We propose a tool that provides current weather forecasts based on city inputs. Starting with GitHub Copilot as a basic version, we will later develop a premier league-level app for more accurate results. The advanced version will be charged for. This is real life project for the welfare of people of our society
Advanced Weather Forecasting through Cutting-Edge Machine Learning Models Welcome to our groundbreaking project on weather forecasting, where we harness the power of advanced machine learning algorithms to predict temperature trends. In this innovative initiative, we employ sophisticated techniques such as linear regression, random forest regression, and decision tree regression to provide accurate and nuanced predictions. Our model takes into account a myriad of factors, including maximum and minimum temperatures, cloud cover, humidity, sun hours, precipitation, pressure, and wind speed.
Unlocking the Potential of Machine Learning Algorithms In the realm of temperature prediction, we transcend traditional methods by utilizing regression and functional regression algorithms. To achieve this, we leverage a comprehensive dataset, dedicating 80% for training and reserving 20% for testing. For instance, in forecasting the temperature for Kanpur, India, we utilize 8 years of historical data for training and 2 years for testing. This approach distinguishes our project from conventional weather forecasting methods, incorporating artificial intelligence to enhance accuracy through models like linear regression, decision tree regression, and random forest regression.
Machine learning has revolutionized weather forecasting, offering unprecedented accuracy and predictability. Looking ahead, we anticipate even greater strides in leveraging these technologies to prevent disasters such as hurricanes, tornadoes, and thunderstorms.
Methodology: Harnessing Data for Precision Our dataset, sourced from Kaggle's "Historical Weather Data for Indian Cities," focuses specifically on Kanpur City. This meticulously collected dataset spans over a decade, from 01-01-2009 to 01-01-2020, offering a wealth of hourly weather data. While we acknowledge that data accuracy cannot be guaranteed, we emphasize the dataset's potential for visualizing climate change due to global warming and forecasting weather patterns for days, weeks, months, and seasons.
In this project, we spotlight the temperature prediction for Kanpur, employing diverse machine learning algorithms and regressions. The historical weather dataset enables us to employ multiple linear regression, decision tree regression, and random forest regression. Visual representations showcase the impact of global warming on factors like precipitation, humidity, and temperature.
The Predictive Power of Machine Learning Models Multiple Linear Regression Despite its high mean absolute error, multiple linear regression provides valuable insights. Our project implementation snapshot reveals its intricacies.
Multiple Linear Regression
Decision Tree Regression With a moderate mean absolute error, decision tree regression offers a balanced accuracy. Explore the actual results from our project implementation.
Decision Tree Regression
Random Forest Regression Outperforming others with a low mean absolute error, random forest regression stands as our most accurate model. Delve into the actual results from our project implementation.
Random Forest Regression
Join us on this cutting-edge journey as we redefine weather forecasting through the lens of advanced machine learning.
Purpose of this project is to predict the temperature using different algorithms like linear regression, random forest regression, and Decision tree regression. The output value should be numerically based on multiple extra factors like maximum temperature, minimum temperature, cloud cover, humidity, and sun hours in a day, precipitation, pressure and wind speed.
There are different methods of foreseeing temperature utilizing Regression and a variety of Functional Regression, in which datasets are utilized to play out the counts and investigation. To Train, the calculations 80% size of information is utilized and 20% size of information is named as a Test set. For Example, if we need to anticipate the temperature of Kanpur, India utilizing these Machine Learning calculations, we will utilize 8 Years of information to prepare the calculations and 2 years of information as a Test dataset. The as opposed to Weather Forecasting utilizing Machine Learning Algorithms which depends essentially on reenactment dependent on Physics and Differential Equations, Artificial Intelligence is additionally utilized for foreseeing temperature: which incorporates models, for example, Linear regression, Decision tree regression, Random forest regression. To finish up, Machine Learning has enormously changed the worldview of Weather estimating with high precision and predictivity. What's more, in the following couple of years greater progression will be made utilizing these advances to precisely foresee the climate to avoid catastrophes like typhoons, Tornados, and Thunderstorms.
The dataset utilized in this arrangement has been gathered from Kaggle which is “Historical Weather Data for Indian Cities” from which we have chosen the data for “Kanpur City”. The dataset was created by keeping in mind the necessity of such historical weather data in the community. The datasets for the top 8 Indian cities as per the population. The dataset was used with the help of the worldweatheronline.com API and the wwo_hist package. The datasets contain hourly weather data from 01-01-2009 to 01-01-2020. The data of each city is for more than 10 years. This data can be used to visualize the change in data due to global warming or can be used to predict the weather for upcoming days, weeks, months, seasons, etc. Note: The data was extracted with the help of worldweatheronline.com API and we cannot guarantee the accuracy of the data. The main target of this dataset can be used to predict the weather for the next day or week with huge amounts of data provided in the dataset. Furthermore, this data can also be used to make visualization which would help to understand the impact of global warming over the various aspects of the weather like precipitation, humidity, temperature, etc.
In this project, we are concentrating on the temperature prediction of Kanpur city with the help of various machine learning algorithms and various regressions. By applying various regressions on the historical weather dataset of Kanpur city we are predicting the temperature like first we are applying Multiple Linear regression, then Decision Tree regression, and after that, we are applying Random Forest Regression.
Historical Weather Dataset of Kanpur City:
Plot for each factor for 10 years
Plot for each factor for 1 year
This regression model has high mean absolute error, hence turned out to be the least accurate model. Given below is a snapshot of the actual result from the project implementation of multiple linear regression.
This regression model has medium mean absolute error, hence turned out to be the little accurate model. Given below is a snapshot of the actual result from the project implementation of multiple linear regression.
This regression model has low mean absolute error, hence turned out to be the more accurate model. Given below is a snapshot of the actual result from the project implementation of multiple linear regression.
