RealCode4You

Get help In Feature Sampling

Using a sampling algorithm can help us reduce the size of the dataset to a point where we can use a better, but more expensive, machine learning algorithm. The key principle here is that the sampling should be done in such a manner that the sample generated should have approximately the same properties as the original dataset. There are mainly 2 types of Sampling in which you can get help

• Sampling without replacement

• Sampling with replacement

 

Dimensionality Reduction Assignment Help

Most real world datasets have a large number of features. For example, consider an fraud detection problem, we might have to deal with a lot of features, also called as dimensions. As the name suggests, dimensionality reduction aims to reduce the number of features - but not simply by selecting a sample of features from the feature-set. Data Analysis algorithms work better if the dimensionality of the dataset is lower. This is mainly because irrelevant features and noise have now been eliminated. The models which are built on top of lower-dimensional data are more understandable and explainable.

 

Get Help In Feature Encoding

Feature encoding is basically performing transformations on the data such that it can be easily accepted as input for machine learning algorithms while still retaining its original meaning. As we all know that better encoding leads to a better model and most algorithms cannot handle the categorical variables unless they are converted into a numerical value. Types of categorical features In Which You Can Get Help

• Binary(yes/no and True/false)

• Ordinal(Low, Medium, high / cold, hot, lava hot etc.)

• Nominal(Cat, dog, tiger / pizza, burger, coke)

​​​

Get Help In Features Scaling

Feature scaling in machine learning is one of the most critical steps during the pre-processing of data before creating a machine learning model. Scaling can make a difference between a weak machine learning model and a better one. The most common techniques of feature scaling are

 

Normalization and Standardization

Normalization is used when we want to bound our values between two numbers, typically, between [0,1] or [-1,1]. While Standardization transforms the data to have zero mean and a variance of 1, they make our data unitless. ​ Where to use feature scaling

• KNN

• K-means

• Principal Component Analysis

• Gradient Descent

 

Where not to use

• Random Forest

• CART

• Gradient Boosted Decision trees

 

​Various Methods: Feature Scaling In Which You Can Get Help Below are the few ways we can do feature scaling:

1. Min-Max Scaler

2. Standard Scaler

3. Max Abs Scaler

4. Robust Scaler

5. Quantile Transformer Scaler

 

Data Compression

Data compression is an essential phase in training a network. The idea is to compress the data so that the same amount of information can be represented by fewer bits. This also helps with the problem of the curse of dimensionality as discussed earlier. A dataset with many attributes is different to train with because it tends to overfit the model. Hence dimensionality reduction techniques need to be applied before the dataset can be used for training.

 

Get Help In AutoEncoder (AE)

Autoencoder is used to learn efficient embeddings of unlabeled data for a given network configuration. The autoencoder consists of two parts, an encoder, and a decoder. The encoder compresses the data from a higher-dimensional space to a lower-dimensional space (also called the latent space), while the decoder does the opposite i.e., convert the latent space back to higher-dimensional space.

 

Get Help In Generative adversarial networks(GANs)

Generative adversarial nets are alternative framework for training generative models in order to avoid the difficulty of approximating many intractable probabilistic computations. Generative adversarial networks (GANs) are one class of models that have