python_files/case_study_classification_C.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "\n",
    "* [Model](#model)\n",
    "* [Visualising accuracy - ROC / AUC](#rocauc)\n",
    "* [Data imbalanced -  SMOTE (oversample the minority class of the target label)](#smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model <a class=\"anchor\" id=\"model\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_original = pd.read_excel(r'creditcardmarketing.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.replace(' ', '_')\n",
    "df.columns= df.columns.str.lower()\n",
    "df.columns= df.columns.str.replace('#_', '')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_miss_values= ['average_balance', 'q1_balance', 'q2_balance', 'q3_balance', 'q4_balance']\n",
    "\n",
    "mean_average_balance = df['average_balance'].mean()\n",
    "df['average_balance'] = df['average_balance'].fillna(mean_average_balance)\n",
    "df['average_balance'] = pd.to_numeric(df['average_balance'], errors = 'coerce')\n",
    "\n",
    "mean_average_balance = df['q1_balance'].mean()\n",
    "df['q1_balance'] = df['q1_balance'].fillna(mean_average_balance)\n",
    "df['q1_balance'] = pd.to_numeric(df['q1_balance'], errors = 'coerce')\n",
    "\n",
    "mean_q2_balance = df['q2_balance'].mean()\n",
    "df['q2_balance'] = df['q2_balance'].fillna(mean_q2_balance)\n",
    "df['q2_balance'] = pd.to_numeric(df['q2_balance'], errors = 'coerce')\n",
    "\n",
    "mean_q3_balance = df['q3_balance'].mean()\n",
    "df['q3_balance'] = df['q3_balance'].fillna(mean_q3_balance)\n",
    "df['q3_balance'] = pd.to_numeric(df['q3_balance'], errors = 'coerce')\n",
    "\n",
    "mean_q4_balance = df['q4_balance'].mean()\n",
    "df['q4_balance'] = df['q4_balance'].fillna(mean_q4_balance)\n",
    "df['q4_balance'] = pd.to_numeric(df['q4_balance'], errors = 'coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['customer_number'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.groupby(['offer_accepted', 'reward', 'mailer_type',\n",
    "       'income_level', 'bank_accounts_open', 'overdraft_protection',\n",
    "       'credit_rating', 'credit_cards_held', 'homes_owned', \n",
    "       'own_your_home', 'average_balance', 'q1_balance', 'q2_balance',\n",
    "       'q3_balance', 'q4_balance'])['household_size'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['household_size'] = pd.qcut(df['household_size'],duplicates='drop', q=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1 of the column  q1_balance : 393.0\n",
      "Q3 of the column  q1_balance : 1520.25\n",
      "IRQ of the column  q1_balance : 1127.25\n",
      "lower_whisker:  -1297.875 , upper whisker:  3211.125\n",
      "Q1 of the column  q2_balance : 664.0\n",
      "Q3 of the column  q2_balance : 1342.0\n",
      "IRQ of the column  q2_balance : 678.0\n",
      "lower_whisker:  -353.0 , upper whisker:  2359.0\n",
      "Q1 of the column  q3_balance : 633.0\n",
      "Q3 of the column  q3_balance : 1461.0\n",
      "IRQ of the column  q3_balance : 828.0\n",
      "lower_whisker:  -609.0 , upper whisker:  2703.0\n",
      "Q1 of the column  q4_balance : 363.0\n",
      "Q3 of the column  q4_balance : 1210.0\n",
      "IRQ of the column  q4_balance : 847.0\n",
      "lower_whisker:  -907.5 , upper whisker:  2480.5\n",
      "Q1 of the column  average_balance : 787.75\n",
      "Q3 of the column  average_balance : 1152.5\n",
      "IRQ of the column  average_balance : 364.75\n",
      "lower_whisker:  240.625 , upper whisker:  1699.625\n"
     ]
    }
   ],
   "source": [
    "cols_outliers = ['q1_balance' , 'q2_balance', 'q3_balance', 'q4_balance', 'average_balance']\n",
    "\n",
    "for col in cols_outliers:\n",
    "    Q1 = df[col].quantile(0.25)\n",
    "    Q3 = df[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    print('Q1 of the column ', col,':', Q1)\n",
    "    print('Q3 of the column ', col,':', Q3)\n",
    "    print('IRQ of the column ', col,':', IQR)\n",
    "    Lower_Whisker = Q1 - 1.5 * IQR\n",
    "    Upper_Whisker = Q3 + 1.5 * IQR\n",
    "    print('lower_whisker: ', Lower_Whisker, ',' , 'upper whisker: ', Upper_Whisker)\n",
    "    #  Outlier Treatment \n",
    "    df = df[df[col]< Upper_Whisker]\n",
    "    df = df[df[col]> Lower_Whisker]\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = df.select_dtypes(include = np.object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = list(df.columns)\n",
    "cols = cols[1:]+[cols[0]]\n",
    "df = df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['average_balance'] = pd.to_numeric(df.average_balance, errors='coerce')\n",
    "df['q1_balance'] = pd.to_numeric(df.q1_balance, errors='coerce')\n",
    "df['q2_balance'] = pd.to_numeric(df.q2_balance, errors='coerce')\n",
    "df['q3_balance'] = pd.to_numeric(df.q3_balance, errors='coerce')\n",
    "df['q4_balance'] = pd.to_numeric(df.q4_balance, errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerics = ['int64', 'float64']\n",
    "df_num = df.select_dtypes(include=numerics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = df.select_dtypes(exclude = ['float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = pd.get_dummies(cat, columns =['reward',\n",
    " 'mailer_type',\n",
    " 'income_level',\n",
    " 'bank_accounts_open',\n",
    " 'overdraft_protection',\n",
    " 'credit_rating',\n",
    " 'credit_cards_held',\n",
    " 'homes_owned',\n",
    " 'household_size',\n",
    " 'own_your_home',\n",
    " 'offer_accepted'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_num.drop(['bank_accounts_open', 'credit_cards_held', 'homes_owned'], axis=1)\n",
    "transformer = Normalizer().fit(X)\n",
    "X_normalised= transformer.transform(X)\n",
    "X_normalised = pd.DataFrame(X_normalised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y= categorical['offer_accepted_Yes']\n",
    "categorical=categorical.drop(['offer_accepted_Yes'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.concatenate((X_normalised, categorical),axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test =train_test_split(X, y, test_size= 0.4, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "classification = LogisticRegression(random_state=0, solver='lbfgs',max_iter=400,\n",
    "                  multi_class='ovr').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score \n",
    "predictions = classification.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9464609800362976"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising accuracy - ROC / AUC  <a class=\"anchor\" id=\"rocauc\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7460218152403342\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics \n",
    "probs = classification.predict_proba(X_test)\n",
    "preds =probs[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "print(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#definitions \n",
    "# tpr = true positive rate \n",
    "# fpr = false positive rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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"
    }
   ],
   "source": [
    "# method to plot AUC visually \n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix \n",
    "\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "accuracy_score(y_test,predictions)\n",
    "confusion_matrix(y_test,predictions)\n",
    "plot_confusion_matrix(classification, X_test, y_test)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data imbalanced -  SMOTE (oversample the minority class of the target label) <a class=\"anchor\" id=\"smote\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    15593\n",
       "0    15593\n",
       "Name: offer_accepted_Yes, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sm, y_sm = smote.fit_resample(X, y)\n",
    "y_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test =train_test_split(X_sm, y_sm, test_size= 0.4, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply model It2 \n",
    "classification = LogisticRegression(random_state=0, solver='lbfgs',max_iter=400,\n",
    "                  multi_class='ovr').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7228857715430862"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy assessment It2\n",
    "predictions = classification.predict(X_test)\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Conf matrix It2 \n",
    "confusion_matrix(y_test,predictions)\n",
    "plot_confusion_matrix(classification, X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "789px",
    "left": "108px",
    "top": "110px",
    "width": "207.594px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}