Project 4 | Logistic Regression Model - Potential Loan Customers Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "### Supervised Learning | Project - 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 1.Read the column description and ensure you understand each attribute well. ( 5 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "               ID\tCustomer ID\n",
    "              Age\tCustomer's age in completed years              \n",
    "        Experience\t#years of professional experience\n",
    "            Income\tAnnual income of the customer ($000)\n",
    "        ZIPCode\t   Home Address ZIP code.\n",
    "        Family     Family size of the customer\n",
    "         CCAvg\t   Avg. spending on credit cards per month ($000)\n",
    "         Education\tEducation Level. 1: Undergrad; 2: Graduate; 3: Advanced/Professional\n",
    "          Mortgage\tValue of house mortgage if any. ($000)\n",
    "     Personal Loan\tDid this customer accept the personal loan offered in the last campaign?\n",
    "     \n",
    "    Securities Account\t Does the customer have a securities account with the bank?\n",
    "\n",
    "        CD Account\tDoes the customer have a certificate of deposit (CD) account with the bank?\n",
    "            Online\tDoes the customer use internet banking facilities?\n",
    "        CreditCard\tDoes the customer use a credit card issued by UniversalBank?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 2.Study the data distribution in each attribute, share your findings. (5 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn.metrics as metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
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       "   ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n",
       "0   1   25           1      49     91107       4    1.6          1         0   \n",
       "1   2   45          19      34     90089       3    1.5          1         0   \n",
       "2   3   39          15      11     94720       1    1.0          1         0   \n",
       "3   4   35           9     100     94112       1    2.7          2         0   \n",
       "4   5   35           8      45     91330       4    1.0          2         0   \n",
       "5   6   37          13      29     92121       4    0.4          2       155   \n",
       "6   7   53          27      72     91711       2    1.5          2         0   \n",
       "7   8   50          24      22     93943       1    0.3          3         0   \n",
       "8   9   35          10      81     90089       3    0.6          2       104   \n",
       "9  10   34           9     180     93023       1    8.9          3         0   \n",
       "\n",
       "   Personal Loan  Securities Account  CD Account  Online  CreditCard  \n",
       "0              0                   1           0       0           0  \n",
       "1              0                   1           0       0           0  \n",
       "2              0                   0           0       0           0  \n",
       "3              0                   0           0       0           0  \n",
       "4              0                   0           0       0           1  \n",
       "5              0                   0           0       1           0  \n",
       "6              0                   0           0       1           0  \n",
       "7              0                   0           0       0           1  \n",
       "8              0                   0           0       1           0  \n",
       "9              1                   0           0       0           0  "
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "bank_loan_data = pd.read_csv(\"Bank_Personal_Loan_Modelling-1.csv\")\n",
    "bank_loan_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 16 columns):\n",
      "ID                    5000 non-null int64\n",
      "Age                   5000 non-null int64\n",
      "Experience            5000 non-null int64\n",
      "Income                5000 non-null int64\n",
      "ZIP Code              5000 non-null int64\n",
      "Family                5000 non-null int64\n",
      "CCAvg                 5000 non-null float64\n",
      "Mortgage              5000 non-null int64\n",
      "Personal Loan         5000 non-null int64\n",
      "Securities Account    5000 non-null int64\n",
      "CD Account            5000 non-null int64\n",
      "Online                5000 non-null int64\n",
      "CreditCard            5000 non-null int64\n",
      "Education_1           5000 non-null uint8\n",
      "Education_2           5000 non-null uint8\n",
      "Education_3           5000 non-null uint8\n",
      "dtypes: float64(1), int64(12), uint8(3)\n",
      "memory usage: 522.5 KB\n"
     ]
    }
   ],
   "source": [
    "bank_loan_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.Get the target column distribution. Your comments ( 3 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Mortgage  \\\n",
       "0   1   25           1      49     91107       4    1.6         0   \n",
       "1   2   45          19      34     90089       3    1.5         0   \n",
       "2   3   39          15      11     94720       1    1.0         0   \n",
       "3   4   35           9     100     94112       1    2.7         0   \n",
       "4   5   35           8      45     91330       4    1.0         0   \n",
       "\n",
       "   Personal Loan  Securities Account  CD Account  Online  CreditCard  \\\n",
       "0              0                   1           0       0           0   \n",
       "1              0                   1           0       0           0   \n",
       "2              0                   0           0       0           0   \n",
       "3              0                   0           0       0           0   \n",
       "4              0                   0           0       0           1   \n",
       "\n",
       "   Education_1  Education_2  Education_3  \n",
       "0            1            0            0  \n",
       "1            1            0            0  \n",
       "2            1            0            0  \n",
       "3            0            1            0  \n",
       "4            0            1            0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_loan_data = pd.get_dummies(bank_loan_data,columns=['Education'])\n",
    "bank_loan_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Experience  Income  Family  CCAvg  Mortgage  Securities Account  \\\n",
       "0   25           1      49       4    1.6         0                   1   \n",
       "1   45          19      34       3    1.5         0                   1   \n",
       "2   39          15      11       1    1.0         0                   0   \n",
       "3   35           9     100       1    2.7         0                   0   \n",
       "4   35           8      45       4    1.0         0                   0   \n",
       "\n",
       "   CD Account  Online  CreditCard  Education_1  Education_2  Education_3  \n",
       "0           0       0           0            1            0            0  \n",
       "1           0       0           0            1            0            0  \n",
       "2           0       0           0            1            0            0  \n",
       "3           0       0           0            0            1            0  \n",
       "4           0       0           1            0            1            0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = bank_loan_data['Personal Loan']\n",
    "X = bank_loan_data.drop(columns=['ID','ZIP Code','Personal Loan'])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e54d6a0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = X.corr()\n",
    "sns.heatmap(corr,cmap=sns.diverging_palette(20, 220, n=100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.Split the data into training and test set in the ratio of 70:30 respectively (2 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,train_size=0.70,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.Use different classification models (Logistic, K-NN and Naïve Bayes) to predict the likelihood of a liability customer buying personal loans (30 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`KNN model for predicting the likelihood of a liability customer buying personal loans`\n",
    "`We see that the model gives maximum accuracy and f1 score with k = 9, so we select k-neighbours to be 9`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with k value of 2 is 0.9033333333333333\n",
      "f1 score with k value of 2 is 0.28571428571428575\n",
      "\n",
      "Accuracy with k value of 3 is 0.9033333333333333\n",
      "f1 score with k value of 3 is 0.4357976653696498\n",
      "\n",
      "Accuracy with k value of 4 is 0.904\n",
      "f1 score with k value of 4 is 0.29411764705882354\n",
      "\n",
      "Accuracy with k value of 5 is 0.906\n",
      "f1 score with k value of 5 is 0.4337349397590362\n",
      "\n",
      "Accuracy with k value of 6 is 0.9066666666666666\n",
      "f1 score with k value of 6 is 0.339622641509434\n",
      "\n",
      "Accuracy with k value of 7 is 0.9053333333333333\n",
      "f1 score with k value of 7 is 0.4083333333333334\n",
      "\n",
      "Accuracy with k value of 8 is 0.9053333333333333\n",
      "f1 score with k value of 8 is 0.330188679245283\n",
      "\n",
      "Accuracy with k value of 9 is 0.9093333333333333\n",
      "f1 score with k value of 9 is 0.4188034188034188\n",
      "\n",
      "Accuracy with k value of 10 is 0.908\n",
      "f1 score with k value of 10 is 0.3365384615384615\n",
      "\n",
      "Accuracy with k value of 11 is 0.9053333333333333\n",
      "f1 score with k value of 11 is 0.39316239316239315\n",
      "\n",
      "Accuracy with k value of 12 is 0.904\n",
      "f1 score with k value of 12 is 0.29411764705882354\n",
      "\n",
      "Accuracy with k value of 13 is 0.9073333333333333\n",
      "f1 score with k value of 13 is 0.39826839826839827\n",
      "\n",
      "Accuracy with k value of 14 is 0.904\n",
      "f1 score with k value of 14 is 0.3076923076923077\n",
      "\n",
      "Accuracy with k value of 15 is 0.906\n",
      "f1 score with k value of 15 is 0.37333333333333335\n",
      "\n",
      "Accuracy with k value of 16 is 0.9013333333333333\n",
      "f1 score with k value of 16 is 0.2884615384615385\n",
      "\n",
      "Accuracy with k value of 17 is 0.9073333333333333\n",
      "f1 score with k value of 17 is 0.37104072398190047\n",
      "\n",
      "Accuracy with k value of 18 is 0.906\n",
      "f1 score with k value of 18 is 0.3380281690140845\n",
      "\n",
      "Accuracy with k value of 19 is 0.904\n",
      "f1 score with k value of 19 is 0.35714285714285715\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "for k in range(2,20):\n",
    "    model = KNeighborsClassifier(n_neighbors=k)\n",
    "    model.fit(X_train,Y_train)\n",
    "    predicted_Species = model.predict(X_test)\n",
    "    print(\"Accuracy with k value of\",k,\"is\",metrics.accuracy_score(Y_test,predicted_Species))\n",
    "    print(\"f1 score with k value of\",k,\"is\", metrics.f1_score(Y_test,predicted_Species))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN = KNeighborsClassifier(n_neighbors=9)\n",
    "KNN.fit(X_train,Y_train)\n",
    "predicted_labels = KNN.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Logistic model for predicting the likelihood of a liability customer buying personal loans`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logReg = LogisticRegression()\n",
    "logReg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_labels_logistic = logReg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Naive Bayes model for predicting the likelihood of a liability customer buying personal loans`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "clf = GaussianNB()\n",
    "clf.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_labels_naive = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6.Print the confusion matrix, classification report, and accuracy for all the above models (8 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`confusion matrix, classification report, and accuracy for KNN model`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN model in predicting Potential Cutomers for Personal Loan:\n",
      "Accuracy Score: 0.9093333333333333\n",
      "Confusion Matrix:\n",
      "[[1315   36]\n",
      " [ 100   49]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95      1351\n",
      "           1       0.58      0.33      0.42       149\n",
      "\n",
      "    accuracy                           0.91      1500\n",
      "   macro avg       0.75      0.65      0.68      1500\n",
      "weighted avg       0.89      0.91      0.90      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"KNN model in predicting Potential Cutomers for Personal Loan:\")\n",
    "print(\"Accuracy Score:\",metrics.accuracy_score(Y_test,predicted_labels))\n",
    "print(\"Confusion Matrix:\")\n",
    "print(metrics.confusion_matrix(Y_test,predicted_labels))\n",
    "print(\"Classification Report:\",metrics.classification_report(Y_test,predicted_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic model in predicting Potential Cutomers for Personal Loan:\n",
      "Accuracy Score: 0.9573333333333334\n",
      "Confusion Matrix:\n",
      "[[1342    9]\n",
      " [  55   94]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.98      1351\n",
      "           1       0.91      0.63      0.75       149\n",
      "\n",
      "    accuracy                           0.96      1500\n",
      "   macro avg       0.94      0.81      0.86      1500\n",
      "weighted avg       0.96      0.96      0.95      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic model in predicting Potential Cutomers for Personal Loan:\")\n",
    "print(\"Accuracy Score:\",metrics.accuracy_score(Y_test,predicted_labels_logistic))\n",
    "print(\"Confusion Matrix:\")\n",
    "print(metrics.confusion_matrix(Y_test,predicted_labels_logistic))\n",
    "print(\"Classification Report:\",metrics.classification_report(Y_test,predicted_labels_logistic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes model in predicting Potential Cutomers for Personal Loan:\n",
      "Accuracy Score: 0.8866666666666667\n",
      "Confusion Matrix:\n",
      "[[1243  108]\n",
      " [  62   87]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.92      0.94      1351\n",
      "           1       0.45      0.58      0.51       149\n",
      "\n",
      "    accuracy                           0.89      1500\n",
      "   macro avg       0.70      0.75      0.72      1500\n",
      "weighted avg       0.90      0.89      0.89      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Naive Bayes model in predicting Potential Cutomers for Personal Loan:\")\n",
    "print(\"Accuracy Score:\",metrics.accuracy_score(Y_test,predicted_labels_naive))\n",
    "print(\"Confusion Matrix:\")\n",
    "print(metrics.confusion_matrix(Y_test,predicted_labels_naive))\n",
    "print(\"Classification Report:\",metrics.classification_report(Y_test,predicted_labels_naive))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 7. Give your reasoning on which is the best model in this case and why it performs better? (7 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`As we see that accuracy and f1-score both are high for logostic model,so logistic model performs best in the use case.`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Logistic Model performs best in predicting the Potential Loan Customers.\n",
    "Since,the accuracy score of this model is 95.73% and we see that in the classification report the f1-score is also high. These are the reasons, we would choose logistic model here.`"
   ]
  }
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