Loan Dataset<\/strong><\/a><\/p>\n# Loan Approval Classifier for Microfinance Institutions Using Logistic Regression\r\n# Project is to predict whether a loan application should be approved or rejected\r\n# based on some parameters\r\n\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import accuracy_score\r\nimport tkinter as tk\r\nfrom tkinter import messagebox\r\n\r\n# Load dataset\r\ndf = pd.read_csv(\"loan_data.csv\")\r\n# print(df.isnull().sum())\r\n# print((df.shape))\r\nX = df[['Age', 'Income', 'LoanAmount', 'CreditScore', 'PreviousLoans']] # Indpended variables\r\ny = df['Approved'] # Depended Variables\r\n#\r\n# # Split data\r\n# # x_train---> Training data set for Independed variable\r\n# # x_test---> Testing data set for Independed variable\r\n# # y_train---> Training data set for depended variable\r\n# # y_test---> Testing data set for depended variable\r\n#\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\r\n# print(len(X_train))\r\n# print(len(X_test))\r\nprint(X_test)\r\n# # Train model\r\nmodel = LogisticRegression()\r\nmodel.fit(X_train, y_train)\r\n#print(model)\r\ny_pred=model.predict(X_test)\r\n#print(y_pred)\r\naccuracy = accuracy_score(y_test,y_pred)\r\n#print(accuracy)\r\n#\r\n# # GUI\r\napp = tk.Tk()\r\napp.title(\"Loan Approval Predictor - Microfinance\")\r\napp.geometry(\"420x500\")\r\napp.configure(bg=\"#f0f0f0\")\r\n\r\n# Entry labels and fields\r\nfields = {\r\n \"Age\": None,\r\n \"Income (\u20b9)\": None,\r\n \"Loan Amount (\u20b9)\": None,\r\n \"Credit Score\": None,\r\n \"Previous Loans Count\": None\r\n}\r\n\r\ntk.Label(app, text=\"Loan Approval Predictor\", font=(\"Helvetica\", 16, \"bold\"), bg=\"#f0f0f0\").pack(pady=10)\r\nframe = tk.Frame(app, bg=\"#f0f0f0\")\r\nframe.pack()\r\n\r\nfor i, label in enumerate(fields):\r\n tk.Label(frame, text=label, font=(\"Arial\", 12), bg=\"#f0f0f0\").grid(row=i, column=0, pady=8, padx=10, sticky=\"w\")\r\n entry = tk.Entry(frame, font=(\"Arial\", 12), width=20)\r\n entry.grid(row=i, column=1, pady=8, padx=10)\r\n fields[label] = entry\r\n\r\n# Prediction function\r\ndef predict_loan():\r\n try:\r\n age = float(fields[\"Age\"].get())\r\n income = float(fields[\"Income (\u20b9)\"].get())\r\n loan_amt = float(fields[\"Loan Amount (\u20b9)\"].get())\r\n credit = float(fields[\"Credit Score\"].get())\r\n prev_loans = int(fields[\"Previous Loans Count\"].get())\r\n features = [[age, income, loan_amt, credit, prev_loans]]\r\n prediction = model.predict(features)[0]\r\n prob = model.predict_proba(features)[0][1]\r\n\r\n # Above line calculates the probability that the loan will be approved.\r\n # Returns the probability of each class (0 or 1) as a list.\r\n # For example: [[0.10, 0.90]] 10% chance of Not Approved (class 0) 90% chance of Approved (class 1)\r\n\r\n msg = f\"Prediction: {'Loan Approved' if prediction == 1 else 'Loan Rejected'}\\n\" \\\r\n f\"Approval Probability: {prob*100:.2f}%\\nModel Accuracy: {accuracy*100:.2f}%\"\r\n messagebox.showinfo(\"Result\", msg)\r\n except ValueError:\r\n messagebox.showerror(\"Invalid Input\", \"Please enter valid numerical values.\")\r\n\r\n# Button\r\ntk.Button(app, text=\"Predict Loan Approval\", command=predict_loan,\r\n font=(\"Arial\", 12), bg=\"#4caf50\", fg=\"white\", padx=10, pady=5).pack(pady=20)\r\n\r\napp.mainloop()\r\n<\/pre>\n <\/p>\n
<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Program 1 Loan Dataset # Loan Approval Classifier for Microfinance Institutions Using Logistic Regression # Project is to predict whether a loan application should be approved or rejected # based on some parameters import...<\/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":[34766,34767,34765,8388,8431,33127,33128,20697],"class_list":["post-145701","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-loan-approval-classifier","tag-loan-approval-classifier-project","tag-loan-approval-classifier-using-logistic-regression","tag-logistic-regression","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project"],"yoast_head":"\n
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