This project aims to analyze weather data and build models to predict weather factors such as maximum temperature and the likelihood of rain tomorrow.
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Python Notebook: The project utilizes a Python notebook to perform all data analysis and model building tasks. The notebook file is named
Weather_Prediction.ipynb
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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. -
Libraries Used:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- TensorFlow (Keras)
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Tasks Covered in the Notebook:
- Exploratory Data Analysis (EDA) to understand the dataset's structure, summary statistics, and visualize relationships between variables.
- Data preprocessing steps such as handling missing values, outlier removal, data standardization, and encoding categorical variables.
- Building an Artificial Neural Network (ANN) model for regression to predict maximum temperature.
- Building an ANN model for classification to predict the likelihood of rain tomorrow.
- Evaluating model performance using metrics such as Mean Squared Error (MSE), R-squared score, Mean Absolute Error (MAE), accuracy, confusion matrix, and ROC curve.
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Instructions for Running the Notebook:
- Ensure that all required libraries are installed in your Python environment.
- Download the
weatherAUS.csv
dataset and place it in the same directory as the notebook. - Open the notebook using Jupyter Notebook or any compatible platform.
- Execute each cell in the notebook