Steps to build a machine learning model

Steps to build a machine learning model: Want to join hands with the current fashion of computer science technology? Want to build an intriguing project of your own in machine learning but doesn’t know where to start. Get started and get startled on this ecstatic journey of yours with us. Pick your platform and we will lead the way.

If I present to you some data about me which may include my traits and features and I ask to use this data to derive some significant or distinctive revelations about me like my age, gender, eye color, eating habits, sleeping pattern and so on. But what happens if I increase this data not just by one or twofold but by manifolds.  Yeah dear, got struck?

 I absolutely believe in hard work and the possibility to achieve anything. But will it be worth the time spending weeks or months to derive such information? I mean common practically think, processing of so much of data for days just to infer whether a person is male or female. Doesn’t sound justifying your hard work. Therefore Smart work is the trend today. Why give away so much valuable time on something like that, which can be invested in something more significant.

But it doesn’t mean that problems of classification, identification like the one stated above are not important. Of course, they are. Actually problems like these need to be implemented through machines which are faster and efficient. It will save our time, increase our performance and analysis with better accuracy with less chance of faults.

To train machines over some data so that they can predict or classify our problem using the provided data sounds just like training a fresher in a company before he/she deals with real world problems.

Well, this replication of ‘machine’ word is done not coincidentally but with a purpose to give you an idea about that. Machine Learning is the field which actually deals with such kind of problems and effectively processes raw data into different categories as per the requirement.

Machine learning programming

This is basically a vital potion of Artificial Intelligence, which you have to drink if you want to join the bigger picture of AI. It enables you to build a machine that is capable of learning from its own experience and enhancing its performance on its own. So basically all your problems will be solved if you are able to fabricate a program for your problem, which can perpetuate in every cycle that involves it.

To build any program, a programming language is a must for its enactment. Python, R programming, Java, Scala, JavaScript are the brisk programming volcanoes of Machine Learning.

So with the language of your choice we will have a candor representation of building a classification or identification model. Here it goes.

Let’s take the very first step i.e. autopsy of our problem

We should be clear about our requirements like What are our possible input variables or features? What is the feature we are hitting to predict? Is the required data available to us? We can categorize our problem as classification, regression, clustering and so on. A very vital part is that the constructive model can only predict output and find the relationship between input and output based on its training. We can relate it as when we prepare for an exam we study only the syllabus given to us and expect to answer only questions that are in sink with the syllabus. But it can’t always be the case as there are exams that don’t have any prescribed syllabus.

The very next step is the crucial one i.e. data arrangement

We can retrieve data related to our problem either directly from the various websites available on the net. Well for a new problem or one with specific scope or genera, data can be collected manually or in-person through a survey or through some organization or using the data available in different forms with distinct departments. Just as if the data requirement is of a specific flower. So either if possible we can get data from the internet or can collect it individually on our own by foraging for the flower and doing research over it or we can visit or ask online to different botanic departments across the various countries to provide us the data for our analysis.

To improve our model first we need to measure its accuracy rate. The load to use what precision measure we should follow depends entirely on the problem type. A classification problem can adhere to metrics like Mean Squared Error (MSE), Root-Mean Squared Error (RMSE), Mean-Absolute Error (MAE), R²  or adjusted R² (i.e. coefficient of determination). For classification problems, we go in hand with metrics like recall, specificity, precision, f1-score and so on. Clustering comes with its own measures which actually does comparative study rather than predicting the accuracy of the model, some of it is Davies-Bouldin Index, Dunn Index, Silhouette Coefficient.

Bifurcation of data or stipulating the Test and Train data set.

We are held with two choices with us once we have the required data for our problem.

Either we can simply bifurcate our data into two containments. One for training purpose and one for evaluating or testing purposes. Well, it could be a problem if the data, on the whole, is not very large or humongous that we can have a good margin for both testing and training part. Such cases may land us to overfitting or underfitting places. In such a scenario better is to have an exclusive and distinct arrangement of data for both the purposes.

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Cleansing the whole data

As we don’t start to give real-world tasks to a fresher when we hire them likewise we don’t just start training our model with the data we retrieved. As the intern is first put through some orientation and teaching stuff first to cleanse his old knowledge and feed it with the new and vital one. Similarly, the data is first cleansed. Now you would be wondering what part of data is needed to be cleansed? What is meant by cleansing? Hold on! get a catch on your breath its not like cleaning the data with rugs and mops. We need to find that whether the data contains any missing value, null value, categorical values or not.

• Missing or null values can be eliminated by either eliminating the row or column or as mostly preferred replacing it with the mean value of that sample column.
• Categorical Values. These are the one which are not in numeric form such as dress color. These values are not processed by many languages so we need to encode it into numeric form using techniques as one hot encoder or min-max encoder of ski learn library.

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Pre-emptive  measures to avoid any anomalies during training

• To classify, group, pick one odd out and cluster data. what is important? Certainly, they should be on the same level, then only we can do it with ease. To attain such fairness and level we use many known techniques in machine learning such as Standardization or Normalization.
• Complex data with a lot of features are difficult to analyze. We can diminish this issue at a great level by comprehending from the whole features to only a few that are important to our problem. this can easily be done using Principal Component Analysis (PAC). It is of great help as it eliminates the correlated data either by vanishing it or merging the columns. It shrinks the dimensionality of our data as vagueness is removed.
• We can also further separate the data set into three parts instead of just two. The validation part is the new one here. The addition of it to the already existing two sets is for the modulating purpose. Inference left by it can be used for modulating some parameters for getting precise results.

• Now comes the most important and cumbersome process. Selection of the algorithm or learning process we desire to use to train our data and build our model. According to the problem and learning type, we will go for the algorithm. Linear regression, logistic regression are few of the regression algorithms. Support Vector Machine(SVM), Naïve Bayes is some classification algorithm. kNN, Decision tree, Random Forest can be used for both problems. k-Means is a clustering algorithm.

• Over-fitting and under-fitting issues can arise. It can be solved by cut short the size of machine learning model or making an addition for regularizing the weights.

Most efficient algorithm for our model

It is highly admirable to say that being selective and picky is not a bad thing, after all, we should have the best and most appropriate algorithm for our model which can provide us with best results.

The question is how to find out the best one? No fuzz at all. Cross-Validation is the key to our lock. It has a scoring strategy that will evaluate the score for all our algorithms we pass through it and obviously the one with its score at zenith is our trophy to pick up.

Last but not the least Tuning the strings of our final model

Well, all the hard work of this machine learning model building is done now. We are just left to do some adjustments in the parameters of our best model. The entities we chose to alter are known as hyperparameters.  We need to do this adjustment to find out the highest competency of our model. Of course, finding the best is just not enough we need to get the best out of best. Now again you would be wondering how to select a needle from a pool of needles. Well, every shortcoming has its breakthrough. Grid Search Cross-Validation does that work for us. It is one of the finest and most used techniques for foraging the best parameters combo.

Inference

Steps to build a machine learning model: With this our fully functional and capable model is built. You can save it and use it forever. We have discussed all the necessary and mandatory steps needed to build a good machine learning model. The code for the listed techniques and algorithms are easily available over the web network. Hope you enjoyed building your own model and gained something good.