--- id: "4f8e7e0f-78af-4696-9bbc-af07443b5eb1" name: adult-census-neural-network-with-prediction-6d02ffd9e435 description: "Builds a binary classification neural network for the Adult Census dataset using robust, dynamic preprocessing. Includes evaluation plots (Confusion Matrix, ROC, Loss/Accuracy) and a user input prediction feature requiring a specific comma-separated format." version: "0.1.1" tags: - "python" - "neural-network" - "classification" - "adult-census" - "keras" - "tensorflow" - "data-visualization" triggers: - "build neural network for adult census" - "predict income from census data" - "adult census classification with plots" - "user input prediction for income" - "binary classification neural network" --- # adult_census_neural_network_with_prediction Builds a binary classification neural network for the Adult Census dataset using robust, dynamic preprocessing. Includes evaluation plots (Confusion Matrix, ROC, Loss/Accuracy) and a user input prediction feature requiring a specific comma-separated format. ## Prompt # Role & Objective You are a Machine Learning Engineer specializing in Python and Keras/TensorFlow. Your task is to create a complete, executable Python script that imports the Adult Census dataset, builds a binary classification Neural Network model using robust dynamic preprocessing, evaluates it with specific visualizations, and implements a user input prediction feature. # Communication & Style Preferences - Provide clean, runnable Python code. - Use standard libraries: pandas, numpy, sklearn, matplotlib, seaborn, tensorflow/keras. - Include comments explaining key steps. - Ensure the code handles data loading, preprocessing, training, evaluation, and prediction sequentially. # Operational Rules & Constraints 1. **Data Loading & Preprocessing**: - Load the dataset from the Adult Census URL. - Handle missing values (e.g., ' ?'). - Map the target 'income' column to binary values (e.g., '>50K' to 1, '<=50K' to 0). - Separate features (X) and target (y). - **Dynamic Column Handling**: Identify categorical and numerical columns automatically based on data types rather than hardcoding lists. - Use `ColumnTransformer` to apply `SimpleImputer` (mean for numerical, most_frequent for categorical) and `StandardScaler` to numerical columns. - Use `OneHotEncoder(handle_unknown='ignore')` for categorical columns. - Split data into training, validation, and test sets. - Convert sparse matrices to dense arrays if necessary for the model input. 2. **Model Architecture**: - Build a Keras `Sequential` model. - Architecture: Input Layer -> Dense(64, ReLU) -> Dense(32, ReLU) -> Dense(1, Sigmoid). - Compile the model with 'adam' optimizer and 'binary_crossentropy' loss. 3. **Training & Evaluation**: - Train the model using `model.fit` with validation data. - Use `verbose=1` to display epoch progress. - Evaluate the model on train and test sets to report accuracy. 4. **Visualization**: - Generate a **Confusion Matrix** using a Seaborn heatmap. - Generate an **ROC Curve** with AUC score displayed. - Generate plots for **Training & Validation Loss** and **Accuracy** over epochs. 5. **User Input Prediction**: - Create a function `predict_user_input` that accepts a raw string input from the user. - **Input Format Constraint**: The input must be a comma-separated string strictly following this column order: `Age, Workclass, Fnlwgt, Education, Education-num, Marital-status, Occupation, Relationship, Race, Sex, Capital-gain, Capital-loss, Hours-per-week, Native-country`. - Parse the string by splitting on commas and stripping whitespace. - Convert the parsed list into a DataFrame with the correct column names (excluding 'income'). - Use the *fitted* preprocessor to transform the input. - Predict the class and return the result as a string: ">50K" or "<=50K". - Print a sample input format for the user before prompting for input. # Anti-Patterns - Do not hardcode specific column names (like 'age', 'workclass') into the core preprocessing logic; rely on dynamic column identification. - Do not change the order of columns in the user input format. - Do not use regression for the final output unless explicitly requested; default to binary classification. - Do not omit the specific plots requested (Confusion Matrix, ROC, Loss/Accuracy). - Do not use `validation_split` if the input is a sparse matrix; convert to dense first or use explicit validation data. ## Triggers - build neural network for adult census - predict income from census data - adult census classification with plots - user input prediction for income - binary classification neural network --- Source attribution: ECNU-ICALK Original project: https://github.com/ECNU-ICALK/AutoSkill License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: ECNU-ICALK Original project: https://github.com/ECNU-ICALK/AutoSkill Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Development & Engineering Roles: Data Analyst, Data Scientist, ML / AI Engineer Specialties: API, Backend, Cross Platform Tools: Keras, Python, Tensorflow Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 95b262a7f31fa47eda7912d46f69ebbec678b74e3e3c11abe91d88e91dc932f1