Data Preprocessing
The textual data that we collect is too messy and unprocessed. The messier the data we train our model on the poorer will be the model’s accuracy of prediction. Therefore for this reason preprocessing is one of the most important step of this process. Cleaning textual data is a lot different from cleaning any other data since it contains vocabulary words from any language instead of numbers. There are various Online Preprocessing Steps Help one could follow for better results some of these are written below:
1: Tokenization
Tokenization is the process of splitting a textual corpora in to a set of words or sentences known as tokens at https://www.allhomeworkassignments.com/ . A corpora when split into words are called word tokenization and when split into sentences is known as sentence tokenization. There are many libraries in python as well as in R which deal with tokenization for example keras, sklearn , textblob , preprocessor etc
2: Remove punctuation
Normally when we extract text from an online source say twitter , the data consists of a lot of punctuations since people tend to build emojis using different punctuation symbols. And also people sometimes use unnecessary punctuations everywhere for example some people use many fullstop symbols together to build a line. All these need to be removed from the data set as these are useless and make no sence. https://www.allhomeworkassignments.com/ can remove these punctuations simply by using Regex library.
3: Stemming
A stemming algorithm is a process of linguistic normalisation, in which the variant forms of a word are reduced to a common form.
4: Lemmatization
Lemmatization is the process of grouping together the different inflected forms of a word so they can be analysed as a single item. Lemmatization is similar to stemming but it brings context to the words. So it links words with similar meaning to one word. Text Preprocessing includes both stemming as well as Lemmatization. Many times people find these two terms confusing. Some treat these two as same. Actually, lemmatization is preferred over Stemming because lemmatization does morphological analysis of the words. Lemmatization could be done in python using NLTK framework.
5: Removing html
Sometimes what happenes is when we extract data from a web page or using some API, the data which https://www.allhomeworkassignments.com/ recieve consists of many html tags such as <h></h> or <p></p>. These tags add no meaning to our data thus these need to be removed. Feature Selection Choosing correct features is directly linked with how well an algorithm is going to perform. Different researchers have used different features for different problems. Choosing the right features for a given problem is very important.
After selecting the features , these features need to be vectorized since the model only understands numbers. This could be done in a number of ways. Some of recommended methods include :
1 : Using a Countvectorizer (sklearn.feature_extraction.text.CountVectorizor)
Countvectorizer is present in python’s sklearn package. The CountVectorizer provides a simple way to both tokenize a collection of text documents and build a vocabulary of known words, but also to encode new documents using that vocabulary.
2 : Using a TFIDF vectorizer(sklearn.feature_extraction.text.TfidfVectorizer)
Tf-idf stands for term frequency-inverse document frequency, and the tf-idf weight is a weight often used in information retrieval and Text Mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus.

k-Nearest Neighbor algorithm represents a machine learning technique used for classifying a set of data into its given target values . KNN could also be used for regression problems but is widely used for classification problems. Now, any classification model needs a target set on which we train the model for its further use. Most of Data Scientists manually set these target values to positive, negative or null. There are other ways of doing this for example there is a library in python known as Textblob that automatically set the target for each tweet just in few lines of code. The data set then is divided into two half's training set and testing set. Now this training as well as test set needs to be transformed into vector formation so as to be fed to the model. The models don’t understand any values other than the vectors. This could be done using another module of the python known as sklearn which contains many classification model as well as different encoders in it. Most commonly used encoding methods include:
1: Count vectorizor 2: Tfidf encoder
Alternately, https://www.bestassignmentsupport.com/ can use deep learning for building a sentiment analyser. In this case libraries like keras, tensorflow and Theono could turn to be helpful
Model evaluation
One of the most common and appropriate technique used by https://www.bestassignmentsupport.com/ for evaluation of a classifier is through confusion matrix. A generalized form of confusion matrix is given below: By applying this technique we can derive the generalized evaluation parameters. These parameters include:
Accuracy : accuracy of a classifier indicates how accurately the classifier has predicted the result.
Precision: precision shows how often the result that is being predicted by the classifier is actually true, when it indicates true. The formula for precision is:
Text Classification is a technique in which we assign targets or categories to textual data in accordance with the context of the data. This method is included in the fundamentals of NLP techniques. Sentiment analysis is actually an application of text classification. Other applications of text classification include spam detection , also a faster emergency response system can be made by classifying panic conversation on social media. Textual data is everywhere, may it be emails , web sites , social media , books or chats. Everywhere the sight goes there is some form of unstructured textual data present. All this data could be made usefull only if we know how to extract it and find usefull patterns in it. Structuring this large data needs scrutinizing effort but this effort could bring a lot of benifit to an individual or organization. Almost all of the text Classification Techniques could be expressed in following steps :
Data Collection
This is the first and a necessary step for building any Machine Learning algorithm as all the machine learning algorithms require some data to train on. Data Collection totally depends on the problem at hand , for example in case of sentiment analysis which is an application text classification, needs any raw text with attached target annotations as positive , negative and null. Similarly depending on the problem we can collect data in any form , may it be reviews of some product of some organization or it could be genre-labeled songs etc. https://www.allhomeworkassignments.com/

What is Data Science?
If you are not know all about data science but want to learn it, then we are ready to help you with data science. I am happy to share our knowledge of data science with you and with share our knowledge with you using python and R in data science. Online Data Science Help is a method which is used to analyze massive data and to find knowledge form them.
Areas which mostly covered using the data science :
Computer Science, Mathematics, Applications
Data visualization: This is used to represent the data in graphical format so that is is easy to learn and complete the project which is easy to understandable by any person. There are multiple visualization technique is used like- Line Plot, Pi Plot, Box Plot, Scatter graph, Bar Plot, Histogram and more other.
Machine learning: This is a branch of artificial intelligence that is totally based on the mathematical algorithms. In this we learn three types of Machine Learning Task- Supervised Machine Learning, Unsupervised Machine Learning and reinforcement Learning at https://www.allhomeworkassignments.com/
Deep learning: This area of machine learning would use the data to carry out model complicated abstractions. It is part of machine learning and it is used to handle complicated machine learning task.
Pattern recognition: Pattern recognition is the process of recognizing patterns by using machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. One of the important aspects of the pattern recognition is its application potential..
Text analytics: This technique is used to handle different type of machine learning test analysis like, analyzing hand written task or other text analyzing. Other important topics which can be handle by Data Scientist
There are many key points needs when we do the Data Science Assignment Help:
Regression Analysis: This most important part of machine learning projects. Regression analysis used to handle non discrete problems. Regression Analysis divided into different categories which is as:
1 - Linear Regression 2 - Logistic regression
Classification Problem: This technique is used to find result using discrete value - good -bad, like - unlike or we can say 0 or 1.
Clustering Analysis: In this data is divided into groups of similar clusters.
What Data Scientist Does?
Collect the information and put it in a usable format. Solve all kinds of business related issues with the help of data driven techniques. Work with different Programming Languages like SAS, R programming and Python. Have extensive knowledge of statistics, especially on statistical tests and distributions Use different analytical techniques like machine learning, text analytics and deep learning. Communicate and easily collaborate with business and IT.
Supervised Learning
Supervised Learning Online Help as the name specifies shows the presence of a superviser as an educator. Essentially supervised learning is a learning wherein we teach or train the machine utilizing information in which the target lables are present. Once the machine has been trained then we present it with data without the presence of a target variable. In the second case the machine itself determines the correct targets for the data presented to it. Some of the most famous Supervised

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