Skip to content

Latest commit

 

History

History
29 lines (23 loc) · 1.72 KB

README.md

File metadata and controls

29 lines (23 loc) · 1.72 KB
Raw

Weather Prediction Project

Purpose

This project aims to analyze weather data and build models to predict weather factors such as maximum temperature and the likelihood of rain tomorrow.

Project Structure

  1. 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.

  2. 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.

  3. Libraries Used:

    • NumPy
    • Pandas
    • Matplotlib
    • Seaborn
    • TensorFlow (Keras)

  4. 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.

  5. 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