# Modeling the data

If you want to explain the data or predict what will happen, you probably want to create a statistical model of your data. There are four common types of algorithms to model data:

- Dimensionality reduction
- Clustering
- Regression
- Classification

These four types of algorithms come from the field of machine learning. The first two types of algorithms i.e. dimensionality reduction and clustering are unsupervised, which means that they create a model based on the features of the data set only. The last two types of algorithms i.e. regression and classification are supervised algorithms, which means that they also incorporate the labels into the model.

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**Dimensionality Reduction**

The aim of this algorithm is to map high-dimensional data points onto a lower dimensional space. The challenge is that we need to keep similar data points close together on the lower-dimensional mapping.

It is often regarded as being part of the exploring step. It is useful when there are too many features for plotting. You can perform a scatter plot matrix but that only shows two features at a time and also this algorithm is useful as a pre processing step for other machine-learning algorithms.

Most dimensionality reduction algorithms are unsupervised that means they don’t utilize the labels of the data points in order to construct the lower dimensional mapping.

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**Clustering**

It can be considered as the most significant unsupervised learning problem so all other problem of this kind, it deals with finding a structure in a set of unlabeled data.

A loose definition of clustering could be the process of organizing objects into groups whose members are same in some way. So a cluster is a collection of objects which are similar between them and are different to the objects belonging to other clusters.

**Regression**

**Regression** is the most commonly used method in forecasting. Regression tries to predict a real valued output (numerical value) of some variable for that individual. Linear and Logistic Regression are some of the most common techniques applied in data analysis.

**Linear regression** can be used for interpolation, but not suitable for predictive analytics. It has many drawbacks when applied to modern data, e.g. sensitivity to both ouliers and cross-correlations (both in the variable and observation domains), and subject to over-fitting. A better solution is piecewise-linear regression, in particular for time series.

**Logistic Regression** can be widely used in scoring, clinical trials and fraud detection, when the respond is binary that is chance of succeeding or failing. It can be well estimated by linear regression after changing the response (logit transform).

It have same problems as linear regression like not robust, model-dependent and computing regression coefficients involves using complex iterative numerically unstable algorithm.

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**Classification**

Using** classification algorithms** you acquire an existing data set and use what you know about it to produce a predictive model for use in classification of future data points. If your goal is to use your dataset and its known subsets to construct a model for predicting the categorization of future data points then in this situation use classification algorithm.

It is used when you see how well your data fits into the dataset’s predefined categories so that you can then construct a predictive model for use in classifying future data points.

When performing classification, use the following points:

- Model predictions are only as good as the underlying data of model.
- Model predictions are only as good as the categorization of the underlying dataset.

** **The final and most important step is to interpret the data. It involves:

- Drawing conclusions from your data
- Evaluating what your results mean
- Communicating your result

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