EDA and modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6dd3376e",
   "metadata": {},
   "source": [
    "## Bank Telemarketing Campaigns Success Prediction  [EDA and model building]\n",
    "\n",
    "**The purpsose of this project** is to evaluate the effectiveness of a banking institution's direct marketing campaigns (phone calls).  We will gain valuable insights from Exploratory Data Analysis [EDA] that will assist telemarketers in determining important success factors for future campaigns.\n",
    "\n",
    "Finally, we will build a **machine learning classifier** capable of accurately predicting whether a given campaign will be successful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "558cfc48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "sns.set_theme(style=\"white\",palette=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1261ab56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in the dataset :41188\n",
      "Number of features in the dataset :21\n"
     ]
    }
   ],
   "source": [
    "# reading in the  dataset\n",
    "df = pd.read_csv(\"bank_marketing.csv\",sep=';')\n",
    "print(f\"Number of rows in the dataset :{len(df)}\")\n",
    "print(f\"Number of features in the dataset :{len(df.columns)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c822a11",
   "metadata": {},
   "source": [
    "## Understanding the data\n",
    "The first step in **exploratory data analysis** is to understand the data. We start by investigating the features, gaining insights into the values contained within them and how they relate to one another and the target feature using statistical and other visual methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b589c0ca",
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## printing out the head of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1771b395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     36548\n",
       "yes     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd762783",
   "metadata": {},
   "source": [
    "Our target feature is represented by **y** and contains the values 'yes' and 'no'. We can give it a more descriptive name and convert the values to numbers, making it easier for future data exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f54ec2ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = df['y'].replace({\"yes\":1,'no':0})\n",
    "df = df.drop('y',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b510ef49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.887346\n",
       "1    0.112654\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking out the new target feature\n",
    "df['target'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41be0f33",
   "metadata": {},
   "source": [
    "Now we'll go over the remaining features and try to learn more about them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c507fc81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  float64\n",
      " 20  target          41188 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(10)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "## printing out the columns information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4422cdbc",
   "metadata": {},
   "source": [
    "There appear to be no missing values in the dataset, but a closer look at some of the records reveals that missing values are stored as **Unknown**, which we will have to deal with later."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c992f1f2",
   "metadata": {},
   "source": [
    "### Examining the Job Feature Column\n",
    "**Question:** Which job has the **highest rate** of term deposit subscriptions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04d1ee88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 12 different categories of jobs in the dataset \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plotting the job column\n",
    "fig,axes = plt.subplots(1,2,sharey=True)\n",
    "fig.set_figheight(4), \n",
    "fig.set_figwidth(15)\n",
    "\n",
    "job_by = df.groupby(\"job\")['target'].mean().sort_values(ascending=False)\n",
    "sns.barplot(x= job_by.values,y=job_by.index,ax=axes[0],palette='deep')\n",
    "sns.countplot(data=df,y='job',ax=axes[1],order=job_by.index,palette='deep')\n",
    "axes[0].set(title=(\"Subscription rate by jobs\"))\n",
    "axes[1].set(title=(\"Jobs by count\"));\n",
    "\n",
    "jobs = df['job'].value_counts()\n",
    "print(f\"There are {len(jobs)} different categories of jobs in the dataset \\n\")\n",
    "\n",
    "\n",
    "## function to show bar level on bars\n",
    "def show_bar_label(ax,fmt):\n",
    "    for i in ax.containers:\n",
    "        ax.bar_label(i,fmt=fmt)\n",
    "        \n",
    "show_bar_label(axes[0],fmt='%.2f')\n",
    "show_bar_label(axes[1],fmt='%.0f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af19237f",
   "metadata": {},
   "source": [
    "Student appears to have the highest subscription rate of all job categories, but given their small sample size, let us conduct a hypothesis test to see if the subscription rate of **0.30%** is acceptable and not due to the chance.\n",
    "\n",
    "**H0** = proportion of student who subscribed is 0.30   \n",
    "**H1** = proportion of student who subscribe is less than 0.30  \n",
    "\n",
    "**using 5% significant level**, we reject the null hyptothesis if z score is lower than the z critical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "04837126",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hypothesis testing\n",
    "p = 0.30\n",
    "q = 1 - p\n",
    "sample_size = 500\n",
    "student_df = df[df['job']=='student'].sample(sample_size, random_state=100)\n",
    "p_hat = student_df['target'].sum()/sample_size\n",
    "std = np.sqrt((p*q)/sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "321e7435",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z_score : 1.07\n",
      "critcal score : -1.64\n"
     ]
    }
   ],
   "source": [
    "z = (p_hat-p)/std\n",
    "z_critical = norm().ppf(0.05)\n",
    "\n",
    "print(f\"z_score : {z:.2f}\")\n",
    "print(f\"critcal score : {z_critical:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b26bbb4",
   "metadata": {},
   "source": [
    "**Conclusion**: At the 5% level of significance, the sample data do not provide sufficient evidence to reject the null hypothesis that the percentage of students who subscribed to a term deposit is less than **30%**.\n",
    "\n",
    "**Recommendation**: Because students are more likely to subscribe than other job categories, marketers should target them more in future campaigns, as they make up a relatively small portion of our current campaign outreach."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b60a20",
   "metadata": {},
   "source": [
    "### Examining the Marital Status Column  \n",
    "**Question:** Does a person's **marital status** influence their likelihood to subscribe?\n",
    "\n",
    "According to the plot below, a customer's **marital status** has no discernible effect on subscription rate, so marital status cannot be used to predict whether someone will subscribe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be47c316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Subscription rate by marital status\")\n",
    "ax = sns.barplot(data=df,y='marital',x='target');\n",
    "show_bar_label(ax,fmt ='   %.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c998c89",
   "metadata": {},
   "source": [
    "### Examining the Education Feature Column\n",
    "**Question:** Is there an effect of **education** on subscription?\n",
    "\n",
    "According to the plot below, **education** has no discernible effect on subscriptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d2a05591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Subscription rate by Education\")\n",
    "ax = sns.barplot(data=df,y='education',x='target');\n",
    "show_bar_label(ax,fmt ='   %.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15ddec9",
   "metadata": {},
   "source": [
    "### Renaming the education columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c12afdb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['education'] = df['education'].replace({'university.degree':\"higher education\",\n",
    "                                               'high.school':'high school',\n",
    "                                                'basic.4y':\"basic\",\n",
    "                                                'basic.6y':\"basic\",\n",
    "                                                'basic.9y':\"basic\",\n",
    "                                                \"professional.course\":\"higher education\",\n",
    "                                                'unknown':'unknown',\n",
    "                                                'illiterate':'illiterate',\n",
    "                                              })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fdf7b4c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Subscription rate by Education\")\n",
    "ax = sns.barplot(data=df,y='education',x='target');\n",
    "show_bar_label(ax,fmt ='   %.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50dc952b",
   "metadata": {},
   "source": [
    "### Examining the Day of Week Feature Column\n",
    "**Question:** Do certain days of week result in a higher subscription rate ?  \n",
    "\n",
    "According to the plot below, a specific **day of the week** is unlikely to produce a higher result than any other day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6bf580b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Subscription rate by Day of Week\")\n",
    "ax = sns.barplot(data=df,x='day_of_week',y='target',ci=None);\n",
    "show_bar_label(ax,fmt ='%.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20e17a5",
   "metadata": {},
   "source": [
    "### Examining the Month Feature Column\n",
    "**Question:** Do certain **months** have a higher rate of subscriptions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aef6b495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1)\n",
    "fig.set_figheight(6),fig.set_figwidth(10)\n",
    "plt.title(\"Subscription rate by Month\")\n",
    "ax = sns.barplot(data=df,x='month',y='target',ci=None);\n",
    "show_bar_label(ax,fmt ='%.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa227820",
   "metadata": {},
   "source": [
    "A quick glance at the plot shows that certain months, such as **March** and **December**, have significantly higher subscription rates among customers. While the sample size appears to be the smallest among the months, hypothesis testing can show if the difference is indeed significant or due to chance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f62731d1",
   "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>dec</th>\n",
       "      <th>mar</th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>apr</th>\n",
       "      <th>nov</th>\n",
       "      <th>jun</th>\n",
       "      <th>aug</th>\n",
       "      <th>jul</th>\n",
       "      <th>may</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>182</td>\n",
       "      <td>546</td>\n",
       "      <td>570</td>\n",
       "      <td>718</td>\n",
       "      <td>2632</td>\n",
       "      <td>4101</td>\n",
       "      <td>5318</td>\n",
       "      <td>6178</td>\n",
       "      <td>7174</td>\n",
       "      <td>13769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dec  mar  sep  oct   apr   nov   jun   aug   jul    may\n",
       "month  182  546  570  718  2632  4101  5318  6178  7174  13769"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df['month'].value_counts(ascending=True)).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53f00ed9",
   "metadata": {},
   "source": [
    "Choosing a null hypothesis of **40%** to really highlight the difference between other months.\n",
    "\n",
    "**H0** = proportion of customers who subscribed in the month of **March**  is equal to  **40%**  \n",
    "**H1** = proportion of customers who subscribe in the month of **March** is lesser than **40%**\n",
    "\n",
    "Using 5% significant level, we reject the null hyptothesis if the  z-score is lower than the z critical, we also carry out hypothesis testing for December, September and October also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d47780e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For dec: z_score : 2.45; critical score: 1.64\n",
      "For mar: z_score : 5.03; critical score: 1.64\n",
      "For sep: z_score : 2.39; critical score: 1.64\n",
      "For oct: z_score : 2.12; critical score: 1.64\n"
     ]
    }
   ],
   "source": [
    "for mon in ['dec','mar','sep','oct']:\n",
    "    p = 0.40\n",
    "    q = 1-p\n",
    "    sample_size = len(df[ df['month'] == mon ])\n",
    "    student_df = df[ df['month'] == mon ]\n",
    "    p_hat = student_df['target'].sum() / sample_size\n",
    "    std = np.sqrt( (p*q) /sample_size )\n",
    "    \n",
    "    z = (p_hat - p) / std\n",
    "    z_critical = norm().ppf(0.95)\n",
    "    print(f\"For {mon}: z_score : {z:.2f}; critical score: {z_critical:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e203bf4d",
   "metadata": {},
   "source": [
    "**Conclusion:** At the 5% level of significance, the sample data does not show enough evidence to reject the null hypothesis.  \n",
    "\n",
    "**Recommendation:** The company should look into whether there are any confounding factors as to why the given months have such a significant effect on subscription rates. They should start by looking into why these months have such a small sample size in the first place."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f591233",
   "metadata": {},
   "source": [
    "### Examining the Poutcome Feature Column [outcome of the previous marketing campaign]\n",
    "\n",
    "**Question:** Does the previous campaign's outcome influence the current campaign?.\n",
    "\n",
    "**Conclusion:** According to the plot below, the previous marketing campaign's success has a **65%** impact on the current campaign's success.\n",
    "\n",
    "**Recommendation:** Marketers should contact more people who have previously subscribed successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7183bdd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Subscription rate by outcome of the previous marketing campaign\")\n",
    "ax = sns.barplot(data=df,x='poutcome',y='target');\n",
    "show_bar_label(ax,fmt ='   %.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "515475ec",
   "metadata": {},
   "source": [
    "### Plotting the remaining categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e06a1565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl = ['housing','default','loan','contact']\n",
    "fig,axes = plt.subplots(1,len(pl),sharey=True)\n",
    "fig.set_figwidth(15)\n",
    "for i in range(len(pl)):\n",
    "    axes[i].set(title=(f\"Subscription rate by {pl[i]}\"))\n",
    "    sns.barplot(data=df,x=pl[i],y='target',ax=axes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb8b626",
   "metadata": {},
   "source": [
    "### Exploring the Numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85e96978",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>40.024060</td>\n",
       "      <td>10.421250</td>\n",
       "      <td>17.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>38.000</td>\n",
       "      <td>47.000</td>\n",
       "      <td>98.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>259.279249</td>\n",
       "      <td>0.000</td>\n",
       "      <td>102.000</td>\n",
       "      <td>180.000</td>\n",
       "      <td>319.000</td>\n",
       "      <td>4918.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>56.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp.var.rate</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>-3.400</td>\n",
       "      <td>-1.800</td>\n",
       "      <td>1.100</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.price.idx</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>92.201</td>\n",
       "      <td>93.075</td>\n",
       "      <td>93.749</td>\n",
       "      <td>93.994</td>\n",
       "      <td>94.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>-50.800</td>\n",
       "      <td>-42.700</td>\n",
       "      <td>-41.800</td>\n",
       "      <td>-36.400</td>\n",
       "      <td>-26.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>0.634</td>\n",
       "      <td>1.344</td>\n",
       "      <td>4.857</td>\n",
       "      <td>4.961</td>\n",
       "      <td>5.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nr.employed</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>5167.035911</td>\n",
       "      <td>72.251528</td>\n",
       "      <td>4963.600</td>\n",
       "      <td>5099.100</td>\n",
       "      <td>5191.000</td>\n",
       "      <td>5228.100</td>\n",
       "      <td>5228.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>0.112654</td>\n",
       "      <td>0.316173</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count         mean         std       min       25%  \\\n",
       "age             41188.0    40.024060   10.421250    17.000    32.000   \n",
       "duration        41188.0   258.285010  259.279249     0.000   102.000   \n",
       "campaign        41188.0     2.567593    2.770014     1.000     1.000   \n",
       "pdays           41188.0   962.475454  186.910907     0.000   999.000   \n",
       "previous        41188.0     0.172963    0.494901     0.000     0.000   \n",
       "emp.var.rate    41188.0     0.081886    1.570960    -3.400    -1.800   \n",
       "cons.price.idx  41188.0    93.575664    0.578840    92.201    93.075   \n",
       "cons.conf.idx   41188.0   -40.502600    4.628198   -50.800   -42.700   \n",
       "euribor3m       41188.0     3.621291    1.734447     0.634     1.344   \n",
       "nr.employed     41188.0  5167.035911   72.251528  4963.600  5099.100   \n",
       "target          41188.0     0.112654    0.316173     0.000     0.000   \n",
       "\n",
       "                     50%       75%       max  \n",
       "age               38.000    47.000    98.000  \n",
       "duration         180.000   319.000  4918.000  \n",
       "campaign           2.000     3.000    56.000  \n",
       "pdays            999.000   999.000   999.000  \n",
       "previous           0.000     0.000     7.000  \n",
       "emp.var.rate       1.100     1.400     1.400  \n",
       "cons.price.idx    93.749    93.994    94.767  \n",
       "cons.conf.idx    -41.800   -36.400   -26.900  \n",
       "euribor3m          4.857     4.961     5.045  \n",
       "nr.employed     5191.000  5228.100  5228.100  \n",
       "target             0.000     0.000     1.000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Summary statistics of the numerical features\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc708681",
   "metadata": {},
   "source": [
    "### Getting an overview through an Heatmap\n",
    "An heatmap can quickly provide an overview to explore the correlation between features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "84197be5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_cols = df.select_dtypes(\"number\")\n",
    "fig,axes = plt.subplots(1,1,sharey=True)\n",
    "fig.set_figwidth(6)\n",
    "fig.set_figheight(6)\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(data=numeric_cols.corr(),vmax=.9 ,center=0, cmap=cmap,\n",
    "            square=True, linewidths=.5 ,annot=True,fmt='.1f',cbar_kws={\"shrink\": .7})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7898cf01",
   "metadata": {},
   "source": [
    "A glance through the plot and we can see most of the features are not correlated with each other but some do have an high correlation such as **'emp.var.rate'** and **'euribor3m'** with perfect correlation of **1**. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c8a4d10",
   "metadata": {},
   "source": [
    "### Exploring the age distribution\n",
    "The age column seems to posses a lot outliers which we will have to handle later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1cac9010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(1,2)\n",
    "fig.set_figwidth(15)\n",
    "fig.set_figheight(6)\n",
    "sns.kdeplot(data=df,x='age',hue='target',ax=axes[0])\n",
    "sns.boxplot(data=df,x='age',ax=axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cea166",
   "metadata": {},
   "source": [
    "## Feature Engineering and Model Building\n",
    "Following the completion of the initial exploration analysis, we will move on to feature engineering and the development of our classification model.\n",
    "\n",
    "It is critical to select an appropriate classification measure for a machine learning model, and this should be done with the objective of meeting the business need in mind. In this context, we will optimize for **recall**, supposing that not misclassifying a potential subscriber is more important than increasing precision.\n",
    "\n",
    "### Model Building\n",
    "We are going to train and fine tune the 3 machine learning models which are listed below:\n",
    "\n",
    "- Logistic Regression\n",
    "- Random Forest\n",
    "- LightGBM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4e3aae",
   "metadata": {},
   "source": [
    "### Dropping the duration column \n",
    "The duration of a call is not known before the call is placed. Also, after the end of the call, the target is obviously known, so the duration feature is not needed for our classification model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f142d6e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"duration\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b5251efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31    1947\n",
       "32    1846\n",
       "33    1833\n",
       "36    1780\n",
       "35    1759\n",
       "      ... \n",
       "89       2\n",
       "91       2\n",
       "94       1\n",
       "87       1\n",
       "95       1\n",
       "Name: age, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6609963f",
   "metadata": {},
   "source": [
    "### Binning the age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03806bdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['age_bins']=list(pd.cut(df['age'],bins=[16,25,30,40,50, 60,100],\n",
    "                      labels=[16,25,30,40,50,60]))\n",
    "fig,axes = plt.subplots(1,1,sharey=True)\n",
    "fig.set_figwidth(15)\n",
    "ax = sns.barplot(data=df,x='age_bins',y='target')\n",
    "show_bar_label(ax,fmt ='       %.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "399febd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30    16385\n",
       "40    10240\n",
       "50     6270\n",
       "25     5717\n",
       "16     1666\n",
       "60      910\n",
       "Name: age_bins, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_bins'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0b09c8e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing packages for building our model\n",
    "from lightgbm import LGBMClassifier\n",
    "from imblearn.pipeline import Pipeline\n",
    "from imblearn.over_sampling import RandomOverSampler, KMeansSMOTE,SVMSMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import  LogisticRegression\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.preprocessing import OneHotEncoder,StandardScaler\n",
    "\n",
    "from sklearn.metrics import f1_score,precision_score,recall_score,confusion_matrix,ConfusionMatrixDisplay,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0327597a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of training_set: 33362\n",
      "Length of validation_set: 3707\n",
      "Length of test_set: 4119\n"
     ]
    }
   ],
   "source": [
    "## Creating our train and test set\n",
    "train  = df.drop(\"target\",axis=1)\n",
    "test  = df['target']\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(train,test,test_size= 0.1, random_state=200)\n",
    "X_train,X_val,y_train,y_val = train_test_split(X_train,y_train,test_size= 0.1, random_state= 200)\n",
    "\n",
    "print(f\"Length of training_set: {len(X_train)}\")\n",
    "print(f\"Length of validation_set: {len(X_val)}\")\n",
    "print(f\"Length of test_set: {len(X_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5c0d35d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## creating a pipeline\n",
    "pipeline = ColumnTransformer([\n",
    "        ('one hot encoding',OneHotEncoder(),['marital','default','housing','loan','month',\n",
    "                                             'education','job', 'contact','poutcome','age_bins',\n",
    "                                            ]),\n",
    "        (\"scaler\",StandardScaler(),['campaign','pdays','previous', \n",
    "                                    'emp.var.rate','cons.price.idx','previous',\n",
    "                                    'cons.conf.idx','nr.employed', 'euribor3m'\n",
    "                                   ]),\n",
    "        (\"drop\",\"drop\",['day_of_week'])\n",
    "],remainder='drop',verbose_feature_names_out=True)\n",
    "\n",
    "pipeline.fit(X_train)\n",
    "X_pipeline = pipeline.transform(X_train)\n",
    "\n",
    "\n",
    "over = SVMSMOTE()\n",
    "under=RandomUnderSampler()\n",
    "\n",
    "steps = [('over', over),\n",
    "         ('under',under)]\n",
    "\n",
    "samp_pipeline = Pipeline(steps=steps)\n",
    "X_sampled_train,y_sampled_train = samp_pipeline.fit_resample(X_pipeline,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eef6a6a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sampled_train,y_sampled_train = samp_pipeline.fit_resample(X_pipeline,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c57228",
   "metadata": {},
   "source": [
    "### 1. Logistic Regression\n",
    "We are going to finally build our first model using logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "3d6e17b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid Search best score: 0.7189627137832273\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'C': 0.1, 'class_weight': {0: 3, 1: 0.7}}"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C':[0.1,0.5,0.9],\n",
    "              'class_weight':[{0:0.1,1:0.9},{0:3,1:0.7}]\n",
    "             }\n",
    "gs = GridSearchCV(LogisticRegression(max_iter=10_000) ,param_grid = param_grid, cv= 5, scoring='f1')\n",
    "gs.fit(X_sampled_train,y_sampled_train)\n",
    "\n",
    "print(f\"Grid Search best score: {gs.best_score_}\")\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "73b23104",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.756277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.854826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 score</th>\n",
       "      <td>0.802537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.789672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "precision  0.756277\n",
       "recall     0.854826\n",
       "f1 score   0.802537\n",
       "accuracy   0.789672"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log = LogisticRegression(C=0.1,max_iter=10000,class_weight={0:0.3,1:0.7})\n",
    "log.fit(X_sampled_train,y_sampled_train);\n",
    "\n",
    "pred =  log.predict_proba(X_sampled_train)[:,1]>=0.488\n",
    "prec  = precision_score(y_sampled_train,pred,pos_label=1)\n",
    "recall  = recall_score(y_sampled_train,pred,pos_label=1)\n",
    "f1 = f1_score(y_sampled_train,pred)\n",
    "acc  = accuracy_score(y_sampled_train,pred)\n",
    "\n",
    "pd.DataFrame([[prec,recall,f1,acc]],columns=['precision','recall','f1 score','accuracy']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33e88799",
   "metadata": {},
   "source": [
    "### Evaluating on the Validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "08b7d72b",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.244915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.746898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 score</th>\n",
       "      <td>0.368873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.722147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "precision  0.244915\n",
       "recall     0.746898\n",
       "f1 score   0.368873\n",
       "accuracy   0.722147"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = pipeline.transform(X_val)\n",
    "pred = log.predict_proba(val)[:,1]>=0.48\n",
    "\n",
    "prec = precision_score(y_val,pred,pos_label=1)\n",
    "recall = recall_score(y_val,pred,pos_label=1)\n",
    "f1 = f1_score(y_val,pred)\n",
    "acc  = accuracy_score(y_val,pred)\n",
    "\n",
    "pd.DataFrame([[prec,recall,f1,acc]],columns=['precision','recall','f1 score','accuracy']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6c7fc346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f27d517da00>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LQRkfH8///u//sn37dg4ePIjD4WD48OEEBfnfg6Mi0n6szPVuTZAuWLCABQsWnPH5unXrWjxm4MCBrFmzptVlnliewuhyuWhoaADA6XRi+OM8JBFpXxamMHr1ifQOYikod+7cyezZs6mvrychIYGSkhLCwsJYvnw5Q4cObe86ioifMCyOevsbS0E5b948pkyZwvTp07HZbBiGwXPPPce8efN4+eWX27uOIuI3zF8FYV7e+Vi6ybhv3z6mTZvmfjTIZrMxdepU9u3b1551ExF/04qZOf7EUlBee+21vPHGG80+e/PNN7nuuuvao04i4q+6aFBaWmbN6XRy7733MmzYMBITEykpKWHHjh2kp6d3WEVFxA+04oFzf2J5mbVvr9JxwQUXMHr06ParlYj4LbMHYvwvJrXMmoh4UyCPegPU19dTWFjI0aNHmz1DqdWDRMTNAFugPke5detW7rnnHurr66mqqiIqKorq6moSExPZvHlze9dRRPxFF33g3NKo929/+1vuuusuPvroI7p168ZHH33ErFmzyMzMbO/6iYg/OTWYY7b5GcvPUU6dOrXZZ3fffTfPPfdce9RJRPxVF308yFJQdu/enaqqKgDi4uLYs2cPlZWV1NTUtGvlRMTPGIDLZPPDoLR0j3LMmDH8+9//5oc//CG33HILU6dOJTg4mPHjx7d3/UTEnwTac5TfNn/+fPc/33nnnQwfPpzq6mr36yFFRODkiHcXHMyx/HjQt6Wmpnq7HiLSFQRaUGZmZrqnMHqSm5vr1QqJiHQ2LQblpEmTOrIepqaOvJDgxlBfV0MscvT92tdVEIsabDV8Head7wq4rvfEiRM7sh4i0hUE+hRGERFTgdaiFBFpLRv4ZRCaUVCKiPeoRSkiYqKLBqWlKYz19fU88cQTpKenc/nllwPwzjvvsHr16natnIj4F5thbfM3loLy4YcfZteuXTz22GPuZysHDRrEiy++2K6VExE/4+L0yHeLm68r2XqWut6bNm0iPz+fyMhIgoKasjUhIYHS0tJ2rZyI+JeAe47y20JCQnA6nc0+q6ioICYmpj3qJCL+qosGpaWu9/jx43nggQf4+uum2RZlZWUsWbKEm266qV0rJyL+xYaFe5S+rmQbWArKe++9lz59+vCjH/2IyspKxo0bR3x8PLNnz27v+omIP+miC/da6nqHhoYyf/585s+fT0VFBT179rS0YIaIBBbbqcV5PemqgzmnutynVFdXu/+5b9++3q2RiEgnY3mFc5vN1uw1tadalF988UX71ExE/E8XHcyxFJQ7d+5s9vPhw4d5+umntYCviDRn4YFyww+D0tJgznfFxcUxf/58fv/733u7PiLizwJ5MOds9u7dS21trTfrIiL+LpC73t99LURtbS179uzR40Ei0ozNODny7Ynhf1lpKSi/+1qIiIgIhg4dSv/+/dujTiLipywtetEVg9LpdPLBBx/w0EMPERqqd9aIiAddtOttOphjt9t599139YC5iJjz8mBOTk4OaWlpDBkyhF27drk/LywsZPLkyYwbN47Jkyezb9++cy7zxNKo97Rp03jqqadoaGiw9KUiEpi8vR5leno6ubm59OnTp9nnixYtIjMzk40bN5KZmcnChQvPucwTj0GZl5cHwOrVq3n22We57LLLuPbaa7nuuuvcm4iIWytalCUlJRw4cKDZVllZ2ezrUlNTcTgczT4rLy+noKCAjIwMADIyMigoKKCioqLNZWY83qNcuHAhGRkZPProo6ZfJCJiddQbYMqUKWcUZWVlMWfOHI+HFxcXk5CQgN1uB5puD8bHx1NcXIxhGG0qi42N9XhOj0F5asriFVdc4fFLRESAVg3m5ObmkpiY2KwoOjq6Xap1rjwGpcvl4oMPPmg2x/u7rrzySq9XSkT8k9XHgwASExNJSkpq9TkcDgelpaU4nU7sdjtOp5OysjIcDgeGYbSpzIzHoKyvr2f+/PktBqXNZmPz5s2tvlAR6aI64PGgXr16kZKSQl5eHhMmTCAvL4+UlBR397mtZZ54DMqIiAgFoYhY5+WgXLp0Kfn5+Rw5coTp06cTExPD+vXrWbx4MdnZ2axYsYLo6GhycnLcx7S1zBO911tEvKY1XW8rFixYwIIFC874fODAgaxZs+asx7S1zBNLgzkiIpZ00WXWPAbltm3bOqoeItIVdNEpjOp6i4h3+WEQmlFQiojXWLlH2ZopjJ2FglJEvEddbxERz2wu8ymMplMcOyEFpYh4jbreIiJm1PUWETGhoBQR8cyGha53h9TEuxSUIuI9alGKiHjWNOrtOQk16i0iAU2j3iIiZtT1FhHxTC1KEREzalGKiJhoxVsY/YmCUkS8Rl1vEREzhmG+hLkfLnGuoBQRr1GLUiz70fQjjPlpBf2H1vHWuhgev7efu+yS0cfJevggcX3q+fL/RfLYPf0oOxgKwMS7DjPh50eIjm2krjqIf78Sw58f6o3L6Y+TvvzL3MXbuHhEOeERTo6Wh/GPFwaQ/0rTn9vFqUeYdf8O4hJr2fV5DL9fcjGHSyJPHmkwffZOxk74GoD8V/ry16eH4p8T9bygiw7mBHXESXJyckhLS2PIkCHs2rWrI07pU+UlwfztvxPI/3vz9wVHxzay8Jl9PP9IIrdcOIxdn0Uyb2WRu/yD16OZPW4wNw+5iBlpQxhwYR0//vmRjq5+QHrp+QuY/uPrmZQ2jiVzU5k6cxcXDP2G6B71zM/5mNV/HMKtY8ay+4sYspedfpfU+In7GXVtKVm3XU3WlGu4YnQZP5i434dX4lun1qM02/xNhwRleno6ubm59OnTpyNO53PvvhbD+//qQeVRe7PPv/+DbyjaFc7beTE0nAjihccSGHBhLX0vqAOguCiM6sqTx9iabuX07n+io6sfkPYXdqexoel3f+o2W2Kfaq66voT9e6N45w0HDfV2cv88iPMvqCQpuQqAG248wNq/DaC8LILyw+GszT2fGzIO+PJSfMpmWAhKP2xRdkjXOzU1tSNO0+klD6lj7+cR7p9P1NopLgojeXAdX+8JB+D6iUeZ87sDdOvu4li5nT/9prevqhtwfnn/dtIzDhAe7mLPzmi2vhfP1Flfsnd3tHufE3XBFB+MpN+A4xwoiqLfgCoKd3d3l+/dHU2/84/7ovqdgwZz5FxFdHPxTXnzVmb18SAiok73Rd5c25M31/ak9/knuOGWCo4e1h9RR1nx6EWsfHwYQy86ykWXldNQH0RERCPfHA1rtl9NVQiRkY0AhEc0Ul0VcrqsOoTIbk6absQF3n3KrjqY0yFdb2lSWx1EZFTzGzSRUS5qq878YzhUGEbRrnCyfnuwo6ongMtlo+DTWM6Lr+PGnxRRWxtMZLeGZvtEdmukpqbpP2B1tcFEdmtsXlZtJxBDEjg9mGO2+RkFZQcq+jKcAd+rdf8cFuHE0f8ERbvCz7q/3Q69k3WP0hfsdgNHUg3793bn/EGnu9Jh4Y0kJlWzf29Td3v/3ijOH1TpLj9/UCX7C7uf8X2B4lSL0mzzNwrKdhBkNwgJcxFkhyA7J//Z4L3XetB/SB2jbzxGSJiL2+4rpbAgwn1/cnxmOT16NbVe+g2q49Y5pWx7J8qXlxIQevQ8wTVjDhEe0UhQkMFlIw9z7dhDfLa1F++9lUDywONcdX0xIaFOMn++m317ojlQ1PTnsnlDEhN/tpdecXXEnlfHxMy9bMpL8vEV+Y7NZVja/E2H3ABbunQp+fn5HDlyhOnTpxMTE8P69es74tQ+kXlPKbf/qtT98w23HOWFxxNY/XgiD/2iP7OXHeTXT+1n57ZIfjsr2b3f90ZUc8cDJUR0axrIeTsvhucfSfTFJQQUw4Abby5i9gPbCQqCsuII/vTEhXywpel3/3D25cycu4O5iz/hy89jyFlwqfvY19b2I7FPDctztwCw8ZW+vLa231nPEzD8LwdN2Qyjcw9BHThwgPT0dHoXDiW4MdTX1RGLgvsGbqvK3zTYavg67C02b95MUlLb/txO/T097/zJBId4vvXQ2HCcI4X/c07n62gaUhUR73EZTZvZPn5GQSki3tNFpzAqKEXEa7rqc5QKShHxGiuj2hr1FhHxvxw0paAUEa+xGQY2kwdpzMo7IwWliHiP6+Rmto+fUVCKiNeoRSkiYqYdHg9KS0sjNDSUsLCmVZzmzp3L1VdfTWFhIdnZ2Rw7doyYmBhycnLo378/gMeytlBQiojXNC3Oazbq3frvffLJJxk8eHCzzxYtWkRmZiYTJkzgn//8JwsXLmTVqlWmZW2hRTFExIuM04v3trR5YVi8vLycgoICMjIyAMjIyKCgoICKigqPZW2lFqWIeI3NBTaTpThPtShLSkrOKIuOjiY6OvqMz+fOnYthGFx++eXcd999FBcXk5CQgN3etBC23W4nPj6e4uJiDMNosSw2NvaM77ZCQSki3tOKV0FMmTLljKKsrCzmzJnT7LPc3FwcDgf19fUsW7aMJUuWcMcdd3irxpYoKEXEe1oxmJObm0tiYvNlBM/WmnQ4HACEhoaSmZnJrFmzePDBByktLcXpdGK323E6nZSVleFwODAMo8WyttI9ShHxGpvLZWkDSExMJCkpqdn23aCsqanh+PGmFeYNw2DDhg2kpKTQq1cvUlJSyMvLAyAvL4+UlBRiY2M9lrWVWpQi4j0G5g+Ut2Isp7y8nDlz5uB0OnG5XAwcOJBFixYBsHjxYrKzs1mxYgXR0dHk5OS4j/NU1hYKShHxHgsPnLfmdbV9+/Zl3bp1Zy0bOHAga9asaXVZWygoRcR79F5vERETCkoRERNWZt1oUQwRCWQ2l4HNJAm1cK+IBDgLXW8/XNlXQSki3mNlLrfuUYpIQHMBJnO9/bBBqaAUEe+xGQY2kyTUwr0iEtjU9RYRMeGy+NIcs+55J6OgFBHvMdz/45mCUkQClqUVzNX1FpFA5rIYlPaOqIz3KChFxHuMrvlibwWliHiPy3UyLD1oy2sYfUxBKSLeY2X1IN2jFJGAZuCXz0maUVCKiPeoRSkiYkL3KEVETKhFKSJiwuU6OY3RgyC1KEUkgBmGgWHS9Tb8cLBHQSki3uMyTs7O8URBKSKBTG9hFBExYVi4R6lRbxEJaGpRioh4ZjhdGE6n5320KIaIBDQrgzk2tShFJJAZFmbmmJV3QgpKEfEew8Awa1EGqUUpIoFMLUrfcJ68MdwYXO/jmkhrGLYaX1dBLGqkFjj9d+1cNNhOYJg8/tNoazjn83S0Th+Uhw8fBqCs714f10RaZ6evKyCtdPjwYZKTk9t0bFRUFD169KCMryzt36NHD6Kiotp0Ll+wGZ184mVdXR07duwgLi4Ou93P3kgk4gecTieHDx9m2LBhhIeHt/l7jh07RlVVlaV9o6KiiImJafO5OlqnD0oREV8L8nUFREQ6OwWliIgJBaWIiAkFpYiICQWliIgJBaWIiAkFpYiICQWlDxQWFjJ58mTGjRvH5MmT2bdvn6+rJB7k5OSQlpbGkCFD2LVrl6+rIz6goPSBRYsWkZmZycaNG8nMzGThwoW+rpJ4kJ6eTm5uLn369PF1VcRHFJQdrLy8nIKCAjIyMgDIyMigoKCAiooKH9dMWpKamorD4fB1NcSHFJQdrLi4mISEBPe8dbvdTnx8PMXFxT6umYi0REEpImJCQdnBHA4HpaWl7rX/nE4nZWVl6tqJdGIKyg7Wq1cvUlJSyMvLAyAvL4+UlBRiY2N9XDMRaYmWWfOBr776iuzsbCorK4mOjiYnJ4cBAwb4ulrSgqVLl5Kfn8+RI0fo2bMnMTExrF+/3tfVkg6koBQRMaGut4iICQWliIgJBaWIiAkFpYiICQWliIgJBaWQnZ3NE088AcDWrVsZN25ch5x3yJAhFBUVnbXs9ttvZ82aNZa+Jy0tjffee69NdTiXYyVwKCj9RFpaGsOHD+fSSy/lqquu4sEHH6S6utrr50lNTWXjxo2m+7388sv87Gc/8/r5RTojBaUfWblyJdu2bWPt2rVs376dP/zhD2fs09jY6IOaiXRtCko/lJCQwNVXX83u3buBpi5sbm4uY8eOZezYsQC8+eabTJgwgdTUVG699VZ27tzpPr6goICJEydy6aWXcs8993DixAl32Ycffsg111zj/rm4uJisrCxGjRrFyJEjWbJkCV999RWLFi3ik08+4dJLLyU1NRWA+vp6cnJyuO6667jqqqtYuHAhdXV17u965plnGD16NKNHj+Yf//iH5evdv38/U6dOZeTIkYwcOZJf/epXVFZWNttn+/bt3HjjjYwYMYIHH3yw2TV5+l2IWKGg9EPFxcVs2bKFlJQU92ebNm3ipZdeYsOGDXz++efMmzePJUuW8OGHHzJ58mR++ctfUl9fT319PbNnz2bChAl89NFHjB8/nvz8/LOex+l0MmPGDHr37s0bb7zBli1buPHGGxk4cCC/+c1vuOSSS9i2bRtbt24F4NFHH6WwsJB169aRn59PWVkZy5cvB2DLli385S9/4S9/+Qv5+fm8//77lq/XMAxmzJjB22+/zWuvvUZJSQlPPfVUs31effVVnn32WV5//XUKCwtZsWIFgMffhYhVCko/Mnv2bFJTU8nMzGTEiBHMnDnTXXb33XcTExNDeHg4L730EpMnT+biiy/GbrczceJEQkJC+OSTT/j0009paGhg2rRphISEMH78eC666KKznu+zzz6jrKyMX//610RGRhIWFuZuPX6XYRisWbOGefPmERMTQ1RUFDNmzHDPiX7ttde4+eabGTx4MJGRkWRlZVm+7uTkZL7//e8TGhpKbGws06dP5z//+U+zfaZMmYLD4SAmJoZZs2a5z+vpdyFiVbCvKyDWLV++nKuuuuqsZd9epu3QoUOsW7eO1atXuz9raGigrKwMm81GQkICNpvNXda7d++zfmdxcTG9e/cmONj8X5OKigpqa2u5+eab3Z8ZhoHL5QKgrKyMYcOGucta81qF8vJyli5dytatW6mursYwDKKjo5vt8+3r7927N2VlZYDn34WIVQrKLuLbwedwOJg5cyazZs06Y7+PPvqI0tJSDMNwH3Po0CH69u17xr4Oh4Pi4mIaGxvPCMtvnw+gZ8+ehIeHs379ehISEs74ru+u4n7o0CHL1/b4449js9l45ZVX6NmzJ5s2bWLJkiXN9vnud8fHx7uvoaXfhYhV6np3QZMmTeLvf/87n376KYZhUFNTw1tvvUVVVRWXXHIJwcHBrFq1isbGRvLz89m+fftZv2f48OHExcXx+OOPU1NTw4kTJ/j444+BpnU1S0tL3ff6goKCmDRpEg8//DDl5eUAlJaW8vbbbwMwfvx41q5dy549e6itreXpp5+2fD3V1dVERkYSHR1NaWkpzzzzzBn7/O1vf6OkpIRjx47xxz/+kRtvvNH0dyFilYKyC7rooot46KGHWLJkCSNGjGDs2LG8/PLLAISGhvLUU0+xdu1aRowYwYYNGxgzZsxZv8dut7Ny5UqKioq4/vrrueaaa3jttdcAGDVqFBdccAGjR49m5MiRANx///0kJyfz05/+lMsuu4w77riDwsJCAK699lqmTZvGtGnTGDNmDKNGjbJ8PVlZWRQUFJCamsrdd9/tHtn/toyMDO68805uuOEG+vbt625BevpdiFil9ShFREyoRSkiYkJBKSJiQkEpImJCQSkiYkJBKSJiQkEpImJCQSkiYkJBKSJiQkEpImLi/wO1ri7To4qo0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## displaying the confusion matrix\n",
    "cm = confusion_matrix(y_val, pred, labels=log.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                           display_labels=log.classes_)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56749903",
   "metadata": {},
   "source": [
    "### 2. Random Forest\n",
    "We'll use **Random Forest** to build our second model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "22bd4595",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid Search best score: 0.8384751682566686\n",
      "{'max_depth': 11, 'min_samples_leaf': 2, 'min_samples_split': 3}\n"
     ]
    }
   ],
   "source": [
    "param_grid={\n",
    "    'max_depth':[9,11],  \n",
    "     'min_samples_leaf': [2,3,5],\n",
    "     'min_samples_split':[2,3,6,7],\n",
    "    }\n",
    "\n",
    "gs = GridSearchCV(RandomForestClassifier(),param_grid = param_grid, cv=5, scoring='f1')\n",
    "gs.fit(X_sampled_train,y_sampled_train)\n",
    "print(f\"Grid Search best score: {gs.best_score_}\")\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1b93dc43",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.804404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.948985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 score</th>\n",
       "      <td>0.870734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.859117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "precision  0.804404\n",
       "recall     0.948985\n",
       "f1 score   0.870734\n",
       "accuracy   0.859117"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(max_depth=11, min_samples_leaf=2, min_samples_split=3 ,class_weight={0:0.3,1:0.7}\n",
    "                            )\n",
    "clf.fit(X_sampled_train,y_sampled_train)\n",
    "pred =  clf.predict_proba(X_sampled_train)[:,1]>=0.43\n",
    "\n",
    "prec  = precision_score(y_sampled_train,pred,pos_label=1)\n",
    "recall  = recall_score(y_sampled_train,pred,pos_label=1)\n",
    "f1 = f1_score(y_sampled_train,pred)\n",
    "acc  = accuracy_score(y_sampled_train,pred)\n",
    "\n",
    "pd.DataFrame([[prec,recall,f1,acc]],columns=['precision','recall','f1 score','accuracy']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64516262",
   "metadata": {},
   "source": [
    "### Evaluating on the Validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "df0d8311",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.266240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.722084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 score</th>\n",
       "      <td>0.389037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.753439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "precision  0.266240\n",
       "recall     0.722084\n",
       "f1 score   0.389037\n",
       "accuracy   0.753439"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = pipeline.transform(X_val)\n",
    "pred = clf.predict_proba(val)[:,1] >= 0.43\n",
    "\n",
    "prec = precision_score(y_val,pred,pos_label=1)\n",
    "recall = recall_score(y_val,pred,pos_label=1)\n",
    "f1 = f1_score(y_val,pred)\n",
    "acc  = accuracy_score(y_val,pred)\n",
    "\n",
    "pd.DataFrame([[prec,recall,f1,acc]],columns=['precision','recall','f1 score','accuracy']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "4c6b6404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f27ceb7a5b0>"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_val, pred, labels=clf.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                           display_labels=clf.classes_)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75267bb",
   "metadata": {},
   "source": [
    "### 3. LightGBM \n",
    "We are going to create our third and final model using **LightGBM**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "97331add",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid Search best score: 0.9312587654432238\n",
      "{'max_depth': 60, 'min_child_samples': 40, 'num_leaves': 300, 'scale_pos_weight': 3}\n"
     ]
    }
   ],
   "source": [
    "param_grid={\n",
    "    'max_depth':[60,70],\n",
    "    'num_leaves': [100,300],\n",
    "    'min_child_samples':[30,40],\n",
    "    'scale_pos_weight':[3,4]\n",
    "    }\n",
    "\n",
    "gs = GridSearchCV(LGBMClassifier(random_state=100), param_grid = param_grid,cv=5,scoring='f1')\n",
    "gs.fit(X_sampled_train,y_sampled_train)\n",
    "print(f\"Grid Search best score: {gs.best_score_}\")\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "0f79ebb6",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.880354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.951350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 score</th>\n",
       "      <td>0.914476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.911027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "precision  0.880354\n",
       "recall     0.951350\n",
       "f1 score   0.914476\n",
       "accuracy   0.911027"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb = LGBMClassifier(max_depth=60,min_child_samples=40,num_leaves=300, random_state=100,scale_pos_weight=3,learning_rate=0.01)\n",
    "lgb.fit(X_sampled_train,y_sampled_train);\n",
    "pred =  lgb.predict_proba(X_sampled_train)[:,1]>=0.5\n",
    "\n",
    "prec  = precision_score(y_sampled_train,pred,pos_label=1)\n",
    "recall  = recall_score(y_sampled_train,pred,pos_label=1)\n",
    "f1 = f1_score(y_sampled_train,pred)\n",
    "acc  = accuracy_score(y_sampled_train,pred)\n",
    "\n",
    "pd.DataFrame([[prec,recall,f1,acc]],columns=['precision','recall','f1 score','accuracy']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a703734",
   "metadata": {},
   "source": [
    "### Evaluating on the Validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "f4c2d15d",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.263538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.724566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 score</th>\n",
       "      <td>0.386499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.749933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "precision  0.263538\n",
       "recall     0.724566\n",
       "f1 score   0.386499\n",
       "accuracy   0.749933"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = pipeline.transform(X_val)\n",
    "pred = lgb.predict_proba(val)[:,1]>=0.4\n",
    "\n",
    "prec = precision_score(y_val,pred,pos_label=1)\n",
    "recall = recall_score(y_val,pred,pos_label=1)\n",
    "f1 = f1_score(y_val,pred)\n",
    "acc  = accuracy_score(y_val,pred)\n",
    "\n",
    "pd.DataFrame([[prec,recall,f1,acc]],columns=['precision','recall','f1 score','accuracy']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d5946314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f27cff24070>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_val, pred, labels=log.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                           display_labels=log.classes_)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e80b96",
   "metadata": {},
   "source": [
    "### Evaluating on the test set\n",
    "Though the logistic regression model has the highest recall in the validation set, the low f1 score and accuracy made us go with the LightGBM model as our model of choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "0f4a24c8",
   "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>logistic</th>\n",
       "      <th>random forest</th>\n",
       "      <th>lightGBM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.189975</td>\n",
       "      <td>0.282238</td>\n",
       "      <td>0.283200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.799578</td>\n",
       "      <td>0.734177</td>\n",
       "      <td>0.746835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 score</th>\n",
       "      <td>0.307007</td>\n",
       "      <td>0.407733</td>\n",
       "      <td>0.410673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.584608</td>\n",
       "      <td>0.754552</td>\n",
       "      <td>0.753338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           logistic  random forest  lightGBM\n",
       "precision  0.189975       0.282238  0.283200\n",
       "recall     0.799578       0.734177  0.746835\n",
       "f1 score   0.307007       0.407733  0.410673\n",
       "accuracy   0.584608       0.754552  0.753338"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xt = pipeline.transform(X_test)\n",
    "log_pred = log.predict_proba(Xt)[:,1]>=0.4\n",
    "rf_pred = clf.predict_proba(Xt)[:,1] >= 0.43\n",
    "lgb_pred = lgb.predict_proba(Xt)[:,1]>=0.4\n",
    "\n",
    "rf_prec = precision_score(y_test,rf_pred,pos_label=1)\n",
    "rf_recall = recall_score(y_test,rf_pred,pos_label=1)\n",
    "rf_f1 = f1_score(y_test,rf_pred)\n",
    "rf_acc  = accuracy_score(y_test,rf_pred)\n",
    "\n",
    "log_prec = precision_score(y_test,log_pred,pos_label=1)\n",
    "log_recall = recall_score(y_test,log_pred,pos_label=1)\n",
    "log_f1 = f1_score(y_test,log_pred)\n",
    "log_acc  = accuracy_score(y_test,log_pred)\n",
    "\n",
    "lgb_prec = precision_score(y_test,lgb_pred,pos_label=1)\n",
    "lgb_recall = recall_score(y_test,lgb_pred,pos_label=1)\n",
    "lgb_f1 = f1_score(y_test,lgb_pred)\n",
    "lgb_acc  = accuracy_score(y_test,lgb_pred)\n",
    "\n",
    "pd.DataFrame([[log_prec,log_recall,log_f1,log_acc],\n",
    "             [rf_prec,rf_recall,rf_f1,rf_acc],\n",
    "              [lgb_prec,lgb_recall,lgb_f1,lgb_acc]]\n",
    "             ,columns=['precision','recall','f1 score','accuracy']\n",
    "             ,index=['logistic','random forest','lightGBM']).T"
   ]
  }
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