{"id":146017,"date":"2025-07-21T11:09:35","date_gmt":"2025-07-21T05:39:35","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146017"},"modified":"2025-07-21T11:09:35","modified_gmt":"2025-07-21T05:39:35","slug":"student-dropout-risk-prediction-using-machine-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/student-dropout-risk-prediction-using-machine-learning\/","title":{"rendered":"ML Project \u2013 Student Dropout Risk Prediction using Gradient Boosting"},"content":{"rendered":"

Program 1<\/h3>\n

Student Dropout Risk Prediction Dataset<\/strong><\/a><\/p>\n

# Step 1: Import libraries\r\n#Student Dropout Risk Prediction\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.ensemble import GradientBoostingClassifier\r\nfrom sklearn.metrics import accuracy_score, confusion_matrix\r\n\r\n# Step 2: Load dataset\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/dropout\/student_dropout_risk_dataset.csv\")\r\ndf.head()\r\ndf.isnull().sum()\r\ndf.shape\r\ndf.info()\r\n\r\n# Step 3: Encode categorical variables\r\nle_gender = LabelEncoder()\r\nle_support = LabelEncoder()\r\nle_job = LabelEncoder()\r\n\r\ndf[\"Gender\"] = le_gender.fit_transform(df[\"Gender\"])\r\ndf[\"ParentalSupport\"] = le_support.fit_transform(df[\"ParentalSupport\"])\r\ndf[\"PartTimeJob\"] = le_job.fit_transform(df[\"PartTimeJob\"])\r\ndf.head()\r\n\r\n# Step 4: Define features and target\r\nX = df.drop(\"DropoutRisk\", axis=1) # Input (Independed)\r\ny = df[\"DropoutRisk\"] # Output Depended\r\ny\r\n\r\n# Step 5: Split data\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\r\nlen(X_test)\r\n\r\n# Step 6: Train the model\r\nmodel = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\r\nmodel.fit(X_train, y_train)\r\n\r\n# Step 7: Evaluate\r\ny_pred = model.predict(X_test)\r\nacc = accuracy_score(y_test, y_pred)\r\ncm = confusion_matrix(y_test, y_pred)\r\n\r\nprint(\"Model Accuracy:\", round(acc * 100, 2), \"%\")\r\nprint(\"Confusion Matrix:\\n\", cm)\r\n\r\n# Step 8: User input for prediction\r\nprint(\"\\n Enter student details to predict dropout risk:\")\r\n\r\ngender = input(\"Gender (Male\/Female): \")\r\nsupport = input(\"Parental Support (Low\/Medium\/High): \")\r\njob = input(\"Part-Time Job? (Yes\/No): \")\r\nage = int(input(\"Age: \"))\r\ngpa = float(input(\"Current GPA (0.0\u20134.0): \"))\r\nattendance = float(input(\"Attendance Rate (0\u2013100%): \"))\r\nstudy_hours = int(input(\"Study Hours Per Week: \"))\r\n\r\n# Encode inputs\r\nif(gender==\"Male\"):\r\n    gender_encoded = 1\r\nelse:\r\n    gender_encoded = 0\r\n\r\nif(support=='Low'):\r\n    support_encoded=0\r\nelif(support=='Medium'):\r\n    support_encoded=1\r\nelse:\r\n    support_encoded=2\r\n\r\nif(job=='Yes'):\r\n    job_encoded=1\r\nelse:\r\n    job_encoded=0\r\n\r\n# Predict\r\ninput_data = pd.DataFrame([{\r\n    \"Age\": age,\r\n    \"Gender\": gender_encoded,\r\n    \"StudyHoursPerWeek\": study_hours,\r\n    \"AttendanceRate\": attendance,\r\n    \"ParentalSupport\": support_encoded,\r\n    \"PartTimeJob\": job_encoded,\r\n    \"CurrentGPA\": gpa\r\n}])\r\n\r\nprediction = model.predict(input_data)\r\nprint(\"\\n Dropout Risk Prediction:\", \" At Risk of Dropping Out\" if prediction == 1 else \" Not at Risk\")\r\n\r\n# Step 9: Plot Feature Importance\r\nimport matplotlib.pyplot as plt\r\n\r\n# Get feature importances\r\nimportances = model.feature_importances_\r\nprint(importances)\r\nfeature_names = X.columns\r\nprint(feature_names)\r\n#Create a bar chart\r\nplt.figure(figsize=(10, 6))\r\nplt.barh(feature_names, importances, color='skyblue')\r\nplt.xlabel(\"Importance Score\")\r\nplt.title(\" Feature Importance \u2013 Dropout Risk Prediction\")\r\nplt.grid(axis='x')\r\nplt.tight_layout()\r\nplt.show()\r\n<\/pre>\n
Program 1 Student Dropout Risk Prediction Dataset # Step 1: Import libraries #Student Dropout Risk Prediction import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.ensemble import GradientBoostingClassifier from sklearn.metrics...<\/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":[34929,8431,33127,33128,20697,34927,34930,34928,34926],"class_list":["post-146017","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-gradient-boosting-in-machine-learning","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project","tag-student-dropout-risk-prediction","tag-student-dropout-risk-prediction-project","tag-student-dropout-risk-prediction-using-gradient-boosting","tag-student-dropout-risk-prediction-using-machine-learning"],"yoast_head":"\nML Project \u2013 Student Dropout Risk Prediction using Gradient Boosting - 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\/student-dropout-risk-prediction-using-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ML Project \u2013 Student Dropout Risk Prediction using Gradient Boosting - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 Student Dropout Risk Prediction Dataset # Step 1: Import libraries #Student Dropout Risk Prediction import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.ensemble import GradientBoostingClassifier from sklearn.metrics...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/student-dropout-risk-prediction-using-machine-learning\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2025-07-21T05:39:35+00:00\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"ML Project \u2013 Student Dropout Risk Prediction using Gradient Boosting - 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