{"id":143651,"date":"2024-12-03T19:10:44","date_gmt":"2024-12-03T13:40:44","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=143651"},"modified":"2025-06-24T15:31:26","modified_gmt":"2025-06-24T10:01:26","slug":"loan-prediction-in-machine-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/loan-prediction-in-machine-learning\/","title":{"rendered":"Machine Learning Project – Loan Prediction"},"content":{"rendered":"

Program 1<\/h3>\n

Loan Dataset<\/strong><\/a><\/p>\n

import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.model_selection import train_test_split\r\nimport seaborn as sn\r\nfrom sklearn.metrics import accuracy_score\r\n\r\n#pandas                    2.2.2\r\n#numpy                     1.24.3\r\n#matplotlib                3.7.1\r\n#scikit-learn              1.5.1\r\n#Python                   3.11.3\r\n\r\n\r\n# DataFrame Prepare\r\ndf_loan=pd.read_csv(\"D:\/\/scikit_data\/loan\/loan_dataset.csv\")\r\ndf_loan.head()\r\n\r\n\r\ntype(df_loan)\r\n\r\n\r\ndf_loan.shape\r\n\r\n\r\n# Find Missing values\r\ndf_loan.isnull().sum()\r\n\r\n\r\n# Drop Missing values\r\ndf_loan=df_loan.dropna()\r\n\r\n\r\n# Find Missing values\r\ndf_loan.isnull().sum()\r\n\r\n\r\ndf_loan.shape\r\n\r\n\r\ndf_loan.head(5)\r\n\r\n\r\n\r\n# Find Categorical Values\r\ndf_loan['Education'].value_counts()\r\n\r\n\r\ndf_loan['Gender'].value_counts()\r\n\r\n\r\ndf_loan['Married'].value_counts()\r\n\r\n\r\ndf_loan.info()\r\n\r\n\r\ndf_loan['Dependents'].value_counts()\r\n\r\n\r\n# Replace  Dependents 3+ values with 4\r\ndf_loan.replace(to_replace='3+',value=4,inplace=True)\r\n\r\n\r\ndf_loan['Dependents'].value_counts()\r\n\r\n\r\n\r\ndf_loan.head()\r\n\r\n\r\n# Data Visulization between Education and Loan Status\r\nsn.countplot(x='Education',hue='Loan_Status',data=df_loan)\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Data Visulization between Dependents and Loan Status\r\nsn.countplot(x='Dependents',hue='Loan_Status',data=df_loan)\r\n\r\n\r\n# Data Visulization between Marrital Status and Loan Status\r\nsn.countplot(x='Married',hue='Loan_Status',data=df_loan)\r\n\r\n\r\n# Data Visulization between Propery Area and Loan Status\r\nsn.countplot(x='Property_Area',hue='Loan_Status',data=df_loan)\r\n\r\n\r\n# Label encoding \r\ndf_loan.head()\r\n\r\n\r\n\r\ndf_loan.replace({'Loan_Status ':{'Y':1,'N':0}},inplace=True)\r\ndf_loan.replace({'Married':{'Yes':1,'No':0}},inplace=True)\r\ndf_loan.replace({'Education':{'Graduate':1,'Not Graduate':0}},inplace=True)\r\ndf_loan.replace({'Property_Area':{'Rural':0,'Semiurban':1,'Urban':2}},inplace=True)\r\n\r\n\r\ndf_loan.head()\r\n\r\n\r\ndf_loan.replace({'Loan_Status':{'Y':1,'N':0}},inplace=True)\r\n\r\n\r\ndf_loan.head()\r\n\r\n\r\ndf_loan.replace({'Gender':{'Male':0,'Female':1}},inplace=True)\r\n\r\n\r\ndf_loan.head()\r\n\r\n\r\ndf_loan.replace({'Self_Employed':{'Yes':1,'No':0}},inplace=True)\r\n\r\n\r\ndf_loan.head()\r\n\r\n\r\n# Model Prepartion (Independed Variable)\r\nx=df_loan.drop(['Loan_ID','Loan_Status'],axis='columns')\r\n\r\n\r\nx\r\n\r\n\r\n# Model Prepartion (Depended Variable)\r\ny=df_loan['Loan_Status']\r\n\r\n\r\ny.shape\r\n\r\n# Split dataset in  training and testing data\r\nx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.1,random_state=2)\r\n\r\n\r\n# x_train ---> training Independed variable\r\n# x_test ---> testing Independed variable\r\n# y_train ---> training Depended variable\r\n# y_test ---> testing Depended variable\r\n\r\n\r\nlen(x_train)\r\n\r\n\r\nlen(x_test)\r\n\r\n\r\n# Model Prepreation\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\n\r\n\r\nmodel=LogisticRegression()\r\n\r\n\r\n# Train Model\r\nmodel.fit(x_train,y_train)\r\n\r\n\r\n# Predication with training data\r\ny_pred_train=model.predict(x_train)\r\n\r\n\r\ny_train.head(10)\r\n\r\n\r\ny_pred_train\r\n\r\n\r\n\r\n# Test data accuracy on training  dataset \r\ntest_data_accuracy=accuracy_score(y_pred_train,y_train)\r\n\r\n\r\nprint(\"Accuracy Score ontraing dataset : \",test_data_accuracy)\r\n\r\n\r\n# Predication with testing data\r\ny_pred_test=model.predict(x_test)\r\n\r\n\r\ny_test\r\n\r\n\r\ny_pred_test\r\n\r\n\r\n# Test data accuracy on testing  dataset \r\ntest_data_accuracy=accuracy_score(y_pred_test,y_test)\r\nprint(\"Accuracy Score on testing dataset : \",test_data_accuracy)\r\n\r\n\r\nmodel.score(x_test,y_test)\r\n\r\n\r\ndf_loan.head(3)\r\n\r\n\r\n# Check with individual value \r\n#model.predict([[0,1,1,1,0,4583,1508.0,128.0,360.0,1.0,0]])\r\nmodel.predict([[0,1,0,1,1,3000,0.0,66.0,360.0,1.0,2]])\r\n<\/pre>\n
 
Program 1 Loan Dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split import seaborn as sn from sklearn.metrics import accuracy_score #pandas 2.2.2 #numpy 1.24.3 #matplotlib 3.7.1...<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[33216,33215,33217,8431,33218,33127,33128,20697],"class_list":["post-143651","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-loan-prediction","tag-loan-prediction-in-machine-learning","tag-loan-prediction-project-in-machine-learning","tag-machine-learning","tag-machine-learning-loan-prediction-project","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project"],"yoast_head":"\nMachine Learning Project - Loan Prediction - DataFlair<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/loan-prediction-in-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning Project - 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