Random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bfbacde7",
   "metadata": {},
   "source": [
    "<div style=\"border-radius:10px;\n",
    "            border:#0b0265 solid;\n",
    "           background-color:green;\n",
    "           font-size:110%;\n",
    "           letter-spacing:0.5px;\n",
    "            text-align: center\">\n",
    "\n",
    "<center><h1 style=\"padding: 25px 0px; color:white; font-weight: bold; font-family: Times New Roman\">\n",
    "Random Forest Ensemble Technique</h1></center>    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98ef18dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import category_encoders as ce\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d01aaf9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1723</th>\n",
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       "      <th>1724</th>\n",
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       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
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       "      <td>more</td>\n",
       "      <td>big</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>5more</td>\n",
       "      <td>more</td>\n",
       "      <td>big</td>\n",
       "      <td>med</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
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       "      <td>more</td>\n",
       "      <td>big</td>\n",
       "      <td>high</td>\n",
       "      <td>vgood</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1728 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          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\n",
       "...     ...    ...    ...   ...    ...   ...    ...\n",
       "1723    low    low  5more  more    med   med   good\n",
       "1724    low    low  5more  more    med  high  vgood\n",
       "1725    low    low  5more  more    big   low  unacc\n",
       "1726    low    low  5more  more    big   med   good\n",
       "1727    low    low  5more  more    big  high  vgood\n",
       "\n",
       "[1728 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"car_evaluation.csv\",header=None)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd9274a0",
   "metadata": {},
   "outputs": [
    {
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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>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
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       "      <td>more</td>\n",
       "      <td>big</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>5more</td>\n",
       "      <td>more</td>\n",
       "      <td>big</td>\n",
       "      <td>med</td>\n",
       "      <td>good</td>\n",
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       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>5more</td>\n",
       "      <td>more</td>\n",
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       "      <td>vgood</td>\n",
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       "<p>1728 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     buying  maint  doors persons 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\n",
       "...     ...    ...    ...     ...      ...    ...    ...\n",
       "1723    low    low  5more    more      med    med   good\n",
       "1724    low    low  5more    more      med   high  vgood\n",
       "1725    low    low  5more    more      big    low  unacc\n",
       "1726    low    low  5more    more      big    med   good\n",
       "1727    low    low  5more    more      big   high  vgood\n",
       "\n",
       "[1728 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = ['buying','maint','doors','persons','lug_boot','safety','class']\n",
    "df.columns=col_names\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1f24b33",
   "metadata": {},
   "outputs": [
    {
     "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   persons   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": [
    "#to get the summary of the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4bc48b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vhigh    432\n",
      "high     432\n",
      "med      432\n",
      "low      432\n",
      "Name: buying, dtype: int64\n",
      "vhigh    432\n",
      "high     432\n",
      "med      432\n",
      "low      432\n",
      "Name: maint, dtype: int64\n",
      "2        432\n",
      "3        432\n",
      "4        432\n",
      "5more    432\n",
      "Name: doors, dtype: int64\n",
      "2       576\n",
      "4       576\n",
      "more    576\n",
      "Name: persons, dtype: int64\n",
      "small    576\n",
      "med      576\n",
      "big      576\n",
      "Name: lug_boot, dtype: int64\n",
      "low     576\n",
      "med     576\n",
      "high    576\n",
      "Name: safety, dtype: int64\n",
      "unacc    1210\n",
      "acc       384\n",
      "good       69\n",
      "vgood      65\n",
      "Name: class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in col_names:\n",
    "    print(df[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8313611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buying      0\n",
       "maint       0\n",
       "doors       0\n",
       "persons     0\n",
       "lug_boot    0\n",
       "safety      0\n",
       "class       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for the missing values in df\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47fd4cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop([\"class\"],axis=1)\n",
    "y=df[\"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ef5924a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "99275364",
   "metadata": {},
   "outputs": [],
   "source": [
    "#enoding the categorical variables\n",
    "encoder = ce.OrdinalEncoder(cols=['buying','maint','doors','persons','lug_boot','safety'])\n",
    "X_train = encoder.fit_transform(X_train)\n",
    "X_test = encoder.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33e84066",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1122</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1209 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      buying  maint  doors  persons  lug_boot  safety\n",
       "840        1      1      1        1         1       1\n",
       "650        1      2      2        1         2       2\n",
       "231        2      2      2        2         3       1\n",
       "176        2      3      3        2         1       2\n",
       "886        3      4      2        3         1       3\n",
       "...      ...    ...    ...      ...       ...     ...\n",
       "1122       3      2      4        2         3       1\n",
       "717        1      2      3        2         3       1\n",
       "1590       4      2      3        3         3       1\n",
       "1289       3      1      1        3         2       2\n",
       "1674       4      1      3        1         2       1\n",
       "\n",
       "[1209 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "80bddd17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ae378352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 0.9387040280210157\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "print(\"Accuracy is:\",acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "daa7c417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ranking features according to their ranks\n",
    "clf = RandomForestClassifier(n_estimators=100)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b506de7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "safety      0.283820\n",
       "persons     0.271967\n",
       "buying      0.180038\n",
       "maint       0.162334\n",
       "lug_boot    0.101841\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feture scores are\n",
    "feature_scores = pd.Series(clf.feature_importances_, index=X_train.columns).sort_values(ascending=False)\n",
    "feature_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b5e7c454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=feature_scores, y=feature_scores.index)\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.ylabel('Features')\n",
    "plt.title(\"Visualizatio of Important Features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6fcd246a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking only important features now for the creation of the model\n",
    "X = df.drop(['class','doors'],axis=1)\n",
    "y = df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "20dbb4c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aa7fcc39",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = ce.OrdinalEncoder(cols=['buying','maint','persons','lug_boot','safety'])\n",
    "X_train = encoder.fit_transform(X_train)\n",
    "X_test = encoder.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "371579e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creation of the more improved model \n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred3=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3dd62bd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model is: 0.9422066549912435\n"
     ]
    }
   ],
   "source": [
    "acc3=accuracy_score(y_test,y_pred3)\n",
    "print(\"Accuracy of the model is:\",acc3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}