{"id":146661,"date":"2025-08-22T11:51:36","date_gmt":"2025-08-22T06:21:36","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146661"},"modified":"2025-08-22T11:51:36","modified_gmt":"2025-08-22T06:21:36","slug":"student-placement-prediction-using-ann","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/student-placement-prediction-using-ann\/","title":{"rendered":"Deep Learning Project &#8211; Student Placement Prediction using ANN"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1y7ken795n9YvRPUrHULp45MBhoYPSvza\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>Placement Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">Student Placement Prediction Using ANN on Academic and Skill Data\r\n\r\nTo build an Artificial Neural Network (ANN) model that predicts whether a student will get placed or not based on their academic records, skills, and personal details\r\n\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\r\nfrom sklearn.metrics import classification_report, confusion_matrix\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense\r\n\r\n# Load dataset\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/placement\/Placement_data_full_class.csv\")\r\n#df.drop(['sl_no', 'salary'], axis=1, inplace=True)\r\ndf.isnull().sum()\r\n\r\n# Encode categorical variables\r\nlabel_encoders = {}\r\nfor column in df.select_dtypes(include=['object']).columns:\r\n    le = LabelEncoder()\r\n    df[column] = le.fit_transform(df[column])\r\n    label_encoders[column] = le\r\ndf.head()\r\n\r\n# Split features and target\r\nX = df.drop('status', axis=1) # Input valeus\r\ny = df['status'] # Out put value\r\ny.head()\r\n\r\n# Normalize features\r\nscaler = StandardScaler()\r\nX_scaled = scaler.fit_transform(X)\r\n\r\n# Train-test split\r\nX_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.4)\r\nlen(X_train)\r\n\r\n# ANN model\r\nmodel = Sequential()\r\nmodel.add(Dense(16, input_dim=X_train.shape[1], activation='relu'))  # Input + I Hidden Layer\r\nmodel.add(Dense(8, activation='relu')) # Hidden Layer\r\nmodel.add(Dense(1, activation='sigmoid')) # OutPut Layer\r\n\r\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\r\n\r\n# Train model and store history\r\nhistory = model.fit(X_train, y_train, epochs=100, batch_size=8, verbose=0)\r\n\r\n# Plot Loss Curve\r\nplt.figure(figsize=(8,5))\r\nplt.plot(history.history['loss'], label='Training Loss')\r\nplt.title('Loss Over Epochs')\r\nplt.xlabel('Epoch')\r\nplt.ylabel('Binary Crossentropy Loss')\r\nplt.legend()\r\nplt.grid(True)\r\nplt.show()\r\n\r\n# Predict and evaluate\r\ny_pred = (model.predict(X_test) &gt; 0.5).astype(\"int32\")\r\n\r\nprint(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\r\n\r\n#Confusion Matrix\r\ncm = confusion_matrix(y_test, y_pred)\r\nplt.figure(figsize=(6,5))\r\nsns.heatmap(cm, annot=True, fmt=\"d\", cmap='Blues')\r\nplt.title(\"Confusion Matrix\")\r\nplt.xlabel(\"Predicted\")\r\nplt.ylabel(\"Actual\")\r\nplt.show()\r\n\r\n# --- Console-based prediction ---\r\nprint(\"\\n--- Predict Placement for a New Student ---\")\r\ndef get_user_input():\r\n    input_data = {}\r\n    for col in X.columns:\r\n        if col in label_encoders:\r\n            classes = label_encoders[col].classes_\r\n            print(f\"{col} options: {list(classes)}\")\r\n            val = input(f\"Enter {col}: \")\r\n            input_data[col] = label_encoders[col].transform([val])[0]\r\n        else:\r\n            val = float(input(f\"Enter {col}: \"))\r\n            input_data[col] = val\r\n    return pd.DataFrame([input_data])\r\n\r\nuser_df = get_user_input()\r\nuser_scaled = scaler.transform(user_df)\r\nuser_pred = model.predict(user_scaled)\r\nresult = \"Placed\" if user_pred[0][0] &gt; 0.5 else \"Not Placed\"\r\nprint(f\"\\n Predicted Placement Status: {result} (Probability: {user_pred[0][0]:.2f})\")\r\n<\/pre>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:13,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1y7ken795n9YvRPUrHULp45MBhoYPSvza\\\/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 Placement Dataset Student Placement Prediction Using ANN on Academic and Skill Data To build an Artificial Neural Network (ANN) model that predicts whether a student will get placed or not based on&#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":[35109,8431,33127,33128,20697,35106,35108,35110,35107],"class_list":["post-146661","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-deep-learning-student-placement-prediction-using-ann","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project","tag-student-placement-prediction","tag-student-placement-prediction-using-ann","tag-student-placement-prediction-using-deep-learning","tag-student-placement-prediction-using-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Deep Learning Project - Student Placement Prediction using ANN - DataFlair<\/title>\n<meta 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