Efficient Spam Email Classification Using Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "820c964f",
   "metadata": {},
   "source": [
    "# Let's start Final ML Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266beebc",
   "metadata": {},
   "source": [
    "## Journal's Name :\n",
    "\n",
    "## Efficient Spam Email Classification Using Machine Learning Algorithms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99b05ea",
   "metadata": {},
   "source": [
    "**توضیحات مقاله** :\n",
    "    مقاله ژورنال به موضوع مبرم تشخیص هرزنامه ایمیل در عصر دیجیتال معاصر می پردازد. از آنجایی که ایمیل یک روش اصلی ارتباط است، شناسایی و کاهش هرزنامه ایمیل به دلیل پیامدهای مهم زمانی، پولی، امنیتی و حریم خصوصی آن بسیار مهم است. این مقاله یک رویکرد مبتنی بر یادگیری ماشینی را برای تشخیص هرزنامه ایمیل، با استفاده از الگوریتم‌های مختلف پیشنهاد می‌کند."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0adb8d27",
   "metadata": {},
   "source": [
    "#### نکته **  توضیحات رو در بیشتر جاها به انگلیسی نوشتم چرا که با زبان فارسی متن بهم ریخته میشد **"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97674092",
   "metadata": {},
   "source": [
    "### import Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e0152876",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f720e039",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2bdb35",
   "metadata": {},
   "source": [
    "### Loading the Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1f308f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('emails.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1150fc0a",
   "metadata": {},
   "source": [
    "### Preprocessing the Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "491df164",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Email No.', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987d6362",
   "metadata": {},
   "source": [
    "### instruction of Dataset :\n",
    "شامل 5172 ردیف، هر ردیف برای هر ایمیل است. 3002 ستون وجود دارد. ستون اول نام ایمیل را نشان می دهد. این نام با اعداد تنظیم شده است و نه نام گیرندگان برای محافظت از حریم خصوصی. آخرین ستون دارای برچسب هایی برای پیش بینی است: 1 برای هرزنامه، 0 برای غیر هرزنامه. 3000 ستون باقی مانده، 3000 کلمه رایج در همه ایمیل ها هستند، پس از حذف نویسه ها/کلمات غیر الفبایی. برای هر ردیف، تعداد هر کلمه (ستون) در آن ایمیل (ردیف) در سلول های مربوطه ذخیره می شود. بنابراین، اطلاعات مربوط به تمام 5172 ایمیل در یک دیتافریم فشرده به جای فایل های متنی جداگانه ذخیره می شود."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26df5b72",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      the  to  ect  and  for  of    a  you  hou  in  ...  connevey  jay  \\\n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0fd3a26",
   "metadata": {},
   "source": [
    "## Part1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0262f589",
   "metadata": {},
   "source": [
    "### Splitting the Dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9b4103",
   "metadata": {},
   "source": [
    "X شامل ویژگی ها (ویژگی ها) مجموعه داده به جز متغیر هدف است.\n",
    "y شامل متغیر هدف است."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "109ce5eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "\n",
    "X = df.drop('Prediction', axis=1)\n",
    "y = df['Prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "acaa3f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ce27a3",
   "metadata": {},
   "source": [
    "### Training and Evaluating Models:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9138ae52",
   "metadata": {},
   "source": [
    "train four different classifiers: Decision Tree, Random Forest, Logistic Regression, and Voting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abfa90a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rashinfarahani/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/rashinfarahani/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('dt', DecisionTreeClassifier()),\n",
       "                             ('rf', RandomForestClassifier()),\n",
       "                             ('lr', LogisticRegression())])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "# Random Forest\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Logistic Regression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# Voting Classifier\n",
    "vc = VotingClassifier(estimators=[('dt', dt), ('rf', rf), ('lr', lr)], voting='hard')\n",
    "vc.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d19e52",
   "metadata": {},
   "source": [
    "### Evaluating Model Performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63632618",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree - Accuracy: 92.37%, RMSE: 0.28\n",
      "Random Forest - Accuracy: 97.58%, RMSE: 0.16\n",
      "Logistic Regression - Accuracy: 96.81%, RMSE: 0.18\n",
      "Voting Classifier - Accuracy: 97.68%, RMSE: 0.15\n"
     ]
    }
   ],
   "source": [
    "models = {'Decision Tree': dt, 'Random Forest': rf, 'Logistic Regression': lr, 'Voting Classifier': vc}\n",
    "for name, model in models.items():\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "    print(f'{name} - Accuracy: {accuracy*100:.2f}%, RMSE: {rmse:.2f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f4ab3d",
   "metadata": {},
   "source": [
    "### حالا با روش ها دیگه امتحان میکنم :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2424d5d3",
   "metadata": {},
   "source": [
    "## Part2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "883428a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3516bee8",
   "metadata": {},
   "source": [
    "### Fill Missing Values with Mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81db7fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values\n",
    "df.fillna(df.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee5d602",
   "metadata": {},
   "source": [
    "### Normalize Numerical Features using StandardScaler:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f163865",
   "metadata": {},
   "source": [
    "تضمین می کند که همه ویژگی ها دارای میانگین 0 و انحراف استاندارد 1 هستند که برای بسیاری از الگوریتم های یادگیری ماشین مهم است."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ff3dcea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize numerical features\n",
    "scaler = StandardScaler()\n",
    "df[df.columns] = scaler.fit_transform(df[df.columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84be9221",
   "metadata": {},
   "source": [
    "### Define Features and Target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17f7a7c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Prediction', axis=1)\n",
    "y = df['Prediction']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65226d7d",
   "metadata": {},
   "source": [
    "### Convert Target to Categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d5e594c",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0.5\n",
    "y = (y > threshold).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95242630",
   "metadata": {},
   "source": [
    "### Split Dataset into Training and Testing Sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5c2b26e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294538ee",
   "metadata": {},
   "source": [
    "### Initialize and Train Logistic Regression Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "417a6ed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rashinfarahani/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e084aa9b",
   "metadata": {},
   "source": [
    "### Make Predictions on the Test Set:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc2bb1c",
   "metadata": {},
   "source": [
    "در نهایت دقت مدل روی مجموعه تست را با استفاده از accuracy_score محاسبه کردم و نتیجه را چاپ کردم. دقت نشان‌دهنده درصد برچسب‌های پیش‌بینی‌شده درست از همه برچسب‌ها در مجموعه آزمایشی است."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9cd061e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.970048309178744\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "#Evaluate Model Performance:\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7951c8cf",
   "metadata": {},
   "source": [
    "### حالا با روش دیگه امتحان میکنم :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa57986",
   "metadata": {},
   "source": [
    "## Part 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d18094f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Email No.','Prediction'])\n",
    "y = df['Prediction']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad162c1",
   "metadata": {},
   "source": [
    "مقدار: نمونه ای از طبقه بندی کننده که با فراپارامترهای پیش فرض آن مقداردهی اولیه شده است."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "aeb93052",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the classifiers\n",
    "classifiers = {\n",
    "    \"Decision Tree\": DecisionTreeClassifier(),\n",
    "    \"Random Forest\": RandomForestClassifier(),\n",
    "    \"Logistic Regression\": LogisticRegression(max_iter=1000),  # Increase max_iter\n",
    "}\n",
    "\n",
    "voting_clf = VotingClassifier(estimators=[(name, clf) for name, clf in classifiers.items()])\n",
    "classifiers[\"Voting Classifier\"] = voting_clf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9a697a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c7a4beb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary to hold the results\n",
    "results = {}\n",
    "\n",
    "for name, clf in classifiers.items():\n",
    "    # Train the classifier\n",
    "    clf.fit(X_train, y_train)\n",
    "    \n",
    "    # Make predictions on the test set\n",
    "    y_pred = clf.predict(X_test)\n",
    "    \n",
    "    # Calculate accuracy and RMSE\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "    \n",
    "    # Store the results\n",
    "    results[name] = {\"Accuracy\": accuracy, \"RMSE\": rmse}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d29c9a7",
   "metadata": {},
   "source": [
    "این خط نتایج DataFrame را چاپ می کند و دقت و امتیازات RMSE را برای هر طبقه بندی کننده نشان می دهد."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cde0ae4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Accuracy      RMSE\n",
      "Decision Tree        0.925604  0.272757\n",
      "Random Forest        0.975845  0.155417\n",
      "Logistic Regression  0.970048  0.173066\n",
      "Voting Classifier    0.980676  0.139010\n"
     ]
    }
   ],
   "source": [
    "results_df = pd.DataFrame(results).T\n",
    "\n",
    "# Display the results\n",
    "print(results_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105c7f31",
   "metadata": {},
   "source": [
    "**مهم**\n",
    "\n",
    "در نهایت، دقت و امتیازات RMSE را برای هر طبقه‌بندی با استفاده از نمودارهای نواری تجسم می‌کنم. این نمودارها مقایسه واضحی از عملکرد طبقه‌بندی‌کننده‌های مختلف ارائه می‌کنند و به ما کمک میکنه بهترین مدل را شناسایی کنم !!."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "25410957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Plot the accuracy scores\n",
    "results_df['Accuracy'].plot(kind='bar', title='Accuracy Scores')\n",
    "plt.show()\n",
    "\n",
    "# Plot the RMSE scores\n",
    "results_df['RMSE'].plot(kind='bar', title='RMSE Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52ee7541",
   "metadata": {},
   "source": [
    "## Part 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fab2a64",
   "metadata": {},
   "source": [
    "### show different information about dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "419bb6fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Email No.  the  to  ect  and  for  of    a  you  hou  ...  connevey  jay  \\\n",
      "0   Email 1    0   0    1    0    0   0    2    0    0  ...         0    0   \n",
      "1   Email 2    8  13   24    6    6   2  102    1   27  ...         0    0   \n",
      "2   Email 3    0   0    1    0    0   0    8    0    0  ...         0    0   \n",
      "3   Email 4    0   5   22    0    5   1   51    2   10  ...         0    0   \n",
      "4   Email 5    7   6   17    1    5   2   57    0    9  ...         0    0   \n",
      "\n",
      "   valued  lay  infrastructure  military  allowing  ff  dry  Prediction  \n",
      "0       0    0               0         0         0   0    0           0  \n",
      "1       0    0               0         0         0   1    0           0  \n",
      "2       0    0               0         0         0   0    0           0  \n",
      "3       0    0               0         0         0   0    0           0  \n",
      "4       0    0               0         0         0   1    0           0  \n",
      "\n",
      "[5 rows x 3002 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5172 entries, 0 to 5171\n",
      "Columns: 3002 entries, Email No. to Prediction\n",
      "dtypes: int64(3001), object(1)\n",
      "memory usage: 118.5+ MB\n",
      "None\n",
      "Email No.     0\n",
      "the           0\n",
      "to            0\n",
      "ect           0\n",
      "and           0\n",
      "             ..\n",
      "military      0\n",
      "allowing      0\n",
      "ff            0\n",
      "dry           0\n",
      "Prediction    0\n",
      "Length: 3002, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "\n",
    "# Display basic information about dataset\n",
    "print(df.info())\n",
    "\n",
    "# And Check for missing values\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7bf67725",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Email No.', 'Prediction'], axis=1)\n",
    "y = df['Prediction']\n",
    "\n",
    "# Split the data into training and testing sets \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "53d3d524",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# scaler = StandardScaler()\n",
    "# X_train_scaled = scaler.fit_transform(X_train)\n",
    "# X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cee8a96",
   "metadata": {},
   "source": [
    "### Initialize classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2d9cad6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_classifier = DecisionTreeClassifier(random_state=42)\n",
    "rf_classifier = RandomForestClassifier(random_state=42)\n",
    "lr_classifier = LogisticRegression(max_iter=1000, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19572196",
   "metadata": {},
   "source": [
    "### Model Training and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8b9a8b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train and evaluate each classifier\n",
    "classifiers = [dt_classifier, rf_classifier, lr_classifier]\n",
    "classifier_names = ['Decision Tree', 'Random Forest', 'Logistic Regression']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fda21027",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Accuracy: 0.9227053140096618\n",
      "Random Forest Accuracy: 0.9777777777777777\n",
      "Logistic Regression Accuracy: 0.9719806763285024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rashinfarahani/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "for clf, name in zip(classifiers, classifier_names):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print(f\"{name} Accuracy: {accuracy}\")\n",
    "\n",
    "#Find the best performing algorithm :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237c45d6",
   "metadata": {},
   "source": [
    "**نکته**:\n",
    "    Voting Classifier: یک طبقه بندی رأی دهی اولیه و آموزش داده می شود. این طبقه بندی کننده اکثریت پیش بینی ها را از سه طبقه بندی کننده می گیرد."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "fd3811e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting Classifier Accuracy: 0.9816425120772947\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rashinfarahani/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Initialize the Voting Classifier\n",
    "voting_clf = VotingClassifier(estimators=[('dt', dt_classifier), ('rf', rf_classifier), ('lr', lr_classifier)], voting='hard')\n",
    "\n",
    "# Train and evaluate the Voting Classifier\n",
    "voting_clf.fit(X_train, y_train)\n",
    "y_pred_voting = voting_clf.predict(X_test)\n",
    "accuracy_voting = accuracy_score(y_test, y_pred_voting)\n",
    "print(f\"Voting Classifier Accuracy: {accuracy_voting}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5624447",
   "metadata": {},
   "source": [
    "### Plot Part :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0c58d1a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Import libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b2c4be38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "583809f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize classifiers\n",
    "dt_classifier = DecisionTreeClassifier(random_state=42)\n",
    "rf_classifier = RandomForestClassifier(random_state=42)\n",
    "lr_classifier = LogisticRegression(random_state=42)\n",
    "svm_classifier = SVC(random_state=42)  # Added Support Vector Machines\n",
    "gb_classifier = GradientBoostingClassifier(random_state=42)  # Added Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d511dafa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Accuracy: 0.9227053140096618\n",
      "Random Forest Accuracy: 0.9777777777777777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rashinfarahani/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.9681159420289855\n",
      "SVM Accuracy: 0.8173913043478261\n",
      "Gradient Boosting Accuracy: 0.9719806763285024\n"
     ]
    }
   ],
   "source": [
    "classifiers = [dt_classifier, rf_classifier, lr_classifier, svm_classifier, gb_classifier]\n",
    "classifier_names = ['Decision Tree', 'Random Forest', 'Logistic Regression', 'SVM', 'Gradient Boosting']\n",
    "\n",
    "# Accuracy Scores\n",
    "accuracy_scores = []\n",
    "\n",
    "for clf, name in zip(classifiers, classifier_names):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracy_scores.append(accuracy)\n",
    "    print(f\"{name} Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ef491ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the performance of each algorithm\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=classifier_names, y=accuracy_scores)\n",
    "plt.title('Accuracy Scores of Different Algorithms')\n",
    "plt.xlabel('Classifier')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim(0.9, 1.0)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "753a4eaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "### import Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b8c9c1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f347709e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calibrated_svc = CalibratedClassifierCV(base_estimator=svm_classifier, method='sigmoid')\n",
    "calibrated_svc.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "#  plot ROC curves for all classifiers including SVM with Platt scaling\n",
    "plt.figure(figsize=(12, 8))\n",
    "for clf, name in zip(classifiers + [calibrated_svc], classifier_names + ['SVM (with Platt Scaling)']):\n",
    "    if hasattr(clf, \"predict_proba\"):\n",
    "        y_scores = clf.predict_proba(X_test)[:, 1]\n",
    "    else:\n",
    "        y_scores = clf.decision_function(X_test)\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_scores)\n",
    "    auc_score = roc_auc_score(y_test, y_scores)\n",
    "    plt.plot(fpr, tpr, label=f'{name} (AUC = {auc_score:.2f})')\n",
    "    \n",
    "\n",
    "    \n",
    "# show plot :    \n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curves for Different Classifiers')\n",
    "plt.legend()\n",
    "plt.show()    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9002ef6",
   "metadata": {},
   "source": [
    "محور x نشان دهنده نرخ مثبت کاذب (FPR) است که نسبت پیش بینی های مثبت کاذب به تعداد کل منفی های واقعی است.\n",
    "محور y نشان دهنده نرخ مثبت واقعی (TPR) است که نسبت پیش بینی های مثبت واقعی به تعداد کل مثبت های واقعی است. TPR همچنین به عنوان حساسیت یا یادآوری شناخته می شود.\n",
    "هر منحنی نشان دهنده عملکرد یک طبقه بندی کننده در آستانه های تصمیم گیری مختلف است.\n",
    "خط چین مورب نشان دهنده یک طبقه بندی تصادفی است.\n",
    "\n",
    "**توضیحات نمودار **:\n",
    "\n",
    "    به طور خلاصه، این طرح امکان مقایسه بصری عملکرد طبقه‌بندی‌کننده‌های مختلف در تمایز بین ایمیل‌های اسپم و غیر هرزنامه را فراهم می‌کند. طبقه بندی کننده با نزدیکترین منحنی به گوشه بالا سمت چپ و با بالاترین امتیاز AUC بهترین عملکرد برای کار در نظر گرفته می شود."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a73071",
   "metadata": {},
   "source": [
    "### At the end:\n",
    "من این روش را هم پیشنهاد میدهم;\n",
    "Boosting Machine (GBM) algorithm   ( به دلیل عملکرد پیش بینی قوی خود شناخته شده است)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "faf07975",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Classifier Accuracy: 0.9719806763285024\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Initialize the Gradient Boosting Classifier\n",
    "gbm_classifier = GradientBoostingClassifier()\n",
    "\n",
    "# Train the classifier on the training data\n",
    "gbm_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred_gbm = gbm_classifier.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy_gbm = accuracy_score(y_test, y_pred_gbm)\n",
    "print(f\"Gradient Boosting Classifier Accuracy: {accuracy_gbm}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dddd6191",
   "metadata": {},
   "source": [
    "## what's the result ?!\n",
    "\n",
    "### Voting Classifier, \n",
    "پیش‌بینی‌های طبقه‌بندی‌کننده‌های متعدد (درخت تصمیم، رگرسیون لجستیک) را ترکیب می‌کند، بالاترین دقت را در بین همه طبقه‌بندی‌کننده‌ها به دست آورد و پس از آن جنگل تصادفی قرار دارد. این نشان می‌دهد که رویکرد گروهی مورد استفاده در طبقه‌بندی رأی‌گیری، عملکرد پیش‌بینی‌کننده را در مقایسه با طبقه‌بندی‌کننده‌های فردی بهبود می‌بخشد.\n",
    "\n",
    "\n",
    "### &\n",
    "The algorithms suggested to address the convergence warning in Logistic Regression include:\n",
    "- Increasing the number of iterations (`max_iter` parameter)\n",
    "- Scaling the data using techniques like standardization (`StandardScaler`)"
   ]
  },
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   "source": [
    "### Rashin Gholijani Farahani - Ml class - Friday "
   ]
  }
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