Looking for someone who is good in Data Science and vector representation

GloVe that relies on a different algorithmic principle but also yields vector representations of words. Within the text file inside the archive, each line contains a word followed by a space and then a series of floating point numbers (also space-separated). The floating point numbers for a word (300 in total) constitute the word vector representation in a 300-dimensional word vector space. Please write code to achieve the following tasks and report the results. Do not use any libraries for the nearest neighbor computation, but instead write your own code for this. You may use any programming language (it is easy to store and manipulate a 300-dimensional array of floating point numbers in almost any programming language). --> Task 1 Determine the 5 nearest neighbours of your first name in terms of the cosine similarity measure, along with the respective cosine similarity scores. For each neighbour, list the word/name, not the vector. Note that you may need to lower-case your name to find it (e.g. “nicole” instead of “Nicole”). If (and only if) your first name is genuinely not covered by the word vector data, then report this fact and use the first name of a celebrity instead. --> Task 2 Write code to create a vector representation for an entire sentence simply by taking the average of all word vectors for words in that sentence. This involves 1) tokenizing a sentence, i.e., splitting it into words, for which you may use a very na¨ıve and imperfect method. Then 2) look up the word vectors for those tokens. Make sure to apply lower-casing if necessary. You may ignore tokens that are not covered by the vocabulary of the word vectors. Finally, 3) take the average, i.e. compute the component-wise sum of the word vectors, and then divide each component by the number of words in the sentence that were covered by the data. Next, choose a random sentence S0 and compute the vector representation of that sentence using the above method. List the nearest neighbour words to that sentence vector (i.e., determine which words in the data have a similar vector representation to the vector for the sentence). --> Task 3 Choose two other sentences S1 and S2 such that S1 is similar in meaning to S0, and S2 is dissimilar in meaning to S0. Create the sentence vectors using the method from Task 2, and report the cosine similarities between the vectors for S0 and S1, and between the vectors for S0 and S2. Explain whether the obtained cosine similarity scores are reasonable and give a brief explanation of why or why not.