Coursera Decision Tree Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d61020c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-2.0.0-py3-none-win_amd64.whl (99.7 MB)\n",
      "Requirement already satisfied: numpy in c:\\users\\mir faisal\\anaconda3\\lib\\site-packages (from xgboost) (1.24.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\mir faisal\\anaconda3\\lib\\site-packages (from xgboost) (1.11.2)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-2.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0538c45a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from xgboost import XGBClassifier\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4299911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/MIR FAISAL/Downloads/heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd274463",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
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       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
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       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>NAP</td>\n",
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       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257bf160",
   "metadata": {},
   "source": [
    "# One Hot Encoding Using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c05b0a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = ['Sex',\n",
    "'ChestPainType',\n",
    "'RestingECG',\n",
    "'ExerciseAngina',\n",
    "'ST_Slope'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1942ead2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(data = df,prefix = variables,columns = variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1328fb26",
   "metadata": {},
   "source": [
    "This will replace the columns with the one-hot encoded ones and keep the columns outside 'columns' argument as it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e64cfb59",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "      <th>ChestPainType_NAP</th>\n",
       "      <th>ChestPainType_TA</th>\n",
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       "      <th>RestingECG_ST</th>\n",
       "      <th>ExerciseAngina_N</th>\n",
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       "      <td>1.5</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "   Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  HeartDisease  \\\n",
       "0   40        140          289          0    172      0.0             0   \n",
       "1   49        160          180          0    156      1.0             1   \n",
       "2   37        130          283          0     98      0.0             0   \n",
       "3   48        138          214          0    108      1.5             1   \n",
       "4   54        150          195          0    122      0.0             0   \n",
       "\n",
       "   Sex_F  Sex_M  ChestPainType_ASY  ...  ChestPainType_NAP  ChestPainType_TA  \\\n",
       "0      0      1                  0  ...                  0                 0   \n",
       "1      1      0                  0  ...                  1                 0   \n",
       "2      0      1                  0  ...                  0                 0   \n",
       "3      1      0                  1  ...                  0                 0   \n",
       "4      0      1                  0  ...                  1                 0   \n",
       "\n",
       "   RestingECG_LVH  RestingECG_Normal  RestingECG_ST  ExerciseAngina_N  \\\n",
       "0               0                  1              0                 1   \n",
       "1               0                  1              0                 1   \n",
       "2               0                  0              1                 1   \n",
       "3               0                  1              0                 0   \n",
       "4               0                  1              0                 1   \n",
       "\n",
       "   ExerciseAngina_Y  ST_Slope_Down  ST_Slope_Flat  ST_Slope_Up  \n",
       "0                 0              0              0            1  \n",
       "1                 0              0              1            0  \n",
       "2                 0              0              0            1  \n",
       "3                 1              0              1            0  \n",
       "4                 0              0              0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d66b9621",
   "metadata": {},
   "source": [
    "now splitting the data into target varibles and features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "24e1c4b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  Sex_F  Sex_M  \\\n",
      "0     40        140          289          0    172      0.0      0      1   \n",
      "1     49        160          180          0    156      1.0      1      0   \n",
      "2     37        130          283          0     98      0.0      0      1   \n",
      "3     48        138          214          0    108      1.5      1      0   \n",
      "4     54        150          195          0    122      0.0      0      1   \n",
      "..   ...        ...          ...        ...    ...      ...    ...    ...   \n",
      "913   45        110          264          0    132      1.2      0      1   \n",
      "914   68        144          193          1    141      3.4      0      1   \n",
      "915   57        130          131          0    115      1.2      0      1   \n",
      "916   57        130          236          0    174      0.0      1      0   \n",
      "917   38        138          175          0    173      0.0      0      1   \n",
      "\n",
      "     ChestPainType_ASY  ChestPainType_ATA  ChestPainType_NAP  \\\n",
      "0                    0                  1                  0   \n",
      "1                    0                  0                  1   \n",
      "2                    0                  1                  0   \n",
      "3                    1                  0                  0   \n",
      "4                    0                  0                  1   \n",
      "..                 ...                ...                ...   \n",
      "913                  0                  0                  0   \n",
      "914                  1                  0                  0   \n",
      "915                  1                  0                  0   \n",
      "916                  0                  1                  0   \n",
      "917                  0                  0                  1   \n",
      "\n",
      "     ChestPainType_TA  RestingECG_LVH  RestingECG_Normal  RestingECG_ST  \\\n",
      "0                   0               0                  1              0   \n",
      "1                   0               0                  1              0   \n",
      "2                   0               0                  0              1   \n",
      "3                   0               0                  1              0   \n",
      "4                   0               0                  1              0   \n",
      "..                ...             ...                ...            ...   \n",
      "913                 1               0                  1              0   \n",
      "914                 0               0                  1              0   \n",
      "915                 0               0                  1              0   \n",
      "916                 0               1                  0              0   \n",
      "917                 0               0                  1              0   \n",
      "\n",
      "     ExerciseAngina_N  ExerciseAngina_Y  ST_Slope_Down  ST_Slope_Flat  \\\n",
      "0                   1                 0              0              0   \n",
      "1                   1                 0              0              1   \n",
      "2                   1                 0              0              0   \n",
      "3                   0                 1              0              1   \n",
      "4                   1                 0              0              0   \n",
      "..                ...               ...            ...            ...   \n",
      "913                 1                 0              0              1   \n",
      "914                 1                 0              0              1   \n",
      "915                 0                 1              0              1   \n",
      "916                 1                 0              0              1   \n",
      "917                 1                 0              0              0   \n",
      "\n",
      "     ST_Slope_Up  \n",
      "0              1  \n",
      "1              0  \n",
      "2              1  \n",
      "3              0  \n",
      "4              1  \n",
      "..           ...  \n",
      "913            0  \n",
      "914            0  \n",
      "915            0  \n",
      "916            0  \n",
      "917            1  \n",
      "\n",
      "[918 rows x 20 columns]\n",
      "0      0\n",
      "1      1\n",
      "2      0\n",
      "3      1\n",
      "4      0\n",
      "      ..\n",
      "913    1\n",
      "914    1\n",
      "915    1\n",
      "916    1\n",
      "917    0\n",
      "Name: HeartDisease, Length: 918, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "features = df.drop(columns = 'HeartDisease',axis = 1)\n",
    "labels = df['HeartDisease']\n",
    "print(features)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d664d30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "918\n"
     ]
    }
   ],
   "source": [
    "print(len(features))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a02fc974",
   "metadata": {},
   "source": [
    "# Splitting The DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0c0b496",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(features,labels, train_size = 0.8, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6edd2472",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train samples: 734\n",
      "validation samples: 184\n",
      "target proportion: 0.5463\n"
     ]
    }
   ],
   "source": [
    "print(f'train samples: {len(X_train)}')\n",
    "print(f'validation samples: {len(X_val)}')\n",
    "print(f'target proportion: {sum(y_train)/len(y_train):.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "823a5f12",
   "metadata": {},
   "source": [
    "# Building The Model\n",
    "# 1. Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "acc970f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700]\n",
    "max_depth_list = [1,2, 3, 4, 8, 16, 32, 64, None] # # None means that there is no depth limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a8db4c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e04746e0d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list_train = []\n",
    "accuracy_list_validation = []\n",
    "for i in min_samples_split_list:\n",
    "    model = DecisionTreeClassifier(min_samples_split = i, random_state = 42).fit(X_train,y_train)\n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_val = accuracy_score(predictions_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_validation.append(accuracy_val)\n",
    "plt.title('Train X Validation Metrics')\n",
    "plt.xlabel('min_samples_split')\n",
    "plt.ylabel('accuracy')\n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_validation)\n",
    "plt.legend(['Train','Validation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d27903b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e04a653f10>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list_train = []\n",
    "accuracy_list_validation = []\n",
    "for j in max_depth_list:\n",
    "    model = DecisionTreeClassifier(max_depth = j,random_state = 42).fit(X_train,y_train)\n",
    "    prediction_train = model.predict(X_train)\n",
    "    prediction_val = model.predict(X_val)\n",
    "    accuracy_train = accuracy_score(prediction_train , y_train)\n",
    "    accuracy_val = accuracy_score(prediction_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_validation.append(accuracy_val)\n",
    "plt.title(\"Train X Validation Metrics\")\n",
    "plt.xlabel(\"max_depth\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40fe3a21",
   "metadata": {},
   "source": [
    "We can see that in general, reducing `max_depth` can help to reduce overfitting.\n",
    "- Reducing `max_depth` from 8 to 4 increases validation accuracy closer to training accuracy, while significantly reducing training accuracy.\n",
    "- The validation accuracy reaches the highest at tree_depth=4. \n",
    "- When the `max_depth` is smaller than 3, both training and validation accuracy decreases.  The tree cannot make enough splits to distinguish positives from negatives (the model is underfitting the training set). \n",
    "- When the `max_depth` is too high ( >= 5), validation accuracy decreases while training accuracy increases, indicating that the model is overfitting to the training set.\n",
    "\n",
    "So we can choose the best values for these two hyper-parameters for our model to be:\n",
    "- `max_depth = 4`\n",
    "- `min_samples_split = 50`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c86e324",
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_tree_model = DecisionTreeClassifier(min_samples_split = 50,\n",
    "                                             max_depth = 3,\n",
    "                                             random_state = 42).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d90d9d1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics train:\n",
      "\tAccuracy score: 0.8542\n",
      "Metrics validation:\n",
      "\tAccuracy score: 0.8424\n"
     ]
    }
   ],
   "source": [
    "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_train),y_train):.4f}\")\n",
    "print(f\"Metrics validation:\\n\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_val),y_val):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c118a4a1",
   "metadata": {},
   "source": [
    "# 2. Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f01c992",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700]\n",
    "max_depth_list = [2, 4, 8, 16, 32, 64, None]\n",
    "n_estimators_list = [10,50,100,500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "892db402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e04a668df0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list_train = []\n",
    "accuracy_list_val = []\n",
    "for min_samples_split in min_samples_split_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(min_samples_split = min_samples_split,\n",
    "                                   random_state = 42).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_val = accuracy_score(predictions_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_val.append(accuracy_val)\n",
    "\n",
    "plt.title('Train x Validation metrics')\n",
    "plt.xlabel('min_samples_split')\n",
    "plt.ylabel('accuracy')\n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_val)\n",
    "plt.legend(['Train','Validation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d1067ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e04a7c50a0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list_train = []\n",
    "accuracy_list_val = []\n",
    "for max_depth in max_depth_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(max_depth = max_depth,\n",
    "                                   random_state = 42).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_val = accuracy_score(predictions_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_val.append(accuracy_val)\n",
    "\n",
    "plt.title('Train x Validation metrics')\n",
    "plt.xlabel('max_depth')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_val)\n",
    "plt.legend(['Train','Validation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cc54c23c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e04a8ae4c0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list_train = []\n",
    "accuracy_list_val = []\n",
    "for estimator in n_estimators_list:\n",
    "    model = RandomForestClassifier(n_estimators = estimator,random_state = 42).fit(X_train,y_train)\n",
    "    predictions_train = model.predict(X_train)\n",
    "    predictions_val = model.predict(X_val)\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_val = accuracy_score(predictions_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_val.append(accuracy_val)\n",
    "plt.title('Train X Validation Metrics')\n",
    "plt.xlabel('n_estimators')\n",
    "plt.ylabel('accuracy')\n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_val)\n",
    "plt.legend(['Train','Validation'])    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2441e7c3",
   "metadata": {},
   "source": [
    "Let's then fit a random forest with the following parameters:\n",
    "\n",
    "max_depth: 16\n",
    "min_samples_split: 10\n",
    "n_estimators: 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5120aa08",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_model = RandomForestClassifier(n_estimators = 100,\n",
    "                                             max_depth = 16, \n",
    "                                             min_samples_split = 10).fit(X_train,y_train)\n",
    "prediction_train = random_forest_model.predict(X_train)\n",
    "accuracy_train = accuracy_score(prediction_train,y_train)\n",
    "prediction_val = random_forest_model.predict(X_val)\n",
    "accuracy_val = accuracy_score(prediction_val,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aa00231a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics train:\n",
      "\tAccuracy score: 0.9278\n",
      "Metrics test:\n",
      "\tAccuracy score: 0.8750\n"
     ]
    }
   ],
   "source": [
    "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_train:.4f}\\nMetrics test:\\n\\tAccuracy score: {accuracy_val:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e3c678f",
   "metadata": {},
   "source": [
    "# 3. XG Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f600f4fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lenght of training Data:  587\n"
     ]
    }
   ],
   "source": [
    "n = int(len(X_train)* 0.8)\n",
    "print(\"The lenght of training Data: \", n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e5809282",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_fit, X_train_eval, y_train_fit, y_train_eval = X_train[:n], X_train[n:], y_train[:n], y_train[n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ec4f32f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.65141\n",
      "[1]\tvalidation_0-logloss:0.61289\n",
      "[2]\tvalidation_0-logloss:0.57971\n",
      "[3]\tvalidation_0-logloss:0.55172\n",
      "[4]\tvalidation_0-logloss:0.53175\n",
      "[5]\tvalidation_0-logloss:0.51401\n",
      "[6]\tvalidation_0-logloss:0.49884\n",
      "[7]\tvalidation_0-logloss:0.48808\n",
      "[8]\tvalidation_0-logloss:0.47906\n",
      "[9]\tvalidation_0-logloss:0.47016\n",
      "[10]\tvalidation_0-logloss:0.46099\n",
      "[11]\tvalidation_0-logloss:0.45347\n",
      "[12]\tvalidation_0-logloss:0.44867\n",
      "[13]\tvalidation_0-logloss:0.44370\n",
      "[14]\tvalidation_0-logloss:0.44137\n",
      "[15]\tvalidation_0-logloss:0.43769\n",
      "[16]\tvalidation_0-logloss:0.43645\n",
      "[17]\tvalidation_0-logloss:0.43211\n",
      "[18]\tvalidation_0-logloss:0.43149\n",
      "[19]\tvalidation_0-logloss:0.42899\n",
      "[20]\tvalidation_0-logloss:0.42878\n",
      "[21]\tvalidation_0-logloss:0.42753\n",
      "[22]\tvalidation_0-logloss:0.42596\n",
      "[23]\tvalidation_0-logloss:0.42669\n",
      "[24]\tvalidation_0-logloss:0.42381\n",
      "[25]\tvalidation_0-logloss:0.42547\n",
      "[26]\tvalidation_0-logloss:0.42418\n",
      "[27]\tvalidation_0-logloss:0.42022\n",
      "[28]\tvalidation_0-logloss:0.41854\n",
      "[29]\tvalidation_0-logloss:0.41675\n",
      "[30]\tvalidation_0-logloss:0.41670\n",
      "[31]\tvalidation_0-logloss:0.41677\n",
      "[32]\tvalidation_0-logloss:0.41277\n",
      "[33]\tvalidation_0-logloss:0.40988\n",
      "[34]\tvalidation_0-logloss:0.41156\n",
      "[35]\tvalidation_0-logloss:0.41340\n",
      "[36]\tvalidation_0-logloss:0.41410\n",
      "[37]\tvalidation_0-logloss:0.41552\n",
      "[38]\tvalidation_0-logloss:0.41682\n",
      "[39]\tvalidation_0-logloss:0.41785\n",
      "[40]\tvalidation_0-logloss:0.41901\n",
      "[41]\tvalidation_0-logloss:0.41970\n",
      "[42]\tvalidation_0-logloss:0.41873\n",
      "[43]\tvalidation_0-logloss:0.42194\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model = XGBClassifier(n_estimators = 500, learning_rate = 0.1,verbosity = 1, random_state = 42)\n",
    "xgb_model.fit(X_train_fit,y_train_fit, eval_set = [(X_train_eval,y_train_eval)], early_stopping_rounds = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e99fa90",
   "metadata": {},
   "source": [
    "Even though we initialized the model to allow up to 500 estimators, the algorithm only fit 43 estimators (over 43 rounds of training).\n",
    "\n",
    "let's see why:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "afe1c580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model.best_iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c276a824",
   "metadata": {},
   "source": [
    "The best round of training was round 33, with a log loss of 4.0988.  \n",
    "- For 10 rounds of training after that (from round 34 to 43), the log loss was higher than this.\n",
    "- Since we set `early_stopping_rounds` to 10, then by the 10th round where the log loss doesn't improve upon the best one, training stops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d957f3d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics train:\n",
      "\tAccuracy score: 0.9469\n",
      "Metrics test:\n",
      "\tAccuracy score: 0.8750\n"
     ]
    }
   ],
   "source": [
    "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_score(xgb_model.predict(X_train),y_train):.4f}\\nMetrics test:\\n\\tAccuracy score: {accuracy_score(xgb_model.predict(X_val),y_val):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b2a02bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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