Loan_Eligibility_Prediction_using_Gradient_Boosting_Classifier_test.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":49462,"status":"ok","timestamp":1644309030315,"user":{"displayName":"Siddhi Jadhav","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08402691859170607175"},"user_tz":-330},"id":"4faWDjO-b7e3","outputId":"9efd6576-2940-4b09-d93d-a1b8054ac64d"},"outputs":[],"source":["#python version 3.8.10\n","'''fancyimpute==0.7.0\n","imblearn==0.0\n","joblib==1.3.1\n","matplotlib==3.7.2\n","numpy==1.24.4\n","pandas==1.3.5\n","scikit_learn==1.3.0\n","scipy==1.10.1\n","seaborn==0.12.2\n","six==1.16.0\n","xgboost==1.7.6'''"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"OtBL3Tn4TZk3"},"outputs":[],"source":["from fancyimpute import KNN, SoftImpute"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"etTcI_2OTfnU"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import os\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import statistics\n","from sklearn.model_selection import train_test_split,GridSearchCV,cross_val_score\n","from sklearn.preprocessing import LabelBinarizer,StandardScaler,OrdinalEncoder\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import roc_auc_score\n","from sklearn.metrics import roc_curve\n","from scipy.stats import boxcox\n","from sklearn.linear_model import LogisticRegression,RidgeClassifier, PassiveAggressiveClassifier\n","from sklearn import metrics\n","from sklearn import preprocessing\n","from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n","from xgboost import plot_importance\n","from matplotlib import pyplot\n","from sklearn.naive_bayes import BernoulliNB\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.svm import LinearSVC\n","from sklearn.tree import DecisionTreeClassifier\n","from xgboost import XGBClassifier\n","import joblib\n","\n","#%matplotlib inline"]},{"cell_type":"code","execution_count":20,"metadata":{"id":"zE18lMsxT90d"},"outputs":[],"source":["import operator"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"wv-OTRB4UGB3"},"outputs":[],"source":["import six\n","import sys\n","sys.modules['sklearn.externals.six'] = six\n","import sklearn.neighbors._base\n","sys.modules['sklearn.neighbors.base'] = sklearn.neighbors._base\n","from sklearn.utils import _safe_indexing\n","sys.modules['sklearn.utils.safe_indexing'] = sklearn.utils._safe_indexing"]},{"cell_type":"code","execution_count":23,"metadata":{"id":"BRTkrzKcUTpW"},"outputs":[],"source":["from imblearn.over_sampling import SMOTE"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"U8D_ER-2UZWT"},"outputs":[],"source":["test = pd.read_csv(\"https://s3.amazonaws.com/hackerday.datascience/358/test_data.csv\")"]},{"cell_type":"code","execution_count":25,"metadata":{"id":"XFGCzoBIUn0Y"},"outputs":[],"source":["cat_cols = ['Term','Years in current job','Home Ownership','Purpose']\n","\n","for c in cat_cols:\n","    test[c] = pd.factorize(test[c])[0]\n","\n"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1644310183580,"user":{"displayName":"Siddhi Jadhav","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08402691859170607175"},"user_tz":-330},"id":"GD_EgreRUqTB","outputId":"5fa03c5b-6343-4ed3-b8e5-fd66e8fec506"},"outputs":[{"name":"stdout","output_type":"stream","text":["[SoftImpute] Max Singular Value of X_init = 220732.319189\n","[SoftImpute] Iter 1: observed MAE=393.868714 rank=4\n","[SoftImpute] Iter 2: observed MAE=393.835770 rank=4\n","[SoftImpute] Iter 3: observed MAE=393.823069 rank=4\n","[SoftImpute] Iter 4: observed MAE=393.816892 rank=4\n","[SoftImpute] Iter 5: observed MAE=393.813120 rank=4\n","[SoftImpute] Iter 6: observed MAE=393.810394 rank=4\n","[SoftImpute] Iter 7: observed MAE=393.808205 rank=4\n","[SoftImpute] Iter 8: observed MAE=393.806333 rank=4\n","[SoftImpute] Iter 9: observed MAE=393.804668 rank=4\n","[SoftImpute] Iter 10: observed MAE=393.803152 rank=4\n","[SoftImpute] Iter 11: observed MAE=393.801748 rank=4\n","[SoftImpute] Iter 12: observed MAE=393.800437 rank=4\n","[SoftImpute] Iter 13: observed MAE=393.799203 rank=4\n","[SoftImpute] Iter 14: observed MAE=393.798037 rank=4\n","[SoftImpute] Iter 15: observed MAE=393.796931 rank=4\n","[SoftImpute] Iter 16: observed MAE=393.795881 rank=4\n","[SoftImpute] Iter 17: observed MAE=393.794882 rank=4\n","[SoftImpute] Iter 18: observed MAE=393.793931 rank=4\n","[SoftImpute] Iter 19: observed MAE=393.793024 rank=4\n","[SoftImpute] Iter 20: observed MAE=393.792160 rank=4\n","[SoftImpute] Iter 21: observed MAE=393.791335 rank=4\n","[SoftImpute] Iter 22: observed MAE=393.790548 rank=4\n","[SoftImpute] Iter 23: observed MAE=393.789797 rank=4\n","[SoftImpute] Iter 24: observed MAE=393.789081 rank=4\n","[SoftImpute] Iter 25: observed MAE=393.788397 rank=4\n","[SoftImpute] Iter 26: observed MAE=393.787744 rank=4\n","[SoftImpute] Iter 27: observed MAE=393.787120 rank=4\n","[SoftImpute] Iter 28: observed MAE=393.786525 rank=4\n","[SoftImpute] Iter 29: observed MAE=393.785956 rank=4\n","[SoftImpute] Iter 30: observed MAE=393.785414 rank=4\n","[SoftImpute] Iter 31: observed MAE=393.784895 rank=4\n","[SoftImpute] Iter 32: observed MAE=393.784401 rank=4\n","[SoftImpute] Iter 33: observed MAE=393.783928 rank=4\n","[SoftImpute] Iter 34: observed MAE=393.783477 rank=4\n","[SoftImpute] Iter 35: observed MAE=393.783046 rank=4\n","[SoftImpute] Iter 36: observed MAE=393.782635 rank=4\n","[SoftImpute] Iter 37: observed MAE=393.782242 rank=4\n","[SoftImpute] Iter 38: observed MAE=393.781867 rank=4\n","[SoftImpute] Iter 39: observed MAE=393.781509 rank=4\n","[SoftImpute] Iter 40: observed MAE=393.781166 rank=4\n","[SoftImpute] Iter 41: observed MAE=393.780840 rank=4\n","[SoftImpute] Iter 42: observed MAE=393.780528 rank=4\n","[SoftImpute] Iter 43: observed MAE=393.780230 rank=4\n","[SoftImpute] Iter 44: observed MAE=393.779946 rank=4\n","[SoftImpute] Iter 45: observed MAE=393.779674 rank=4\n","[SoftImpute] Iter 46: observed MAE=393.779415 rank=4\n","[SoftImpute] Iter 47: observed MAE=393.779167 rank=4\n","[SoftImpute] Iter 48: observed MAE=393.778931 rank=4\n","[SoftImpute] Iter 49: observed MAE=393.778705 rank=4\n","[SoftImpute] Iter 50: observed MAE=393.778489 rank=4\n","[SoftImpute] Iter 51: observed MAE=393.778284 rank=4\n","[SoftImpute] Iter 52: observed MAE=393.778087 rank=4\n","[SoftImpute] Iter 53: observed MAE=393.777899 rank=4\n","[SoftImpute] Iter 54: observed MAE=393.777720 rank=4\n","[SoftImpute] Iter 55: observed MAE=393.777549 rank=4\n","[SoftImpute] Iter 56: observed MAE=393.777385 rank=4\n","[SoftImpute] Iter 57: observed MAE=393.777229 rank=4\n","[SoftImpute] Iter 58: observed MAE=393.777080 rank=4\n","[SoftImpute] Iter 59: observed MAE=393.776938 rank=4\n","[SoftImpute] Iter 60: observed MAE=393.776802 rank=4\n","[SoftImpute] Iter 61: observed MAE=393.776672 rank=4\n","[SoftImpute] Iter 62: observed MAE=393.776548 rank=4\n","[SoftImpute] Iter 63: observed MAE=393.776430 rank=4\n","[SoftImpute] Iter 64: observed MAE=393.776317 rank=4\n","[SoftImpute] Iter 65: observed MAE=393.776209 rank=4\n","[SoftImpute] Iter 66: observed MAE=393.776106 rank=4\n","[SoftImpute] Iter 67: observed MAE=393.776008 rank=4\n","[SoftImpute] Iter 68: observed MAE=393.775914 rank=4\n","[SoftImpute] Iter 69: observed MAE=393.775824 rank=4\n","[SoftImpute] Iter 70: observed MAE=393.775739 rank=4\n","[SoftImpute] Iter 71: observed MAE=393.775657 rank=4\n","[SoftImpute] Iter 72: observed MAE=393.775579 rank=4\n","[SoftImpute] Iter 73: observed MAE=393.775504 rank=4\n","[SoftImpute] Iter 74: observed MAE=393.775433 rank=4\n","[SoftImpute] Iter 75: observed MAE=393.775365 rank=4\n","[SoftImpute] Iter 76: observed MAE=393.775300 rank=4\n","[SoftImpute] Iter 77: observed MAE=393.775238 rank=4\n","[SoftImpute] Iter 78: observed MAE=393.775179 rank=4\n","[SoftImpute] Iter 79: observed MAE=393.775122 rank=4\n","[SoftImpute] Iter 80: observed MAE=393.775068 rank=4\n","[SoftImpute] Iter 81: observed MAE=393.775017 rank=4\n","[SoftImpute] Iter 82: observed MAE=393.774968 rank=4\n","[SoftImpute] Iter 83: observed MAE=393.774921 rank=4\n","[SoftImpute] Stopped after iteration 83 for lambda=4414.646384\n"]}],"source":["#Imputing missing data with soft impute\n","updated_test_data=pd.DataFrame(data=SoftImpute().fit_transform(test[test.columns[3:19]],), columns=test[test.columns[3:19]].columns, index=test.index)\n"]},{"cell_type":"code","execution_count":27,"metadata":{"id":"XmjixH60Ur9F"},"outputs":[],"source":["#Getting the dataset ready pd.get dummies function for dropping the dummy variables\n","test_data = pd.get_dummies(updated_test_data, drop_first=True)"]},{"cell_type":"code","execution_count":28,"metadata":{"id":"ibd-MEBNUvIP"},"outputs":[],"source":["gbm_pickle = joblib.load('GBM_Model_version1.pkl')"]},{"cell_type":"code","execution_count":29,"metadata":{"id":"9Bgr14kwUw7P"},"outputs":[],"source":["y_pred = gbm_pickle.predict(test_data)\n"]},{"cell_type":"code","execution_count":30,"metadata":{"id":"UlVUl9WoUyqG"},"outputs":[],"source":["y_pred = gbm_pickle.predict_proba(test_data)\n"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"jqMwBR84Uz_w"},"outputs":[],"source":["y_pred_1=np.where(y_pred ==0, 'Loan Approved', 'Loan Rejected')\n"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"k-cGMFUxU1Ah"},"outputs":[],"source":["test['Loan Status']=y_pred_1\n","\n","test.to_csv('Output_Test.csv',index=False)\n"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":582,"status":"ok","timestamp":1644310240851,"user":{"displayName":"Siddhi Jadhav","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08402691859170607175"},"user_tz":-330},"id":"aSMsJWZtU3Ll","outputId":"5ec38bcf-a61f-4958-ad64-e5aa3f3b152b"},"outputs":[{"data":{"text/plain":["array([['Loan Rejected', 'Loan Rejected'],\n","       ['Loan Rejected', 'Loan Rejected'],\n","       ['Loan Rejected', 'Loan Rejected'],\n","       ['Loan Rejected', 'Loan Rejected'],\n","       ['Loan Rejected', 'Loan Rejected'],\n","       ['Loan Rejected', 'Loan Rejected'],\n","       ['Loan Rejected', 'Loan Rejected'],\n","       ['Loan Rejected', 'Loan Rejected'],\n","       ['Loan Rejected', 'Loan Rejected']], dtype='<U13')"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["y_pred_1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tKOVXlErU_-a"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UgYb25djVE-M"},"outputs":[],"source":[]}],"metadata":{"colab":{"collapsed_sections":[],"name":"Loan_Eligibility_Prediction_using_Gradient_Boosting_Classifier_test.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.10"}},"nbformat":4,"nbformat_minor":0}