Heart Failure Prediction using various ML Classification Algorithms.ipynb

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    "# Comparison of Classification Algorithms for Binary Classification Problems\n",
    "\n",
    "## Heart Failure Prediction\n",
    "\n",
    "#### Link to Dataset: https://archive-beta.ics.uci.edu/dataset/519/heart+failure+clinical+records\n",
    "\n",
    "The Lifecycle of the project will have following steps:\n",
    "\n",
    "1. <b>Objective</b>: The objective of this project is to evaluate and compare the important metrics of classification algorithms viz. Logistic Regression, K-Nearest Neighbors, Support Vector Classifiers, Decision Trees and Random Forest. We will test the Accuracy, Precision, Recall and F1 Score of all these algorithms.\n",
    "\n",
    "2. <b>Data Collection</b>: The data is taken from UCI ML Repository.\n",
    "\n",
    "    This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features.\n",
    "\n",
    "    Features꞉\n",
    "    - age꞉ age of the patient (years)\n",
    "    - anaemia꞉ decrease of red blood cells or hemoglobin (boolean)\n",
    "    - high blood pressure꞉ if the patient has hypertension (boolean)\n",
    "    - creatinine phosphokinase (CPK)꞉ level of the CPK enzyme in the blood (mcg/L)\n",
    "    - diabetes꞉ if the patient has diabetes (boolean)\n",
    "    - ejection fraction꞉ percentage of blood leaving the heart at each contraction (percentage)\n",
    "    - platelets꞉ platelets in the blood (kiloplatelets/mL)\n",
    "    - sex꞉ Male=1 or Female=0 (binary)\n",
    "    - serum creatinine꞉ level of serum creatinine in the blood (mg/dL)\n",
    "    - serum sodium꞉ level of serum sodium in the blood (mEq/L)\n",
    "    - smoking꞉ if the patient smokes or not 1=yes or 0=no (boolean)\n",
    "    - time꞉ follow‑up period (days)\n",
    "    - death event꞉ if the patient deceased during the follow‑up period (boolean)\n",
    "\n",
    "3. <b>Exploratory Data Analysis</b>: In this step we will analyse the dependent and independent variables, check the relationship between the variables by plotting histplots and bar charts. We will also check for inconsistencies in data or null values if any.\n",
    "\n",
    "4. <b>Model Selection</b>: As this is a classification task, we will be working with classification algorithms such as Logistic Regression, K-Nearest Neighbors, Support Vector Classifier, Decision Tree Classifier and Random Forest Classifier. \n",
    "\n",
    "5. <b>Model Training</b>: We will be training the model across all the 5 algorithms and evaluate the metrics of each model.\n",
    "\n",
    "6. <b>Hyperparameter Tuning</b>: We will perform Hyperparameter tuning to optimize the performance of the models by selecting the best combination of hyperparameters.\n",
    "\n",
    "7. <b>Evaluating Model Performance</b>: We will use the metrics Accuracy, Precision, Recall and F1 Score to compare the performance of each model and select the best model for our problem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04320de2",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1420125c",
   "metadata": {},
   "source": [
    "## Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e486bfda",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pprint import pprint\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ba97d66",
   "metadata": {},
   "source": [
    "## Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "63415324",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_link = 'https://raw.githubusercontent.com/grvnair/heart-failure-prediction/main/heart_failure_clinical_records_dataset.csv'\n",
    "\n",
    "df = pd.read_csv(dataset_link)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc660c9",
   "metadata": {},
   "source": [
    "## Analysing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ddbe9849",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
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       "      <td>1.9</td>\n",
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       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
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       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.9</td>\n",
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       "      <td>1</td>\n",
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       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  75.0        0                       582         0                 20   \n",
       "1  55.0        0                      7861         0                 38   \n",
       "2  65.0        0                       146         0                 20   \n",
       "3  50.0        1                       111         0                 20   \n",
       "4  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1  265000.00               1.9           130    1   \n",
       "1                    0  263358.03               1.1           136    1   \n",
       "2                    0  162000.00               1.3           129    1   \n",
       "3                    0  210000.00               1.9           137    1   \n",
       "4                    0  327000.00               2.7           116    0   \n",
       "\n",
       "   smoking  time  DEATH_EVENT  \n",
       "0        0     4            1  \n",
       "1        0     6            1  \n",
       "2        1     7            1  \n",
       "3        0     7            1  \n",
       "4        0     8            1  "
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     "metadata": {},
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c7b586da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of rows and columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a3bbc72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         0\n",
       "anaemia                     0\n",
       "creatinine_phosphokinase    0\n",
       "diabetes                    0\n",
       "ejection_fraction           0\n",
       "high_blood_pressure         0\n",
       "platelets                   0\n",
       "serum_creatinine            0\n",
       "serum_sodium                0\n",
       "sex                         0\n",
       "smoking                     0\n",
       "time                        0\n",
       "DEATH_EVENT                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ddb1837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       299 non-null    float64\n",
      " 1   anaemia                   299 non-null    int64  \n",
      " 2   creatinine_phosphokinase  299 non-null    int64  \n",
      " 3   diabetes                  299 non-null    int64  \n",
      " 4   ejection_fraction         299 non-null    int64  \n",
      " 5   high_blood_pressure       299 non-null    int64  \n",
      " 6   platelets                 299 non-null    float64\n",
      " 7   serum_creatinine          299 non-null    float64\n",
      " 8   serum_sodium              299 non-null    int64  \n",
      " 9   sex                       299 non-null    int64  \n",
      " 10  smoking                   299 non-null    int64  \n",
      " 11  time                      299 non-null    int64  \n",
      " 12  DEATH_EVENT               299 non-null    int64  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c305c923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
       "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
       "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
       "       'DEATH_EVENT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99994340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    203\n",
       "1     96\n",
       "Name: DEATH_EVENT, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value Counts for Deceased Patients\n",
    "\n",
    "df['DEATH_EVENT'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285aa231",
   "metadata": {},
   "source": [
    "1 signifies deceased and 0 signifies not deceased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a26c4f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df['DEATH_EVENT'].value_counts(), labels=[0, 1], colors=sns.color_palette('Paired'), \n",
    "        autopct='%.0f%%', startangle=90, explode=[0.07, 0])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3343363b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value counts for ANAEMIA : \n",
      "0    170\n",
      "1    129\n",
      "Name: anaemia, dtype: int64 \n",
      "\n",
      "Value counts for DIABETES : \n",
      "0    174\n",
      "1    125\n",
      "Name: diabetes, dtype: int64 \n",
      "\n",
      "Value counts for HIGH_BLOOD_PRESSURE : \n",
      "0    194\n",
      "1    105\n",
      "Name: high_blood_pressure, dtype: int64 \n",
      "\n",
      "Value counts for SEX : \n",
      "1    194\n",
      "0    105\n",
      "Name: sex, dtype: int64 \n",
      "\n",
      "Value counts for SMOKING : \n",
      "0    203\n",
      "1     96\n",
      "Name: smoking, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "list_col = ['anaemia', 'diabetes', 'high_blood_pressure', 'sex', 'smoking']\n",
    "\n",
    "for col in list_col:\n",
    "    print(\"Value counts for {} : \\n{} \\n\".format(col.upper(), df[col].value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80dfc94c",
   "metadata": {},
   "source": [
    "#### We will create a grouped bar chart to show the count of all the features which has binary values. I have relied on the matplotlib documentation for making Grouped bar chart. \n",
    "#### - Link: https://matplotlib.org/stable/gallery/lines_bars_and_markers/barchart.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05853491",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_list = ['anaemia', 'diabetes', 'high_blood_pressure', 'sex', 'smoking']\n",
    "value_ones = [np.count_nonzero(df['anaemia'] == 1), np.count_nonzero(df['diabetes'] == 1),\n",
    "             np.count_nonzero(df['high_blood_pressure'] == 1), np.count_nonzero(df['sex'] == 1),\n",
    "             np.count_nonzero(df['smoking'] == 1)]\n",
    "value_zeros = [np.count_nonzero(df['anaemia'] == 0), np.count_nonzero(df['diabetes'] == 0),\n",
    "             np.count_nonzero(df['high_blood_pressure'] == 0), np.count_nonzero(df['sex'] == 0),\n",
    "             np.count_nonzero(df['smoking'] == 0)]\n",
    "\n",
    "x = np.arange(len(features_list))\n",
    "width = 0.35\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "bar1 = ax.bar(x - width/2, value_ones, width, label = 1, color = 'lightcoral')\n",
    "bar2 = ax.bar(x + width/2, value_zeros, width, label = 0, color = 'cornflowerblue')\n",
    "\n",
    "ax.set_xticks(x, features_list, size = 15)\n",
    "\n",
    "ax.bar_label(bar1, fontweight = 'bold')\n",
    "ax.bar_label(bar2, fontweight = 'bold')\n",
    "ax.set_title(\"Grouped Chart to Analyse Binary Features\", size = 20, weight = 'heavy')\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9589f839",
   "metadata": {},
   "source": [
    "##### We will now create a Heatmap to get insights on the relationship between different features on the dataset. Below is an helpful article to create heatmaps.\n",
    "\n",
    "##### Article - https://medium.com/@szabo.bibor/how-to-create-a-seaborn-correlation-heatmap-in-python-834c0686b88e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea7fb355",
   "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>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088006</td>\n",
       "      <td>-0.081584</td>\n",
       "      <td>-0.101012</td>\n",
       "      <td>0.060098</td>\n",
       "      <td>0.093289</td>\n",
       "      <td>-0.052354</td>\n",
       "      <td>0.159187</td>\n",
       "      <td>-0.045966</td>\n",
       "      <td>0.065430</td>\n",
       "      <td>0.018668</td>\n",
       "      <td>-0.224068</td>\n",
       "      <td>0.253729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anaemia</th>\n",
       "      <td>0.088006</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.190741</td>\n",
       "      <td>-0.012729</td>\n",
       "      <td>0.031557</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>-0.043786</td>\n",
       "      <td>0.052174</td>\n",
       "      <td>0.041882</td>\n",
       "      <td>-0.094769</td>\n",
       "      <td>-0.107290</td>\n",
       "      <td>-0.141414</td>\n",
       "      <td>0.066270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <td>-0.081584</td>\n",
       "      <td>-0.190741</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009639</td>\n",
       "      <td>-0.044080</td>\n",
       "      <td>-0.070590</td>\n",
       "      <td>0.024463</td>\n",
       "      <td>-0.016408</td>\n",
       "      <td>0.059550</td>\n",
       "      <td>0.079791</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>-0.009346</td>\n",
       "      <td>0.062728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>-0.101012</td>\n",
       "      <td>-0.012729</td>\n",
       "      <td>-0.009639</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004850</td>\n",
       "      <td>-0.012732</td>\n",
       "      <td>0.092193</td>\n",
       "      <td>-0.046975</td>\n",
       "      <td>-0.089551</td>\n",
       "      <td>-0.157730</td>\n",
       "      <td>-0.147173</td>\n",
       "      <td>0.033726</td>\n",
       "      <td>-0.001943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ejection_fraction</th>\n",
       "      <td>0.060098</td>\n",
       "      <td>0.031557</td>\n",
       "      <td>-0.044080</td>\n",
       "      <td>-0.004850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024445</td>\n",
       "      <td>0.072177</td>\n",
       "      <td>-0.011302</td>\n",
       "      <td>0.175902</td>\n",
       "      <td>-0.148386</td>\n",
       "      <td>-0.067315</td>\n",
       "      <td>0.041729</td>\n",
       "      <td>-0.268603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <td>0.093289</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>-0.070590</td>\n",
       "      <td>-0.012732</td>\n",
       "      <td>0.024445</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049963</td>\n",
       "      <td>-0.004935</td>\n",
       "      <td>0.037109</td>\n",
       "      <td>-0.104615</td>\n",
       "      <td>-0.055711</td>\n",
       "      <td>-0.196439</td>\n",
       "      <td>0.079351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platelets</th>\n",
       "      <td>-0.052354</td>\n",
       "      <td>-0.043786</td>\n",
       "      <td>0.024463</td>\n",
       "      <td>0.092193</td>\n",
       "      <td>0.072177</td>\n",
       "      <td>0.049963</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.041198</td>\n",
       "      <td>0.062125</td>\n",
       "      <td>-0.125120</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>0.010514</td>\n",
       "      <td>-0.049139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_creatinine</th>\n",
       "      <td>0.159187</td>\n",
       "      <td>0.052174</td>\n",
       "      <td>-0.016408</td>\n",
       "      <td>-0.046975</td>\n",
       "      <td>-0.011302</td>\n",
       "      <td>-0.004935</td>\n",
       "      <td>-0.041198</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.189095</td>\n",
       "      <td>0.006970</td>\n",
       "      <td>-0.027414</td>\n",
       "      <td>-0.149315</td>\n",
       "      <td>0.294278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_sodium</th>\n",
       "      <td>-0.045966</td>\n",
       "      <td>0.041882</td>\n",
       "      <td>0.059550</td>\n",
       "      <td>-0.089551</td>\n",
       "      <td>0.175902</td>\n",
       "      <td>0.037109</td>\n",
       "      <td>0.062125</td>\n",
       "      <td>-0.189095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027566</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.087640</td>\n",
       "      <td>-0.195204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.065430</td>\n",
       "      <td>-0.094769</td>\n",
       "      <td>0.079791</td>\n",
       "      <td>-0.157730</td>\n",
       "      <td>-0.148386</td>\n",
       "      <td>-0.104615</td>\n",
       "      <td>-0.125120</td>\n",
       "      <td>0.006970</td>\n",
       "      <td>-0.027566</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.445892</td>\n",
       "      <td>-0.015608</td>\n",
       "      <td>-0.004316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking</th>\n",
       "      <td>0.018668</td>\n",
       "      <td>-0.107290</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>-0.147173</td>\n",
       "      <td>-0.067315</td>\n",
       "      <td>-0.055711</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>-0.027414</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.445892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022839</td>\n",
       "      <td>-0.012623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>-0.224068</td>\n",
       "      <td>-0.141414</td>\n",
       "      <td>-0.009346</td>\n",
       "      <td>0.033726</td>\n",
       "      <td>0.041729</td>\n",
       "      <td>-0.196439</td>\n",
       "      <td>0.010514</td>\n",
       "      <td>-0.149315</td>\n",
       "      <td>0.087640</td>\n",
       "      <td>-0.015608</td>\n",
       "      <td>-0.022839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.526964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEATH_EVENT</th>\n",
       "      <td>0.253729</td>\n",
       "      <td>0.066270</td>\n",
       "      <td>0.062728</td>\n",
       "      <td>-0.001943</td>\n",
       "      <td>-0.268603</td>\n",
       "      <td>0.079351</td>\n",
       "      <td>-0.049139</td>\n",
       "      <td>0.294278</td>\n",
       "      <td>-0.195204</td>\n",
       "      <td>-0.004316</td>\n",
       "      <td>-0.012623</td>\n",
       "      <td>-0.526964</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               age   anaemia  creatinine_phosphokinase  \\\n",
       "age                       1.000000  0.088006                 -0.081584   \n",
       "anaemia                   0.088006  1.000000                 -0.190741   \n",
       "creatinine_phosphokinase -0.081584 -0.190741                  1.000000   \n",
       "diabetes                 -0.101012 -0.012729                 -0.009639   \n",
       "ejection_fraction         0.060098  0.031557                 -0.044080   \n",
       "high_blood_pressure       0.093289  0.038182                 -0.070590   \n",
       "platelets                -0.052354 -0.043786                  0.024463   \n",
       "serum_creatinine          0.159187  0.052174                 -0.016408   \n",
       "serum_sodium             -0.045966  0.041882                  0.059550   \n",
       "sex                       0.065430 -0.094769                  0.079791   \n",
       "smoking                   0.018668 -0.107290                  0.002421   \n",
       "time                     -0.224068 -0.141414                 -0.009346   \n",
       "DEATH_EVENT               0.253729  0.066270                  0.062728   \n",
       "\n",
       "                          diabetes  ejection_fraction  high_blood_pressure  \\\n",
       "age                      -0.101012           0.060098             0.093289   \n",
       "anaemia                  -0.012729           0.031557             0.038182   \n",
       "creatinine_phosphokinase -0.009639          -0.044080            -0.070590   \n",
       "diabetes                  1.000000          -0.004850            -0.012732   \n",
       "ejection_fraction        -0.004850           1.000000             0.024445   \n",
       "high_blood_pressure      -0.012732           0.024445             1.000000   \n",
       "platelets                 0.092193           0.072177             0.049963   \n",
       "serum_creatinine         -0.046975          -0.011302            -0.004935   \n",
       "serum_sodium             -0.089551           0.175902             0.037109   \n",
       "sex                      -0.157730          -0.148386            -0.104615   \n",
       "smoking                  -0.147173          -0.067315            -0.055711   \n",
       "time                      0.033726           0.041729            -0.196439   \n",
       "DEATH_EVENT              -0.001943          -0.268603             0.079351   \n",
       "\n",
       "                          platelets  serum_creatinine  serum_sodium       sex  \\\n",
       "age                       -0.052354          0.159187     -0.045966  0.065430   \n",
       "anaemia                   -0.043786          0.052174      0.041882 -0.094769   \n",
       "creatinine_phosphokinase   0.024463         -0.016408      0.059550  0.079791   \n",
       "diabetes                   0.092193         -0.046975     -0.089551 -0.157730   \n",
       "ejection_fraction          0.072177         -0.011302      0.175902 -0.148386   \n",
       "high_blood_pressure        0.049963         -0.004935      0.037109 -0.104615   \n",
       "platelets                  1.000000         -0.041198      0.062125 -0.125120   \n",
       "serum_creatinine          -0.041198          1.000000     -0.189095  0.006970   \n",
       "serum_sodium               0.062125         -0.189095      1.000000 -0.027566   \n",
       "sex                       -0.125120          0.006970     -0.027566  1.000000   \n",
       "smoking                    0.028234         -0.027414      0.004813  0.445892   \n",
       "time                       0.010514         -0.149315      0.087640 -0.015608   \n",
       "DEATH_EVENT               -0.049139          0.294278     -0.195204 -0.004316   \n",
       "\n",
       "                           smoking      time  DEATH_EVENT  \n",
       "age                       0.018668 -0.224068     0.253729  \n",
       "anaemia                  -0.107290 -0.141414     0.066270  \n",
       "creatinine_phosphokinase  0.002421 -0.009346     0.062728  \n",
       "diabetes                 -0.147173  0.033726    -0.001943  \n",
       "ejection_fraction        -0.067315  0.041729    -0.268603  \n",
       "high_blood_pressure      -0.055711 -0.196439     0.079351  \n",
       "platelets                 0.028234  0.010514    -0.049139  \n",
       "serum_creatinine         -0.027414 -0.149315     0.294278  \n",
       "serum_sodium              0.004813  0.087640    -0.195204  \n",
       "sex                       0.445892 -0.015608    -0.004316  \n",
       "smoking                   1.000000 -0.022839    -0.012623  \n",
       "time                     -0.022839  1.000000    -0.526964  \n",
       "DEATH_EVENT              -0.012623 -0.526964     1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a570055b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Heatmap')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "mask = np.triu(np.ones_like(df.corr(), dtype = np.bool))\n",
    "heatmap = sns.heatmap(df.corr(), mask = mask, annot=True, cmap='BrBG_r')\n",
    "heatmap.set_title(\"Correlation Heatmap\", size = 20, weight = 'heavy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5634ad2f",
   "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>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DEATH_EVENT</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_creatinine</th>\n",
       "      <td>0.294278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.253729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <td>0.079351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anaemia</th>\n",
       "      <td>0.066270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <td>0.062728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>-0.001943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.004316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking</th>\n",
       "      <td>-0.012623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platelets</th>\n",
       "      <td>-0.049139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_sodium</th>\n",
       "      <td>-0.195204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ejection_fraction</th>\n",
       "      <td>-0.268603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>-0.526964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          DEATH_EVENT\n",
       "DEATH_EVENT                  1.000000\n",
       "serum_creatinine             0.294278\n",
       "age                          0.253729\n",
       "high_blood_pressure          0.079351\n",
       "anaemia                      0.066270\n",
       "creatinine_phosphokinase     0.062728\n",
       "diabetes                    -0.001943\n",
       "sex                         -0.004316\n",
       "smoking                     -0.012623\n",
       "platelets                   -0.049139\n",
       "serum_sodium                -0.195204\n",
       "ejection_fraction           -0.268603\n",
       "time                        -0.526964"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[['DEATH_EVENT']].sort_values(['DEATH_EVENT'], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2ae510fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap = sns.heatmap(df.corr()[['DEATH_EVENT']].sort_values('DEATH_EVENT', ascending = False), annot = True, cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "872ac5b2",
   "metadata": {},
   "source": [
    "#### Let us analyse other features by plotting Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "004345c5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols = 5, figsize=(15,5))\n",
    "\n",
    "sns.histplot(df['creatinine_phosphokinase'], ax=ax[0], color = 'orchid')\n",
    "sns.histplot(df['ejection_fraction'], ax=ax[1], color = 'palegreen')\n",
    "sns.histplot(df['platelets'], ax=ax[2], color = 'lightgray')\n",
    "sns.histplot(df['serum_creatinine'], ax=ax[3], color = 'mediumpurple')\n",
    "sns.histplot(df['serum_sodium'], ax=ax[4], color = 'coral')\n",
    "\n",
    "ax[0].set_ylabel('')\n",
    "ax[1].set_ylabel('')\n",
    "ax[2].set_ylabel('')\n",
    "ax[3].set_ylabel('')\n",
    "ax[4].set_ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4feb26d",
   "metadata": {},
   "source": [
    "I have referred to Matplotlib's documentation to get the available colors to chose from. \n",
    "https://matplotlib.org/stable/gallery/color/named_colors.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a55ee2",
   "metadata": {},
   "source": [
    "## Train test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6db807e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('DEATH_EVENT', axis = 1)\n",
    "y = df['DEATH_EVENT']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c91b632",
   "metadata": {},
   "source": [
    "# Model Training through different classification Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c52c73cf",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b5e0b5ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression()\n",
    "log_model = log_reg.fit(X_train, y_train)\n",
    "log_y_pred = log_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2d07a0f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8 \n",
      "Precision: 0.8823529411764706 \n",
      "Recall:0.6 \n",
      "F1 Score: 0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "log_accuracy = accuracy_score(y_test, log_y_pred)\n",
    "log_precision = precision_score(y_test, log_y_pred)\n",
    "log_recall = recall_score(y_test, log_y_pred)\n",
    "log_f1score = f1_score(y_test, log_y_pred)\n",
    "\n",
    "print(\"Accuracy: {} \\nPrecision: {} \\nRecall:{} \\nF1 Score: {}\".format(log_accuracy,\n",
    "                                                                      log_precision,\n",
    "                                                                      log_recall,\n",
    "                                                                      log_f1score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1426dcf",
   "metadata": {},
   "source": [
    "## K-Nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c5f4eef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the KNN Model with 1 neighbors is 0.5333333333333333\n",
      "Accuracy of the KNN Model with 2 neighbors is 0.5666666666666667\n",
      "Accuracy of the KNN Model with 3 neighbors is 0.5333333333333333\n",
      "Accuracy of the KNN Model with 4 neighbors is 0.55\n",
      "Accuracy of the KNN Model with 5 neighbors is 0.5333333333333333\n",
      "Accuracy of the KNN Model with 6 neighbors is 0.5333333333333333\n",
      "Accuracy of the KNN Model with 7 neighbors is 0.5333333333333333\n",
      "Accuracy of the KNN Model with 8 neighbors is 0.55\n",
      "Accuracy of the KNN Model with 9 neighbors is 0.55\n",
      "Accuracy of the KNN Model with 10 neighbors is 0.5333333333333333\n"
     ]
    }
   ],
   "source": [
    "# I will be checking the accuracy of KNN Model by looping through the number of neighbors\n",
    "\n",
    "for i in range(1, 11):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn_model = knn.fit(X_train, y_train)\n",
    "    knn_y_pred = knn_model.predict(X_test)\n",
    "    print(\"Accuracy of the KNN Model with {} neighbors is {}\".format(i, accuracy_score(y_test, knn_y_pred)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e4083dce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Maximum Accuracy of the KNN Model is 56.67% with number of Neighbors = 2\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 2)\n",
    "knn_model = knn.fit(X_train, y_train)\n",
    "knn_y_pred = knn_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ca88d209",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5666666666666667 \n",
      "Precision; 0.4 \n",
      "Recall: 0.08 \n",
      "F1 Score: 0.13333333333333333\n"
     ]
    }
   ],
   "source": [
    "knn_accuracy = accuracy_score(y_test, knn_y_pred)\n",
    "knn_precision = precision_score(y_test, knn_y_pred)\n",
    "knn_recall = recall_score(y_test, knn_y_pred)\n",
    "knn_f1score = f1_score(y_test, knn_y_pred)\n",
    "\n",
    "print(\"Accuracy: {} \\nPrecision; {} \\nRecall: {} \\nF1 Score: {}\".format(knn_accuracy,\n",
    "                                                                       knn_precision,\n",
    "                                                                       knn_recall,\n",
    "                                                                       knn_f1score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb605773",
   "metadata": {},
   "source": [
    "## Support Vector Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd21d8a4",
   "metadata": {},
   "source": [
    "In Support Vector Classifiers, we have 4 different Kernels that directly impact the Metrics of the model. So, we will test the SVC model against all these 4 Hyperparameters.\n",
    "\n",
    "SVC Documentation: https://scikit-learn.org/stable/modules/svm.html#svm-kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "34e57846",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the SVC Model with linear kernel is 0.75\n",
      "Accuracy of the SVC Model with poly kernel is 0.5833333333333334\n",
      "Accuracy of the SVC Model with rbf kernel is 0.5833333333333334\n",
      "Accuracy of the SVC Model with sigmoid kernel is 0.5833333333333334\n"
     ]
    }
   ],
   "source": [
    "for k in ('linear', 'poly', 'rbf', 'sigmoid'):\n",
    "    svc = SVC(kernel=k)\n",
    "    svc_model = svc.fit(X_train, y_train)\n",
    "    svc_y_pred = svc_model.predict(X_test)\n",
    "    print(\"Accuracy of the SVC Model with {} kernel is {}\".format(k, accuracy_score(y_test, svc_y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d14a69",
   "metadata": {},
   "source": [
    "The highest accuracy of the Support Vector Classifier is with the Linear Kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c73488b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='linear')\n",
    "svc_model = svc.fit(X_train, y_train)\n",
    "svc_y_pred = svc_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "269eeb66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.75 \n",
      "Precision: 0.8125 \n",
      "Recall: 0.52 \n",
      "F1 Score: 0.6341463414634146\n"
     ]
    }
   ],
   "source": [
    "svc_accuracy = accuracy_score(y_test, svc_y_pred)\n",
    "svc_precision = precision_score(y_test, svc_y_pred)\n",
    "svc_recall = recall_score(y_test, svc_y_pred)\n",
    "svc_f1score = f1_score(y_test, svc_y_pred)\n",
    "\n",
    "print(\"Accuracy: {} \\nPrecision: {} \\nRecall: {} \\nF1 Score: {}\".format(svc_accuracy,\n",
    "                                                                       svc_precision,\n",
    "                                                                       svc_recall,\n",
    "                                                                       svc_f1score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf35684",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier\n",
    "\n",
    "Decision Tree Classifiers has different Hyperparameters that can be tuned to get maximum efficiency. We will be looping through different criterion and max_depth for building this model.\n",
    "\n",
    "Decision Tree Documentation: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2a18b085",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Maximum Accuracy of the Model with max depth 1 is 0.75\n",
      "The Maximum Accuracy of the Model with max depth 2 is 0.6833333333333333\n",
      "The Maximum Accuracy of the Model with max depth 3 is 0.7166666666666667\n",
      "The Maximum Accuracy of the Model with max depth 4 is 0.6833333333333333\n",
      "The Maximum Accuracy of the Model with max depth 5 is 0.75\n",
      "The Maximum Accuracy of the Model with max depth 6 is 0.7833333333333333\n",
      "The Maximum Accuracy of the Model with max depth 7 is 0.7166666666666667\n",
      "The Maximum Accuracy of the Model with max depth 8 is 0.65\n",
      "The Maximum Accuracy of the Model with max depth 9 is 0.6666666666666666\n",
      "The Maximum Accuracy of the Model with max depth 10 is 0.7\n"
     ]
    }
   ],
   "source": [
    "#for i in ('gini', 'entropy'):\n",
    "for j in range(1, 11):\n",
    "    dec = DecisionTreeClassifier(max_depth=j)\n",
    "    dec_model = dec.fit(X_train, y_train)\n",
    "    dec_y_pred = dec_model.predict(X_test)\n",
    "    # mean_acc = accuracy_score(y_test, dec_y_pred)\n",
    "    print(\"The Maximum Accuracy of the Model with max depth {} is {}\".format(j, accuracy_score(y_test, dec_y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6ad682d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dec = DecisionTreeClassifier(max_depth=1)\n",
    "dec_model = dec.fit(X_train, y_train)\n",
    "dec_y_pred = dec_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9c06d3f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.75 \n",
      "Precision: 0.8125 \n",
      "Recall: 0.52 \n",
      "F1 Score: 0.6341463414634146\n"
     ]
    }
   ],
   "source": [
    "dec_accuracy = accuracy_score(y_test, dec_y_pred)\n",
    "dec_precision = precision_score(y_test, dec_y_pred)\n",
    "dec_recall = recall_score(y_test, dec_y_pred)\n",
    "dec_f1score = f1_score(y_test, dec_y_pred)\n",
    "\n",
    "print(\"Accuracy: {} \\nPrecision: {} \\nRecall: {} \\nF1 Score: {}\".format(dec_accuracy,\n",
    "                                                                       dec_precision,\n",
    "                                                                       dec_recall,\n",
    "                                                                       dec_f1score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8c784e",
   "metadata": {},
   "source": [
    "## Random Forest Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030b07f2",
   "metadata": {},
   "source": [
    "There are various Hyperparameters in Random Forest that can be tuned to get the best results. We will be using the RandomisedSearchCV from sklearn to compare the Hyperparameters and the model performance. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ab4956",
   "metadata": {},
   "source": [
    "I have tuned the hyperparameters by referring to the sklearn documentation and an article on Random Forest Hyperparameter Tuning by Will Koehrsen.\n",
    "\n",
    "- Random Forest Documentation: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
    "- Hyperparameter Tuning the Random Forest in Python: https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f11ab953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e08ebfb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': [True, False],\n",
      " 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None],\n",
      " 'max_features': ['auto', 'sqrt'],\n",
      " 'min_samples_leaf': [1, 2, 4],\n",
      " 'min_samples_split': [2, 5, 10],\n",
      " 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}\n"
     ]
    }
   ],
   "source": [
    "# number of trees in forest\n",
    "n_estimators = [int(i) for i in np.linspace(200, 2000, 10)]\n",
    "\n",
    "# number of features to consider when looking for the best split\n",
    "max_features = ['auto', 'sqrt']\n",
    "\n",
    "# maximum depth of the tree\n",
    "max_depth = [int(i) for i in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "\n",
    "# minimum number of samples required to split an internal node\n",
    "min_samples_split = [2, 5, 10]\n",
    "\n",
    "# minimum number of samples required to be at a leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "\n",
    "# method of selecting samples for training\n",
    "bootstrap = [True, False]\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "              'max_features': max_features,\n",
    "              'max_depth': max_depth,\n",
    "              'min_samples_split': min_samples_split,\n",
    "              'min_samples_leaf': min_samples_leaf,\n",
    "              'bootstrap': bootstrap}\n",
    "\n",
    "pprint(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1e2feaa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_random = RandomizedSearchCV(estimator= rf, param_distributions=random_grid, n_iter = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f7226806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(estimator=RandomForestClassifier(), n_iter=100,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [200, 400, 600, 800,\n",
       "                                                         1000, 1200, 1400, 1600,\n",
       "                                                         1800, 2000]})"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5f1e6cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 400,\n",
       " 'min_samples_split': 5,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 90,\n",
       " 'bootstrap': True}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "181387ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rf = RandomForestClassifier(n_estimators=600, \n",
    "                                 min_samples_split=2, \n",
    "                                 min_samples_leaf=1, \n",
    "                                 max_features='sqrt', \n",
    "                                 max_depth=70,\n",
    "                                bootstrap=True)\n",
    "\n",
    "rf_model = best_rf.fit(X_train, y_train)\n",
    "rf_y_pred = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fee312f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7333333333333333 \n",
      "Precision: 0.8461538461538461 \n",
      "Recall: 0.44 \n",
      "F1 Score: 0.5789473684210525\n"
     ]
    }
   ],
   "source": [
    "rf_accuracy = accuracy_score(y_test, rf_y_pred)\n",
    "rf_precision = precision_score(y_test, rf_y_pred)\n",
    "rf_recall = recall_score(y_test, rf_y_pred)\n",
    "rf_f1score = f1_score(y_test, rf_y_pred)\n",
    "\n",
    "print(\"Accuracy: {} \\nPrecision: {} \\nRecall: {} \\nF1 Score: {}\".format(rf_accuracy,\n",
    "                                                                       rf_precision,\n",
    "                                                                       rf_recall,\n",
    "                                                                       rf_f1score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f11a57a2",
   "metadata": {},
   "source": [
    "#### Now we will create a dictionary to compare all the metrics from each classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1deb0621",
   "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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K-Nearest Neighbors</th>\n",
       "      <td>0.57</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Support Vector Classifier</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Accuracy  Precision  Recall  F1 Score\n",
       "Logistic Regression            0.80       0.88    0.60      0.71\n",
       "K-Nearest Neighbors            0.57       0.40    0.08      0.13\n",
       "Support Vector Classifier      0.75       0.81    0.52      0.63\n",
       "Decision Tree                  0.75       0.81    0.52      0.63\n",
       "Random Forest                  0.73       0.85    0.44      0.58"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_list = [log_accuracy, knn_accuracy, svc_accuracy, dec_accuracy, rf_accuracy]\n",
    "precision_list = [log_precision, knn_precision, svc_precision, dec_precision, rf_precision]\n",
    "recall_list = [log_recall, knn_recall, svc_recall, dec_recall, rf_recall]\n",
    "f1_list = [log_f1score, knn_f1score, svc_f1score, dec_f1score, rf_f1score]\n",
    "\n",
    "columns = ['Accuracy', 'Precision', 'Recall', 'F1 Score']\n",
    "\n",
    "eval_df = pd.DataFrame([accuracy_list, precision_list, recall_list,f1_list], index = columns)\n",
    "eval_df = eval_df.transpose()\n",
    "eval_df.rename({0:'Logistic Regression', 1:'K-Nearest Neighbors', 2:'Support Vector Classifier', 3:'Decision Tree', 4:'Random Forest'}, inplace=True)\n",
    "eval_df = eval_df.round(2)\n",
    "eval_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1940dbe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 4, figsize=(12, 4))\n",
    "\n",
    "sns.barplot(eval_df.index, eval_df.Accuracy,  ax = ax[0])\n",
    "for i, v in enumerate(eval_df.Accuracy.values):\n",
    "    ax[0].text(round(i, 2), v, str(v), fontweight='bold', ha='center', va='bottom')\n",
    "\n",
    "sns.barplot(eval_df.index, eval_df.Precision,  ax = ax[1])\n",
    "for i, v in enumerate(eval_df.Precision.values):\n",
    "    ax[1].text(i, v, str(v), fontweight='bold', ha='center', va='bottom')\n",
    "    \n",
    "sns.barplot(eval_df.index, eval_df.Recall, ax = ax[2])\n",
    "for i, v in enumerate(eval_df.Recall.values):\n",
    "    ax[2].text(i, v, str(v), fontweight='bold', ha='center', va='bottom')\n",
    "    \n",
    "sns.barplot(eval_df.index, eval_df['F1 Score'], ax = ax[3])\n",
    "for i, v in enumerate(eval_df['F1 Score'].values):\n",
    "    ax[3].text(i, v, str(v), fontweight='bold', ha='center', va='bottom')\n",
    "\n",
    "ax[0].set_title(\"Accuracy Score\", size=15, weight='heavy')\n",
    "ax[1].set_title(\"Precision Score\", size=15, weight='heavy')\n",
    "ax[2].set_title(\"Recall Score\", size=15, weight='heavy')\n",
    "ax[3].set_title(\"F1 Score\", size=15, weight='heavy')\n",
    "\n",
    "ax[0].set_xlabel('')\n",
    "ax[1].set_xlabel('')\n",
    "ax[2].set_xlabel('')\n",
    "ax[3].set_xlabel('')\n",
    "\n",
    "ax[0].set_ylabel('')\n",
    "ax[1].set_ylabel('')\n",
    "ax[2].set_ylabel('')\n",
    "ax[3].set_ylabel('')\n",
    "\n",
    "ax[0].set_xticklabels(eval_df.index, rotation = 90)\n",
    "ax[1].set_xticklabels(eval_df.index, rotation = 90)\n",
    "ax[2].set_xticklabels(eval_df.index, rotation = 90)\n",
    "ax[3].set_xticklabels(eval_df.index, rotation = 90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fb3bea",
   "metadata": {},
   "source": [
    "### Looking at the bar graphs above, we can clearly see that the Logisitic Regression Algorithm does a better job in all the metrics as compared to all the other classifiers. "
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}