Hierarchial Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7e329d5d",
   "metadata": {},
   "source": [
    "Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4a131a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "from sklearn.decomposition import PCA \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7d831816",
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv(r\"C:\\Users\\vinay\\OneDrive\\Desktop\\Christ\\SEM2\\MACHINE LEARNING\\DATASETS\\german_credit_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa4fdc4",
   "metadata": {},
   "source": [
    "### 1. Data Display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "40a5d3fb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Saving accounts</th>\n",
       "      <th>Checking account</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>NaN</td>\n",
       "      <td>little</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>radio/TV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>moderate</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>radio/TV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "      <td>furniture/equipment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Age     Sex  Job Housing Saving accounts Checking account  \\\n",
       "0           0   67    male    2     own             NaN           little   \n",
       "1           1   22  female    2     own          little         moderate   \n",
       "2           2   49    male    1     own          little              NaN   \n",
       "3           3   45    male    2    free          little           little   \n",
       "4           4   53    male    2    free          little           little   \n",
       "\n",
       "   Credit amount  Duration              Purpose  \n",
       "0           1169         6             radio/TV  \n",
       "1           5951        48             radio/TV  \n",
       "2           2096        12            education  \n",
       "3           7882        42  furniture/equipment  \n",
       "4           4870        24                  car  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() # for first 5 records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3d526024",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Age</th>\n",
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       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Saving accounts</th>\n",
       "      <th>Checking account</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>995</td>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1736</td>\n",
       "      <td>12</td>\n",
       "      <td>furniture/equipment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>996</td>\n",
       "      <td>40</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>3857</td>\n",
       "      <td>30</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>997</td>\n",
       "      <td>38</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>NaN</td>\n",
       "      <td>804</td>\n",
       "      <td>12</td>\n",
       "      <td>radio/TV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>998</td>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>1845</td>\n",
       "      <td>45</td>\n",
       "      <td>radio/TV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>999</td>\n",
       "      <td>27</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>moderate</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4576</td>\n",
       "      <td>45</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Age     Sex  Job Housing Saving accounts Checking account  \\\n",
       "995         995   31  female    1     own          little              NaN   \n",
       "996         996   40    male    3     own          little           little   \n",
       "997         997   38    male    2     own          little              NaN   \n",
       "998         998   23    male    2    free          little           little   \n",
       "999         999   27    male    2     own        moderate         moderate   \n",
       "\n",
       "     Credit amount  Duration              Purpose  \n",
       "995           1736        12  furniture/equipment  \n",
       "996           3857        30                  car  \n",
       "997            804        12             radio/TV  \n",
       "998           1845        45             radio/TV  \n",
       "999           4576        45                  car  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail() #for last 5 records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "73d9ea97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Age', 'Sex', 'Job', 'Housing', 'Saving accounts',\n",
       "       'Checking account', 'Credit amount', 'Duration', 'Purpose'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns #to know about the feaures in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea1bebc1",
   "metadata": {},
   "source": [
    "#### 1.1 Removing unnecessary colum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a3b15bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['Unnamed: 0'],axis=1,inplace=True) #removing unwanted column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cfded013",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Saving accounts</th>\n",
       "      <th>Checking account</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>NaN</td>\n",
       "      <td>little</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>radio/TV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>moderate</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>radio/TV</td>\n",
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      "text/plain": [
       "   Age     Sex  Job Housing Saving accounts Checking account  Credit amount  \\\n",
       "0   67    male    2     own             NaN           little           1169   \n",
       "1   22  female    2     own          little         moderate           5951   \n",
       "\n",
       "   Duration   Purpose  \n",
       "0         6  radio/TV  \n",
       "1        48  radio/TV  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2) #verifying our dropped column is removed from dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4ea6e5",
   "metadata": {},
   "source": [
    "#### 1.2 To display basic information about dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d5d21e5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Age               1000 non-null   int64 \n",
      " 1   Sex               1000 non-null   object\n",
      " 2   Job               1000 non-null   int64 \n",
      " 3   Housing           1000 non-null   object\n",
      " 4   Saving accounts   817 non-null    object\n",
      " 5   Checking account  606 non-null    object\n",
      " 6   Credit amount     1000 non-null   int64 \n",
      " 7   Duration          1000 non-null   int64 \n",
      " 8   Purpose           1000 non-null   object\n",
      "dtypes: int64(4), object(5)\n",
      "memory usage: 70.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d7e9b520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 9)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #provides the size of dataframe with number of rows and columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9214c4d",
   "metadata": {},
   "source": [
    "#### 1.3 Counting null and duplicate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "87aff3e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                   0\n",
       "Sex                   0\n",
       "Job                   0\n",
       "Housing               0\n",
       "Saving accounts     183\n",
       "Checking account    394\n",
       "Credit amount         0\n",
       "Duration              0\n",
       "Purpose               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "39455e29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of missing data column wise:\n",
      "----------------------------------------\n",
      "Age : 0.0 %\n",
      "Sex : 0.0 %\n",
      "Job : 0.0 %\n",
      "Housing : 0.0 %\n",
      "Saving accounts : 18.3 %\n",
      "Checking account : 39.4 %\n",
      "Credit amount : 0.0 %\n",
      "Duration : 0.0 %\n",
      "Purpose : 0.0 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of missing data column wise:\") #to print the missing value report\n",
    "print('-'*40)\n",
    "for i in data.columns:\n",
    "    print(f\"{i} : {((data[i].isnull().sum())/data.shape[0])*100} %\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2a2099d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum() # to check if duplicate rows are there or not"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c9c617",
   "metadata": {},
   "source": [
    "#### 1.4 Checking the 5 point statistical summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4d0c842d",
   "metadata": {},
   "outputs": [
    {
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       "<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>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.546000</td>\n",
       "      <td>1.904000</td>\n",
       "      <td>3271.258000</td>\n",
       "      <td>20.903000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.375469</td>\n",
       "      <td>0.653614</td>\n",
       "      <td>2822.736876</td>\n",
       "      <td>12.058814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1365.500000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2319.500000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3972.250000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18424.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age          Job  Credit amount     Duration\n",
       "count  1000.000000  1000.000000    1000.000000  1000.000000\n",
       "mean     35.546000     1.904000    3271.258000    20.903000\n",
       "std      11.375469     0.653614    2822.736876    12.058814\n",
       "min      19.000000     0.000000     250.000000     4.000000\n",
       "25%      27.000000     2.000000    1365.500000    12.000000\n",
       "50%      33.000000     2.000000    2319.500000    18.000000\n",
       "75%      42.000000     2.000000    3972.250000    24.000000\n",
       "max      75.000000     3.000000   18424.000000    72.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe() #gives statistical summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3c16f8",
   "metadata": {},
   "source": [
    "## 2. Handling null values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4cd4a2",
   "metadata": {},
   "source": [
    "**Interpretation:** The above plot shows that most of the people have little fund in their Savings Account."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "247c9ac8",
   "metadata": {},
   "source": [
    "**Interpretation:** The above plot shows that equal number of people have little and moderate funds in their Checking Account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "003be862",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import skew # to import stats function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2fd89aab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age              1.020739\n",
       "Job             -0.374295\n",
       "Credit amount    1.949628\n",
       "Duration         1.094184\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8315ec79",
   "metadata": {},
   "source": [
    "**Interpretation:** The above are the values of skewness for different numerical columns which shows except job feature rest of them are highly skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01730dc5",
   "metadata": {},
   "source": [
    "### 2.1 Filling the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "21ded29a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Saving accounts'].fillna('little',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65d3f2f",
   "metadata": {},
   "source": [
    "**Reason:** Since savings account has more people with little funds hence we replaced null values with little class value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "70bc10ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Saving accounts'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2985d4ee",
   "metadata": {},
   "source": [
    "##### Savings account values filling\n",
    "**Reason:** Since savings account has equal number of people with moderate and little funds we will equally replace these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8b1474e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ca = data['Checking account']\n",
    "ca1 = ca[:501]\n",
    "ca2 = ca[501:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d6c77c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "008fe2c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "157d6520",
   "metadata": {},
   "outputs": [],
   "source": [
    "ca1.fillna('little',inplace=True)\n",
    "ca2.fillna('moderate',inplace=True)\n",
    "ca_new = pd.concat([ca1,ca2],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b6046ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3c275b54",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Checking Account']=ca_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a20dd7ad",
   "metadata": {
    "scrolled": false
   },
   "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>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Saving accounts</th>\n",
       "      <th>Checking account</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Checking Account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>moderate</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "      <td>car</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>moderate</td>\n",
       "      <td>1736</td>\n",
       "      <td>12</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>40</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>3857</td>\n",
       "      <td>30</td>\n",
       "      <td>car</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>38</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>moderate</td>\n",
       "      <td>804</td>\n",
       "      <td>12</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>1845</td>\n",
       "      <td>45</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>27</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>moderate</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4576</td>\n",
       "      <td>45</td>\n",
       "      <td>car</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age     Sex  Job Housing Saving accounts Checking account  Credit amount  \\\n",
       "0     67    male    2     own          little           little           1169   \n",
       "1     22  female    2     own          little         moderate           5951   \n",
       "2     49    male    1     own          little           little           2096   \n",
       "3     45    male    2    free          little           little           7882   \n",
       "4     53    male    2    free          little           little           4870   \n",
       "..   ...     ...  ...     ...             ...              ...            ...   \n",
       "995   31  female    1     own          little         moderate           1736   \n",
       "996   40    male    3     own          little           little           3857   \n",
       "997   38    male    2     own          little         moderate            804   \n",
       "998   23    male    2    free          little           little           1845   \n",
       "999   27    male    2     own        moderate         moderate           4576   \n",
       "\n",
       "     Duration              Purpose Checking Account  \n",
       "0           6             radio/TV           little  \n",
       "1          48             radio/TV         moderate  \n",
       "2          12            education           little  \n",
       "3          42  furniture/equipment           little  \n",
       "4          24                  car           little  \n",
       "..        ...                  ...              ...  \n",
       "995        12  furniture/equipment         moderate  \n",
       "996        30                  car           little  \n",
       "997        12             radio/TV         moderate  \n",
       "998        45             radio/TV           little  \n",
       "999        45                  car         moderate  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "28e1c232",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['Checking account','Checking account'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b4a757fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 0\n",
       "Sex                 0\n",
       "Job                 0\n",
       "Housing             0\n",
       "Saving accounts     0\n",
       "Credit amount       0\n",
       "Duration            0\n",
       "Purpose             0\n",
       "Checking Account    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c82e095",
   "metadata": {},
   "source": [
    "**All missing values have been handled.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8825eb21",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(),annot=True,cmap='viridis') #Correlation plot for numerical columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f94d8d1",
   "metadata": {},
   "source": [
    "**Interpretation:** Above plot shows that duration and credit amount has high positive correlation and age and duration have very little negative correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a3225ecd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize=(10,10)) #histogram showing distribution of numerical features\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d52bdd0c",
   "metadata": {},
   "source": [
    "**Interpretation:** Above histogram plots show the skewness we calculated above and verifies it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8f8679f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Sex', ylabel='Credit amount'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data['Sex'],y=data['Credit amount']) #boxplot shows 5 point statistical summary in plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344f1e9d",
   "metadata": {},
   "source": [
    "**Interpretation:** Plot above shows that Males and Females are taking almost the same amount of credit from bank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a8191432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Job', ylabel='Credit amount'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data['Job'],y=data['Credit amount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66087a0c",
   "metadata": {},
   "source": [
    "**Interpretation:** Plot above shows that people with job title 3 i.e. highly skilled take more loan as they can repay it due to high salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "05373079",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Sex', ylabel='Duration'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data['Sex'],y=data['Duration'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c16af0",
   "metadata": {},
   "source": [
    "**Interpretation:** Plot above shows that Males and females take loan for almost same duration from bank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9a21797f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car                    337\n",
       "radio/TV               280\n",
       "furniture/equipment    181\n",
       "business                97\n",
       "education               59\n",
       "repairs                 22\n",
       "domestic appliances     12\n",
       "vacation/others         12\n",
       "Name: Purpose, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Purpose'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b2c1f0ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Housing', ylabel='Credit amount'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data['Housing'],y=data['Credit amount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb4a0bec",
   "metadata": {},
   "source": [
    "**Interpretation:** Plot above shows that people living in free house taking more loan since they might not have house mortgages as compared to other category people so they have more money at disposal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f27c8bf",
   "metadata": {},
   "source": [
    "## 3. Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b626f55b",
   "metadata": {},
   "source": [
    "#### 3.1 Encoding the categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6f8222c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "data['Housing']=data['Housing'].astype('category')\n",
    "data['Housing_n']=data['Housing'].cat.codes\n",
    "\n",
    "data['Sex']=data['Sex'].astype('category')\n",
    "data['Sex_n']=data['Sex'].cat.codes\n",
    "\n",
    "data['Saving accounts']=data['Saving accounts'].astype('category')\n",
    "data['Savings_Account_n']=data['Saving accounts'].cat.codes\n",
    "\n",
    "data['Purpose']=data['Purpose'].astype('category')\n",
    "data['Purpose_n']=data['Purpose'].cat.codes\n",
    "\n",
    "data['Checking Account']=data['Checking Account'].astype('category')\n",
    "data['Checking_Account_n']=data['Checking Account'].cat.codes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5fde919a",
   "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>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Saving accounts</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Checking Account</th>\n",
       "      <th>Housing_n</th>\n",
       "      <th>Sex_n</th>\n",
       "      <th>Savings_Account_n</th>\n",
       "      <th>Purpose_n</th>\n",
       "      <th>Checking_Account_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>little</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>moderate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>little</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>little</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "      <td>car</td>\n",
       "      <td>little</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>1736</td>\n",
       "      <td>12</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>moderate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>40</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>3857</td>\n",
       "      <td>30</td>\n",
       "      <td>car</td>\n",
       "      <td>little</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>38</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>804</td>\n",
       "      <td>12</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>moderate</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>1845</td>\n",
       "      <td>45</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>little</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>27</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4576</td>\n",
       "      <td>45</td>\n",
       "      <td>car</td>\n",
       "      <td>moderate</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age     Sex  Job Housing Saving accounts  Credit amount  Duration  \\\n",
       "0     67    male    2     own          little           1169         6   \n",
       "1     22  female    2     own          little           5951        48   \n",
       "2     49    male    1     own          little           2096        12   \n",
       "3     45    male    2    free          little           7882        42   \n",
       "4     53    male    2    free          little           4870        24   \n",
       "..   ...     ...  ...     ...             ...            ...       ...   \n",
       "995   31  female    1     own          little           1736        12   \n",
       "996   40    male    3     own          little           3857        30   \n",
       "997   38    male    2     own          little            804        12   \n",
       "998   23    male    2    free          little           1845        45   \n",
       "999   27    male    2     own        moderate           4576        45   \n",
       "\n",
       "                 Purpose Checking Account  Housing_n  Sex_n  \\\n",
       "0               radio/TV           little          1      1   \n",
       "1               radio/TV         moderate          1      0   \n",
       "2              education           little          1      1   \n",
       "3    furniture/equipment           little          0      1   \n",
       "4                    car           little          0      1   \n",
       "..                   ...              ...        ...    ...   \n",
       "995  furniture/equipment         moderate          1      0   \n",
       "996                  car           little          1      1   \n",
       "997             radio/TV         moderate          1      1   \n",
       "998             radio/TV           little          0      1   \n",
       "999                  car         moderate          1      1   \n",
       "\n",
       "     Savings_Account_n  Purpose_n  Checking_Account_n  \n",
       "0                    0          5                   0  \n",
       "1                    0          5                   1  \n",
       "2                    0          3                   0  \n",
       "3                    0          4                   0  \n",
       "4                    0          1                   0  \n",
       "..                 ...        ...                 ...  \n",
       "995                  0          4                   1  \n",
       "996                  0          1                   0  \n",
       "997                  0          5                   1  \n",
       "998                  0          5                   0  \n",
       "999                  1          1                   1  \n",
       "\n",
       "[1000 rows x 14 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "76fa2b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['Sex','Housing','Saving accounts','Checking Account','Purpose'],axis=1,inplace=True) #dropping encoded columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "98ccdd6c",
   "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>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Housing_n</th>\n",
       "      <th>Sex_n</th>\n",
       "      <th>Savings_Account_n</th>\n",
       "      <th>Purpose_n</th>\n",
       "      <th>Checking_Account_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1736</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>3857</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>804</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>1845</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>4576</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Job  Credit amount  Duration  Housing_n  Sex_n  Savings_Account_n  \\\n",
       "0     67    2           1169         6          1      1                  0   \n",
       "1     22    2           5951        48          1      0                  0   \n",
       "2     49    1           2096        12          1      1                  0   \n",
       "3     45    2           7882        42          0      1                  0   \n",
       "4     53    2           4870        24          0      1                  0   \n",
       "..   ...  ...            ...       ...        ...    ...                ...   \n",
       "995   31    1           1736        12          1      0                  0   \n",
       "996   40    3           3857        30          1      1                  0   \n",
       "997   38    2            804        12          1      1                  0   \n",
       "998   23    2           1845        45          0      1                  0   \n",
       "999   27    2           4576        45          1      1                  1   \n",
       "\n",
       "     Purpose_n  Checking_Account_n  \n",
       "0            5                   0  \n",
       "1            5                   1  \n",
       "2            3                   0  \n",
       "3            4                   0  \n",
       "4            1                   0  \n",
       "..         ...                 ...  \n",
       "995          4                   1  \n",
       "996          1                   0  \n",
       "997          5                   1  \n",
       "998          5                   0  \n",
       "999          1                   1  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data #new encoded dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2a3faf52",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = data.drop(['Credit amount'], axis=1) #creating new dataframes with features and target/label variable\n",
    "df_label = data['Credit amount']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3da167",
   "metadata": {},
   "source": [
    "#### 3.2 Scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "67acf3de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "standardized = StandardScaler()\n",
    "standardized.fit(df_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "192de169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.76645648,  0.14694918, -1.23647786, ..., -0.4634091 ,\n",
       "         1.07326286, -0.97649891],\n",
       "       [-1.19140394,  0.14694918,  2.24819436, ..., -0.4634091 ,\n",
       "         1.07326286,  0.67299249],\n",
       "       [ 1.18331231, -1.38377145, -0.73866754, ..., -0.4634091 ,\n",
       "         0.06170503, -0.97649891],\n",
       "       ...,\n",
       "       [ 0.21583532,  0.14694918, -0.73866754, ..., -0.4634091 ,\n",
       "         1.07326286,  0.67299249],\n",
       "       [-1.10345149,  0.14694918,  1.9992892 , ..., -0.4634091 ,\n",
       "         1.07326286, -0.97649891],\n",
       "       [-0.75164167,  0.14694918,  1.9992892 , ...,  0.77897455,\n",
       "        -0.9498528 ,  0.67299249]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data = standardized.transform(df_features)\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "73cd7540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 8)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f619fe",
   "metadata": {},
   "source": [
    "### Applying PCA on the scaled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3f4d9d63",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, 1:].values\n",
    "y = data['Credit amount'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6147c271",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,1000):\n",
    "    if df_label[i]>0 and df_label[i]<=5000:\n",
    "        df_label[i] = 1\n",
    "    elif df_label[i]>5000 and df_label[i]<=10000:\n",
    "        df_label[i] = 2\n",
    "    elif df_label[i]>10000 and df_label[i]<=15000:\n",
    "        df_label[i] = 3\n",
    "    elif df_label[i]>15000 and df_label[i]<20000:\n",
    "        df_label[i] = 4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a106535",
   "metadata": {},
   "source": [
    "**Credit Class Categories** </br>\n",
    "Class 1: <5000 Euro </br>\n",
    "Class 2: >5000 and <=10,000 Euro </br>\n",
    "Class 3: >10,000 and <=15,000 Euro </br>\n",
    "Class 4: >15,000 and <20,000 Euro </br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8c7deeea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "pca = PCA().fit(scaled_data)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "xi = np.arange(1, 9, step=1)\n",
    "y = np.cumsum(pca.explained_variance_ratio_)\n",
    "\n",
    "plt.ylim(0.0,1.1)\n",
    "plt.plot(xi, y, marker='o', linestyle='--', color='b')\n",
    "\n",
    "plt.xlabel('Number of Components')\n",
    "plt.xticks(np.arange(0, 9, step=1)) #change from 0-based array index to 1-based human-readable label\n",
    "plt.ylabel('Cumulative variance (%)')\n",
    "plt.title('The number of components needed to explain variance')\n",
    "\n",
    "plt.axhline(y=0.90, color='r', linestyle='-')\n",
    "plt.text(0.5, 0.85, '90% cut-off threshold', color = 'red', fontsize=16)\n",
    "\n",
    "ax.grid(axis='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abfbe2aa",
   "metadata": {},
   "source": [
    "**Interpretation:** 6 features explain 90% variation in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b3a775a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19676937, 0.14770458, 0.13023777, 0.12667016, 0.11540989,\n",
       "       0.10539807, 0.09934507, 0.07846509])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ae63ed3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00083699,  1.90148799, -1.87057466],\n",
       "       [-0.43716877, -2.15232066,  0.36142743],\n",
       "       [ 0.18070777,  1.60615877, -1.3839129 ],\n",
       "       ...,\n",
       "       [ 0.14489704,  0.59518162, -0.49836724],\n",
       "       [ 1.49171107, -1.56499328, -0.97056016],\n",
       "       [ 0.94738   , -1.20845425,  1.69073059]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=3)\n",
    "    \n",
    "x_pca = pca.fit_transform(scaled_data)\n",
    "x_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c89725e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "83e350e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00083699,  1.90148799, -1.87057466],\n",
       "       [-0.43716877, -2.15232066,  0.36142743],\n",
       "       [ 0.18070777,  1.60615877, -1.3839129 ],\n",
       "       ...,\n",
       "       [ 0.14489704,  0.59518162, -0.49836724],\n",
       "       [ 1.49171107, -1.56499328, -0.97056016],\n",
       "       [ 0.94738   , -1.20845425,  1.69073059]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3869bc39",
   "metadata": {},
   "source": [
    "#### Projecting three components on 3D Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f56146a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca_df = pd.DataFrame(x_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cf375c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_pca_df =pd.concat([x_pca_df,df_label],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "90189511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Component 1=%{x}<br>Component 2=%{y}<br>Component 3=%{z}<br>Credit amount=%{marker.color}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": [
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           "#636efa",
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          "geo": {
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           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
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          "hovermode": "closest",
          "mapbox": {
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          "polar": {
           "angularaxis": {
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
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            "linecolor": "white",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "fig = px.scatter_3d(new_pca_df, x=0, y=1, z=2,color='Credit amount',labels={'0':\"Component 1\",'1':\"Component 2\",'2':\"Component 3\"})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ca623dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pc = pd.DataFrame(pca.components_, columns = df_features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "1084971d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Housing_n</th>\n",
       "      <th>Sex_n</th>\n",
       "      <th>Savings_Account_n</th>\n",
       "      <th>Purpose_n</th>\n",
       "      <th>Checking_Account_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.442133</td>\n",
       "      <td>0.314557</td>\n",
       "      <td>0.344417</td>\n",
       "      <td>-0.577426</td>\n",
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       "      <td>-0.074463</td>\n",
       "      <td>-0.187705</td>\n",
       "      <td>0.060818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.493836</td>\n",
       "      <td>-0.530272</td>\n",
       "      <td>-0.576173</td>\n",
       "      <td>-0.164068</td>\n",
       "      <td>0.153366</td>\n",
       "      <td>0.264374</td>\n",
       "      <td>0.037950</td>\n",
       "      <td>0.145565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.127929</td>\n",
       "      <td>0.100843</td>\n",
       "      <td>0.191066</td>\n",
       "      <td>0.113264</td>\n",
       "      <td>-0.050449</td>\n",
       "      <td>0.688548</td>\n",
       "      <td>-0.339456</td>\n",
       "      <td>0.576416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age       Job  Duration  Housing_n     Sex_n  Savings_Account_n  \\\n",
       "0  0.442133  0.314557  0.344417  -0.577426  0.457223          -0.074463   \n",
       "1  0.493836 -0.530272 -0.576173  -0.164068  0.153366           0.264374   \n",
       "2 -0.127929  0.100843  0.191066   0.113264 -0.050449           0.688548   \n",
       "\n",
       "   Purpose_n  Checking_Account_n  \n",
       "0  -0.187705            0.060818  \n",
       "1   0.037950            0.145565  \n",
       "2  -0.339456            0.576416  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "3a0b4513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(114.0, 0.5, 'Principal Components')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "sns.heatmap(df_pc, cmap = 'Blues')\n",
    "plt.title('Principal Components correlation with the features')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Principal Components')\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e14b5825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00083699,  1.90148799, -1.87057466,  0.07190151,  0.97620508,\n",
       "         0.62558269],\n",
       "       [-0.43716877, -2.15232066,  0.36142743,  1.48777694, -0.18459834,\n",
       "         0.63304484],\n",
       "       [ 0.18070777,  1.60615877, -1.3839129 , -0.7248761 , -0.09054347,\n",
       "        -0.34107267],\n",
       "       ...,\n",
       "       [ 0.14489704,  0.59518162, -0.49836724,  1.2704384 , -0.0541123 ,\n",
       "        -0.43670135],\n",
       "       [ 1.49171107, -1.56499328, -0.97056016,  0.75957174,  0.79988813,\n",
       "        -0.85298356],\n",
       "       [ 0.94738   , -1.20845425,  1.69073059, -0.31123253, -0.29511188,\n",
       "        -0.83601008]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=6)\n",
    "    \n",
    "x_pca = pca.fit_transform(scaled_data)\n",
    "x_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8023f6b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca_df = pd.DataFrame(x_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "bc04baea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_pca_df, df_label, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb5648ca",
   "metadata": {},
   "source": [
    "## 4. Model Building"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3a59817d",
   "metadata": {},
   "source": [
    "### Hierachial Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "22ae736e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import library for hierarchial cluster for ploting dendogram\n",
    "import scipy.cluster.hierarchy as sch\n",
    "dendogram=sch.dendrogram(sch.linkage(x_pca_df,method='ward'))\n",
    "plt.title('Dendogram')\n",
    "plt.xlabel('Components')\n",
    "plt.ylabel('Euclidean distances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "2e025865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 0, 4, 4, 2, 0, 2, 0, 3, 3, 3, 4, 3, 2, 4, 0, 4, 2, 4, 2,\n",
       "       0, 0, 1, 1, 1, 2, 1, 0, 2, 1, 0, 0, 3, 1, 0, 1, 1, 1, 2, 2, 1, 3,\n",
       "       4, 3, 2, 2, 0, 2, 1, 0, 1, 0, 0, 0, 1, 3, 3, 3, 0, 1, 0, 0, 3, 4,\n",
       "       3, 2, 0, 3, 3, 1, 4, 3, 0, 4, 1, 1, 0, 1, 3, 2, 3, 4, 4, 3, 3, 0,\n",
       "       2, 1, 4, 4, 3, 1, 2, 0, 4, 0, 1, 0, 0, 1, 3, 1, 0, 1, 0, 0, 3, 2,\n",
       "       0, 3, 0, 3, 2, 1, 3, 3, 2, 2, 3, 3, 1, 0, 3, 0, 1, 1, 0, 3, 3, 1,\n",
       "       1, 4, 3, 3, 2, 2, 1, 3, 2, 3, 1, 1, 3, 0, 3, 4, 1, 2, 4, 1, 1, 2,\n",
       "       0, 3, 4, 4, 0, 1, 2, 3, 2, 0, 2, 2, 3, 2, 3, 0, 0, 3, 3, 1, 1, 4,\n",
       "       1, 2, 1, 4, 0, 0, 4, 2, 0, 1, 4, 0, 2, 3, 2, 0, 0, 1, 1, 3, 3, 3,\n",
       "       0, 1, 2, 0, 0, 1, 0, 0, 1, 1, 0, 2, 1, 3, 4, 0, 4, 3, 0, 1, 1, 4,\n",
       "       1, 3, 1, 0, 0, 1, 2, 4, 1, 1, 1, 2, 2, 1, 1, 1, 0, 0, 3, 1, 3, 4,\n",
       "       0, 3, 2, 0, 4, 0, 1, 3, 2, 3, 3, 2, 1, 1, 1, 4, 2, 4, 1, 4, 0, 4,\n",
       "       0, 1, 0, 3, 4, 1, 0, 2, 0, 1, 1, 1, 3, 4, 3, 2, 2, 4, 1, 4, 0, 3,\n",
       "       0, 1, 4, 4, 0, 0, 4, 0, 0, 3, 3, 0, 1, 2, 4, 3, 1, 4, 2, 2, 3, 2,\n",
       "       3, 0, 1, 2, 3, 0, 1, 1, 1, 4, 1, 3, 0, 1, 0, 2, 3, 0, 1, 2, 1, 1,\n",
       "       4, 2, 3, 3, 1, 1, 3, 3, 1, 1, 3, 3, 2, 0, 1, 3, 1, 2, 0, 2, 3, 3,\n",
       "       0, 1, 2, 0, 4, 1, 1, 3, 0, 1, 3, 1, 1, 0, 0, 3, 1, 3, 0, 1, 3, 0,\n",
       "       4, 3, 1, 4, 0, 4, 1, 4, 3, 1, 0, 3, 3, 1, 2, 3, 0, 2, 0, 3, 1, 1,\n",
       "       4, 1, 0, 2, 1, 0, 0, 0, 0, 1, 4, 1, 2, 2, 3, 0, 4, 0, 3, 0, 0, 3,\n",
       "       2, 3, 2, 0, 0, 2, 3, 0, 1, 2, 1, 4, 4, 1, 4, 1, 1, 1, 2, 2, 4, 3,\n",
       "       4, 3, 1, 4, 3, 3, 3, 1, 4, 2, 0, 3, 1, 0, 0, 3, 0, 4, 3, 1, 1, 0,\n",
       "       3, 1, 0, 4, 3, 1, 3, 4, 1, 3, 0, 1, 1, 3, 2, 1, 0, 1, 3, 0, 2, 2,\n",
       "       2, 0, 4, 4, 0, 0, 1, 4, 2, 1, 0, 1, 1, 1, 1, 2, 3, 0, 1, 1, 3, 0,\n",
       "       2, 1, 1, 0, 0, 0, 1, 1, 0, 3, 0, 3, 0, 1, 1, 3, 0, 2, 3, 0, 2, 1,\n",
       "       1, 0, 1, 3, 0, 1, 1, 1, 4, 3, 0, 3, 1, 0, 1, 1, 0, 4, 3, 1, 3, 0,\n",
       "       3, 2, 1, 0, 4, 1, 2, 3, 1, 1, 4, 1, 3, 0, 0, 3, 0, 2, 1, 3, 3, 1,\n",
       "       3, 3, 1, 3, 3, 3, 0, 1, 0, 0, 3, 1, 4, 3, 0, 1, 4, 4, 4, 3, 2, 3,\n",
       "       1, 3, 3, 0, 0, 0, 4, 3, 4, 3, 3, 2, 0, 4, 1, 0, 2, 3, 2, 3, 3, 0,\n",
       "       1, 0, 3, 0, 0, 0, 0, 3, 4, 1, 1, 0, 2, 4, 3, 1, 1, 3, 3, 1, 2, 1,\n",
       "       3, 1, 3, 1, 1, 1, 1, 0, 1, 2, 0, 1, 0, 1, 4, 0, 0, 4, 0, 1, 3, 1,\n",
       "       1, 0, 1, 1, 3, 1, 2, 3, 0, 1, 0, 0, 0, 2, 2, 3, 1, 1, 4, 3, 4, 1,\n",
       "       1, 1, 0, 0, 1, 0, 1, 2, 1, 3, 1, 0, 1, 2, 1, 3, 1, 1, 2, 0, 4, 0,\n",
       "       3, 2, 0, 3, 3, 1, 2, 4, 0, 1, 0, 0, 1, 1, 1, 0, 1, 2, 3, 2, 3, 2,\n",
       "       2, 3, 3, 2, 1, 1, 3, 2, 3, 3, 3, 0, 0, 3, 2, 1, 1, 2, 1, 0, 3, 3,\n",
       "       0, 0, 4, 3, 3, 3, 2, 3, 0, 1, 0, 0, 4, 3, 1, 0, 0, 1, 1, 3, 1, 4,\n",
       "       3, 3, 0, 1, 0, 3, 0, 3, 2, 4, 1, 2, 1, 3, 0, 2, 2, 0, 0, 1, 3, 0,\n",
       "       1, 1, 2, 3, 4, 3, 0, 0, 0, 3, 3, 1, 3, 0, 1, 4, 0, 3, 0, 1, 0, 0,\n",
       "       0, 3, 2, 3, 1, 1, 0, 1, 1, 0, 1, 0, 3, 0, 0, 0, 2, 3, 0, 3, 3, 4,\n",
       "       3, 3, 4, 1, 1, 0, 3, 0, 1, 1, 0, 0, 4, 4, 0, 0, 1, 4, 0, 0, 4, 1,\n",
       "       3, 0, 2, 0, 3, 2, 0, 3, 3, 1, 0, 3, 2, 1, 2, 3, 1, 2, 4, 1, 0, 1,\n",
       "       0, 0, 0, 4, 1, 4, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 3, 2, 3, 1, 0, 0,\n",
       "       0, 1, 1, 1, 0, 1, 3, 3, 3, 3, 3, 2, 0, 3, 0, 4, 2, 1, 3, 2, 3, 0,\n",
       "       4, 4, 3, 0, 1, 0, 1, 3, 0, 2, 3, 2, 3, 1, 0, 0, 0, 0, 1, 0, 3, 3,\n",
       "       2, 0, 1, 1, 0, 0, 2, 3, 3, 2, 1, 1, 4, 1, 1, 0, 0, 2, 1, 3, 2, 3,\n",
       "       1, 0, 1, 0, 3, 0, 3, 2, 4, 0, 0, 0, 1, 0, 3, 0, 1, 3, 0, 4, 0, 1,\n",
       "       1, 1, 1, 1, 0, 3, 0, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import library for Agglomerative Clustering\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc=AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='ward')\n",
    "y_hc=hc.fit_predict(x_pca_df)\n",
    "y_hc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8565c2f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the clustered data\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "plt.scatter(x_pca_df.iloc[y_hc == 0, 0], x_pca_df.iloc[y_hc == 0, 1],c='green', label='cluster 1')\n",
    "plt.scatter(x_pca_df.iloc[y_hc == 1, 0], x_pca_df.iloc[y_hc == 1, 1],c='blue', label='cluster 2')\n",
    "plt.scatter(x_pca_df.iloc[y_hc == 2, 0], x_pca_df.iloc[y_hc == 2, 1],c='yellow', label='cluster 3')\n",
    "plt.scatter(x_pca_df.iloc[y_hc == 3, 0], x_pca_df.iloc[y_hc == 3, 1],c='violet', label='cluster 4')\n",
    "plt.scatter(x_pca_df.iloc[y_hc == 4, 0], x_pca_df.iloc[y_hc == 4, 1],c='red', label='cluster 5')\n",
    "plt.legend()\n",
    "plt.title('Analysis')\n",
    "plt.xlabel('Components')\n",
    "plt.ylabel('Smoothness')\n",
    "ax.set_aspect('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631227f1",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0197f16",
   "metadata": {},
   "source": [
    "In this lab, we have tried to gain the knowledge about data and its variables, further we did some preprocessing to the data in order to bring it into more analyst friendly mode, laterly we implemented various graphs using various libraries in order to get valuable insights. Furthermore, with that we perform dimensionality reduction which can help the banks to predict credit amount for the customers. We implemented and evaluated KMeans clustering to get accuracy in terms of predicting credit amount category. "
   ]
  }
 ],
 "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}