Welcome to the My first Time Series project repository! 😀
- This project focuses on predicting weather patterns using machine learning techniques. Weather forecasting is crucial for planning daily activities, agriculture, transportation, and various industries. By analyzing historical weather data collected over time (known as time series data), this model aims to forecast future weather conditions accurately.
- The core of this project revolves around processing and analyzing large datasets of historical weather observations. These datasets typically include measurements such as temperature, humidity, Dew point recorded daily. Using Python and popular machine learning library scikit-learn, we can build predictive models that learn from these historical patterns to make forecasts.
- Machine learning offers powerful tools to detect complex patterns and relationships within data that traditional forecasting methods might miss. By training models on vast amounts of historical weather data, we can leverage algorithms like regression, decision trees, and SVMs to make predictions with reasonable accuracy.
- Data Preparation: Scripts and notebooks to clean, preprocess, and visualize weather data.
- Model Development: Implementations of various machine learning models tailored for time series forecasting.
- Evaluation: Techniques to assess model performance and validate predictions against actual weather outcomes.
- Deployment: Guidelines and examples for deploying models in real-world applications.