraa/PythonCodes/archive/car_evaluation.csv\"\n",
    "df = pd.read_csv(data, header=None)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "941f5a65-42af-4eb5-8044-4f1143090e57",
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    {
     "data": {
      "text/plain": [
       "(1728, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
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   "execution_count": 5,
   "id": "601d352f-4a72-4f74-ab31-228bafd3a4a1",
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       "       0      1  2  3      4     5      6\n",
       "0  vhigh  vhigh  2  2  small   low  unacc\n",
       "1  vhigh  vhigh  2  2  small   med  unacc\n",
       "2  vhigh  vhigh  2  2  small  high  unacc\n",
       "3  vhigh  vhigh  2  2    med   low  unacc\n",
       "4  vhigh  vhigh  2  2    med   med  unacc"
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   "source": [
    "df.head()"
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   "id": "ea4dccee-852d-44d8-b7cf-0491654bd010",
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   "outputs": [],
   "source": [
    "col_names = [\"buying\",\"maint\",\"doors\",\"person\",\"lug_boot\", \"safety\", \"class\"]\n",
    "df.columns = col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8598c377-aa4b-4e38-9b63-004b41e04424",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>person</th>\n",
       "      <th>lug_boot</th>\n",
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      "text/plain": [
       "  buying  maint doors person lug_boot safety  class\n",
       "0  vhigh  vhigh     2      2    small    low  unacc\n",
       "1  vhigh  vhigh     2      2    small    med  unacc\n",
       "2  vhigh  vhigh     2      2    small   high  unacc\n",
       "3  vhigh  vhigh     2      2      med    low  unacc\n",
       "4  vhigh  vhigh     2      2      med    med  unacc"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
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   "execution_count": 8,
   "id": "0e431b8e-4fba-4c13-9880-a6b5c0166283",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1728 entries, 0 to 1727\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   buying    1728 non-null   object\n",
      " 1   maint     1728 non-null   object\n",
      " 2   doors     1728 non-null   object\n",
      " 3   person    1728 non-null   object\n",
      " 4   lug_boot  1728 non-null   object\n",
      " 5   safety    1728 non-null   object\n",
      " 6   class     1728 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "398b25a7-4370-47f6-865d-cacd491f3e38",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buying\n",
      "vhigh    432\n",
      "high     432\n",
      "med      432\n",
      "low      432\n",
      "Name: count, dtype: int64\n",
      "maint\n",
      "vhigh    432\n",
      "high     432\n",
      "med      432\n",
      "low      432\n",
      "Name: count, dtype: int64\n",
      "doors\n",
      "2        432\n",
      "3        432\n",
      "4        432\n",
      "5more    432\n",
      "Name: count, dtype: int64\n",
      "person\n",
      "2       576\n",
      "4       576\n",
      "more    576\n",
      "Name: count, dtype: int64\n",
      "lug_boot\n",
      "small    576\n",
      "med      576\n",
      "big      576\n",
      "Name: count, dtype: int64\n",
      "safety\n",
      "low     576\n",
      "med     576\n",
      "high    576\n",
      "Name: count, dtype: int64\n",
      "class\n",
      "unacc    1210\n",
      "acc       384\n",
      "good       69\n",
      "vgood      65\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in col_names:\n",
    "  print(df[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d126840-ffea-406b-83fe-1bf8cbf377ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "unacc    1210\n",
       "acc       384\n",
       "good       69\n",
       "vgood      65\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7ece025-47ad-4f7d-8447-b07d3b20c2d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buying      0\n",
       "maint       0\n",
       "doors       0\n",
       "person      0\n",
       "lug_boot    0\n",
       "safety      0\n",
       "class       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dfe34797-396c-49e7-8c3f-f3329fc26ac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop([\"class\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97ef31fc-e3c9-4433-a738-7f3ee3c53d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a1a456e-bb3b-4442-979b-04ed7ffdbf21",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04687683-8385-475f-bfd0-803367546f07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1209, 6)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2185fcb-0301-400d-8c51-d2070c1cd024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(519, 6)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "040be215-27f0-4091-b9fc-cffad73da20f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buying      object\n",
       "maint       object\n",
       "doors       object\n",
       "person      object\n",
       "lug_boot    object\n",
       "safety      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "26aaedf9-49e6-4fcb-8dca-a6391505bd6e",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>person</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
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       "      <th>1178</th>\n",
       "      <td>med</td>\n",
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       "      <td>high</td>\n",
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       "      <td>more</td>\n",
       "      <td>small</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>low</td>\n",
       "      <td>med</td>\n",
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       "      <td>4</td>\n",
       "      <td>med</td>\n",
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       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>med</td>\n",
       "      <td>med</td>\n",
       "      <td>5more</td>\n",
       "      <td>2</td>\n",
       "      <td>big</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>med</td>\n",
       "      <td>high</td>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "     buying maint  doors person lug_boot safety\n",
       "1178    med   med  5more      4      big   high\n",
       "585    high  high      3   more    small    low\n",
       "1552    low   med      3      4      med    med\n",
       "1169    med   med  5more      2      big   high\n",
       "1033    med  high      4      2      big    med"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ec6ea24a-df38-4dc1-b274-9ff8da860734",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install category_encoders\n",
    "import category_encoders as ce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "66105da4-d4af-4d77-b411-4d177aad8ff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = ce.OrdinalEncoder(cols=[\"buying\",\"maint\",\"doors\",\"person\",\"lug_boot\", \"safety\"])\n",
    "x_train = encoder.fit_transform(x_train)\n",
    "x_test = encoder.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "043aff3d-5d1c-48b8-9dd0-ccc73e20fb33",
   "metadata": {},
   "outputs": [
    {
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       "      <th>lug_boot</th>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      buying  maint  doors  person  lug_boot  safety\n",
       "1178       1      1      1       1         1       1\n",
       "585        2      2      2       2         2       2\n",
       "1552       3      1      2       1         3       3\n",
       "1169       1      1      1       3         1       1\n",
       "1033       1      2      3       3         1       3"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1acef833-8fb2-4a21-9ca1-d28ef182b7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>person</th>\n",
       "      <th>lug_boot</th>\n",
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      "text/plain": [
       "      buying  maint  doors  person  lug_boot  safety\n",
       "599        2      2      3       3         3       1\n",
       "1201       1      4      4       1         3       3\n",
       "628        2      2      1       3         1       3\n",
       "1498       3      2      1       1         3       3\n",
       "1263       1      4      3       2         3       2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ac324fee-9a2f-45f3-a1e4-94db34ba2de5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=10,random_state=0)\n",
    "rfc.fit(x_train,y_train)\n",
    "y_pred = rfc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5190005e-c076-48c9-9722-10562cd2f51e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.8863\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Model Accuracy: {0:0.4f}\".format(accuracy_score(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c67c6952-74e2-42c3-86a6-8335e7020806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy with  100 decision tree: 0.9037\n"
     ]
    }
   ],
   "source": [
    "rfc_100 = RandomForestClassifier(n_estimators=100,random_state=0)\n",
    "rfc_100.fit(x_train,y_train)\n",
    "y_pred_100 = rfc_100.predict(x_test)\n",
    "print(\"Model Accuracy with  100 decision tree: {0:0.4f}\".format(accuracy_score(y_test,y_pred_100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b2e2bbb-edfe-4220-9460-5d531e228e01",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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