jayeshmanani/Decision-Tree-Classifier-using-scikit-learn
In this notebook, we will use scikit-learn to perform a decision tree based classification of weather data. The file daily_weather.csv is a comma-separated file that contains weather data. This data comes from a weather station located in San Diego, California. The weather station is equipped with sensors that capture weather-related measurements such as air temperature, air pressure, and relative humidity. Data was collected for a period of three years, from September 2011 to September 2014, to ensure that sufficient data for different seasons and weather conditions is captured. Let's now check all the columns in the data.
This project helps you understand how different weather conditions like temperature, pressure, and humidity might lead to specific weather outcomes, such as rain or clear skies. By inputting your historical weather measurements, it outputs a simple decision-making model that forecasts likely weather events. Anyone who needs to make decisions based on predicting local weather patterns would find this useful.
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Use this if you have a dataset of local weather measurements and want a straightforward way to predict future weather conditions.
Not ideal if you need highly accurate, complex long-range weather forecasting models or real-time operational weather predictions.
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Oct 12, 2018
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