{"id":22752044,"url":"https://github.com/zeuscoderbe/eda-and-model-fnn","last_synced_at":"2025-10-24T05:16:11.956Z","repository":{"id":235091744,"uuid":"790033992","full_name":"ZeusCoderBE/EDA-and-Model-FNN","owner":"ZeusCoderBE","description":"Exploratory analysis and visualization of Australian weather data. 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The notebook file is named `Weather_Prediction.ipynb`.\n\n2. **Data:** The dataset used in this project is stored in a CSV file named `weatherAUS.csv`. This dataset contains approximately 10 years of daily weather observations from various locations across Australia, including 145460 records and 23 attributes.\n\n3. **Libraries Used:**\n   - NumPy\n   - Pandas\n   - Matplotlib\n   - Seaborn\n   - TensorFlow (Keras)\n\n4. **Tasks Covered in the Notebook:**\n   - Exploratory Data Analysis (EDA) to understand the dataset's structure, summary statistics, and visualize relationships between variables.\n   - Data preprocessing steps such as handling missing values, outlier removal, data standardization, and encoding categorical variables.\n   - Building an Artificial Neural Network (ANN) model for regression to predict maximum temperature.\n   - Building an ANN model for classification to predict the likelihood of rain tomorrow.\n   - Evaluating model performance using metrics such as Mean Squared Error (MSE), R-squared score, Mean Absolute Error (MAE), accuracy, confusion matrix, and ROC curve.\n   \n5. **Instructions for Running the Notebook:**\n   - Ensure that all required libraries are installed in your Python environment.\n   - Download the `weatherAUS.csv` dataset and place it in the same directory as the notebook.\n   - Open the notebook using Jupyter Notebook or any compatible platform.\n   - Execute each cell in the notebook\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzeuscoderbe%2Feda-and-model-fnn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzeuscoderbe%2Feda-and-model-fnn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzeuscoderbe%2Feda-and-model-fnn/lists"}