Lab4_logistic-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "46bca7dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2447a0d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('loan.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "78f1ad37",
   "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>Income</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Loan Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35</td>\n",
       "      <td>50000</td>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>60000</td>\n",
       "      <td>720</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>45000</td>\n",
       "      <td>680</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>80000</td>\n",
       "      <td>750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>70000</td>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Income  Credit Score  Loan Status\n",
       "0   35   50000           700            1\n",
       "1   42   60000           720            1\n",
       "2   28   45000           680            0\n",
       "3   55   80000           750            1\n",
       "4   48   70000           730            1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c6ba9cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(166, 4)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "84ce9b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.\tSplit the dataset into independent variables (features) and the dependent variable (target).\n",
    "target=df['Loan Status']\n",
    "features= df.drop(['Loan Status'],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "441a0ee2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95718f4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cdfd3970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f0e233d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "041445f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b07e582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9411764705882353\n",
      "Precision: 0.9354838709677419\n",
      "Recall: 1.0\n",
      "F1-score: 0.9666666666666666\n"
     ]
    }
   ],
   "source": [
    "#4.\tEvaluate the model's performance using metrics like accuracy, precision, recall, and F1-score.\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred)\n",
    "print(\"Precision:\", precision)\n",
    "\n",
    "# Recall\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(\"Recall:\", recall)\n",
    "\n",
    "# F1-score\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(\"F1-score:\", f1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0a94ba20",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "831bbb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_probs)\n",
    "\n",
    "# Compute Area Under the ROC Curve (AUC)\n",
    "auc = roc_auc_score(y_test, y_pred_probs)\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label='ROC curve (area = %0.2f)' % auc)\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "437442ab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}