LogisticReg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-weight:bold;\"><i style=\"color:teal;\">MACHINE LEARNING</i></h1>\n",
    "\n",
    "<h5 style=\"font-weight:bold;\"><i style=\"color:teal;\">By:</i></h5>\n",
    "<i style=\"color: black; font-weight:bold;\"><br/> - Safae NABET<br/> - Hanae ELKORRI</i><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"font-weight:bold;\"><i style=\"color:tomato;\">Classification using logistic regression:</i></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "eXYmASnIfUWY"
   },
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "_Q0BWiL5i_Fi",
    "outputId": "3acea6b3-424e-490a-ce5a-3006373f67cb"
   },
   "outputs": [
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       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>151.20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118898 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  ...  reservation_status_No-Show\n",
       "0                0  ...                           0\n",
       "1                1  ...                           0\n",
       "2                2  ...                           0\n",
       "3                3  ...                           0\n",
       "4                4  ...                           0\n",
       "...            ...  ...                         ...\n",
       "118893      119385  ...                           0\n",
       "118894      119386  ...                           0\n",
       "118895      119387  ...                           0\n",
       "118896      119388  ...                           0\n",
       "118897      119389  ...                           0\n",
       "\n",
       "[118898 rows x 43 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "df = pd.read_csv('hotel_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "g8hGa_Y-jDaV"
   },
   "outputs": [],
   "source": [
    "#Building model and Splitting the dataset into the Training set and Test set\n",
    "\n",
    "X = df.loc[:, df.columns != 'is_canceled']\n",
    "y = df['is_canceled']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0SZK716ojJcF",
    "outputId": "afd8a13f-4409-4ef7-9d9a-26e2b9ec2f34"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Multiple Linear Regression to the Training set\n",
    "\n",
    "regressor = LogisticRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "hyjD7a5xjPF_"
   },
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "twufLozNjWwN",
    "outputId": "dc2efd42-4ccf-4db9-b8f5-78ab47d8bf19"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12315,  2530],\n",
       "       [ 4244,  4691]])"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "80ciGITTjYhj",
    "outputId": "2e8d6ab3-66d2-4485-936d-69a23647b8ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.83      0.78     14845\n",
      "           1       0.65      0.53      0.58      8935\n",
      "\n",
      "    accuracy                           0.72     23780\n",
      "   macro avg       0.70      0.68      0.68     23780\n",
      "weighted avg       0.71      0.72      0.71     23780\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "LogisticReg.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
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