python_files/case_study_classification_A-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "\n",
    "* [Model](#Model)\n",
    "* [Visualising accuracy - ROC / AUC](#rocauc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model <a class=\"anchor\" id=\"model\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_original = pd.read_excel(r'creditcardmarketing.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_miss_values= ['average_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_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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['customer_number','q1_balance'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1 of the column  q2_balance : 658.0\n",
      "Q3 of the column  q2_balance : 1344.0\n",
      "IRQ of the column  q2_balance : 686.0\n",
      "lower_whisker:  -371.0 , upper whisker:  2373.0\n",
      "Q1 of the column  q3_balance : 632.0\n",
      "Q3 of the column  q3_balance : 1497.0\n",
      "IRQ of the column  q3_balance : 865.0\n",
      "lower_whisker:  -665.5 , upper whisker:  2794.5\n",
      "Q1 of the column  q4_balance : 375.0\n",
      "Q3 of the column  q4_balance : 1205.5\n",
      "IRQ of the column  q4_balance : 830.5\n",
      "lower_whisker:  -870.75 , upper whisker:  2451.25\n",
      "Q1 of the column  average_balance : 791.75\n",
      "Q3 of the column  average_balance : 1152.0\n",
      "IRQ of the column  average_balance : 360.25\n",
      "lower_whisker:  251.375 , upper whisker:  1692.375\n"
     ]
    }
   ],
   "source": [
    "# find quartiles\n",
    "cols_outliers = [ '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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = df.select_dtypes(include = np.object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = list(df.columns)\n",
    "cols = cols[1:]+[cols[0]]\n",
    "df = df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['average_balance'] = pd.to_numeric(df.average_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": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerics = ['int64', 'float64']\n",
    "df_num = df.select_dtypes(include=numerics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = df.select_dtypes(exclude = ['float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col=cat.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_num.drop(['bank_accounts_open', 'credit_cards_held', 'homes_owned', \"household_size\"], 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": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y= categorical['offer_accepted_Yes']\n",
    "categorical=categorical.drop(['offer_accepted_Yes'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.concatenate((X_normalised, categorical),axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 52,
   "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": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score \n",
    "predictions = classification.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 0, 0, 1], dtype=uint8)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7034989272157122"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.770045343064312\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": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#definitions \n",
    "# tpr = true positive rate \n",
    "# fpr = false positive rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 59,
   "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"
   ]
  }
 ],
 "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",
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