credit_risk_analysis.ipynb

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    "## Credit Risk Analysis \n",
    "### For the Coursera course \n",
    "### Supervised Machine Learning: Classification by IBM\n",
    "\n",
    "###### By Adithya Narayan Samal"
   ]
  },
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    "#### this dataset has been take from kaggle, [note as the dataset is very large we only consider a small part of the data to train models]\n",
    "\n",
    "dataset by RAMESH MEHTA,you can download the dataset from\n",
    "https://www.kaggle.com/datasets/rameshmehta/credit-risk-analysis"
   ]
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  {
   "cell_type": "markdown",
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    "#### Main objective of the analysis that specifies whether your model will be focused on prediction or interpretation and the benefits that your analysis provides to the business or stakeholders of this data.\n",
    "\n",
    "The Main Aim of this analysis is to build a model that predicts has accurate prediction and explain the importance of the features by explaining the model(we use simple models as they easier to understand) (we try to predict if a person will default on their loan payments)"
   ]
  },
  {
   "cell_type": "markdown",
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    "#### Brief description of the data set you chose, a summary of its attributes, and an outline of what you are trying to accomplish with this analysis.\n",
    "\n",
    "The dataset contains loan data for loans issued through the year 2007 - 2015\n",
    "as the loan has a lot of attributes, we only consider ones which have some Correlation with the default_ind(default indicator) of the loan which is our Target Variable,\n",
    "\n",
    "some of its attributes are,\n",
    "\n",
    "default_ind--> this shows if the particular person has defaulted the payment of the instalment \n",
    "\n",
    "loan_amnt --> Amount of money requested by the borrower.\n",
    "\n",
    "int_rate --> Interest rate of the loan.\n",
    "\n",
    "grade --> Loan grade with categories A, B, C, D, E, F, G\n",
    "\n",
    "sub_grade --> sub categories ranging from 1-5 for the grade\n",
    "\n",
    "annual_inc --> Borrowers annual income\n",
    "\n",
    "purpose --> The primary purpose of borrowing.\n",
    "\n",
    "installments –-> Monthly amount payments for opted loan\n",
    "\n",
    "term –-> duration of the loan until it’s paid off\n",
    "\n",
    "as this dataset contains about 73 features we ignore most of them on the basis of correlation and null/missing values,\n",
    "this data is heavily unbalanced about 5.42% of all loans considered as defaulted"
   ]
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  {
   "cell_type": "markdown",
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    "#### Brief summary of data exploration and actions taken for data cleaning and feature engineering\n",
    "\n",
    "for data cleaning,we first take the drop all columns which have more than 1% of null values, then we drop all null/missing value\n",
    "this removes most of the columns so we work with that is left, \n",
    "we then transform rows as such as Grade,sub_grade for Grade who's values can be A,B,C etc we use dummy encoding and for sub grade which is whatever the grade attribute is with an additional value from 1-5 (eg A1,B3,D4) for subgrade we simply just consider the number (eg for A5 we only consider 5)\n",
    "\n",
    "We apply binary encoding for categorical variables with two possible values,and apply dummy encoding for the other categorical attributes and we also convert the Date type attributes to 3 new attributes that being day,month,year\n",
    "\n",
    "we also try to look at the correlation between the remaining attributes and default_ind but we dont eliminate any attributes, \n",
    "we try to explain how the attributes which has comparitively high correlation with default_indm but we notice that most of the attributes are pretty similar for both defaulters and non-defaulters,\n",
    "\n"
   ]
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  {
   "cell_type": "markdown",
   "id": "a68edb19",
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   "source": [
    "#### Summary of training at least three different classifier models, preferably of different nature in explainability and predictability. For example, you can start with a simple logistic regression as a baseline, adding other models or ensemble models. Preferably, all your models use the same training and test splits, or the same cross-validation method\n",
    "\n",
    "Here is a summary of the diffrent models Trained along with the hyperparameters and performance metrics\n",
    "\n",
    "<table style=\"font-size: 16px;\">\n",
    "  <tr>\n",
    "    <th><center>  Model  </center></th>\n",
    "    <th><center>  Hyperparameters  </center></th>\n",
    "    <th><center>  Performance Metrics  </center></th>\n",
    "  </tr>\n",
    "    <tr>\n",
    "        <td><center>Gaussian Naive Bayes</center></td>\n",
    "        <td><center>GaussianNB(priors= [0.94575, 0.05425])</center></td>\n",
    "        <td><center>\n",
    "Accuracy :  0.8640791832281194<br>\n",
    "Recall : 0.9342672413793104<br>\n",
    "Precision : 0.2768788588460719<br>\n",
    "F1 Score: 0.4271637378879947<br>\n",
    "AUC : 0.89716040272509<br></center></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><center>Logistic Regression</center></td>\n",
    "        <td><center>LogisticRegression(class_weight={0: 0.05425,1:0.94575},C=0.01,penalty='l2')</center></td>\n",
    "        <td><center>\n",
    "Accuracy :  0.9936676798378926<br>\n",
    "Recall : 0.9673132183908046<br>\n",
    "Precision : 0.9200546634779638<br>\n",
    "F1 Score: 0.9430922780598845<br>\n",
    "AUC : 0.98124622600628<br>\n",
    "            </center></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><center>Decision Tree</center></td>\n",
    "        <td><center>DecisionTreeClassifier(random_state=123)</center></td>\n",
    "        <td><center>\n",
    "Accuracy :  0.9536669004754111<br>\n",
    "Recall : 0.24209770114942528<br>\n",
    "Precision : 0.7154989384288747<br>\n",
    "F1 Score: 0.3617820719269994<br>\n",
    "AUC : 0.6182882407683673<br>\n",
    "            </center></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><center>Random Forest</center></td>\n",
    "        <td><center>RandomForestClassifier(n_estimators=69,random_state=123)</center></td>\n",
    "        <td><center>\n",
    "Accuracy :  0.9976424284934923<br>\n",
    "Recall : 0.9568965517241379<br>\n",
    "Precision : 0.999624765478424<br>\n",
    "F1 Score: 0.9777940906588365<br>\n",
    "AUC : 0.9784379750792094<br></center></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><center>KNN</center></td>\n",
    "        <td><center>\n",
    "KNeighborsClassifier(n_neighbors=3)\n",
    "            </center></td>\n",
    "        <td><center>\n",
    "Accuracy :  0.9536669004754111<br>\n",
    "Recall : 0.24209770114942528<br>\n",
    "Precision : 0.7154989384288747<br>\n",
    "F1 Score: 0.3617820719269994<br>\n",
    "AUC : 0.6182882407683673<br>\n",
    "            </center></td>\n",
    "    </tr>\n",
    "    </table>\n",
    "    \n",
    "Logistic Regression and Random forest are the ideal models from what we can observe in the table above,"
   ]
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   "cell_type": "markdown",
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    "#### A paragraph explaining which of your classifier models you recommend as a final model that best fits your needs in terms of accuracy and explainability.\n",
    "\n",
    "Although Both Logistic Regression and Random Forest have similar Accuracy,F1 Score and AUC, although Random Forest holds a slightly better performance it is better to use logistic Regression as the final model, as it is less resource intensive and not as likely to overfit, and mainly it is easier to understand by looking at the weights of the Coeff of each feature where as to understand feature importance in Randomforest we will have to go through a large and Complex tree,\n",
    "\n",
    "Which is why i would suggest Logistic Regression as the final model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab1b1c1",
   "metadata": {},
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    "#### Summary Key Findings and Insights, which walks your reader through the main drivers of your model and insights from your data derived from your classifier model.\n",
    "\n",
    "out_prncp,out_prncp_inv,total_rec_prncp,loan_amnt,funded_amnt,funded_amnt_inv,total_pymnt_inv,total_pymnt,last_pymnt_amnt,recoveries...,these are some of the features which have the most impact on if a person defaults on their loan or not,(according to Logistic Regression)\n",
    "\n",
    "Some of the features considered by Randomforest are-->\n",
    "recoveries,collection_recovery_fee,\ttotal_rec_prncp,out_prncp,last_pymnt_amnt,total_pymnt_inv,out_prncp_inv,total_pymnt\t,funded_amnt,loan_amnt\n",
    "\n",
    "some othere key findings are \n",
    "\n",
    "<ul>\n",
    "    <li>most of the features like int_rate are similar for both defaulters,non-defaulters although it has similar range for both, it is marginally higher for defaulters<br><br></li>\n",
    "    <li>some features like collection_recovery_fee which are related with recovery fee can have more significance as they are mostly zero for non-defaulters , making it a effective feature when deciding if someone is likely to default, this is probably because you only pay recovery related fees when you have defaulted atleast once, and maybe if someone had defaulted once they might default again<br><br></li>\n",
    "    <li>We can see that most of the features considered important has relation to recoveries, and most of the important features are similar for both Logistic Regression, and RandomForest<br></li>   \n",
    "</ul>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54131010",
   "metadata": {},
   "source": [
    "##### Suggestions for next steps in analyzing this data, which may include suggesting revisiting this model after adding specific data features that may help you achieve a better explanation or a better prediction.\n",
    "\n",
    "Some models like SVM were not used as they take a lot of time to train if dataset has a lot of features, so we can try a SVM and as we have a huge quantity of data we can also try using Deep Learning,Neural Networks for better performance,\n",
    "\n",
    "we can also try including more features as in this case i removed/discarded a lot of potentially usefull features because of stroage and processing power constraints, if we get better resources we can try being less selective and include more of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2106765e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed8ff555",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ignore warnings\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f7ba3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('data.csv',low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72028a34",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>855969 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  member_id  loan_amnt  funded_amnt  funded_amnt_inv  \\\n",
       "0        1077501    1296599       5000         5000           4975.0   \n",
       "1        1077430    1314167       2500         2500           2500.0   \n",
       "2        1077175    1313524       2400         2400           2400.0   \n",
       "3        1076863    1277178      10000        10000          10000.0   \n",
       "4        1075358    1311748       3000         3000           3000.0   \n",
       "...          ...        ...        ...          ...              ...   \n",
       "855964  36371250   39102635      10000        10000          10000.0   \n",
       "855965  36441262   39152692      24000        24000          24000.0   \n",
       "855966  36271333   38982739      13000        13000          13000.0   \n",
       "855967  36490806   39222577      12000        12000          12000.0   \n",
       "855968  36271262   38982659      20000        20000          20000.0   \n",
       "\n",
       "              term  int_rate  installment grade sub_grade  ... il_util  \\\n",
       "0        36 months     10.65       162.87     B        B2  ...     NaN   \n",
       "1        60 months     15.27        59.83     C        C4  ...     NaN   \n",
       "2        36 months     15.96        84.33     C        C5  ...     NaN   \n",
       "3        36 months     13.49       339.31     C        C1  ...     NaN   \n",
       "4        60 months     12.69        67.79     B        B5  ...     NaN   \n",
       "...            ...       ...          ...   ...       ...  ...     ...   \n",
       "855964   36 months     11.99       332.10     B        B5  ...     NaN   \n",
       "855965   36 months     11.99       797.03     B        B5  ...     NaN   \n",
       "855966   60 months     15.99       316.07     D        D2  ...     NaN   \n",
       "855967   60 months     19.99       317.86     E        E3  ...     NaN   \n",
       "855968   36 months     11.99       664.20     B        B5  ...     NaN   \n",
       "\n",
       "       open_rv_12m open_rv_24m  max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
       "0              NaN         NaN         NaN      NaN              NaN    NaN   \n",
       "1              NaN         NaN         NaN      NaN              NaN    NaN   \n",
       "2              NaN         NaN         NaN      NaN              NaN    NaN   \n",
       "3              NaN         NaN         NaN      NaN              NaN    NaN   \n",
       "4              NaN         NaN         NaN      NaN              NaN    NaN   \n",
       "...            ...         ...         ...      ...              ...    ...   \n",
       "855964         NaN         NaN         NaN      NaN          17100.0    NaN   \n",
       "855965         NaN         NaN         NaN      NaN          10200.0    NaN   \n",
       "855966         NaN         NaN         NaN      NaN          18000.0    NaN   \n",
       "855967         NaN         NaN         NaN      NaN          27000.0    NaN   \n",
       "855968         NaN         NaN         NaN      NaN          41700.0    NaN   \n",
       "\n",
       "       total_cu_tl inq_last_12m default_ind  \n",
       "0              NaN          NaN           0  \n",
       "1              NaN          NaN           1  \n",
       "2              NaN          NaN           0  \n",
       "3              NaN          NaN           0  \n",
       "4              NaN          NaN           0  \n",
       "...            ...          ...         ...  \n",
       "855964         NaN          NaN           0  \n",
       "855965         NaN          NaN           0  \n",
       "855966         NaN          NaN           0  \n",
       "855967         NaN          NaN           0  \n",
       "855968         NaN          NaN           0  \n",
       "\n",
       "[855969 rows x 73 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size=df.shape[0]\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0e88a0d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dropped_list=[]\n",
    "for x in df:\n",
    "    if (df[x].count()<(size*0.99)) :\n",
    "        df.drop([x],axis=1,inplace=True)\n",
    "        dropped_list.append(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3fb2ed24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['emp_title',\n",
       " 'emp_length',\n",
       " 'desc',\n",
       " 'mths_since_last_delinq',\n",
       " 'mths_since_last_record',\n",
       " 'last_pymnt_d',\n",
       " 'next_pymnt_d',\n",
       " 'mths_since_last_major_derog',\n",
       " 'annual_inc_joint',\n",
       " 'dti_joint',\n",
       " 'verification_status_joint',\n",
       " 'tot_coll_amt',\n",
       " 'tot_cur_bal',\n",
       " 'open_acc_6m',\n",
       " 'open_il_6m',\n",
       " 'open_il_12m',\n",
       " 'open_il_24m',\n",
       " 'mths_since_rcnt_il',\n",
       " 'total_bal_il',\n",
       " 'il_util',\n",
       " 'open_rv_12m',\n",
       " 'open_rv_24m',\n",
       " 'max_bal_bc',\n",
       " 'all_util',\n",
       " 'total_rev_hi_lim',\n",
       " 'inq_fi',\n",
       " 'total_cu_tl',\n",
       " 'inq_last_12m']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "272cdc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id     member_id  loan_amnt  funded_amnt  funded_amnt_inv  term   int_rate  installment  grade  sub_grade  home_ownership  annual_inc  verification_status  issue_d  pymnt_plan  purpose  title  zip_code  addr_state  dti    delinq_2yrs  earliest_cr_line  inq_last_6mths  open_acc  pub_rec  revol_bal  revol_util  total_acc  initial_list_status  out_prncp  out_prncp_inv  total_pymnt  total_pymnt_inv  total_rec_prncp  total_rec_int  total_rec_late_fee  recoveries  collection_recovery_fee  last_pymnt_amnt  last_credit_pull_d  collections_12_mths_ex_med  policy_code  application_type  acc_now_delinq  default_ind\n",
       "False  False      False      False        False            False  False     False        False  False      False           False       False                False    False       False    False  False     False       False  False        False             False           False     False    False      False       False      False                False      False          False        False            False            False          False               False       False                    False            False               False                       False        False             False           False          855385\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we deal with the non-numeric attributes\n",
    "df.dropna(inplace=True)\n",
    "df.isna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d0106c7",
   "metadata": {
    "scrolled": true
   },
   "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>sub_grade</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C4</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C5</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A4</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C5</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E1</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>F2</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sub_grade grade\n",
       "0        B2     B\n",
       "1        C4     C\n",
       "2        C5     C\n",
       "3        C1     C\n",
       "4        B5     B\n",
       "5        A4     A\n",
       "6        C5     C\n",
       "7        E1     E\n",
       "8        F2     F\n",
       "9        B5     B"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#as sub_grade and grade are related, we can assume if grade is like A, subgrade will be some thing like A5 or A4\n",
    "df[['sub_grade','grade']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0819f014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    428822\n",
       "RENT        342312\n",
       "OWN          84063\n",
       "OTHER          142\n",
       "NONE            43\n",
       "ANY              3\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['home_ownership'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2115ca8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['term']=df['term'].apply(lambda x : int(x.split(\" \")[1]))\n",
    "#for sub grade, we do ordinal encoding for grade column and only consider the subgrade part ie for A4 we consider 4,\n",
    "df['sub_grade']=df['sub_grade'].apply(lambda x : int(x[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb52229c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['initial_list_status']=df['initial_list_status'].apply(lambda x:0 if x == 'f' else 1)\n",
    "df['pymnt_plan']=df['pymnt_plan'].apply(lambda x:0 if x == 'n' else 1)\n",
    "df['application_type']=df['application_type'].apply(lambda x:0 if x == 'INDIVIDUAL' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b1e29a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dealing with date based columns,dropping columns\n",
    "date_list=['issue_d','earliest_cr_line','last_credit_pull_d']\n",
    "drop_list=['title','zip_code','addr_state','id']\n",
    "for x in  date_list:\n",
    "    df[x]=pd.to_datetime(df[x])\n",
    "    df[x+\"_day\"]=df[x].dt.day\n",
    "    df[x+\"_month\"]=df[x].dt.month\n",
    "    df[x+\"_year\"]=df[x].dt.year\n",
    "    df.drop(labels=[x],inplace=True,axis=1)\n",
    "df.drop(labels=drop_list,axis=1,inplace=True) \n",
    "#ordinal encoding of the remaining columns\n",
    "df=pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23e362a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cr=df.corr()\n",
    "df_cols=df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51784c82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['int_rate', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'default_ind', 'last_credit_pull_d_day']\n",
      "int_rate :  0.15500366390293305\n",
      "total_rec_late_fee :  0.14071868635751128\n",
      "recoveries :  0.4757988426510833\n",
      "collection_recovery_fee :  0.3307443207798686\n",
      "default_ind :  1.0\n",
      "last_credit_pull_d_day :  0.17658276317654667\n"
     ]
    }
   ],
   "source": [
    "high_cor_list=[]\n",
    "for x in df_cols:\n",
    "    if (df_cr['default_ind'][x]>0.10 and df_cr['default_ind'][x]>0) or (df_cr['default_ind'][x]<-0.30 and df_cr['default_ind'][x]<0):\n",
    "        high_cor_list.append(x)\n",
    "print(high_cor_list)\n",
    "for x in high_cor_list:\n",
    "    print(x+\" : \",df_cr['default_ind'][x])\n",
    "#as we have very low correlation among the columns, we have to consider every column,\n",
    "low_cor_list=[]\n",
    "for x in df_cols:\n",
    "    if (df_cr['default_ind'][x]==0):\n",
    "        low_cor_list.append(x)\n",
    "for x in low_cor_list:\n",
    "    print(x+\" : \",df_cr['default_ind'][x])\n",
    "# as all columns have some non-zero correlation we dont drop any,\n",
    "# we can see that 'last_credit_pull_d_day ' has somewhat high correlation, however this is probably just coincidence and does not mean anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "970dd3d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# as the dataset is too large, we use  to take a sample and well use it as our dataset to save time in training models,\n",
    "from sklearn.model_selection import train_test_split\n",
    "df_maj,df_min=train_test_split( df,test_size=0.2, random_state=42, stratify=df['default_ind'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "edca7b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.94575\n",
       "1    0.05425\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_maj['default_ind'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e981fe4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.94575\n",
       "1    0.05425\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_min['default_ind'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "09dad1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we see that both have the same ratio of Defaults, we take df_min, as our main dataset,\n",
    "old_df=df.copy()\n",
    "df=df_min.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2016061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1faea886e50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['default_ind'],df['recoveries'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d26e771",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group=df.groupby('default_ind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "167dad64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default_ind  recoveries\n",
       "0            0.00          1.000000\n",
       "1            0.00          0.480228\n",
       "             10.40         0.000323\n",
       "             10.70         0.000323\n",
       "             11.29         0.000323\n",
       "                             ...   \n",
       "             21096.30      0.000108\n",
       "             24833.68      0.000108\n",
       "             25000.29      0.000108\n",
       "             26308.47      0.000108\n",
       "             33520.27      0.000108\n",
       "Name: recoveries, Length: 4736, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group['recoveries'].value_counts(normalize=True)\n",
    "# we can see that people who did not default have 0 recoveries, so there is a tie between recoveries,\n",
    "#the relationship might be if you are a defaulty you might have to pay 'recoveries'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "89afb7d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default_ind  grade_A\n",
       "0            0          0.824711\n",
       "             1          0.175289\n",
       "1            0          0.943325\n",
       "             1          0.056675\n",
       "Name: grade_A, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group['grade_A'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2f85bf59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default_ind  grade_B\n",
       "0            0          0.704412\n",
       "             1          0.295588\n",
       "1            0          0.789570\n",
       "             1          0.210430\n",
       "Name: grade_B, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group['grade_B'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f86b61ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default_ind  grade_C\n",
       "0            0          0.723856\n",
       "             1          0.276144\n",
       "1            0          0.716087\n",
       "             1          0.283913\n",
       "Name: grade_C, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group['grade_C'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "99ac37f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default_ind  grade_D\n",
       "0            0          0.849026\n",
       "             1          0.150974\n",
       "1            0          0.769852\n",
       "             1          0.230148\n",
       "Name: grade_D, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group['grade_D'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9cda2325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default_ind  grade_E\n",
       "0            0          0.925740\n",
       "             1          0.074260\n",
       "1            0          0.864778\n",
       "             1          0.135222\n",
       "Name: grade_E, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group['grade_E'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e9fd9e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default_ind  grade_F\n",
       "0            0          0.977187\n",
       "             1          0.022813\n",
       "1            0          0.935136\n",
       "             1          0.064864\n",
       "Name: grade_F, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group['grade_F'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "188566e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default_ind  grade_G\n",
       "0            0          0.995068\n",
       "             1          0.004932\n",
       "1            0          0.981252\n",
       "             1          0.018748\n",
       "Name: grade_G, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group['grade_G'].value_counts(normalize=True)\n",
    "# we see that the grade they belong to doesnt have much impact to if they default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "97e5e81b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sub_grade  default_ind\n",
       "1          0              0.950567\n",
       "           1              0.049433\n",
       "2          0              0.946477\n",
       "           1              0.053523\n",
       "3          0              0.943320\n",
       "           1              0.056680\n",
       "4          0              0.944057\n",
       "           1              0.055943\n",
       "5          0              0.944063\n",
       "           1              0.055937\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# we know all the sub_grades(1-5) have similar presence(proportions/counts)\n",
    "df_group=df.groupby('sub_grade')\n",
    "df_group['default_ind'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c41ea9a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='sub_grade,default_ind'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_group['default_ind'].value_counts(normalize=True).plot(kind='barh')\n",
    "#we see something similar here, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "156ece33",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df.copy()\n",
    "df2=df2.sort_values(['int_rate'])\n",
    "df2.reset_index(inplace=True)\n",
    "default_ind_0=df2['default_ind']==0\n",
    "default_ind_1=df2['default_ind']==1\n",
    "df3=df2[default_ind_1]\n",
    "df4=df2[default_ind_0]\n",
    "df3.reset_index(inplace=True)\n",
    "df4.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0796d7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9281.000000\n",
       "mean       16.006910\n",
       "std         4.291598\n",
       "min         5.420000\n",
       "25%        13.050000\n",
       "50%        15.650000\n",
       "75%        18.920000\n",
       "max        28.990000\n",
       "Name: int_rate, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#default_ind=1\n",
    "df3['int_rate'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3358874c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    161796.000000\n",
       "mean         13.038807\n",
       "std           4.313886\n",
       "min           5.320000\n",
       "25%           9.760000\n",
       "50%          12.690000\n",
       "75%          15.610000\n",
       "max          28.990000\n",
       "Name: int_rate, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#default_ind=0\n",
    "df4['int_rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "42b51c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(df3['int_rate'],c='blue')#default_ind=1\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(df4['int_rate'],c='green')#default_ind=0\n",
    "plt.show()\n",
    "\n",
    "#both defaulty and non-defaulty have similar intrest rate min,max and average, even the curve is similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b7de3d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2=df.copy()\n",
    "df2=df2.sort_values(['collection_recovery_fee'])\n",
    "df2.reset_index(inplace=True)\n",
    "default_ind_0=df2['default_ind']==0\n",
    "default_ind_1=df2['default_ind']==1\n",
    "df3=df2[default_ind_1]\n",
    "df4=df2[default_ind_0]\n",
    "df3.reset_index(inplace=True)\n",
    "df4.reset_index(inplace=True)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(df3['collection_recovery_fee'],c='blue')#default_ind=1\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(df4['collection_recovery_fee'],c='green')#default_ind=0\n",
    "plt.show()\n",
    "# we see that 'collection_recovery_fee' is 0 for all defaultys, so this feature,might have some significance,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "951af599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2=df.copy()\n",
    "df2=df2.sort_values(['total_rec_late_fee'])\n",
    "df2.reset_index(inplace=True)\n",
    "default_ind_0=df2['default_ind']==0\n",
    "default_ind_1=df2['default_ind']==1\n",
    "df3=df2[default_ind_1]\n",
    "df4=df2[default_ind_0]\n",
    "df3.reset_index(inplace=True)\n",
    "df4.reset_index(inplace=True)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(df3['total_rec_late_fee'],c='blue')#default_ind=1\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(df4['total_rec_late_fee'],c='green')#default_ind=0\n",
    "plt.show()\n",
    "#again we got similar graphs and the are quite similar,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b4ed8052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9281.000000\n",
       "mean        2.360642\n",
       "std        10.090214\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         0.000000\n",
       "max       358.680000\n",
       "Name: total_rec_late_fee, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['total_rec_late_fee'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0b5dffc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    161796.000000\n",
       "mean          0.209447\n",
       "std           2.990831\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           0.000000\n",
       "max         286.747566\n",
       "Name: total_rec_late_fee, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['total_rec_late_fee'].describe()\n",
    "# we can see that the mean, and the maximum is much lower for non-defaulty(default_ind=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4764a24",
   "metadata": {},
   "source": [
    "### We Now Build Models,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "61d114bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import recall_score,precision_score,accuracy_score,f1_score,roc_auc_score\n",
    "def performance_eval(y,ypred):\n",
    "    print(\"Accuracy : \",accuracy_score(y,ypred))\n",
    "    print(\"Recall :\",recall_score(y,ypred))\n",
    "    print(\"Precision :\",precision_score(y,ypred))\n",
    "    print(\"F1 Score:\",f1_score(y,ypred))\n",
    "    print(\"AUC :\",roc_auc_score(y,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "39403403",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train_test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_df,test_df=train_test_split(df,test_size=0.3, random_state=42, stratify=df['default_ind'])\n",
    "y_train=train_df['default_ind']\n",
    "x_train=train_df.drop('default_ind',axis=1)\n",
    "\n",
    "y_test=test_df['default_ind']\n",
    "x_test=test_df.drop('default_ind',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5e7e333d",
   "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>GridSearchCV(cv=7, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 5, 7, 9, 11]}, scoring=&#x27;f1&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=7, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 5, 7, 9, 11]}, scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=7, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': [3, 5, 7, 9, 11]}, scoring='f1')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "params={'n_neighbors':[3,5,7,9,11]}\n",
    "knn_mod=KNeighborsClassifier()\n",
    "knn_grid=GridSearchCV(knn_mod,param_grid=params,cv=7,scoring='f1')\n",
    "knn_grid.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "18015751",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 3}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ea1f2924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.9536669004754111\n",
      "Recall : 0.24209770114942528\n",
      "Precision : 0.7154989384288747\n",
      "F1 Score: 0.3617820719269994\n",
      "AUC : 0.6182882407683673\n"
     ]
    }
   ],
   "source": [
    "#best knn, the model\n",
    "knn_mod=KNeighborsClassifier(n_neighbors=3)\n",
    "knn_mod.fit(x_train,y_train)\n",
    "ypred=knn_mod.predict(x_test)\n",
    "performance_eval(y_test,ypred)\n",
    "# the model doesn't perform very well,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f8f3e1ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.9536669004754111\n",
      "Recall : 0.24209770114942528\n",
      "Precision : 0.7154989384288747\n",
      "F1 Score: 0.3617820719269994\n",
      "AUC : 0.6182882407683673\n"
     ]
    }
   ],
   "source": [
    "#decision tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt=DecisionTreeClassifier(random_state=123)\n",
    "dt_to_plot=dt.fit(x_train,y_train)\n",
    "dt=dt.predict(x_test)\n",
    "performance_eval(y_test,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e52e7153",
   "metadata": {},
   "outputs": [
    {
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       " Text(0.9501631563334322, 0.6964285714285714, 'gini = 0.0\\nsamples = 30524\\nvalue = [30524, 0]'),\n",
       " Text(0.9572827054286562, 0.7321428571428571, 'x[17] <= 6.5\\ngini = 0.041\\nsamples = 48\\nvalue = [47, 1]'),\n",
       " Text(0.9549095223969148, 0.6964285714285714, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.9596558884603975, 0.6964285714285714, 'gini = 0.0\\nsamples = 47\\nvalue = [47, 0]'),\n",
       " Text(0.9715218036191041, 0.7678571428571429, 'x[16] <= 95.75\\ngini = 0.022\\nsamples = 178\\nvalue = [176, 2]'),\n",
       " Text(0.9667754375556215, 0.7321428571428571, 'x[39] <= 6.5\\ngini = 0.011\\nsamples = 174\\nvalue = [173, 1]'),\n",
       " Text(0.9644022545238802, 0.6964285714285714, 'x[22] <= 959.565\\ngini = 0.124\\nsamples = 15\\nvalue = [14, 1]'),\n",
       " Text(0.9620290714921388, 0.6607142857142857, 'x[6] <= 295.035\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.9596558884603975, 0.625, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.9644022545238802, 0.625, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.9667754375556215, 0.6607142857142857, 'gini = 0.0\\nsamples = 13\\nvalue = [13, 0]'),\n",
       " Text(0.9691486205873628, 0.6964285714285714, 'gini = 0.0\\nsamples = 159\\nvalue = [159, 0]'),\n",
       " Text(0.9762681696825868, 0.7321428571428571, 'x[1] <= 21350.0\\ngini = 0.375\\nsamples = 4\\nvalue = [3, 1]'),\n",
       " Text(0.9738949866508454, 0.6964285714285714, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.9786413527143281, 0.6964285714285714, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.9905072678730347, 0.8392857142857143, 'x[11] <= 10.0\\ngini = 0.04\\nsamples = 97\\nvalue = [95, 2]'),\n",
       " Text(0.9857609018095521, 0.8035714285714286, 'x[28] <= 23.125\\ngini = 0.021\\nsamples = 95\\nvalue = [94, 1]'),\n",
       " Text(0.9833877187778107, 0.7678571428571429, 'x[10] <= 15.66\\ngini = 0.444\\nsamples = 3\\nvalue = [2, 1]'),\n",
       " Text(0.9810145357460694, 0.7321428571428571, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.9857609018095521, 0.7321428571428571, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.9881340848412934, 0.7678571428571429, 'gini = 0.0\\nsamples = 92\\nvalue = [92, 0]'),\n",
       " Text(0.9952536339365173, 0.8035714285714286, 'x[5] <= 18.06\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.9928804509047761, 0.7678571428571429, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.9976268169682587, 0.7678571428571429, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.4624560097847449, 0.9464285714285714, 'gini = 0.0\\nsamples = 3373\\nvalue = [0, 3373]')]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "plot_tree(dt_to_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "349df49b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.9976424284934923\n",
      "Recall : 0.9568965517241379\n",
      "Precision : 0.999624765478424\n",
      "F1 Score: 0.9777940906588365\n",
      "AUC : 0.9784379750792094\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc=RandomForestClassifier(n_estimators=69,random_state=123)\n",
    "rfc.fit(x_train,y_train)\n",
    "ypred=rfc.predict(x_test)\n",
    "performance_eval(y_test,ypred)\n",
    "\n",
    "# we see that the results are pretty good, it has high accuracy,AUC, and a good precision,Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bb4da9a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=7,\n",
       "             estimator=LogisticRegression(class_weight={0: 0.05425,\n",
       "                                                        1: 0.94575}),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 0, 1, 10],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None]},\n",
       "             scoring=&#x27;f1&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=7,\n",
       "             estimator=LogisticRegression(class_weight={0: 0.05425,\n",
       "                                                        1: 0.94575}),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 0, 1, 10],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None]},\n",
       "             scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(class_weight={0: 0.05425, 1: 0.94575})</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(class_weight={0: 0.05425, 1: 0.94575})</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=7,\n",
       "             estimator=LogisticRegression(class_weight={0: 0.05425,\n",
       "                                                        1: 0.94575}),\n",
       "             param_grid={'C': [0.01, 0.1, 0, 1, 10],\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet', None]},\n",
       "             scoring='f1')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lg=LogisticRegression(class_weight={0: 0.05425, 1: 0.94575})\n",
    "params={'C':[0.01,0.1,0,1,10],'penalty':['l1', 'l2', 'elasticnet', None]}\n",
    "lg_grid=GridSearchCV(lg,param_grid=params,cv=7,scoring='f1')\n",
    "lg_grid.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ae7fe261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.01, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_grid.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "98a874bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.9936676798378926\n",
      "Recall : 0.9673132183908046\n",
      "Precision : 0.9200546634779638\n",
      "F1 Score: 0.9430922780598845\n",
      "AUC : 0.98124622600628\n"
     ]
    }
   ],
   "source": [
    "#best logistic regression\n",
    "lr=LogisticRegression(class_weight={0: 0.05425, 1: 0.94575},C=0.01,penalty='l2')\n",
    "lr.fit(x_train,y_train)\n",
    "ypred=lr.predict(x_test)\n",
    "performance_eval(y_test,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4b462bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'priors': [0.94575, 0.05425]}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb = GaussianNB()\n",
    "params={'priors':[[0.94575,0.05425],None]}\n",
    "nb_grid=GridSearchCV(nb,param_grid=params,cv=7,scoring='f1')\n",
    "nb_grid.fit(x_train,y_train)\n",
    "nb_grid.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5b55869b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.8640791832281194\n",
      "Recall : 0.9342672413793104\n",
      "Precision : 0.27687885884607194\n",
      "F1 Score: 0.4271637378879947\n",
      "AUC : 0.89716040272509\n"
     ]
    }
   ],
   "source": [
    "nb = GaussianNB(priors= [0.94575, 0.05425])\n",
    "nb.fit(x_train,y_train)\n",
    "ypred=nb.predict(x_test)\n",
    "performance_eval(y_test,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "014f7645",
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_param_weights=pd.DataFrame(zip([x_train.columns.to_list()][0],lr.coef_.tolist()[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1e3fff45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>member_id</td>\n",
       "      <td>-1.062964e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>7.406546e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>funded_amnt</td>\n",
       "      <td>7.375541e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>funded_amnt_inv</td>\n",
       "      <td>7.261827e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>term</td>\n",
       "      <td>3.487377e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>purpose_other</td>\n",
       "      <td>7.070247e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>purpose_renewable_energy</td>\n",
       "      <td>2.023633e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>purpose_small_business</td>\n",
       "      <td>3.869925e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>purpose_vacation</td>\n",
       "      <td>1.281302e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>purpose_wedding</td>\n",
       "      <td>3.545767e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0             1\n",
       "0                  member_id -1.062964e-07\n",
       "1                  loan_amnt  7.406546e-04\n",
       "2                funded_amnt  7.375541e-04\n",
       "3            funded_amnt_inv  7.261827e-04\n",
       "4                       term  3.487377e-06\n",
       "..                       ...           ...\n",
       "67             purpose_other  7.070247e-09\n",
       "68  purpose_renewable_energy  2.023633e-10\n",
       "69    purpose_small_business  3.869925e-09\n",
       "70          purpose_vacation  1.281302e-09\n",
       "71           purpose_wedding  3.545767e-10\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_param_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fe66f7e9",
   "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>1</th>\n",
       "      <th>absoulute_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>7.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-2.316220e-05</td>\n",
       "      <td>1.047809e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.762482e-04</td>\n",
       "      <td>2.563658e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9.923144e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.003067e-10</td>\n",
       "      <td>3.408619e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.087537e-08</td>\n",
       "      <td>5.295893e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.773216e-07</td>\n",
       "      <td>6.192984e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.406546e-04</td>\n",
       "      <td>9.923144e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1  absoulute_value\n",
       "count  7.200000e+01     7.200000e+01\n",
       "mean  -2.316220e-05     1.047809e-04\n",
       "std    2.762482e-04     2.563658e-04\n",
       "min   -9.923144e-04     0.000000e+00\n",
       "25%    1.003067e-10     3.408619e-09\n",
       "50%    1.087537e-08     5.295893e-08\n",
       "75%    4.773216e-07     6.192984e-06\n",
       "max    7.406546e-04     9.923144e-04"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "logistic_param_weights['absoulute_value']=logistic_param_weights[1].apply(lambda x:math.sqrt(x*x))\n",
    "logistic_param_weights.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bc9e2c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>absoulute_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>out_prncp</td>\n",
       "      <td>-0.000992</td>\n",
       "      <td>0.000992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>out_prncp_inv</td>\n",
       "      <td>-0.000992</td>\n",
       "      <td>0.000992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>total_rec_prncp</td>\n",
       "      <td>-0.000873</td>\n",
       "      <td>0.000873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>0.000741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>funded_amnt</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>0.000738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>funded_amnt_inv</td>\n",
       "      <td>0.000726</td>\n",
       "      <td>0.000726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>total_pymnt_inv</td>\n",
       "      <td>-0.000625</td>\n",
       "      <td>0.000625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>total_pymnt</td>\n",
       "      <td>-0.000625</td>\n",
       "      <td>0.000625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>last_pymnt_amnt</td>\n",
       "      <td>-0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>recoveries</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1  absoulute_value\n",
       "19        out_prncp -0.000992         0.000992\n",
       "20    out_prncp_inv -0.000992         0.000992\n",
       "23  total_rec_prncp -0.000873         0.000873\n",
       "1         loan_amnt  0.000741         0.000741\n",
       "2       funded_amnt  0.000738         0.000738\n",
       "3   funded_amnt_inv  0.000726         0.000726\n",
       "22  total_pymnt_inv -0.000625         0.000625\n",
       "21      total_pymnt -0.000625         0.000625\n",
       "28  last_pymnt_amnt -0.000471         0.000471\n",
       "26       recoveries  0.000194         0.000194"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_param_weights.sort_values(by=['absoulute_value'],ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "87d70b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_feature_imp=pd.DataFrame(zip([x_train.columns.to_list()][0],rfc.feature_importances_.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c27c9669",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>absoulute_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.013889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.034031</td>\n",
       "      <td>0.034031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000764</td>\n",
       "      <td>0.000764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.006189</td>\n",
       "      <td>0.006189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.210248</td>\n",
       "      <td>0.210248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1  absoulute_value\n",
       "count  72.000000        72.000000\n",
       "mean    0.013889         0.013889\n",
       "std     0.034031         0.034031\n",
       "min     0.000000         0.000000\n",
       "25%     0.000135         0.000135\n",
       "50%     0.000764         0.000764\n",
       "75%     0.006189         0.006189\n",
       "max     0.210248         0.210248"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "rfc_feature_imp['absoulute_value']=rfc_feature_imp[1].apply(lambda x:math.sqrt(x*x))\n",
    "rfc_feature_imp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2943697c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>0</th>\n",
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       "      <th>absoulute_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>recoveries</td>\n",
       "      <td>0.210248</td>\n",
       "      <td>0.210248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>collection_recovery_fee</td>\n",
       "      <td>0.131294</td>\n",
       "      <td>0.131294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>total_rec_prncp</td>\n",
       "      <td>0.113195</td>\n",
       "      <td>0.113195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>out_prncp</td>\n",
       "      <td>0.070504</td>\n",
       "      <td>0.070504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>last_pymnt_amnt</td>\n",
       "      <td>0.068871</td>\n",
       "      <td>0.068871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>total_pymnt_inv</td>\n",
       "      <td>0.053084</td>\n",
       "      <td>0.053084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>out_prncp_inv</td>\n",
       "      <td>0.048432</td>\n",
       "      <td>0.048432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>total_pymnt</td>\n",
       "      <td>0.044764</td>\n",
       "      <td>0.044764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>funded_amnt</td>\n",
       "      <td>0.036633</td>\n",
       "      <td>0.036633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0.028127</td>\n",
       "      <td>0.028127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0         1  absoulute_value\n",
       "26               recoveries  0.210248         0.210248\n",
       "27  collection_recovery_fee  0.131294         0.131294\n",
       "23          total_rec_prncp  0.113195         0.113195\n",
       "19                out_prncp  0.070504         0.070504\n",
       "28          last_pymnt_amnt  0.068871         0.068871\n",
       "22          total_pymnt_inv  0.053084         0.053084\n",
       "20            out_prncp_inv  0.048432         0.048432\n",
       "21              total_pymnt  0.044764         0.044764\n",
       "2               funded_amnt  0.036633         0.036633\n",
       "1                 loan_amnt  0.028127         0.028127"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_feature_imp.sort_values(by=['absoulute_value'],ascending=False).head(10)"
   ]
  }
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