Machine Learning Algorithms

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Machine Learning Algorithms


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KNN Algorithm

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When do we use KNN algorithm? KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry. To evaluate any technique we generally look at 3 important aspects: 1. Ease to interpret output 2. Calculation time 3. Predictive Power Let us take a few examples to  place KNN in the scale : KNN algorithm fairs across all parameters of considerations. It is commonly used for its easy of interpretation and low calculation time. How does the KNN algorithm work? Let’s take a simple case to understand this algorithm. Following is a spread of red circles (RC) and green squares (GS) : You intend to find out the class of the blue star (BS) . BS can either be RC or GS and nothing else. The “K” is KNN algorithm is the nearest neighbors we wish to take vote from. Let’s say K = 3. Hence, we will now make a circle with BS as center just as big as to enclose only three data points on the p...
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Use amCharts to visualize Google Analytics data

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Use amCharts to visualize Google Analytics data We can use amCharts to visualize google analytical data.Before we can extract data from your Analytics account, you will need to set up an application and API access to it and further more steps are available. An example : link : https://www.amcharts.com/kbase/use-amcharts-to-visualize-google-analytics-data/
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What are the steps used in Machine Learning?

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What are the steps used in Machine Learning? There are 5 basic steps used to perform a machine learning task: Collecting data : Be it the raw data from excel, access, text files etc., this step (gathering past data) forms the foundation of the future learning. The better the variety, density and volume of relevant data, better the learning prospects for the machine becomes. Preparing the data : Any analytical process thrives on the quality of the data used. One needs to spend time determining the quality of data and then taking steps for fixing issues such as missing data and treatment of outliers. Exploratory analysis is perhaps one method to study the nuances of the data in details thereby burgeoning the nutritional content of the data. Training a model : This step involves choosing the appropriate algorithm and representation of data in the form of the model. The cleaned data is split into two parts – train and test (proportion depending on the p...
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