Random Forest  Section part 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "36fa3e3a",
   "metadata": {},
   "source": [
    "# Random Forests\n",
    "1. It have the ability to greatly increase the performance based on expanding ideas from the Decision Tree.\n",
    "2. Random Forests are known as ensemble lerners, since they rely on an ensemble of models (multiple decision trees)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6fddbc31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc662c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"penguins_size.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b136ca7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>culmen_length_mm</th>\n",
       "      <th>culmen_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species     island  culmen_length_mm  culmen_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Torgersen              39.1             18.7              181.0   \n",
       "1  Adelie  Torgersen              39.5             17.4              186.0   \n",
       "2  Adelie  Torgersen              40.3             18.0              195.0   \n",
       "3  Adelie  Torgersen               NaN              NaN                NaN   \n",
       "4  Adelie  Torgersen              36.7             19.3              193.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3750.0    MALE  \n",
       "1       3800.0  FEMALE  \n",
       "2       3250.0  FEMALE  \n",
       "3          NaN     NaN  \n",
       "4       3450.0  FEMALE  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d461c311",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5833da20",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>culmen_length_mm</th>\n",
       "      <th>culmen_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3650.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species     island  culmen_length_mm  culmen_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Torgersen              39.1             18.7              181.0   \n",
       "1  Adelie  Torgersen              39.5             17.4              186.0   \n",
       "2  Adelie  Torgersen              40.3             18.0              195.0   \n",
       "4  Adelie  Torgersen              36.7             19.3              193.0   \n",
       "5  Adelie  Torgersen              39.3             20.6              190.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3750.0    MALE  \n",
       "1       3800.0  FEMALE  \n",
       "2       3250.0  FEMALE  \n",
       "4       3450.0  FEMALE  \n",
       "5       3650.0    MALE  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19ca5438",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(df.drop('species',axis = 1 ), drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "86631c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2953209d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b199e84a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79924a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2077e84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators = 10,\n",
    "                           max_features='auto', \n",
    "                           random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9da735a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=10, random_state=101)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1c67b090",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d794d79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8cba2eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "168bab26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2a14bf5cac0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(rfc, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b9a0c976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Adelie       0.97      0.95      0.96        41\n",
      "   Chinstrap       0.92      0.96      0.94        23\n",
      "      Gentoo       1.00      1.00      1.00        37\n",
      "\n",
      "    accuracy                           0.97       101\n",
      "   macro avg       0.96      0.97      0.97       101\n",
      "weighted avg       0.97      0.97      0.97       101\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd35d371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.31867744, 0.1018487 , 0.17343398, 0.21316964, 0.14512091,\n",
       "       0.03720114, 0.00632264, 0.00422556])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1dbdc29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}