Creating a fully-automated system that can use today’s weather data for a given location to predict whether it will rain at the location tomorrow.

⚠️ Frameworks and Libraries

Scikit-Learn: Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python.
Matplotlib : Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
Numpy: Caffe-based Single Shot-Multibox Detector (SSD) model used to detect faces
Pandas: pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
Seaborn : Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures.
Plotly : The plotly Python library is an interactive, open-source plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.

📁 Datasets

🔄 Source

This dataset is a collected from Kaggle repository named Rain In Australia. This is a collection of daily weather data from previous 10 years in Australia.

📖 Data Preprocessing

Data pre-processing is an important step for the creation of a machine learning model. Initially, data may not be clean or in the required format for the model which can cause misleading outcomes. In pre-processing of data, we transform data into our required format. It is used to deal with noises, duplicates, and missing values of the dataset. Data pre-processing has the activities like importing datasets, splitting datasets, attribute scaling, etc. Preprocessing of data is required for improving the accuracy of the model.

🚀 Installation

The Code is written in Python 3.7. If you don’t have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:

Clone the repo

git clone https://github.com/Chaganti-Reddy/Weather-Prediction-Australia.git

Change your directory to the cloned repo

cd Weather-Prediction-Australia

Now, run the following command in your Terminal/Command Prompt to install the libraries required

python3 -m virtualenv my_env

source my_env/bin/activate

pip3 install -r requirements.txt

💡 How to Run

Open terminal. Go into the cloned project directory and type the following command:

python3 Weather-Prediction.py

📂 Directory Tree

├── assets
│   ├── main1.jpg
│   ├── main.png
│   ├── Sigmoid.png
│   └── Training.png
├── aussie_rain.joblib
├── LICENSE
├── README.md
├── requirements.txt
├── test_inputs.parquet
├── test_targets.parquet
├── train_inputs.parquet
├── train_targets.parquet
├── val_inputs.parquet
├── val_targets.parquet
├── weather-dataset-rattle-package
│   └── weatherAUS.csv
├── Weather-Prediction.ipynb
└── Weather-Prediction.py

Weather Prediction Australia