diff --git a/Term Deposit Prediction/Dataset/README.md b/Term Deposit Prediction/Dataset/README.md
new file mode 100644
index 000000000..841ec0cc6
--- /dev/null
+++ b/Term Deposit Prediction/Dataset/README.md	
@@ -0,0 +1,6 @@
+The dataset used in this project is related to a bank marketing campaign, containing various client attributes and the outcome of the marketing campaign.
+
+You can download the dataset from the following sources:
+
+- Kaggle Dataset: [Bank Marketing Dataset](https://www.kaggle.com/datasets/henriqueyamahata/bank-marketing/data)
+- Direct Download: `dataset.csv`
diff --git a/Term Deposit Prediction/Dataset/dataset.csv b/Term Deposit Prediction/Dataset/dataset.csv
new file mode 100644
index 000000000..8c47ee2f0
--- /dev/null
+++ b/Term Deposit Prediction/Dataset/dataset.csv	
@@ -0,0 +1,41189 @@
+age,job,marital,education,default,housing,loan,contact,month,day_of_week,duration,campaign,pdays,previous,poutcome,emp.var.rate,cons.price.idx,cons.conf.idx,euribor3m,nr.employed,y
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+34,technician,married,unknown,no,yes,no,cellular,nov,tue,138,1,1,4,success,-1.1,94.767,-50.8,1.046,4963.6,no
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+58,admin.,married,high.school,no,yes,no,cellular,nov,tue,173,1,999,2,failure,-1.1,94.767,-50.8,1.046,4963.6,no
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+60,admin.,married,unknown,no,no,no,cellular,nov,tue,787,1,7,2,success,-1.1,94.767,-50.8,1.046,4963.6,yes
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+61,admin.,married,high.school,no,no,no,telephone,nov,wed,508,4,999,0,nonexistent,-1.1,94.767,-50.8,1.044,4963.6,no
+31,admin.,single,university.degree,no,yes,no,cellular,nov,thu,260,1,999,0,nonexistent,-1.1,94.767,-50.8,1.041,4963.6,yes
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+41,unemployed,married,basic.9y,no,no,no,cellular,nov,thu,597,2,3,2,success,-1.1,94.767,-50.8,1.041,4963.6,yes
+28,self-employed,single,university.degree,no,yes,no,cellular,nov,thu,97,2,6,3,success,-1.1,94.767,-50.8,1.041,4963.6,no
+25,student,single,high.school,no,no,no,cellular,nov,thu,244,1,999,0,nonexistent,-1.1,94.767,-50.8,1.041,4963.6,yes
+54,technician,married,unknown,no,yes,no,cellular,nov,thu,222,1,999,1,failure,-1.1,94.767,-50.8,1.041,4963.6,no
+54,unemployed,married,professional.course,no,yes,no,telephone,nov,thu,200,2,10,4,failure,-1.1,94.767,-50.8,1.041,4963.6,no
+42,admin.,married,university.degree,no,yes,no,cellular,nov,thu,419,3,999,1,failure,-1.1,94.767,-50.8,1.041,4963.6,no
+58,unemployed,married,university.degree,no,yes,no,cellular,nov,thu,344,2,999,0,nonexistent,-1.1,94.767,-50.8,1.041,4963.6,yes
+31,services,married,high.school,no,yes,no,cellular,nov,fri,202,3,999,0,nonexistent,-1.1,94.767,-50.8,1.04,4963.6,no
+33,management,married,university.degree,no,yes,no,cellular,nov,fri,237,2,999,0,nonexistent,-1.1,94.767,-50.8,1.04,4963.6,yes
+37,admin.,married,university.degree,no,yes,no,cellular,nov,fri,315,1,999,0,nonexistent,-1.1,94.767,-50.8,1.04,4963.6,yes
+43,admin.,divorced,university.degree,no,yes,no,cellular,nov,fri,741,4,999,2,failure,-1.1,94.767,-50.8,1.04,4963.6,yes
+29,technician,single,professional.course,no,no,yes,cellular,nov,fri,127,1,6,2,success,-1.1,94.767,-50.8,1.04,4963.6,no
+35,admin.,single,professional.course,no,yes,no,cellular,nov,fri,92,3,999,4,failure,-1.1,94.767,-50.8,1.04,4963.6,no
+26,admin.,single,university.degree,no,no,no,cellular,nov,fri,528,3,999,0,nonexistent,-1.1,94.767,-50.8,1.04,4963.6,yes
+41,admin.,married,university.degree,no,yes,no,cellular,nov,fri,252,4,999,0,nonexistent,-1.1,94.767,-50.8,1.04,4963.6,yes
+25,technician,single,professional.course,no,yes,no,cellular,nov,fri,712,2,19,1,success,-1.1,94.767,-50.8,1.04,4963.6,yes
+35,admin.,single,professional.course,no,yes,no,cellular,nov,fri,397,3,999,0,nonexistent,-1.1,94.767,-50.8,1.04,4963.6,yes
+30,admin.,single,university.degree,no,yes,no,cellular,nov,mon,324,2,999,0,nonexistent,-1.1,94.767,-50.8,1.039,4963.6,no
+41,blue-collar,married,basic.9y,no,yes,no,cellular,nov,mon,371,2,999,1,failure,-1.1,94.767,-50.8,1.039,4963.6,no
+41,technician,married,professional.course,no,no,no,cellular,nov,mon,526,2,6,1,success,-1.1,94.767,-50.8,1.039,4963.6,yes
+26,management,single,university.degree,no,yes,no,cellular,nov,mon,112,5,999,0,nonexistent,-1.1,94.767,-50.8,1.039,4963.6,no
+67,housemaid,divorced,professional.course,no,yes,no,cellular,nov,mon,655,2,5,5,success,-1.1,94.767,-50.8,1.039,4963.6,yes
+41,technician,married,professional.course,no,yes,no,cellular,nov,mon,185,2,999,0,nonexistent,-1.1,94.767,-50.8,1.039,4963.6,no
+31,housemaid,single,university.degree,no,no,no,telephone,nov,mon,152,5,999,0,nonexistent,-1.1,94.767,-50.8,1.039,4963.6,no
+41,technician,married,professional.course,no,yes,no,cellular,nov,mon,545,2,999,0,nonexistent,-1.1,94.767,-50.8,1.039,4963.6,yes
+31,housemaid,single,university.degree,no,no,no,cellular,nov,mon,159,4,999,0,nonexistent,-1.1,94.767,-50.8,1.039,4963.6,no
+35,technician,divorced,basic.4y,no,no,no,cellular,nov,tue,363,1,999,0,nonexistent,-1.1,94.767,-50.8,1.035,4963.6,yes
+35,technician,divorced,basic.4y,no,yes,no,cellular,nov,tue,514,1,9,4,success,-1.1,94.767,-50.8,1.035,4963.6,yes
+33,admin.,married,university.degree,no,no,no,cellular,nov,tue,843,1,999,0,nonexistent,-1.1,94.767,-50.8,1.035,4963.6,yes
+33,admin.,married,university.degree,no,yes,no,cellular,nov,tue,510,1,999,1,failure,-1.1,94.767,-50.8,1.035,4963.6,no
+60,blue-collar,married,basic.4y,no,yes,no,cellular,nov,tue,347,2,4,1,success,-1.1,94.767,-50.8,1.035,4963.6,no
+35,technician,divorced,basic.4y,no,yes,no,cellular,nov,tue,385,3,4,2,success,-1.1,94.767,-50.8,1.035,4963.6,yes
+54,admin.,married,professional.course,no,no,no,cellular,nov,tue,1868,2,10,1,success,-1.1,94.767,-50.8,1.035,4963.6,yes
+38,housemaid,divorced,university.degree,no,no,no,cellular,nov,wed,403,2,999,0,nonexistent,-1.1,94.767,-50.8,1.03,4963.6,yes
+32,admin.,married,university.degree,no,no,no,telephone,nov,wed,651,1,999,1,failure,-1.1,94.767,-50.8,1.03,4963.6,yes
+32,admin.,married,university.degree,no,yes,no,cellular,nov,wed,236,3,999,0,nonexistent,-1.1,94.767,-50.8,1.03,4963.6,no
+38,entrepreneur,married,university.degree,no,no,no,cellular,nov,wed,144,2,999,0,nonexistent,-1.1,94.767,-50.8,1.03,4963.6,no
+62,services,married,high.school,no,yes,no,cellular,nov,wed,154,5,999,0,nonexistent,-1.1,94.767,-50.8,1.03,4963.6,no
+40,management,divorced,university.degree,no,yes,no,cellular,nov,wed,293,2,999,4,failure,-1.1,94.767,-50.8,1.03,4963.6,no
+33,student,married,professional.course,no,yes,no,telephone,nov,thu,112,1,999,0,nonexistent,-1.1,94.767,-50.8,1.031,4963.6,yes
+31,admin.,single,university.degree,no,yes,no,cellular,nov,thu,353,1,999,0,nonexistent,-1.1,94.767,-50.8,1.031,4963.6,yes
+62,retired,married,university.degree,no,yes,no,cellular,nov,thu,329,1,999,2,failure,-1.1,94.767,-50.8,1.031,4963.6,yes
+62,retired,married,university.degree,no,yes,no,cellular,nov,thu,208,1,1,6,success,-1.1,94.767,-50.8,1.031,4963.6,yes
+34,student,single,unknown,no,yes,no,cellular,nov,thu,180,1,999,2,failure,-1.1,94.767,-50.8,1.031,4963.6,no
+38,housemaid,divorced,high.school,no,yes,yes,cellular,nov,thu,360,1,999,0,nonexistent,-1.1,94.767,-50.8,1.031,4963.6,no
+57,retired,married,professional.course,no,yes,no,cellular,nov,thu,124,6,999,0,nonexistent,-1.1,94.767,-50.8,1.031,4963.6,no
+62,retired,married,university.degree,no,no,no,cellular,nov,thu,483,2,6,3,success,-1.1,94.767,-50.8,1.031,4963.6,yes
+64,retired,divorced,professional.course,no,yes,no,cellular,nov,fri,151,3,999,0,nonexistent,-1.1,94.767,-50.8,1.028,4963.6,no
+36,admin.,married,university.degree,no,no,no,cellular,nov,fri,254,2,999,0,nonexistent,-1.1,94.767,-50.8,1.028,4963.6,no
+37,admin.,married,university.degree,no,yes,no,cellular,nov,fri,281,1,999,0,nonexistent,-1.1,94.767,-50.8,1.028,4963.6,yes
+29,unemployed,single,basic.4y,no,yes,no,cellular,nov,fri,112,1,9,1,success,-1.1,94.767,-50.8,1.028,4963.6,no
+73,retired,married,professional.course,no,yes,no,cellular,nov,fri,334,1,999,0,nonexistent,-1.1,94.767,-50.8,1.028,4963.6,yes
+46,blue-collar,married,professional.course,no,no,no,cellular,nov,fri,383,1,999,0,nonexistent,-1.1,94.767,-50.8,1.028,4963.6,no
+56,retired,married,university.degree,no,yes,no,cellular,nov,fri,189,2,999,0,nonexistent,-1.1,94.767,-50.8,1.028,4963.6,no
+44,technician,married,professional.course,no,no,no,cellular,nov,fri,442,1,999,0,nonexistent,-1.1,94.767,-50.8,1.028,4963.6,yes
+74,retired,married,professional.course,no,yes,no,cellular,nov,fri,239,3,999,1,failure,-1.1,94.767,-50.8,1.028,4963.6,no
diff --git a/Term Deposit Prediction/Images/README.md b/Term Deposit Prediction/Images/README.md
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+++ b/Term Deposit Prediction/Images/README.md	
@@ -0,0 +1,61 @@
+1. **Age Distribution**:
+    - Most clients are in the age range of 30-40 years.
+    - Age distribution is visualized using a pie chart.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/2c7fae04-f030-4e9c-b3e3-e621390a6d2d)
+
+
+2. **Job Distribution**:
+    - The most common job types are blue-collar, management, and technician.
+    - Job distribution is visualized using a bar chart.
+    
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/b0d6a595-40c0-46f5-9b2f-3b2ac2728097)
+
+
+3. **Education Level**:
+    - Most clients have a university degree, followed by high school education.
+    - Education distribution is visualized using a bar chart.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/11d9f16c-7233-4e3d-b7d4-21e387123f92)
+
+
+4. **Loan Status**:
+    - Majority of clients do not have personal loans, housing loans, or defaults.
+    - Loan status distribution is visualized using a pie chart.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/a14127e0-d8fc-4b78-a9ad-9d573d0ef489)
+
+
+5. **Marital Status**:
+    - Most clients are married, followed by single and divorced.
+    - Marital status distribution is visualized using a pie chart.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/2de201d4-a4d4-4d4a-8b39-587068057977)
+
+
+6. **Contact Type**:
+    - The preferred contact type is cellular.
+    - Contact type distribution is visualized using a histogram.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/e6de612d-8229-4aef-93f3-2abcd6ce78a7)
+
+
+7. **Call Duration**:
+    - Total call duration peaks in certain months and days of the week.
+    - Call duration by month and day is visualized using line charts.
+
+
+    <div align='center'>
+    <img src='https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/720037be-9dec-412c-9fd2-657298bac6c9' width=600/>
+    <img src='https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/82dea548-9d84-479f-9791-f713fcb718a2' width=600/>
+    </div>
+
+8. **Correlation Analysis**:
+    - Heatmap reveals the correlation between numerical features.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/2b5505c4-b4c4-4a43-bfe0-87065db6f15a)
+
+
+9. **Models Performance Comparision**:
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/8ecf69f4-e2da-4ead-8683-0ce3c14ad324)
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diff --git a/Term Deposit Prediction/Models/Notebook.ipynb b/Term Deposit Prediction/Models/Notebook.ipynb
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@@ -0,0 +1,736221 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4c3c49be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries for data manipulation, visualization, and modeling\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objs as go"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b5cf52a4",
+   "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset\n",
+    "df=pd.read_csv('dataset.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d3b65139",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(41188, 21)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7b6e7b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
+       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
+       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
+       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f2adce6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Counts for job:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>job</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>admin.</td>\n",
+       "      <td>10422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>9254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>technician</td>\n",
+       "      <td>6743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>services</td>\n",
+       "      <td>3969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>management</td>\n",
+       "      <td>2924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>retired</td>\n",
+       "      <td>1720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>entrepreneur</td>\n",
+       "      <td>1456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>self-employed</td>\n",
+       "      <td>1421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>housemaid</td>\n",
+       "      <td>1060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>unemployed</td>\n",
+       "      <td>1014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>student</td>\n",
+       "      <td>875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>330</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              job  Count\n",
+       "0          admin.  10422\n",
+       "1     blue-collar   9254\n",
+       "2      technician   6743\n",
+       "3        services   3969\n",
+       "4      management   2924\n",
+       "5         retired   1720\n",
+       "6    entrepreneur   1456\n",
+       "7   self-employed   1421\n",
+       "8       housemaid   1060\n",
+       "9      unemployed   1014\n",
+       "10        student    875\n",
+       "11        unknown    330"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for marital:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>marital</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>married</td>\n",
+       "      <td>24928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>single</td>\n",
+       "      <td>11568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>divorced</td>\n",
+       "      <td>4612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    marital  Count\n",
+       "0   married  24928\n",
+       "1    single  11568\n",
+       "2  divorced   4612\n",
+       "3   unknown     80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for education:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>education</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>university.degree</td>\n",
+       "      <td>12168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>high.school</td>\n",
+       "      <td>9515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>6045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>professional.course</td>\n",
+       "      <td>5243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>basic.4y</td>\n",
+       "      <td>4176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>basic.6y</td>\n",
+       "      <td>2292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>1731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>illiterate</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             education  Count\n",
+       "0    university.degree  12168\n",
+       "1          high.school   9515\n",
+       "2             basic.9y   6045\n",
+       "3  professional.course   5243\n",
+       "4             basic.4y   4176\n",
+       "5             basic.6y   2292\n",
+       "6              unknown   1731\n",
+       "7           illiterate     18"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for default:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>default</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>no</td>\n",
+       "      <td>32588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>8597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>yes</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   default  Count\n",
+       "0       no  32588\n",
+       "1  unknown   8597\n",
+       "2      yes      3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for housing:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>housing</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>yes</td>\n",
+       "      <td>21576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>no</td>\n",
+       "      <td>18622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>990</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   housing  Count\n",
+       "0      yes  21576\n",
+       "1       no  18622\n",
+       "2  unknown    990"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for loan:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>loan</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>no</td>\n",
+       "      <td>33950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>yes</td>\n",
+       "      <td>6248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>990</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      loan  Count\n",
+       "0       no  33950\n",
+       "1      yes   6248\n",
+       "2  unknown    990"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for contact:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>contact</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cellular</td>\n",
+       "      <td>26144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>telephone</td>\n",
+       "      <td>15044</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     contact  Count\n",
+       "0   cellular  26144\n",
+       "1  telephone  15044"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for month:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>month</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>may</td>\n",
+       "      <td>13769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>jul</td>\n",
+       "      <td>7174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>aug</td>\n",
+       "      <td>6178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>jun</td>\n",
+       "      <td>5318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nov</td>\n",
+       "      <td>4101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>apr</td>\n",
+       "      <td>2632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>oct</td>\n",
+       "      <td>718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sep</td>\n",
+       "      <td>570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>mar</td>\n",
+       "      <td>546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>dec</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  month  Count\n",
+       "0   may  13769\n",
+       "1   jul   7174\n",
+       "2   aug   6178\n",
+       "3   jun   5318\n",
+       "4   nov   4101\n",
+       "5   apr   2632\n",
+       "6   oct    718\n",
+       "7   sep    570\n",
+       "8   mar    546\n",
+       "9   dec    182"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for day_of_week:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>day_of_week</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>thu</td>\n",
+       "      <td>8623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>mon</td>\n",
+       "      <td>8514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>wed</td>\n",
+       "      <td>8134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>tue</td>\n",
+       "      <td>8090</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>fri</td>\n",
+       "      <td>7827</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  day_of_week  Count\n",
+       "0         thu   8623\n",
+       "1         mon   8514\n",
+       "2         wed   8134\n",
+       "3         tue   8090\n",
+       "4         fri   7827"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for poutcome:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>poutcome</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>35563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>failure</td>\n",
+       "      <td>4252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>success</td>\n",
+       "      <td>1373</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      poutcome  Count\n",
+       "0  nonexistent  35563\n",
+       "1      failure   4252\n",
+       "2      success   1373"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Counts for y:\n",
+      "\n"
+     ]
+    },
+    {
+     "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>y</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>no</td>\n",
+       "      <td>36548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>yes</td>\n",
+       "      <td>4640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     y  Count\n",
+       "0   no  36548\n",
+       "1  yes   4640"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "categorical_cols = df.select_dtypes(include=['object', 'category']).columns\n",
+    "\n",
+    "# Get the count of all categorical features column values\n",
+    "categorical_counts = {col: df[col].value_counts() for col in categorical_cols}\n",
+    "\n",
+    "# Print the counts in a more readable format\n",
+    "for col, counts in categorical_counts.items():\n",
+    "    print(f\"Counts for {col}:\\n\")\n",
+    "    display(pd.DataFrame(counts).reset_index().rename(columns={'index': col, col: 'Count'}))\n",
+    "    print(\"\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dca4827b",
+   "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>NaN</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 month  \\\n",
+       "0   56  housemaid  married     basic.4y      no      no   no  telephone   may   \n",
+       "1   57   services  married  high.school     NaN      no   no  telephone   may   \n",
+       "2   37   services  married  high.school      no     yes   no  telephone   may   \n",
+       "3   40     admin.  married     basic.6y      no      no   no  telephone   may   \n",
+       "4   56   services  married  high.school      no      no  yes  telephone   may   \n",
+       "\n",
+       "  day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
+       "0         mon  ...         1    999         0  nonexistent          1.1   \n",
+       "1         mon  ...         1    999         0  nonexistent          1.1   \n",
+       "2         mon  ...         1    999         0  nonexistent          1.1   \n",
+       "3         mon  ...         1    999         0  nonexistent          1.1   \n",
+       "4         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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Replace 'unknown' values with NaN\n",
+    "df1= df.replace('unknown', np.nan)\n",
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8e345d80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30488, 21)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1=df1.dropna()\n",
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f9efb93d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_2bb71_row0_col0, #T_2bb71_row1_col0, #T_2bb71_row2_col0, #T_2bb71_row3_col0, #T_2bb71_row4_col0, #T_2bb71_row4_col2, #T_2bb71_row5_col0, #T_2bb71_row6_col0, #T_2bb71_row6_col2, #T_2bb71_row7_col0, #T_2bb71_row7_col1, #T_2bb71_row7_col3, #T_2bb71_row7_col4, #T_2bb71_row7_col5, #T_2bb71_row7_col6, #T_2bb71_row7_col7, #T_2bb71_row8_col0, #T_2bb71_row9_col0 {\n",
+       "  background-color: #f7fbff;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row0_col1, #T_2bb71_row0_col3, #T_2bb71_row0_col4, #T_2bb71_row0_col5, #T_2bb71_row0_col6, #T_2bb71_row2_col7 {\n",
+       "  background-color: #f5f9fe;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row0_col2 {\n",
+       "  background-color: #f0f6fd;\n",
+       "  color: #000000;\n",
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+       "#T_2bb71_row0_col7, #T_2bb71_row6_col7 {\n",
+       "  background-color: #f3f8fe;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row1_col1 {\n",
+       "  background-color: #ecf4fb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row1_col2, #T_2bb71_row9_col1, #T_2bb71_row9_col3, #T_2bb71_row9_col4, #T_2bb71_row9_col5, #T_2bb71_row9_col6, #T_2bb71_row9_col7 {\n",
+       "  background-color: #08306b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_2bb71_row1_col3, #T_2bb71_row2_col1, #T_2bb71_row2_col2, #T_2bb71_row2_col3, #T_2bb71_row2_col4, #T_2bb71_row2_col5, #T_2bb71_row3_col3, #T_2bb71_row4_col1, #T_2bb71_row4_col3, #T_2bb71_row4_col4, #T_2bb71_row4_col5, #T_2bb71_row5_col3, #T_2bb71_row5_col4, #T_2bb71_row5_col5, #T_2bb71_row8_col1, #T_2bb71_row8_col3, #T_2bb71_row8_col4, #T_2bb71_row8_col5, #T_2bb71_row8_col6 {\n",
+       "  background-color: #f5fafe;\n",
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+       "#T_2bb71_row1_col4, #T_2bb71_row6_col3 {\n",
+       "  background-color: #f2f7fd;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row1_col5 {\n",
+       "  background-color: #eff6fc;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row1_col6 {\n",
+       "  background-color: #eaf2fb;\n",
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+       "#T_2bb71_row1_col7 {\n",
+       "  background-color: #084082;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_2bb71_row2_col6, #T_2bb71_row4_col6, #T_2bb71_row4_col7, #T_2bb71_row5_col1, #T_2bb71_row5_col2, #T_2bb71_row5_col6, #T_2bb71_row5_col7, #T_2bb71_row8_col2, #T_2bb71_row8_col7 {\n",
+       "  background-color: #f6faff;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row3_col1, #T_2bb71_row3_col7 {\n",
+       "  background-color: #d1e2f3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row3_col2 {\n",
+       "  background-color: #1d6cb1;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_2bb71_row3_col4 {\n",
+       "  background-color: #d0e1f2;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row3_col5, #T_2bb71_row3_col6 {\n",
+       "  background-color: #d0e2f2;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row6_col1, #T_2bb71_row6_col4, #T_2bb71_row6_col5, #T_2bb71_row6_col6 {\n",
+       "  background-color: #f2f8fd;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row7_col2 {\n",
+       "  background-color: #f4f9fe;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2bb71_row9_col2 {\n",
+       "  background-color: #bad6eb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_2bb71\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_2bb71_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
+       "      <th id=\"T_2bb71_level0_col1\" class=\"col_heading level0 col1\" >mean</th>\n",
+       "      <th id=\"T_2bb71_level0_col2\" class=\"col_heading level0 col2\" >std</th>\n",
+       "      <th id=\"T_2bb71_level0_col3\" class=\"col_heading level0 col3\" >min</th>\n",
+       "      <th id=\"T_2bb71_level0_col4\" class=\"col_heading level0 col4\" >25%</th>\n",
+       "      <th id=\"T_2bb71_level0_col5\" class=\"col_heading level0 col5\" >50%</th>\n",
+       "      <th id=\"T_2bb71_level0_col6\" class=\"col_heading level0 col6\" >75%</th>\n",
+       "      <th id=\"T_2bb71_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row0\" class=\"row_heading level0 row0\" >age</th>\n",
+       "      <td id=\"T_2bb71_row0_col0\" class=\"data row0 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row0_col1\" class=\"data row0 col1\" >39.030012</td>\n",
+       "      <td id=\"T_2bb71_row0_col2\" class=\"data row0 col2\" >10.333529</td>\n",
+       "      <td id=\"T_2bb71_row0_col3\" class=\"data row0 col3\" >17.000000</td>\n",
+       "      <td id=\"T_2bb71_row0_col4\" class=\"data row0 col4\" >31.000000</td>\n",
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+       "      <td id=\"T_2bb71_row0_col7\" class=\"data row0 col7\" >95.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row1\" class=\"row_heading level0 row1\" >duration</th>\n",
+       "      <td id=\"T_2bb71_row1_col0\" class=\"data row1 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row1_col1\" class=\"data row1 col1\" >259.484092</td>\n",
+       "      <td id=\"T_2bb71_row1_col2\" class=\"data row1 col2\" >261.714262</td>\n",
+       "      <td id=\"T_2bb71_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
+       "      <td id=\"T_2bb71_row1_col4\" class=\"data row1 col4\" >103.000000</td>\n",
+       "      <td id=\"T_2bb71_row1_col5\" class=\"data row1 col5\" >181.000000</td>\n",
+       "      <td id=\"T_2bb71_row1_col6\" class=\"data row1 col6\" >321.000000</td>\n",
+       "      <td id=\"T_2bb71_row1_col7\" class=\"data row1 col7\" >4918.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row2\" class=\"row_heading level0 row2\" >campaign</th>\n",
+       "      <td id=\"T_2bb71_row2_col0\" class=\"data row2 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row2_col1\" class=\"data row2 col1\" >2.521451</td>\n",
+       "      <td id=\"T_2bb71_row2_col2\" class=\"data row2 col2\" >2.720150</td>\n",
+       "      <td id=\"T_2bb71_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
+       "      <td id=\"T_2bb71_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
+       "      <td id=\"T_2bb71_row2_col5\" class=\"data row2 col5\" >2.000000</td>\n",
+       "      <td id=\"T_2bb71_row2_col6\" class=\"data row2 col6\" >3.000000</td>\n",
+       "      <td id=\"T_2bb71_row2_col7\" class=\"data row2 col7\" >43.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row3\" class=\"row_heading level0 row3\" >pdays</th>\n",
+       "      <td id=\"T_2bb71_row3_col0\" class=\"data row3 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row3_col1\" class=\"data row3 col1\" >956.332295</td>\n",
+       "      <td id=\"T_2bb71_row3_col2\" class=\"data row3 col2\" >201.373292</td>\n",
+       "      <td id=\"T_2bb71_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
+       "      <td id=\"T_2bb71_row3_col4\" class=\"data row3 col4\" >999.000000</td>\n",
+       "      <td id=\"T_2bb71_row3_col5\" class=\"data row3 col5\" >999.000000</td>\n",
+       "      <td id=\"T_2bb71_row3_col6\" class=\"data row3 col6\" >999.000000</td>\n",
+       "      <td id=\"T_2bb71_row3_col7\" class=\"data row3 col7\" >999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row4\" class=\"row_heading level0 row4\" >previous</th>\n",
+       "      <td id=\"T_2bb71_row4_col0\" class=\"data row4 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row4_col1\" class=\"data row4 col1\" >0.194273</td>\n",
+       "      <td id=\"T_2bb71_row4_col2\" class=\"data row4 col2\" >0.522788</td>\n",
+       "      <td id=\"T_2bb71_row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
+       "      <td id=\"T_2bb71_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
+       "      <td id=\"T_2bb71_row4_col5\" class=\"data row4 col5\" >0.000000</td>\n",
+       "      <td id=\"T_2bb71_row4_col6\" class=\"data row4 col6\" >0.000000</td>\n",
+       "      <td id=\"T_2bb71_row4_col7\" class=\"data row4 col7\" >7.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row5\" class=\"row_heading level0 row5\" >emp.var.rate</th>\n",
+       "      <td id=\"T_2bb71_row5_col0\" class=\"data row5 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row5_col1\" class=\"data row5 col1\" >-0.071510</td>\n",
+       "      <td id=\"T_2bb71_row5_col2\" class=\"data row5 col2\" >1.610399</td>\n",
+       "      <td id=\"T_2bb71_row5_col3\" class=\"data row5 col3\" >-3.400000</td>\n",
+       "      <td id=\"T_2bb71_row5_col4\" class=\"data row5 col4\" >-1.800000</td>\n",
+       "      <td id=\"T_2bb71_row5_col5\" class=\"data row5 col5\" >1.100000</td>\n",
+       "      <td id=\"T_2bb71_row5_col6\" class=\"data row5 col6\" >1.400000</td>\n",
+       "      <td id=\"T_2bb71_row5_col7\" class=\"data row5 col7\" >1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row6\" class=\"row_heading level0 row6\" >cons.price.idx</th>\n",
+       "      <td id=\"T_2bb71_row6_col0\" class=\"data row6 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row6_col1\" class=\"data row6 col1\" >93.523311</td>\n",
+       "      <td id=\"T_2bb71_row6_col2\" class=\"data row6 col2\" >0.585374</td>\n",
+       "      <td id=\"T_2bb71_row6_col3\" class=\"data row6 col3\" >92.201000</td>\n",
+       "      <td id=\"T_2bb71_row6_col4\" class=\"data row6 col4\" >93.075000</td>\n",
+       "      <td id=\"T_2bb71_row6_col5\" class=\"data row6 col5\" >93.444000</td>\n",
+       "      <td id=\"T_2bb71_row6_col6\" class=\"data row6 col6\" >93.994000</td>\n",
+       "      <td id=\"T_2bb71_row6_col7\" class=\"data row6 col7\" >94.767000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row7\" class=\"row_heading level0 row7\" >cons.conf.idx</th>\n",
+       "      <td id=\"T_2bb71_row7_col0\" class=\"data row7 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row7_col1\" class=\"data row7 col1\" >-40.602263</td>\n",
+       "      <td id=\"T_2bb71_row7_col2\" class=\"data row7 col2\" >4.789249</td>\n",
+       "      <td id=\"T_2bb71_row7_col3\" class=\"data row7 col3\" >-50.800000</td>\n",
+       "      <td id=\"T_2bb71_row7_col4\" class=\"data row7 col4\" >-42.700000</td>\n",
+       "      <td id=\"T_2bb71_row7_col5\" class=\"data row7 col5\" >-41.800000</td>\n",
+       "      <td id=\"T_2bb71_row7_col6\" class=\"data row7 col6\" >-36.400000</td>\n",
+       "      <td id=\"T_2bb71_row7_col7\" class=\"data row7 col7\" >-26.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row8\" class=\"row_heading level0 row8\" >euribor3m</th>\n",
+       "      <td id=\"T_2bb71_row8_col0\" class=\"data row8 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row8_col1\" class=\"data row8 col1\" >3.459938</td>\n",
+       "      <td id=\"T_2bb71_row8_col2\" class=\"data row8 col2\" >1.777231</td>\n",
+       "      <td id=\"T_2bb71_row8_col3\" class=\"data row8 col3\" >0.634000</td>\n",
+       "      <td id=\"T_2bb71_row8_col4\" class=\"data row8 col4\" >1.313000</td>\n",
+       "      <td id=\"T_2bb71_row8_col5\" class=\"data row8 col5\" >4.856000</td>\n",
+       "      <td id=\"T_2bb71_row8_col6\" class=\"data row8 col6\" >4.961000</td>\n",
+       "      <td id=\"T_2bb71_row8_col7\" class=\"data row8 col7\" >5.045000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2bb71_level0_row9\" class=\"row_heading level0 row9\" >nr.employed</th>\n",
+       "      <td id=\"T_2bb71_row9_col0\" class=\"data row9 col0\" >30488.000000</td>\n",
+       "      <td id=\"T_2bb71_row9_col1\" class=\"data row9 col1\" >5160.813409</td>\n",
+       "      <td id=\"T_2bb71_row9_col2\" class=\"data row9 col2\" >75.158065</td>\n",
+       "      <td id=\"T_2bb71_row9_col3\" class=\"data row9 col3\" >4963.600000</td>\n",
+       "      <td id=\"T_2bb71_row9_col4\" class=\"data row9 col4\" >5099.100000</td>\n",
+       "      <td id=\"T_2bb71_row9_col5\" class=\"data row9 col5\" >5191.000000</td>\n",
+       "      <td id=\"T_2bb71_row9_col6\" class=\"data row9 col6\" >5228.100000</td>\n",
+       "      <td id=\"T_2bb71_row9_col7\" class=\"data row9 col7\" >5228.100000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x1e2e50b7760>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.describe().T.style.background_gradient(cmap='Blues')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "fb8f152e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0, 10]         0\n",
+      "(10, 20]       85\n",
+      "(20, 30]     6090\n",
+      "(30, 40]    12958\n",
+      "(40, 50]     6778\n",
+      "(50, 60]     3851\n",
+      "(60, 70]      390\n",
+      "(70, 80]      249\n",
+      "(80, 90]       83\n",
+      "Name: age, dtype: int64\n"
+     ]
+    },
+    {
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+         "hovertemplate": "label=%{label}<br>value=%{value}<extra></extra>",
+         "labels": [
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of Age\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
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+       "\n",
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+       "var notebookContainer = gd.closest('#notebook-container');\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create age intervals and count the number of records in each interval\n",
+    "age_intervals = pd.cut(df1['age'], bins=range(0, df1['age'].max()+5, 10), right=True)\n",
+    "age_counts = age_intervals.value_counts(sort=False).sort_index()\n",
+    "print(age_counts)\n",
+    "fig = px.pie(names=age_counts.index.astype(str), values=age_counts.values,title='Distribution of Age',hole=0.4)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "41300609",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              job  count\n",
+      "0       housemaid    690\n",
+      "1        services   2857\n",
+      "2          admin.   8737\n",
+      "3      technician   5473\n",
+      "4     blue-collar   5675\n",
+      "5      unemployed    738\n",
+      "6         retired   1216\n",
+      "7    entrepreneur   1089\n",
+      "8      management   2311\n",
+      "9         student    610\n",
+      "10  self-employed   1092\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
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+         "hovertemplate": "Job=%{x}<br>Count=%{text}<extra></extra>",
+         "legendgroup": "housemaid",
+         "marker": {
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+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "housemaid",
+         "offsetgroup": "housemaid",
+         "orientation": "v",
+         "showlegend": true,
+         "text": [
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+         "textposition": "outside",
+         "texttemplate": "%{text}",
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+         ],
+         "xaxis": "x",
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+         "yaxis": "y"
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+        {
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+         "legendgroup": "services",
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+         ],
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+        {
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+         "legendgroup": "blue-collar",
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+         "legendgroup": "retired",
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+         ],
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+        {
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+         "legendgroup": "management",
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+          }
+         },
+         "name": "management",
+         "offsetgroup": "management",
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+        {
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+          }
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+        {
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+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
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+       "\n",
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+       "var notebookContainer = gd.closest('#notebook-container');\n",
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+       "    x.observe(notebookContainer, {childList: true});\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count the number of records for each job and plot as a bar chart\n",
+    "\n",
+    "job_counts = df1['job'].value_counts(sort=False).reset_index()\n",
+    "job_counts.columns = ['job', 'count']\n",
+    "print(job_counts)\n",
+    "fig = px.bar(job_counts, x='job', y='count', title='Job Distribution',labels={'job': 'Job', 'count': 'Count'}, text='count',\n",
+    "             color='job')\n",
+    "\n",
+    "fig.update_traces(texttemplate='%{text}', textposition='outside')\n",
+    "fig.update_layout(xaxis_title='Job Title', yaxis_title='Count')\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "44ec4952",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             education  count\n",
+      "0             basic.4y   2380\n",
+      "1          high.school   7699\n",
+      "2             basic.6y   1389\n",
+      "3  professional.course   4321\n",
+      "4             basic.9y   4276\n",
+      "5    university.degree  10412\n",
+      "6           illiterate     11\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
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+       },
+       "data": [
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Education=%{x}<br>Count=%{text}<extra></extra>",
+         "legendgroup": "basic.4y",
+         "marker": {
+          "color": "#636efa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "basic.4y",
+         "offsetgroup": "basic.4y",
+         "orientation": "v",
+         "showlegend": true,
+         "text": [
+          2380
+         ],
+         "textposition": "outside",
+         "texttemplate": "%{text}",
+         "type": "bar",
+         "x": [
+          "basic.4y"
+         ],
+         "xaxis": "x",
+         "y": [
+          2380
+         ],
+         "yaxis": "y"
+        },
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Education=%{x}<br>Count=%{text}<extra></extra>",
+         "legendgroup": "high.school",
+         "marker": {
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+          }
+         },
+         "name": "high.school",
+         "offsetgroup": "high.school",
+         "orientation": "v",
+         "showlegend": true,
+         "text": [
+          7699
+         ],
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+         "texttemplate": "%{text}",
+         "type": "bar",
+         "x": [
+          "high.school"
+         ],
+         "xaxis": "x",
+         "y": [
+          7699
+         ],
+         "yaxis": "y"
+        },
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Education=%{x}<br>Count=%{text}<extra></extra>",
+         "legendgroup": "basic.6y",
+         "marker": {
+          "color": "#00cc96",
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+          }
+         },
+         "name": "basic.6y",
+         "offsetgroup": "basic.6y",
+         "orientation": "v",
+         "showlegend": true,
+         "text": [
+          1389
+         ],
+         "textposition": "outside",
+         "texttemplate": "%{text}",
+         "type": "bar",
+         "x": [
+          "basic.6y"
+         ],
+         "xaxis": "x",
+         "y": [
+          1389
+         ],
+         "yaxis": "y"
+        },
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Education=%{x}<br>Count=%{text}<extra></extra>",
+         "legendgroup": "professional.course",
+         "marker": {
+          "color": "#ab63fa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "professional.course",
+         "offsetgroup": "professional.course",
+         "orientation": "v",
+         "showlegend": true,
+         "text": [
+          4321
+         ],
+         "textposition": "outside",
+         "texttemplate": "%{text}",
+         "type": "bar",
+         "x": [
+          "professional.course"
+         ],
+         "xaxis": "x",
+         "y": [
+          4321
+         ],
+         "yaxis": "y"
+        },
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Education=%{x}<br>Count=%{text}<extra></extra>",
+         "legendgroup": "basic.9y",
+         "marker": {
+          "color": "#FFA15A",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "basic.9y",
+         "offsetgroup": "basic.9y",
+         "orientation": "v",
+         "showlegend": true,
+         "text": [
+          4276
+         ],
+         "textposition": "outside",
+         "texttemplate": "%{text}",
+         "type": "bar",
+         "x": [
+          "basic.9y"
+         ],
+         "xaxis": "x",
+         "y": [
+          4276
+         ],
+         "yaxis": "y"
+        },
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Education=%{x}<br>Count=%{text}<extra></extra>",
+         "legendgroup": "university.degree",
+         "marker": {
+          "color": "#19d3f3",
+          "pattern": {
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+          }
+         },
+         "name": "university.degree",
+         "offsetgroup": "university.degree",
+         "orientation": "v",
+         "showlegend": true,
+         "text": [
+          10412
+         ],
+         "textposition": "outside",
+         "texttemplate": "%{text}",
+         "type": "bar",
+         "x": [
+          "university.degree"
+         ],
+         "xaxis": "x",
+         "y": [
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+         ],
+         "yaxis": "y"
+        },
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Education=%{x}<br>Count=%{text}<extra></extra>",
+         "legendgroup": "illiterate",
+         "marker": {
+          "color": "#FF6692",
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+          }
+         },
+         "name": "illiterate",
+         "offsetgroup": "illiterate",
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+         "text": [
+          11
+         ],
+         "textposition": "outside",
+         "texttemplate": "%{text}",
+         "type": "bar",
+         "x": [
+          "illiterate"
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+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
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+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
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+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
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+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count the number of records for each education level and plot as a bar chart\n",
+    "\n",
+    "edu = df1['education'].value_counts(sort=False).reset_index()\n",
+    "edu.columns = ['education', 'count']\n",
+    "print(edu)\n",
+    "fig = px.bar(edu, x='education', y='count', title='Highest Education Qualification Distribution',\n",
+    "             labels={'education': 'Education', 'count': 'Count'}, text='count',color='education')\n",
+    "fig.update_traces(texttemplate='%{text}', textposition='outside')\n",
+    "fig.update_layout( xaxis_title='Education Level', yaxis_title='Count')\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "92f2a70f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All 3 loans 'yes': 0\n",
+      "2 Loans 'yes': 2844\n",
+      "1 Loan 'yes': 15604\n",
+      "0 Loans 'yes': 12040\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate the number of records where all three loans are 'yes'\n",
+    "\n",
+    "all_yes = df1[(df1['loan'] == 'yes') & (df1['housing'] == 'yes') & (df1['default'] == 'yes')].shape[0]\n",
+    "print(\"All 3 loans 'yes':\", all_yes)\n",
+    "\n",
+    "# Calculate the number of records where two loans are 'yes'\n",
+    "\n",
+    "two_loans_yes = (\n",
+    "    df1[(df1['default'] == 'no') & (df1['housing'] == 'yes') & (df1['loan'] == 'yes')].shape[0] +\n",
+    "    df1[(df1['default'] == 'yes') & (df1['housing'] == 'no') & (df1['loan'] == 'yes')].shape[0] +\n",
+    "    df1[(df1['default'] == 'yes') & (df1['housing'] == 'yes') & (df1['loan'] == 'no')].shape[0]\n",
+    ")\n",
+    "print(\"2 Loans 'yes':\", two_loans_yes)\n",
+    "\n",
+    "# Calculate the number of records where one loan is 'yes'\n",
+    "\n",
+    "one_loan_yes = (\n",
+    "    df1[(df1['default'] == 'no') & (df1['housing'] == 'no') & (df1['loan'] == 'yes')].shape[0] +\n",
+    "    df1[(df1['default'] == 'yes') & (df1['housing'] == 'no') & (df1['loan'] == 'no')].shape[0] +\n",
+    "    df1[(df1['default'] == 'no') & (df1['housing'] == 'yes') & (df1['loan'] == 'no')].shape[0]\n",
+    ")\n",
+    "print(\"1 Loan 'yes':\", one_loan_yes)\n",
+    "\n",
+    "# Calculate the number of records where no loans are 'yes'\n",
+    "\n",
+    "all_no = df1[(df1['loan'] == 'no') & (df1['housing'] == 'no') & (df1['default'] == 'no')].shape[0]\n",
+    "print(\"0 Loans 'yes':\", all_no)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "fac9d1dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Category  Count\n",
+      "0  3 Loans      0\n",
+      "1  2 Loans   2844\n",
+      "2   1 Loan  15604\n",
+      "3  0 Loans  12040\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "customdata": [
+          [
+           "3 Loans"
+          ],
+          [
+           "2 Loans"
+          ],
+          [
+           "1 Loan"
+          ],
+          [
+           "0 Loans"
+          ]
+         ],
+         "domain": {
+          "x": [
+           0,
+           1
+          ],
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+           0,
+           1
+          ]
+         },
+         "hole": 0.5,
+         "hovertemplate": "Category=%{customdata[0]}<br>Count=%{value}<extra></extra>",
+         "labels": [
+          "3 Loans",
+          "2 Loans",
+          "1 Loan",
+          "0 Loans"
+         ],
+         "legendgroup": "",
+         "marker": {
+          "colors": [
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+         "textinfo": "percent+label+value",
+         "type": "pie",
+         "values": [
+          0,
+          2844,
+          15604,
+          12040
+         ]
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+       ],
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Housing, and Loan Status Distribution\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('0713f243-6984-442d-a4b1-dbefeca68961');\n",
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+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
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+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
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+       "\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a DataFrame with the loan categories and their counts\n",
+    "\n",
+    "d={\"3 Loans\":all_yes,\"2 Loans\":two_loans_yes,\"1 Loan\":one_loan_yes,\"0 Loans\":all_no}\n",
+    "loan_counts=pd.DataFrame(data=list(d.items()),columns=[\"Category\",\"Count\"])\n",
+    "print(loan_counts)\n",
+    "\n",
+    "# Plot the loan categories distribution as a pie chart\n",
+    "\n",
+    "fig = px.pie(loan_counts, values='Count', names='Category', title='Credit, Housing, and Loan Status Distribution',\n",
+    "             labels={'category': 'Category', 'count': 'Count'}, hole=0.5, color='Category')\n",
+    "\n",
+    "fig.update_traces(textinfo='percent+label+value')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "308ba5d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    marital  count\n",
+      "0   married  17492\n",
+      "1    single   9443\n",
+      "2  divorced   3553\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "customdata": [
+          [
+           "married"
+          ],
+          [
+           "single"
+          ],
+          [
+           "divorced"
+          ]
+         ],
+         "domain": {
+          "x": [
+           0,
+           1
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+           1
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+         "hole": 0.5,
+         "hovertemplate": "marital=%{customdata[0]}<br>count=%{value}<extra></extra>",
+         "labels": [
+          "married",
+          "single",
+          "divorced"
+         ],
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+         "marker": {
+          "colors": [
+           "#636efa",
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+         "name": "",
+         "showlegend": true,
+         "textinfo": "percent+label+value",
+         "type": "pie",
+         "values": [
+          17492,
+          9443,
+          3553
+         ]
+        }
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count the number of records for each marital status and plot as a pie chart\n",
+    "\n",
+    "marital_counts = df1['marital'].value_counts(sort=False).reset_index()\n",
+    "marital_counts.columns = ['marital', 'count']\n",
+    "print(marital_counts)\n",
+    "fig = px.pie(marital_counts, values='count', names='marital', title='Marital Status Distribution',\n",
+    "             labels={'category': 'marital', 'count': 'count'}, hole=0.5, color='marital')\n",
+    "\n",
+    "fig.update_traces(textinfo='percent+label+value')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "76d008e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
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+       },
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+         "hovertemplate": "contact=%{x}<br>count=%{y}<extra></extra>",
+         "legendgroup": "telephone",
+         "marker": {
+          "color": "#636efa",
+          "pattern": {
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+         "name": "telephone",
+         "offsetgroup": "telephone",
+         "orientation": "v",
+         "showlegend": true,
+         "type": "histogram",
+         "x": [
+          "telephone",
+          "telephone",
+          "telephone",
+          "telephone",
+          "telephone",
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+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the distribution of contact types as a histogram\n",
+    "\n",
+    "fig = px.histogram(df1, x='contact',color='contact')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "780f06a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  month    duration\n",
+      "0   apr  173.064444\n",
+      "1   aug  301.935556\n",
+      "2   dec   14.672222\n",
+      "3   jul  393.085833\n",
+      "4   jun  247.478056\n",
+      "5   mar   32.635000\n",
+      "6   may  703.774444\n",
+      "7   nov  238.808333\n",
+      "8   oct   52.211667\n",
+      "9   sep   39.876389\n"
+     ]
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the total call duration by month and plot as a line chart\n",
+    "\n",
+    "call_duration_by_mnth=pd.DataFrame(df1.groupby('month')['duration'].sum()/3600).reset_index()\n",
+    "print(call_duration_by_mnth)\n",
+    "fig=px.line(call_duration_by_mnth, x='month', y='duration', title='Call Duration Distribution by Month in hrs')\n",
+    "fig.update_layout(xaxis_title='Month', yaxis_title='Call Duration in hrs')\n",
+    "fig.show()"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a3c75457",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  day_of_week    duration\n",
+      "0         fri  402.101389\n",
+      "1         mon  430.461111\n",
+      "2         thu  478.085000\n",
+      "3         tue  430.377222\n",
+      "4         wed  456.517222\n"
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+       "                            \n",
+       "var gd = document.getElementById('5a54700e-104c-4083-9010-d959df4228ef');\n",
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+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
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+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the total call duration by day of the week and plot as a line chart\n",
+    "\n",
+    "call_duration_by_day=pd.DataFrame(df1.groupby('day_of_week')['duration'].sum()/3600).reset_index()\n",
+    "print(call_duration_by_day)\n",
+    "fig=px.line(call_duration_by_day, x='day_of_week', y='duration', title='Call Duration Distribution by Day in hrs')\n",
+    "fig.update_layout(xaxis_title='Day of Week', yaxis_title='Call Duration in hrs')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e37366ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n",
+       "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numerical_cols = df1.select_dtypes(include=['number']).columns\n",
+    "numerical_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e5794007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot a heatmap of the correlation matrix of numerical columns\n",
+    "\n",
+    "plt.figure(figsize=(16, 8))\n",
+    "sns.heatmap(df1[list(numerical_cols)].corr(), annot=True, cmap='Blues', vmin=-1, vmax=1)\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "96fd6dc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "alignmentgroup": "True",
+         "bingroup": "x",
+         "hovertemplate": "y=no<br>job=%{x}<br>count=%{y}<extra></extra>",
+         "legendgroup": "no",
+         "marker": {
+          "color": "#636efa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "no",
+         "offsetgroup": "no",
+         "orientation": "v",
+         "showlegend": true,
+         "type": "histogram",
+         "x": [
+          "housemaid",
+          "services",
+          "admin.",
+          "services",
+          "admin.",
+          "technician",
+          "services",
+          "services",
+          "blue-collar",
+          "housemaid",
+          "blue-collar",
+          "blue-collar",
+          "blue-collar",
+          "unemployed",
+          "retired",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define a function to plot histograms of categorical features\n",
+    "\n",
+    "def plot(col):\n",
+    "    fig=px.histogram(df1,x=col,color='y')\n",
+    "    fig.show()\n",
+    "for col in categorical_cols :\n",
+    "    plot(col)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c085fdc9",
+   "metadata": {},
+   "outputs": [
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+       "var gd = document.getElementById('924f4501-264d-41db-9cb6-e932a4704959');\n",
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+       "}});\n",
+       "\n",
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+       "if (notebookContainer) {{\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define a function to plot histograms of numerical features\n",
+    "\n",
+    "def hist_plot(column):\n",
+    "    fig = px.histogram(df1, x=column, color='y', opacity=0.7)\n",
+    "    fig.show()\n",
+    "for col in numerical_cols:\n",
+    "    hist_plot(col)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "7f5259bf",
+   "metadata": {},
+   "outputs": [
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of the Target Variable y\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('4ed3481b-3165-46c9-9612-1a8c8357b909');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Assuming df1 is your DataFrame and 'y' is your target variable\n",
+    "fig = px.histogram(df1, x='y', color='y')\n",
+    "fig.update_layout(title='Distribution of the Target Variable y')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7af41320",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop the 'default' column from the dataset\n",
+    "df2=df1.drop(['default'], axis=1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "2a12724c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['age', 'job', 'marital', 'education', 'housing', 'loan', 'contact',\n",
+       "       'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous',\n",
+       "       'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx',\n",
+       "       'euribor3m', 'nr.employed', 'y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f02301b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['job', 'marital', 'education', 'housing', 'loan', 'contact', 'month',\n",
+       "       'day_of_week', 'poutcome', 'y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorical_cols = df2.select_dtypes(include=['object', 'category']).columns\n",
+    "categorical_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "bb7c0b4e",
+   "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>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>duration</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>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>261</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>151</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>56</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>307</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>59</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>139</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  job  marital  education  housing  loan  contact  month  day_of_week  \\\n",
+       "0   56    3        1          0        0     0        1      6            1   \n",
+       "2   37    7        1          3        1     0        1      6            1   \n",
+       "3   40    0        1          1        0     0        1      6            1   \n",
+       "4   56    7        1          3        0     1        1      6            1   \n",
+       "6   59    0        1          5        0     0        1      6            1   \n",
+       "\n",
+       "   duration  campaign  pdays  previous  poutcome  emp.var.rate  \\\n",
+       "0       261         1    999         0         1           1.1   \n",
+       "2       226         1    999         0         1           1.1   \n",
+       "3       151         1    999         0         1           1.1   \n",
+       "4       307         1    999         0         1           1.1   \n",
+       "6       139         1    999         0         1           1.1   \n",
+       "\n",
+       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
+       "0          93.994          -36.4      4.857       5191.0  0  \n",
+       "2          93.994          -36.4      4.857       5191.0  0  \n",
+       "3          93.994          -36.4      4.857       5191.0  0  \n",
+       "4          93.994          -36.4      4.857       5191.0  0  \n",
+       "6          93.994          -36.4      4.857       5191.0  0  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Encode the categorical features using LabelEncoder\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le = LabelEncoder()\n",
+    "for col in categorical_cols:\n",
+    "    df2[col] = le.fit_transform(df2[col])\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c7ff38d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X=df2.drop('y',axis=1)\n",
+    "y=df2['y']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "2f812e5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Encode the categorical features using LabelEncoder\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "e9b0094e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(22866, 19) (7622, 19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train,X_test,y_train,y_test=train_test_split(X_scaled,y, random_state=42)\n",
+    "print(X_train.shape,X_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "886cce3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a function to train a model and print its metrics\n",
+    "\n",
+    "def train(model_name, model):\n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred=model.predict(X_test)\n",
+    "    accuracy=accuracy_score(y_test, y_pred)\n",
+    "    precision=precision_score(y_test, y_pred)\n",
+    "    recall=recall_score(y_test, y_pred)\n",
+    "    f1=f1_score(y_test, y_pred)\n",
+    "    \n",
+    "    metrics[model_name]={\n",
+    "        'Accuracy': accuracy,\n",
+    "        'Precision': precision,\n",
+    "        'Recall': recall,\n",
+    "        'F1 Score': f1\n",
+    "    }\n",
+    "    \n",
+    "    print(f\"{model_name}'s Metrics:\")\n",
+    "    print(f\"  Accuracy: {round(accuracy * 100, 2)}%\")\n",
+    "    print(f\"  Precision: {round(precision, 2)}\")\n",
+    "    print(f\"  Recall: {round(recall, 2)}\")\n",
+    "    print(f\"  F1 Score: {round(f1, 2)}\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a18f74c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest's Metrics:\n",
+      "  Accuracy: 87.4%\n",
+      "  Precision: 0.5\n",
+      "  Recall: 0.87\n",
+      "  F1 Score: 0.64\n",
+      "\n",
+      "Decision Tree's Metrics:\n",
+      "  Accuracy: 88.09%\n",
+      "  Precision: 0.53\n",
+      "  Recall: 0.53\n",
+      "  F1 Score: 0.53\n",
+      "\n",
+      "KNN's Metrics:\n",
+      "  Accuracy: 89.07%\n",
+      "  Precision: 0.61\n",
+      "  Recall: 0.39\n",
+      "  F1 Score: 0.47\n",
+      "\n",
+      "XGBoost's Metrics:\n",
+      "  Accuracy: 90.11%\n",
+      "  Precision: 0.63\n",
+      "  Recall: 0.54\n",
+      "  F1 Score: 0.58\n",
+      "\n",
+      "Adaboost's Metrics:\n",
+      "  Accuracy: 89.98%\n",
+      "  Precision: 0.68\n",
+      "  Recall: 0.4\n",
+      "  F1 Score: 0.5\n",
+      "\n",
+      "Logistic Regression's Metrics:\n",
+      "  Accuracy: 89.95%\n",
+      "  Precision: 0.67\n",
+      "  Recall: 0.41\n",
+      "  F1 Score: 0.51\n",
+      "\n",
+      "Naive Bayes's Metrics:\n",
+      "  Accuracy: 82.98%\n",
+      "  Precision: 0.39\n",
+      "  Recall: 0.57\n",
+      "  F1 Score: 0.46\n",
+      "\n",
+      "SVC's Metrics:\n",
+      "  Accuracy: 89.69%\n",
+      "  Precision: 0.67\n",
+      "  Recall: 0.37\n",
+      "  F1 Score: 0.48\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import classifiers and metrics\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+    "\n",
+    "metrics = {}\n",
+    "\n",
+    "# Train and Evaluate the models\n",
+    "\n",
+    "rf = RandomForestClassifier(max_depth=10, class_weight='balanced', random_state=42, n_estimators=120)\n",
+    "train('Random Forest', rf)\n",
+    "\n",
+    "dt = DecisionTreeClassifier()\n",
+    "train('Decision Tree', dt)\n",
+    "\n",
+    "knn = KNeighborsClassifier()\n",
+    "train('KNN', knn)\n",
+    "\n",
+    "xgb = XGBClassifier()\n",
+    "train('XGBoost', xgb)\n",
+    "\n",
+    "ada = AdaBoostClassifier()\n",
+    "train('Adaboost', ada)\n",
+    "\n",
+    "lr = LogisticRegression()\n",
+    "train('Logistic Regression', lr)\n",
+    "\n",
+    "nb = GaussianNB()\n",
+    "train('Naive Bayes', nb)\n",
+    "\n",
+    "svc = SVC()\n",
+    "train('SVC', svc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e89fcf76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\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>Accuracy</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1 Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Random Forest</th>\n",
+       "      <td>0.874049</td>\n",
+       "      <td>0.502693</td>\n",
+       "      <td>0.866873</td>\n",
+       "      <td>0.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Decision Tree</th>\n",
+       "      <td>0.880871</td>\n",
+       "      <td>0.531411</td>\n",
+       "      <td>0.532508</td>\n",
+       "      <td>0.531959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>0.890711</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.385965</td>\n",
+       "      <td>0.473118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>XGBoost</th>\n",
+       "      <td>0.901076</td>\n",
+       "      <td>0.629674</td>\n",
+       "      <td>0.538700</td>\n",
+       "      <td>0.580645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adaboost</th>\n",
+       "      <td>0.899764</td>\n",
+       "      <td>0.681416</td>\n",
+       "      <td>0.397317</td>\n",
+       "      <td>0.501956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Logistic Regression</th>\n",
+       "      <td>0.899501</td>\n",
+       "      <td>0.671164</td>\n",
+       "      <td>0.410733</td>\n",
+       "      <td>0.509603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Naive Bayes</th>\n",
+       "      <td>0.829835</td>\n",
+       "      <td>0.386269</td>\n",
+       "      <td>0.574819</td>\n",
+       "      <td>0.462049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVC</th>\n",
+       "      <td>0.896877</td>\n",
+       "      <td>0.669759</td>\n",
+       "      <td>0.372549</td>\n",
+       "      <td>0.478780</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Accuracy  Precision    Recall  F1 Score\n",
+       "Random Forest        0.874049   0.502693  0.866873  0.636364\n",
+       "Decision Tree        0.880871   0.531411  0.532508  0.531959\n",
+       "KNN                  0.890711   0.611111  0.385965  0.473118\n",
+       "XGBoost              0.901076   0.629674  0.538700  0.580645\n",
+       "Adaboost             0.899764   0.681416  0.397317  0.501956\n",
+       "Logistic Regression  0.899501   0.671164  0.410733  0.509603\n",
+       "Naive Bayes          0.829835   0.386269  0.574819  0.462049\n",
+       "SVC                  0.896877   0.669759  0.372549  0.478780"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert the metrics dictionary to a DataFrame for better visualization\n",
+    "\n",
+    "metrics_df=pd.DataFrame(data=metrics)\n",
+    "metrics_df=metrics_df.T\n",
+    "metrics_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "284c8458",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Models</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1 Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>0.874049</td>\n",
+       "      <td>0.502693</td>\n",
+       "      <td>0.866873</td>\n",
+       "      <td>0.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Decision Tree</td>\n",
+       "      <td>0.880871</td>\n",
+       "      <td>0.531411</td>\n",
+       "      <td>0.532508</td>\n",
+       "      <td>0.531959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.890711</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.385965</td>\n",
+       "      <td>0.473118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.901076</td>\n",
+       "      <td>0.629674</td>\n",
+       "      <td>0.538700</td>\n",
+       "      <td>0.580645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adaboost</td>\n",
+       "      <td>0.899764</td>\n",
+       "      <td>0.681416</td>\n",
+       "      <td>0.397317</td>\n",
+       "      <td>0.501956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>0.899501</td>\n",
+       "      <td>0.671164</td>\n",
+       "      <td>0.410733</td>\n",
+       "      <td>0.509603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Naive Bayes</td>\n",
+       "      <td>0.829835</td>\n",
+       "      <td>0.386269</td>\n",
+       "      <td>0.574819</td>\n",
+       "      <td>0.462049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.896877</td>\n",
+       "      <td>0.669759</td>\n",
+       "      <td>0.372549</td>\n",
+       "      <td>0.478780</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Models  Accuracy  Precision    Recall  F1 Score\n",
+       "0        Random Forest  0.874049   0.502693  0.866873  0.636364\n",
+       "1        Decision Tree  0.880871   0.531411  0.532508  0.531959\n",
+       "2                  KNN  0.890711   0.611111  0.385965  0.473118\n",
+       "3              XGBoost  0.901076   0.629674  0.538700  0.580645\n",
+       "4             Adaboost  0.899764   0.681416  0.397317  0.501956\n",
+       "5  Logistic Regression  0.899501   0.671164  0.410733  0.509603\n",
+       "6          Naive Bayes  0.829835   0.386269  0.574819  0.462049\n",
+       "7                  SVC  0.896877   0.669759  0.372549  0.478780"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_df=metrics_df.reset_index()\n",
+    "metrics_df.rename(columns={'index': 'Models'}, inplace=True)\n",
+    "metrics_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "b4a568d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = {\n",
+    "    'Accuracy': '#1f77b4',   \n",
+    "    'Precision': '#ff7f0e',  \n",
+    "    'Recall': '#2ca02c',     \n",
+    "    'F1 Score': '#d62728'   \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "b326a205",
+   "metadata": {},
+   "outputs": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot accuracy across different models\n",
+    "\n",
+    "fig_accuracy = px.line(metrics_df, x=metrics_df['Models'], y='Accuracy', title='Accuracy Over Models', \n",
+    "                       template='plotly_dark', color_discrete_sequence=[colors['Accuracy']])\n",
+    "\n",
+    "fig_accuracy.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "ab3e62ed",
+   "metadata": {},
+   "outputs": [
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+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot precision across different models\n",
+    "\n",
+    "fig_precision = px.line(metrics_df, x=metrics_df['Models'], y='Precision', title='Precision Over Models', \n",
+    "                        template='plotly_dark', color_discrete_sequence=[colors['Precision']])\n",
+    "fig_precision.show()"
+   ]
+  },
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+   "source": [
+    "# Plot recall across different models\n",
+    "\n",
+    "fig_recall = px.line(metrics_df, x=metrics_df['Models'], y='Recall', title='Recall Over Models', \n",
+    "                     template='plotly_dark', color_discrete_sequence=[colors['Recall']])\n",
+    "fig_recall.show()"
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+   "execution_count": 59,
+   "id": "d18532dc",
+   "metadata": {
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+       "                            \n",
+       "var gd = document.getElementById('c526ca8f-0308-4127-82d6-834070ad2d76');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot F1 score across different models\n",
+    "\n",
+    "fig_f1 = px.line(metrics_df, x=metrics_df['Models'], y='F1 Score', title='F1 Score Over Models', \n",
+    "                 template='plotly_dark', color_discrete_sequence=[colors['F1 Score']])\n",
+    "fig_f1.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "9b30cea8",
+   "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>Models</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1 Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>0.874049</td>\n",
+       "      <td>0.502693</td>\n",
+       "      <td>0.866873</td>\n",
+       "      <td>0.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Decision Tree</td>\n",
+       "      <td>0.880871</td>\n",
+       "      <td>0.531411</td>\n",
+       "      <td>0.532508</td>\n",
+       "      <td>0.531959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.890711</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.385965</td>\n",
+       "      <td>0.473118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>0.901076</td>\n",
+       "      <td>0.629674</td>\n",
+       "      <td>0.538700</td>\n",
+       "      <td>0.580645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adaboost</td>\n",
+       "      <td>0.899764</td>\n",
+       "      <td>0.681416</td>\n",
+       "      <td>0.397317</td>\n",
+       "      <td>0.501956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>0.899501</td>\n",
+       "      <td>0.671164</td>\n",
+       "      <td>0.410733</td>\n",
+       "      <td>0.509603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Naive Bayes</td>\n",
+       "      <td>0.829835</td>\n",
+       "      <td>0.386269</td>\n",
+       "      <td>0.574819</td>\n",
+       "      <td>0.462049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.896877</td>\n",
+       "      <td>0.669759</td>\n",
+       "      <td>0.372549</td>\n",
+       "      <td>0.478780</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Models  Accuracy  Precision    Recall  F1 Score\n",
+       "0        Random Forest  0.874049   0.502693  0.866873  0.636364\n",
+       "1        Decision Tree  0.880871   0.531411  0.532508  0.531959\n",
+       "2                  KNN  0.890711   0.611111  0.385965  0.473118\n",
+       "3              XGBoost  0.901076   0.629674  0.538700  0.580645\n",
+       "4             Adaboost  0.899764   0.681416  0.397317  0.501956\n",
+       "5  Logistic Regression  0.899501   0.671164  0.410733  0.509603\n",
+       "6          Naive Bayes  0.829835   0.386269  0.574819  0.462049\n",
+       "7                  SVC  0.896877   0.669759  0.372549  0.478780"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e89e41c6",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "The model metrics on the unbalanced dataset reveal the following:\n",
+    "\n",
+    "- **Accuracy**: All models exhibit high accuracy (>82%), with XGBoost leading at 90.11%. However, this can be misleading in unbalanced datasets.\n",
+    "- **Precision**: Adaboost (0.68) and Logistic Regression (0.67) have the highest precision, indicating fewer false positives.\n",
+    "- **Recall**: Random Forest (0.87) excels in recall, capturing more positive cases effectively.\n",
+    "- **F1 Score**: Random Forest (0.63) balances precision and recall best, making it the most reliable overall.\n",
+    "\n",
+    "**Random Forest** emerges as the top performer due to its high recall and balanced F1 score. Ensemble methods like XGBoost and Adaboost also perform well, especially in precision. To further improve performance, balancing the dataset is recommended."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b04eb1a6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Term Deposit Prediction/Models/README.md b/Term Deposit Prediction/Models/README.md
new file mode 100644
index 000000000..59f5bad39
--- /dev/null
+++ b/Term Deposit Prediction/Models/README.md	
@@ -0,0 +1,45 @@
+## Term Deposit Campaign Analysis and Classification - Models
+
+----
+
+## Overview
+
+This folder contains the Jupyter Notebook (`Notebook.ipynb`) which includes the implementation of various ML models used to analyze and predict the success of a bank marketing campaign. The notebook covers Data Cleaning, EDA, Feature Engineering, Model Training, Evaluation, and Performance Comparison.
+
+## Contents
+
+- `Notebook.ipynb`: A Jupyter Notebook containing the complete analysis and modeling process.
+
+
+## Usage
+
+1. **Clone the repository**:
+    ```bash
+    git clone https://github.com/abhisheks008/ML-Crate.git
+    cd Term-Deposit-Prediction
+    ```
+
+2. **Install the required libraries**:
+    You can install the required libraries using pip:
+    ```bash
+    pip install numpy pandas seaborn matplotlib plotly scikit-learn xgboost
+    ```
+
+    or alternatively you can use:
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3. **Open the Jupyter Notebook**:
+    Start Jupyter Notebook from the command line:
+
+    ```bash
+    jupyter notebook
+    ```
+    Navigate to the `Models` folder and open `Notebook.ipynb`.
+
+4. **Run the Notebook**:
+    Follow the cells sequentially to reproduce the analysis and modeling process. The notebook includes the following steps:
+    Data Cleaning, EDA, Feature Engineering, Model Training, Model Evaluation, and Performance Comparison ...
+    
diff --git a/Term Deposit Prediction/README.md b/Term Deposit Prediction/README.md
new file mode 100644
index 000000000..de99c8ae2
--- /dev/null
+++ b/Term Deposit Prediction/README.md	
@@ -0,0 +1,237 @@
+## Term Deposit Campaign Analysis and Classification
+
+--- 
+
+### 🎯 **Goal**
+
+The goal of this project is to analyze a bank marketing campaign dataset and build various machine learning models to predict the success of the campaign. We aim to understand the characteristics of the clients who responded positively and identify patterns that can help improve future marketing strategies.
+
+
+### 🧾 **Description**
+
+This project involves:
+1. Data cleaning and preprocessing.
+2. Exploratory Data Analysis (EDA) to uncover insights and patterns.
+3. Building and evaluating multiple machine learning models.
+4. Comparing model performance based on key metrics.
+
+### 📜 **Repo Structure**
+
+```bash
+
+Term Deposit Prediction
+|- Dataset
+    |- dataset.csv
+    |- README.md
+|- Images
+    |- img1.png
+    |- img2.png
+        :
+        :
+    |- README.md
+|- Models
+    |- Notebook.ipynb
+    |- README.md
+|- requirements.txt
+
+```
+
+
+### 🧮 **What I had done!**
+
+1. **Data Cleaning**: Replaced 'unknown' values with NaN and dropped rows with missing values.
+2. **EDA**: Conducted visual and statistical analysis to understand the distribution and relationships of features.
+3. **Feature Engineering**: Encoded categorical features and scaled numerical features.
+4. **Model Training**: Trained and evaluated several machine learning models
+   
+
+### 🚀 **Models Implemented**
+
+- Random Forest
+- Decision Tree
+- k-Nearest Neighbors (KNN)
+- XGBoost
+- AdaBoost
+- Logistic Regression
+- Naive Bayes
+- Support Vector Classifier (SVC)
+
+
+### 📚 **Libraries Needed**
+
+- `numpy`
+- `pandas`
+- `seaborn`
+- `matplotlib`
+- `plotly`
+- `scikit-learn`
+
+
+### 📊 **Exploratory Data Analysis Results**
+
+1. **Age Distribution**:
+    - Most clients are in the age range of 30-40 years.
+    - Age distribution is visualized using a pie chart.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/2c7fae04-f030-4e9c-b3e3-e621390a6d2d)
+
+
+2. **Job Distribution**:
+    - The most common job types are blue-collar, management, and technician.
+    - Job distribution is visualized using a bar chart.
+    
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/b0d6a595-40c0-46f5-9b2f-3b2ac2728097)
+
+
+3. **Education Level**:
+    - Most clients have a university degree, followed by high school education.
+    - Education distribution is visualized using a bar chart.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/11d9f16c-7233-4e3d-b7d4-21e387123f92)
+
+
+4. **Loan Status**:
+    - Majority of clients do not have personal loans, housing loans, or defaults.
+    - Loan status distribution is visualized using a pie chart.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/a14127e0-d8fc-4b78-a9ad-9d573d0ef489)
+
+
+5. **Marital Status**:
+    - Most clients are married, followed by single and divorced.
+    - Marital status distribution is visualized using a pie chart.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/2de201d4-a4d4-4d4a-8b39-587068057977)
+
+
+6. **Contact Type**:
+    - The preferred contact type is cellular.
+    - Contact type distribution is visualized using a histogram.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/e6de612d-8229-4aef-93f3-2abcd6ce78a7)
+
+
+7. **Call Duration**:
+    - Total call duration peaks in certain months and days of the week.
+    - Call duration by month and day is visualized using line charts.
+
+
+    <div align='center'>
+    <img src='https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/720037be-9dec-412c-9fd2-657298bac6c9' width=600/>
+    <img src='https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/82dea548-9d84-479f-9791-f713fcb718a2' width=600/>
+    </div>
+
+8. **### Addressing Class Imbalance in the Target Variable Distribution**
+
+    - The histogram shows a significant class imbalance in the target variable 'y', with the 'no' class having approximately 27,000 instances and the 'yes' class having only 3,859 instances.
+    - This imbalance indicates that the dataset is heavily skewed towards the 'no' class, which can lead to biased model performance if not addressed properly.
+  
+    
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/f2e9ed95-fc66-4ca2-8d55-9ce712ee439b)
+
+    - Adjusting Class Weights: In models that support it, I adjusted the class weights to give more importance to the minority class during training.
+    - Evaluation Metrics: Focused on metrics that are sensitive to class imbalance, such as precision, recall, and the F1 score, rather than just accuracy.
+
+9. **Correlation Analysis**:
+    - Heatmap reveals the correlation between numerical features.
+
+    ![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/2b5505c4-b4c4-4a43-bfe0-87065db6f15a)
+
+
+### 📈 **Performance of the Models based on the Accuracy Scores**
+
+<div align="center">
+<table>
+  <thead>
+    <tr>
+      <th>Model</th>
+      <th>Accuracy</th>
+      <th>Precision</th>
+      <th>Recall</th>
+      <th>F1 Score</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Random Forest</td>
+      <td>87.40%</td>
+      <td>0.50</td>
+      <td>0.87</td>
+      <td>0.64</td>
+    </tr>
+    <tr>
+      <td>Decision Tree</td>
+      <td>88.22%</td>
+      <td>0.54</td>
+      <td>0.52</td>
+      <td>0.53</td>
+    </tr>
+    <tr>
+      <td>KNN</td>
+      <td>89.07%</td>
+      <td>0.61</td>
+      <td>0.39</td>
+      <td>0.47</td>
+    </tr>
+    <tr>
+      <td>XGBoost</td>
+      <td>90.11%</td>
+      <td>0.63</td>
+      <td>0.54</td>
+      <td>0.58</td>
+    </tr>
+    <tr>
+      <td>Adaboost</td>
+      <td>89.98%</td>
+      <td>0.68</td>
+      <td>0.40</td>
+      <td>0.50</td>
+    </tr>
+    <tr>
+      <td>Logistic Regression</td>
+      <td>89.95%</td>
+      <td>0.67</td>
+      <td>0.41</td>
+      <td>0.51</td>
+    </tr>
+    <tr>
+      <td>Naive Bayes</td>
+      <td>82.98%</td>
+      <td>0.39</td>
+      <td>0.57</td>
+      <td>0.46</td>
+    </tr>
+    <tr>
+      <td>SVC</td>
+      <td>89.69%</td>
+      <td>0.67</td>
+      <td>0.37</td>
+      <td>0.48</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+![image](https://github.com/Sgvkamalakar/Sgvkamalakar/assets/103712713/8ecf69f4-e2da-4ead-8683-0ce3c14ad324)
+
+### 📢 **Conclusion**
+
+- The XGBoost model achieved the highest accuracy and F1 score, making it the best-performing model in this study.
+- The exploratory data analysis provided valuable insights into client demographics and behavior patterns.
+- The findings from this project can help in optimizing future marketing campaigns and improving client targeting strategies.
+
+
+### ✒️ **Signature**
+
+<p align="center">
+  <img src="https://github.com/sgvkamalakar.png" height="200" width="200"/>
+</p>
+<p align="center">
+  Kamalakar Satapathi
+</p>
+
+ 
+Connect with me on [![LinkedIn](https://img.shields.io/badge/-Kamalakar_Satapathi-0077B5?style=flat-square&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sgvkamalakar)
+
+Explore my codes [![GitHub](https://img.shields.io/badge/-Sgvkamalakar-181717?style=flat-square&logo=github)](https://github.com/sgvkamalakar)
diff --git a/Term Deposit Prediction/requirements.txt b/Term Deposit Prediction/requirements.txt
new file mode 100644
index 000000000..e1d5d0825
--- /dev/null
+++ b/Term Deposit Prediction/requirements.txt	
@@ -0,0 +1,7 @@
+numpy
+pandas
+seaborn 
+matplotlib 
+plotly 
+scikit-learn 
+xgboost