Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere at a given place and using meteorology to project how the atmosphere will change. In this exercise, you will use machine learning to predict the weather. You are given a dataset of weather history, and you're asked to give a model that predicts the weather, in this dataset you're also given no description about the dataset, this will make know how to deal with a dataset that you don't know anything about it.
Dataset name
weather.csv
Features
Formatted Date.Summary.Precip Type.Temperature (C).Apparent Temperature (C).Humidity: pupil-teacher ratio by the townWind Speed (km/h).Wind Bearing (degrees).Visibility (km).Loud Cover.Pressure (millibars).Daily Summary.
Target Variable
Temperature.
We want to predict the temperature variable given the other variables, so your task is to create a model that can do this task, this is a multiple linear regression model, you can use polynomial if you want to also.
The repository of this course can be found at this Link, in which you can find in it some code example, lessons and so one to help you get started with your assignment. If you need extra help you can get it by making an issue in this repository, tag me (@Younes-Charfaoui) and then describe what do you need, We will review the solution and reply to all kind of help.