{"id":146623,"date":"2025-08-18T12:27:27","date_gmt":"2025-08-18T06:57:27","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146623"},"modified":"2025-08-18T12:27:27","modified_gmt":"2025-08-18T06:57:27","slug":"diabetes-prediction-using-gradient-boosting","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/diabetes-prediction-using-gradient-boosting\/","title":{"rendered":"ML Project &#8211; Diabetes Prediction using Gradient Boosting"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1PDqQrUmLuJW1Sd95vS-XTFYhvjkmbEKw\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>Diabetes Prediction Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\"># Import libraries\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# Load dataset\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/diabetes\/diabetes_prediction_dataset.csv\")\r\ndf.info()\r\n\r\n# Label Encoding for categorical column\r\nle = LabelEncoder()\r\ndf[\"Gender\"] = le.fit_transform(df[\"Gender\"])  # Male = 1, Female = 0\r\ndf.head()\r\n\r\n# Separate Independed and depended variables\r\nX = df.drop(\"Outcome\", axis=1) # Input\r\ny = df[\"Outcome\"] #Output\r\ny\r\n\r\n#  Split dataset\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#  Train Gradient Boosting Classifier\r\n\r\nmodel = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\r\nmodel.fit(X_train, y_train) #Trained\r\n\r\n#  Evaluate model\r\ny_pred = model.predict(X_test)\r\nacc = accuracy_score(y_test, y_pred)\r\ncm = confusion_matrix(y_test, y_pred)\r\nprint(\" Model Performance\")\r\nprint(f\"Accuracy: {acc:.2f}\")\r\nprint(\"Confusion Matrix:\")\r\nprint(cm)\r\n\r\n# Step 8: User input for prediction (updated)\r\nprint(\"\\n Enter patient details to predict diabetes (1=Diabetic, 0=Not Diabetic):\")\r\n\r\nGender = input(\"Gender (Male\/Female): \").strip().capitalize()\r\nif Gender == \"Female\":\r\n    Pregnancies = int(input(\"Number of Pregnancies: \"))\r\n    Gender_encoded=0\r\nelse:\r\n    Pregnancies=0\r\n    Gender_encoded=1\r\n    # Not applicable to males\r\n\r\nGlucose = int(input(\"Glucose Level: \"))\r\nBloodPressure = int(input(\"Blood Pressure: \"))\r\nSkinThickness = int(input(\"Skin Thickness: \"))\r\nInsulin = int(input(\"Insulin Level: \"))\r\nBMI = float(input(\"BMI: \"))\r\nDPF = float(input(\"Diabetes Pedigree Function: \"))\r\nAge = int(input(\"Age: \"))\r\n\r\n\r\n# Create input DataFrame\r\ninput_data = pd.DataFrame([{\r\n    \"Pregnancies\": Pregnancies,\r\n    \"Glucose\": Glucose,\r\n    \"BloodPressure\": BloodPressure,\r\n    \"SkinThickness\": SkinThickness,\r\n    \"Insulin\": Insulin,\r\n    \"BMI\": BMI,\r\n    \"DiabetesPedigreeFunction\": DPF,\r\n    \"Age\": Age,\r\n    \"Gender\": Gender_encoded\r\n}])\r\n\r\n# Predict\r\nresult = model.predict(input_data)[0]\r\nprint(f\"\\n Prediction: {'Diabetic' if result == 1 else 'Not Diabetic'}\")\r\n<\/pre>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:16,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1PDqQrUmLuJW1Sd95vS-XTFYhvjkmbEKw\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[],&quot;broken&quot;:false,&quot;last_checked&quot;:null,&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Diabetes Prediction Dataset # Import libraries 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 import accuracy_score, confusion_matrix # Load dataset df =&#46;&#46;&#46;<\/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":[35079,35077,35078,8431,33127,33128,35080,20697],"class_list":["post-146623","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-diabetes-prediction","tag-diabetes-prediction-using-gradient-boosting","tag-diabetes-prediction-using-machine-learning","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-program-on-diabetes-prediction","tag-machine-learning-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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