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.model_selection import train_test_split\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import accuracy_score, classification_report\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nimport os\r\n# Load dataset\r\ndf = pd.read_csv(\"admission_data.csv\")\r\n#print(df.isnull().sum())\r\n\r\n# Features and target\r\nX = df[['GRE', 'TOEFL', 'SOP', 'CGPA', 'Research']] # Independed variable\r\ny = df['Admitted'] # Depended\r\n\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\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(X_test)\r\n\r\n\r\n#Train model\r\nmodel = LogisticRegression()\r\nmodel.fit(X_train, y_train)\r\nprint(model)\r\n\r\n# Predict\r\ny_pred = model.predict(X_test)\r\n\r\n# # Accuracy\r\nprint(\"Accuracy:\", accuracy_score(y_test, y_pred))\r\n#print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\r\n\r\n# # Predict new student\r\nprint(\"\\n--- Predict Admission ---\")\r\ngre = float(input(\"Enter GRE Score: \"))\r\ntoefl = float(input(\"Enter TOEFL Score: \"))\r\nsop = float(input(\"Enter SOP Score (1-5): \"))\r\ncgpa = float(input(\"Enter CGPA (out of 10): \"))\r\nresearch = int(input(\"Research Experience (1 = Yes, 0 = No): \"))\r\n\r\nadmit_prob = model.predict_proba([[gre, toefl, sop, cgpa, research]])[0][1]\r\nprint(admit_prob*100)\r\nadmit_class = model.predict([[gre, toefl, sop, cgpa, research]])[0]\r\nprint(admit_class)\r\n\r\nprint(f\"\\nPredicted Chance of Admission: {admit_prob*100:.2f}%\")\r\nprint(\"Prediction:\", \"Admitted\" if admit_class == 1 else \"Not Admitted\")\r\n# os.system('cls')\r\n# # Plot correlation heatmap\r\n# # sns.heatmap(df.corr(), annot=True, cmap=\"coolwarm\")\r\n# # plt.title(\"Feature Correlation Matrix\")\r\n# # plt.show()<\/pre>\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":[34756,34755,8388,8431,34754,33127,33128,20697],"class_list":["post-145679","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-admission-chance-predictor","tag-admission-chance-predictor-using-logistic-regression","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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