Importance_Features(RandomForrest_DecisionTree).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83b48fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import (\n",
    "    confusion_matrix,\n",
    "    accuracy_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score\n",
    ")\n",
    "from sklearn.metrics import classification_report\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eaf87bf",
   "metadata": {},
   "source": [
    "## Loading Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ecddff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('cancer-patient_datasets_Change.csv')\n",
    "#df.head()\n",
    "#df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49cf225a",
   "metadata": {},
   "source": [
    "## Specify Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f544e514",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df['Level']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49585eee",
   "metadata": {},
   "source": [
    "### Split Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67d84293",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 7\n",
    "train_data, test_data = train_test_split(df, test_size=0.3, \n",
    "random_state=seed, stratify=target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc77cc01",
   "metadata": {},
   "source": [
    "## Separate the independent and target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "669fa39a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate the independent and target variables from training data\n",
    "train_x = train_data.drop(columns=['index','Patient Id','Level'],axis=1)\n",
    "train_y = train_data['Level']\n",
    "# separate the independent and target variables from test data\n",
    "test_x = test_data.drop(columns=['index','Patient Id','Level'],axis=1)\n",
    "test_y = test_data['Level']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7b5413",
   "metadata": {},
   "source": [
    "## Normalization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "642fd20a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import MinMaxScaler\n",
    "#scaler = MinMaxScaler()\n",
    "#train_x = scaler.fit_transform(train_x)\n",
    "#test_x = scaler.fit_transform(test_x)\n",
    "#print(train_x)\n",
    "#print(test_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7fbe89",
   "metadata": {},
   "source": [
    "## Standarization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64e17b46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfrom sklearn.preprocessing import StandardScaler\\nscaling = StandardScaler()\\ntrain_x = scaling.fit_transform(train_x)\\ntest_x = scaling.fit_transform(test_x)\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaling = StandardScaler()\n",
    "train_x = scaling.fit_transform(train_x)\n",
    "test_x = scaling.fit_transform(test_x)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "052de89c",
   "metadata": {},
   "source": [
    "## Build Random Forrest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27a0c373",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f3663676",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "# Fit the model to the training data\n",
    "rf_classifier.fit(train_x, train_y)\n",
    "\n",
    "# Make predictions on the test set\n",
    "rf_pred_train = rf_classifier.predict(train_x)\n",
    "rf_pred_test = rf_classifier.predict(test_x)\n",
    "# Evaluate the accuracy of the model\n",
    "#accuracy = accuracy_score(test_y, y_pred)\n",
    "#print(f\"Accuracy: {accuracy:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edcfc10b",
   "metadata": {},
   "source": [
    "### Build Decesion Tree Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb91398d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt_classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 42)\n",
    "dt_classifier.fit(train_x, train_y)\n",
    "dt_pred_train = dt_classifier.predict(train_x)\n",
    "dt_pred_test = dt_classifier.predict(test_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d76fb72",
   "metadata": {},
   "source": [
    "### Importances Features of Random Forrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4b83a7c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. Feature 4 (Dust Allergy): 0.10491506933380756\n",
      "2. Feature 9 (Obesity): 0.09986709230928557\n",
      "3. Feature 14 (Fatigue): 0.08955135776462748\n",
      "4. Feature 13 (Coughing of Blood): 0.08611821114201806\n",
      "5. Feature 5 (OccuPational Hazards): 0.07403336320546414\n",
      "6. Feature 3 (Alcohol use): 0.07393931824790946\n",
      "7. Feature 18 (Swal0ing Difficulty): 0.06449439424178507\n",
      "8. Feature 17 (Wheezing): 0.055667372053385726\n",
      "9. Feature 16 (Shortness of Breath): 0.04670314929367123\n",
      "10. Feature 22 (Snoring): 0.044495956308013465\n",
      "11. Feature 20 (Frequent Cold): 0.0402700284359111\n",
      "12. Feature 6 (Genetic Risk): 0.03578224978677818\n",
      "13. Feature 15 (Weight Loss): 0.03324383599960726\n",
      "14. Feature 19 (Clubbing of Finger Nails): 0.032403471617952026\n",
      "15. Feature 7 (chronic Lung Disease): 0.023455266669950032\n",
      "16. Feature 2 (Air Pollution): 0.021825779116205427\n",
      "17. Feature 12 (Chest Pain): 0.01754137636452373\n",
      "18. Feature 11 (Passive Smoker): 0.016640167201724447\n",
      "19. Feature 21 (Dry Cough): 0.014901208449008888\n",
      "20. Feature 8 (Balanced Diet): 0.010460516576029161\n",
      "21. Feature 0 (Age): 0.007691170426940136\n",
      "22. Feature 10 (Smoking): 0.005919645455401953\n",
      "23. Feature 1 (Gender): 7.999999999999999e-05\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get feature importances\n",
    "feature_importances = rf_classifier.feature_importances_\n",
    "\n",
    "# Sort the features by their importance\n",
    "sorted_indices = np.argsort(feature_importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "for i, feature_index in enumerate(sorted_indices):\n",
    "    print(f\"{i + 1}. Feature {feature_index} ({train_x.columns[feature_index]}): {feature_importances[feature_index]}\")\n",
    "\n",
    "# Plot the feature importances\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(range(train_x.shape[1]), feature_importances[sorted_indices], align=\"center\")\n",
    "plt.xticks(range(train_x.shape[1]), np.array(train_x.columns)[sorted_indices], rotation=90)\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel(\"Importance\")\n",
    "plt.title(\"Feature Importances in Random Forest Classifier (Cancer Dataset)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bfd9af2",
   "metadata": {},
   "source": [
    "### Compare Decesion Tree and Random Forrest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e50d9ec2",
   "metadata": {},
   "source": [
    "### Compare Both Model(Using Accuracy and F1Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "93447136",
   "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>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision tree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forrest</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model  Train Accuracy  Test Accuracy  F1_Score\n",
       "0   Decision tree             1.0            1.0       1.0\n",
       "1  Random Forrest             1.0            1.0       1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare = pd.DataFrame({'Model': ['Decision tree',\n",
    "                                  'Random Forrest'], \n",
    "                        'Train Accuracy': [accuracy_score(train_y, dt_pred_train),\n",
    "                                     accuracy_score(train_y,rf_pred_train)],                       \n",
    "                       'Test Accuracy': [accuracy_score(test_y, dt_pred_test),\n",
    "                                     accuracy_score(test_y,rf_pred_test)],\n",
    "                       'F1_Score': [f1_score(test_y,dt_pred_test),\n",
    "                                     f1_score(test_y,rf_pred_test)],\n",
    "                       })\n",
    "compare.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8a712b",
   "metadata": {},
   "source": [
    "### Importance Features of Both Model(Random Forrest and Decesion Tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "85740ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance=pd.DataFrame({\n",
    "    'rf_classifier':rf_classifier.feature_importances_,\n",
    "    'dt_classifier':dt_classifier.feature_importances_\n",
    "},index=df.drop(columns=['index','Patient Id','Level']).columns)\n",
    "feature_importance.sort_values(by='rf_classifier',ascending=True,inplace=True)\n",
    "#Arrange Index\n",
    "index = np.arange(len(feature_importance))\n",
    "fig, ax = plt.subplots(figsize=(18,8))\n",
    "rfc_feature=ax.barh(index,feature_importance['rf_classifier'],0.4,color='purple',label='Random Forest')\n",
    "dt_feature=ax.barh(index+0.4,feature_importance['dt_classifier'],0.4,color='lightgreen',label='Decision Tree')\n",
    "ax.set(yticks=index+0.4,yticklabels=feature_importance.index)\n",
    "\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4540372",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d8f1215",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3778b6ce",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}