Before you start this tutorial, complete the Raspberry Pi online simulator tutorial or one of the device tutorials; for example, Raspberry Pi with node. In these articles, you set up your Azure IoT device and IoT hub, and you deploy a sample application to run on your device.

The application sends collected sensor data to your IoT hub.

rainfall prediction using machine learning github

Machine learning is a technique of data science that helps computers learn from existing data to forecast future behaviors, outcomes, and trends. Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

You learn how to use Azure Machine Learning to do weather forecast chance of rain using the temperature and humidity data from your Azure IoT hub. The chance of rain is the output of a prepared weather prediction model. The model is built upon historic data to forecast chance of rain based on temperature and humidity. In this section you get the weather prediction model from the Azure AI Library. Then you add an R-script module to the model to clean the temperature and humidity data.

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Lastly, you deploy the model as a predictive web service. Go to the weather prediction model page. For the model to behave correctly, the temperature and humidity data must be convertible to numeric data.

In this section, you add an R-script module to the weather prediction model that removes any rows that have data values for temperature or humidity that cannot be converted to numeric values. On the left-side of the Azure Machine Learning Studio window, click the arrow to expand the tools panel.

Enter "Execute" into the search box. Select the Execute R Script module. Delete the connection between the Clean Missing Data and the Execute R Script modules and then connect the inputs and outputs of the new module as shown. Select the new Execute R Script module to open its properties window. Copy and paste the following code into the R Script box.I recently participated in the Kaggle-hosted data science competition How Much Did It Rain II where the goal was to predict a set of hourly rainfall levels from sequences of weather radar measurements.

I came in first! I describe my approach in this blog post. My research lab supervisor Dr John Pinney and I were in the midst of developing a set of deep learning tools for our current research programme when this competition came along. Due to some overlaps in the statistical tools and data sets neural networks and variable-length sequences, in particular I saw it as a good opportunity to validate some of our ideas in a different context at least that is my post hoc justification for the time spent on this competition!

In the near future I will post about the research project and how it relates to this problem. The goal of the competition was to use sequences of polarmetric weather radar data to predict a set of hourly rain gauge measurements recorded over several months in over the US midwestern corn-growing states.

These radar readings represent instantaneous precipitation rates and is used to estimate rainfall levels over a wider area e. For the contest, each radar measurement was condensed into 22 features.

These include the minutes past the top of the hour that the radar observation was carried out, the distance of the rain gauge from the radar, and various reflectivity and differential phase readings of both the vertical column above and the areas surrounding the rain gauge. Up to 19 radar records are given per hourly rain gauge reading and as few as a single record ; interestingly the number of radar measurements provided should itself contain some information on the rainfall levels as it is apparently not uncommon for meteorologists to request multiple radar sweeps when there are fast-moving storms.

The preditions were evaluated based on the mean absolute error MAE relative to actual rain gauge readings. Nevertheless, the underlying structural similarities are compelling enough to suggest that RNNs are well-suited for the problem.

In the previous version of this contest which I did not participate ingradient boosting was the undisputed star of the show; neural networks, to my knowledge, were not deployed with much success. If RNNs would prove to be as unreasonably effective here as they are in an increasing array of problems in machine learning, then I might have a chance of coming up with a unique and good solution.

For a overview of RNNs, the blog post by Andrej Karpathy is as good a general introduction to the subject as you will find anywhere.

Additionally, I used scikit-learn to implement the cross-validation splits, and pandas and NumPy to process and format the data and submission files. To this day I have an irrational aversion to Matplotlib even with Seaborn and so all my plots were done in R.

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It was well-documented from the start, and much discussed in the competition forum, that a large proportion of the hourly rain gauge measurements were not to be trusted e. Given that some of these values are several orders of magnitude higher than what is physically possible anywhere on earth, the MAE values participants were reporting were dominated by these extreme outliers.

However, since the evaluation metric was the MAE rather than the root-mean-square error RMSEone can simply view the outliers as an annoying source of extrinsic noise in the evaluation scores; the absolute values of the MAE are however, in my view, close to meaningless without prior knowledge of typical weather patterns in the US midwest. The approach I and many others took was simply to exclude from the training set the rain gauges with readings above 70mm.

Over the course of the competition I experimented with several different thresholds from 53mm to 73mm, and did a few runs where I removed this pre-processing step altogether. The weather radar sequences varied in length from one to 19 readings per hourly rain gauge record. Furthermore these readings were taken at seemingly random points within the hour. In other words, this was not your typical time-series dataset EEG waveforms, daily stock market prices, etc.

One attractive feature of RNNs is that they accept input sequences of varying lengths due to weight sharing in the hidden layers. Because of this, I did not do any pre-processing beyond removing the outliers as described above and replacing any missing radar feature values with zero; I retained each timestamp as a component in the feature vector and preserved the sequential nature of the input. The idea was to implement an end-to-end learning framework and, for this reason, with not a small touch of laziness thrown in, I did not bother to implement any feature engineering.

The training set consists of data from the first 20 days of each month and the test set data from the remaining days. This ensures that both sets are more or less independent. However, as was pointed out in the competition forum, because the calendar time and location information is omitted, it is impossible to construct a local validation holdout subset that is truly independent from the rest of the training set; specifically, there is no way of ensuring that any two gauge readings are not correlated in time or space.

This had implications in that it was very difficult to detect cases of overfitting without submissions to the public leaderboard see Training section below for more details. One common method to reduce overfitting is to augment the training set via label-preserving transformations on the data.Photo by Ricardo Gomez Angel on Unsplash.

The Compressive Strength of Concrete determines the quality of Concrete.

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This is generally determined by a standard crushing test on a concrete cylinder. This requires engineers to build small concrete cylinders with different combinations of raw materials and test these cylinders for strength variations with a change in each raw material. The recommended wait time for testing the cylinder is 28 days to ensure correct results. This consumes a lot of time and requires lot of labour to prepare different prototypes and test them. Also, this method is prone to human error and one small mistake can cause the wait time to drastically increase.

One way of reducing the wait time and reducing the amount of combinations to try is to make use of digital simulations, where we can provide information to the computer about what we know and the computer tries different combinations to predict the compressive strength.

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This way we can reduce the amount of combinations we can try physically and reduce the amount of time for experimentation. But, to design such software we have to know the relations between all the raw materials and how one material affects the strength. It is possible to derive mathematical equations and run simulations based on these equations, but we cannot expect the relations to be same in real-world. Also, these tests have been performed for many number of time now and we have enough real-world data that can be used for predictive modelling.

In this article, we are going to analyse Concrete Compressive Strength dataset and build Machine Learning models to predict the compressive strength. This notebook containing all the code can be used in parallel. The dataset consists of instances with 9 attributes and has no missing values. There are 8 input variables and 1 output variable. We shall explore the data to see how input features are affecting compressive strength.

rainfall prediction using machine learning github

The first step in a Data Science project is to understand the data and gain insights from the data before doing any modelling. This includes checking for any missing values, plotting the features with respect to the target variable, observing the distributions of all the features and so on. Lets import the data and start analysing.

Lets check the correlations between the input features, this will give an idea about how each variable is affecting all other variables. This can be done by calculating Pearson correlations between the features as shown in the code below. Also, Age and Super Plasticizer are other two factors influencing Compressive strength. These correlations are useful to understand the data in detail, as they give an idea about how a variable is affecting the other.

We can further use a pairplot in seaborn to plot pair wise relations between all the features and distributions of features along the diagonal.Many parts of the source code do not follow good coding practices recommended for python pep8 style guide. Although this requires some effort, it will increase the readability of the package library.

A good summary of the standard can be found here. Smart India Hackathon - Personal website. Add a description, image, and links to the rainfall-prediction topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the rainfall-prediction topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content. Here are 5 public repositories matching this topic Language: All Filter by language.

Star Code Issues Pull requests. Open Make code pep8 compilant. Some Read more. Star 3. Advance warning system for flood with rainfall analysis. Updated Aug 3, Jupyter Notebook. Star 0. Updated Aug 22, Jupyter Notebook.

Updated Feb 5, Jupyter Notebook.Last Updated on August 5, Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables. A powerful type of neural network designed to handle sequence dependence is called recurrent neural networks. The Long Short-Term Memory network or LSTM network is a type of recurrent neural network used in deep learning because very large architectures can be successfully trained.

In this post, you will discover how to develop LSTM networks in Python using the Keras deep learning library to address a demonstration time-series prediction problem.

ML | Rainfall prediction using Linear regression

After completing this tutorial you will know how to implement and develop LSTM networks for your own time series prediction problems and other more general sequence problems. You will know:. In this tutorial, we will develop a number of LSTMs for a standard time series prediction problem. These examples will show you exactly how you can develop your own differently structured LSTM networks for time series predictive modeling problems.

Discover how to build models for multivariate and multi-step time series forecasting with LSTMs and more in my new bookwith 25 step-by-step tutorials and full source code. The example in this post is quite dated, I have better examples available for using LSTMs on time series, see:.

The problem we are going to look at in this post is theInternational Airline Passengers prediction problem. This is a problem where, given a year and a month, the task is to predict the number of international airline passengers in units of 1, The data ranges from January to Decemberor 12 years, with observations. We can load this dataset easily using the Pandas library.

We are not interested in the date, given that each observation is separated by the same interval of one month. Therefore, when we load the dataset we can exclude the first column. Once loaded we can easily plot the whole dataset. The code to load and plot the dataset is listed below.

rainfall prediction using machine learning github

You can also see some periodicity to the dataset that probably corresponds to the Northern Hemisphere vacation period. Normally, it is a good idea to investigate various data preparation techniques to rescale the data and to make it stationary. As such, it can be used to create large recurrent networks that in turn can be used to address difficult sequence problems in machine learning and achieve state-of-the-art results.

A block has components that make it smarter than a classical neuron and a memory for recent sequences. Each unit is like a mini-state machine where the gates of the units have weights that are learned during the training procedure. That is, given the number of passengers in units of thousands this month, what is the number of passengers next month? This assumes a working SciPy environment with the Keras deep learning library installed.This article is a continuation of the prior article in a three part series on using Machine Learning in Python to predict weather temperatures for the city of Lincoln, Nebraska in the United States based off data collected from Weather Underground's API services.

In the first article of the series, Using Machine Learning to Predict the Weather: Part 1I described how to extract the data from Weather Underground, parse it, and clean it. For a summary of the topics for each of the articles presented in this series, please see the introduction to the prior article. The focus of this article will be to describe the processes and steps required to build a rigorous Linear Regression model to predict future mean daily temperature values based off the dataset built in the prior article.

In the third article of the series, Using Machine Learning to Predict the Weather: Part 3I describe how the processes and steps required to build a Neural Network using Google's TensorFlow to predict future mean daily temperatures.

rainfall prediction using machine learning github

Using this method I can then compare the results to the Linear Regression model. So, if you would like to follow along without going through the somewhat painful experience of gathering, processing, and cleaning the data described in the prior article then pull down the pickle file and use the following code to deserialize the data back into a Pandas DataFrame for use in this section. If you receive an error stating No module named 'pandas. To avoid this I have since then included a CSV file in the repo which contains the data from the end of part 1 that you can read in using the following code instead:.

Linear regression aims to apply a set of assumptions primary regarding linear relationships and numerical techniques to predict an outcome Y, aka the dependent variable based off of one or more predictors X's independent variables with the end goal of establishing a model mathematical formula to predict outcomes given only the predictor values with some amount of uncertainty.

That last term in the equation for the Linear Regression is a very important one. A key assumption required by the linear regression technique is that you have a linear relationship between the dependent variable and each independent variable.

One way to assess the linearity between our independent variable, which for now will be the mean temperature, and the other independent variables is to calculate the Pearson correlation coefficient. The Pearson correlation coefficient r is a measurement of the amount of linear correlation between equal length arrays which outputs a value ranging -1 to 1. Correlation values ranging from 0 to 1 represent increasingly strong positive correlation. By this I mean that two data series are positively correlated when values in one data series increase simultaneously with the values in the other series and, as they both go up in increasingly equal magnitude the Pearson correlation value will approach 1.

Correlation values from 0 to -1 are said to be inversely, or negatively, correlated in that when the values of one series increase the corresponding values in the opposite series decrease but, as changes in magnitude between the series become equal with opposite direction the correlation value will approach Pearson correlation values that closely straddle either side of zero are suggestive to have a weak linear relationship, becoming weaker as the value approaches zero.

Opinions vary among statisticians and stats books on clear-cut boundaries for the levels of strength of a correlation coefficient. However, I have found that a generally accepted set of classifications for the strengths of correlation are as follows:. To assess the correlation in this data I will call the corr method of the Pandas DataFrame object.

This will output the correlation values from most negatively correlated to the most positively correlated.

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In selecting features to include in this linear regression model, I would like to error on the side of being slightly less permissive in including variables with moderate or lower correlation coefficients. So I will be removing the features that have correlation values less than the absolute value of 0. Also, since the "mintempm" and "maxtempm" variables are for the same day as the prediction variable "meantempm", I will be removing those also i.

With this information, I can now create a new DataFrame that only contains my variables of interest. Because most people, myself included, are much more accustomed to looking at visuals to assess and verify patterns, I will be graphing each of these selected predictors to prove to myself that there is in fact a linear relationship. To do this I will utilize matplotlib's pyplot module. For this plot I would like to have the dependent variable "meantempm" be the consistent y-axis along all of the 18 predictor variables plots.

One way to accomplish this is to create a grid of plots. Instead I will create a grid structure with six rows of three columns in order to avoid sacrificing clarity in the graphs. From the plots above it is recognizable that all the remaining predictor variables show a good linear relationship with the response variable "meantempm".

Additionally, it is also worth noting that the relationships all look uniformly randomly distributed. By this I mean there appears to be relatively equal variation in the spread of values devoid of any fanning or cone shape.

A uniform random distribution of spread along the points is also another important assumption of Linear Regression using Ordinary Least Squares algorithm. A robust Linear Regression model should utilize statistical tests for selecting meaningful, statistically significant, predictors to include.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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Introduction: This machine learning project learnt and predicted rainfall behavior based on 14 weather features. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. This machine learning project learnt and predicted rainfall behavior based on 14 weather features.

Python Jupyter Notebook. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Leoll initial commit. Latest commit fa0a4af Mar 21, Kaggle-Rainfall-Prediction Introduction: This machine learning project learnt and predicted rainfall behavior based on 14 weather features.

Credits to: Chen Lu, Leona Li. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.

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