Random Forest Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53f61d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "plt.rcParams['figure.figsize'] = [30,15]\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.tree import plot_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c220d0e5",
   "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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_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  bill_length_mm  bill_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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"penguins.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b823223d",
   "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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_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  bill_length_mm  bill_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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7f9bb05",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['bill_length_mm','bill_depth_mm','flipper_length_mm','body_mass_g']]\n",
    "y = df['sex']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e96b04b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.93\n"
     ]
    }
   ],
   "source": [
    "rfm = RandomForestClassifier()\n",
    "rfm.fit(X_train, y_train)\n",
    "y_pred = rfm.predict(X_test)\n",
    "print('Accuracy: {:.2f}'.format(rfm.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ff3b4aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "rfm = RandomForestClassifier(n_estimators=500,criterion='entropy', max_depth=5)\n",
    "rfm.fit(X_train, y_train)\n",
    "y_pred = rfm.predict(X_test)\n",
    "print('Accuracy: {:.2f}'.format(rfm.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec2f62e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_size_inches(16,4)\n",
    "\n",
    "df[df.sex=='Male'].plot(kind='scatter',x='bill_length_mm',y='bill_depth_mm',color='orange', label='Male', ax = ax1)\n",
    "df[df.sex=='Female'].plot(kind='scatter',x='bill_length_mm',y='bill_depth_mm',color='blue', label='Female',ax=ax1)\n",
    "\n",
    "df[df.sex=='Male'].plot.scatter(x='flipper_length_mm',y='body_mass_g',color='orange', label='Male', ax = ax2)\n",
    "df[df.sex=='Female'].plot.scatter(x='flipper_length_mm',y='body_mass_g',color='blue', label='Female',ax=ax2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d43ad91",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/prabhatm/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Female'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of the output using input\n",
    "rfm.predict([[40,18, 195, 3250]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9ecf3233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Female       0.97      0.95      0.96        60\n",
      "        Male       0.93      0.95      0.94        40\n",
      "\n",
      "    accuracy                           0.95       100\n",
      "   macro avg       0.95      0.95      0.95       100\n",
      "weighted avg       0.95      0.95      0.95       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "11efb4f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score: 0.95\n"
     ]
    }
   ],
   "source": [
    "# Accuracy\n",
    "print(\"Test Accuracy Score:\", rfm.score(X_test, y_test)) #Predicted correctly/Total values"
   ]
  }
 ],
 "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
}