{"id":145700,"date":"2025-06-30T18:31:09","date_gmt":"2025-06-30T13:01:09","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=145700"},"modified":"2025-06-30T18:31:09","modified_gmt":"2025-06-30T13:01:09","slug":"suspicious-login-detection-using-logistic-regression","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/suspicious-login-detection-using-logistic-regression\/","title":{"rendered":"ML Project – Suspicious Login Detection using Logistic Regression"},"content":{"rendered":"

Program 1<\/h3>\n

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

# Suspicious Login Detection Using Logistic Regression\r\n# Detect whether a login attempt is normal or suspicious\r\n# based on parameter like login time, location, device type, and previous failed attempts.\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(\"login_data.csv\")\r\n\r\n# Features and label\r\nX = df[['LoginTime', 'LoginLocation', 'DeviceType', 'FailedAttempts']] # Independed\r\ny = df['Suspicious'] # Depended\r\n\r\n# Split data\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(y_train))\r\n# print(len(X_test))\r\n# print(len(y_test))\r\n\r\n# Train model\r\nmodel = LogisticRegression()\r\nmodel.fit(X_train, y_train)\r\nprint(model)\r\n#\r\n# # Evaluate\r\ny_pred = model.predict(X_test)\r\n# print(y_pred)\r\n# print(\"Accuracy:\", accuracy_score(y_test, y_pred))\r\n\r\n# # Predict single input (Example)\r\n\r\n\r\ndef predict_login():\r\n    try:\r\n        time = int(entry_time.get())\r\n        location = int(entry_location.get())\r\n        device = int(entry_device.get())\r\n        attempts = int(entry_attempts.get())\r\n        features=[[time, location, device, attempts]]\r\n        result = model.predict(features)[0]\r\n        msg = \"Suspicious Login Detected!\" if result == 1 else \"Login is Normal.\"\r\n        messagebox.showinfo(\"Prediction Result\", msg)\r\n    except ValueError:\r\n        messagebox.showerror(\"Error\", \"Please enter valid integer values.\")\r\n\r\napp = tk.Tk()\r\napp.title(\"Suspicious Login Detection\")\r\napp.geometry(\"400x300\")\r\n\r\ntk.Label(app, text=\"Login Time (0\u201323)\",font=(\"Helvetica\", 10, \"bold\")).pack()\r\nentry_time = tk.Entry(app)\r\nentry_time.pack()\r\n\r\ntk.Label(app, text=\"Login Location (1=Known, 0=Unknown)\",font=(\"Helvetica\", 10, \"bold\")).pack()\r\nentry_location = tk.Entry(app)\r\nentry_location.pack()\r\n\r\ntk.Label(app, text=\"Device Type (1=Known, 0=Unknown)\",font=(\"Helvetica\", 10, \"bold\")).pack()\r\nentry_device = tk.Entry(app)\r\nentry_device.pack()\r\n\r\ntk.Label(app, text=\"Failed Attempts\",font=(\"Helvetica\", 10, \"bold\")).pack()\r\nentry_attempts = tk.Entry(app)\r\nentry_attempts.pack()\r\n\r\ntk.Button(app, text=\"Predict\", command=predict_login,font=(\"Arial\", 12), bg=\"blue\", fg=\"white\", padx=10, pady=5).pack(pady=10)\r\napp.mainloop()<\/pre>\n
 <\/p>\n
 
Program 1 Login Dataset # Suspicious Login Detection Using Logistic Regression # Detect whether a login attempt is normal or suspicious # based on parameter like login time, location, device type, and previous failed...<\/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":[8388,8431,33127,33128,20697,34764,34762,34763,34761],"class_list":["post-145700","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-logistic-regression","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project","tag-ml-suspicious-login-detection","tag-suspicious-login-detection","tag-suspicious-login-detection-project","tag-suspicious-login-detection-using-logistic-regression"],"yoast_head":"\nML Project - Suspicious Login Detection using Logistic Regression - 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\/suspicious-login-detection-using-logistic-regression\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ML Project - 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