Admission Dataset<\/strong><\/a><\/p>\n# Admission Chance Predictor Based on Test Scores and Profile\r\n# This project uses a Logistic Regression model to predict\r\n# whether a student will be admitted (1) or not admitted (0) based on:\r\n# GRE score , TOEFL score ,SOP (Statement of Purpose) score, CGPA, Research experience\r\n\r\nimport pandas as pd\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.model_selection import train_test_split\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(\"admission_data.csv\")\r\n#print(df.isnull().sum())\r\n# # Features and labels\r\nX = df[['GRE', 'TOEFL', 'SOP', 'CGPA', 'Research']] # Independed variables\r\ny = df['Admitted'] # Depended variables\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# # Train-test split\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(X_test)\r\n# # Train model\r\nmodel = LogisticRegression()\r\nmodel.fit(X_train, y_train) # Trained\r\nprint(model)\r\n#\r\n# # Accuracy on test set\r\naccuracy = accuracy_score(y_test, model.predict(X_test))\r\nprint(accuracy)\r\n#\r\n# GUI setup\r\napp = tk.Tk()\r\napp.title(\"Admission Predictor - GUI Version\")\r\napp.geometry(\"500x500\")\r\napp.config(bg=\"#eef2f3\")\r\napp.resizable(False,False)\r\n# Entry fields\r\nlabels = {\r\n \"GRE Score\": None,\r\n \"TOEFL Score\": None,\r\n \"SOP Score (1-5)\": None,\r\n \"CGPA (out of 10)\": None,\r\n \"Research (0 or 1)\": None\r\n}\r\n\r\ntk.Label(app, text=\"Admission Chance Predictor\", font=(\"Helvetica\", 16, \"bold\"), bg=\"#eef2f3\").pack(pady=10)\r\nframe = tk.Frame(app, bg=\"#eef2f3\")\r\nframe.pack()\r\nfor i, label in enumerate(labels):\r\n tk.Label(frame, text=label, font=(\"Arial\", 12), bg=\"#eef2f3\").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 labels[label] = entry\r\n\r\n# Prediction function\r\ndef predict_admission():\r\n try:\r\n gre = float(labels[\"GRE Score\"].get())\r\n toefl = float(labels[\"TOEFL Score\"].get())\r\n sop = float(labels[\"SOP Score (1-5)\"].get())\r\n cgpa = float(labels[\"CGPA (out of 10)\"].get())\r\n research = int(labels[\"Research (0 or 1)\"].get())\r\n\r\n features = [[gre, toefl, sop, cgpa, research]]\r\n prob = model.predict_proba(features)[0][1]\r\n #Above line calculates the probability that the student will be admitted.\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 Admitted (class 0) 90% chance of Admitted (class 1)\r\n\r\n pred = model.predict(features)[0]\r\n\r\n result = f\"Prediction: {'Admitted' if pred == 1 else 'Not Admitted'}\\n\" \\\r\n f\"Probability: {prob*100:.2f}%\\n\" \\\r\n f\"Model Accuracy (on test set): {accuracy*100:.2f}%\"\r\n\r\n messagebox.showinfo(\"Admission Prediction\", result)\r\n\r\n except ValueError:\r\n messagebox.showerror(\"Invalid Input\", \"Please enter valid numeric values.\")\r\n\r\n# Button\r\ntk.Button(app, text=\"Predict Admission\", command=predict_admission,\r\n font=(\"Arial\", 12), bg=\"#4caf50\", fg=\"white\", padx=10, pady=5).pack(pady=20)\r\n\r\napp.mainloop()<\/pre>\n <\/p>\n
<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Program 1 Admission Dataset # Admission Chance Predictor Based on Test Scores and Profile # This project uses a Logistic Regression model to predict # whether a student will be admitted (1) or not...<\/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":[34756,34755,34758,34757,8388,8431,34754,33127,33128,20697],"class_list":["post-145680","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-admission-chance-predictor","tag-admission-chance-predictor-using-logistic-regression","tag-admission-chance-predictor-using-logistic-regression-gui-based","tag-admission-chance-predictor-using-logistic-regression-in-ml","tag-logistic-regression","tag-machine-learning","tag-machine-learning-admission-chance-predictor-using-logistic-regression","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project"],"yoast_head":"\n
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