"You are provided a selection of norms for English and German across a range of variables. The
norms rely on human judgements and/or semi-automatic extensions regarding degrees of concreteness, valence, arousal, imageability and further perception modalities. In addition, you are provided corpus-based frequency lists as well as distributional co-occurrence scores.
The goal of your project is to first analyse a subset of the norm data and then to explore whether
judgements are related across modalities and to corpus-based frequency and semantic diversity.

Task: Write a report about your findings (Your report should be 5 - 8 pages long (excluding the bibliography))"

(Unfortunately, the corpus frequency and distributional information files are too large to be uploaded here. I hope there's another way to provide the files in case this assignment is being done by someone. It is a beginners R course, so only basic plots and statistics should be in the report, you can s

What you need to do:
 
Part I Data collection
Write a Python script that harvests the tweets of the three Twitter accounts the study focuses on. Get the contents of their tweets and when they were tweeted. Write the information into a data file (an xlsx file, not a CSV file see the documentation below). You will need to upload both the Python script and the data file that you generated as part of the assignment.

Part II Data analysis
Analyse the file that is provided, named twitterdata.xlsx. The analysis will a descriptive analysis of the tweets, in which you will compare how the three accounts in focus have tweeted (and how that possibly changed over time). This will require to draw a random sample of 50 COVID tweets per account (See the additional documentation on how to do that).
Be creative in how you handle the analysis. You will have to upload the Python script in which you perform the analysis (data cleaning if necessary and analysis/visualisati

-Read the pdf (ML Report and Case Study Guidance and Grading Scheme).
-This is a 5000 words report that consists of two parts (sample report attached).

Part one: ML Project on Python (3000 words)
- Dataset is Telecom_customer_churn.csv
- telecomChurn.ipynb is the actual code used so far (you can work on it further please feel free to add/edit the codes  etc..)
- File "ML Project Presentation and feed back" contains the presentation that was presented to the professors and at the end has the professors feedback in the last slide.
- Sample codes also provided in the file Sample Code.zip

Part two: Case Study on Spotify (2000 words):
under the pdf: MarrBernardWard_2019_26_Spotify_ArtificialIntelligence

-Read the pdf (ML Report and Case Study Guidance and Grading Scheme).
-This is a 5000 words report that consists of two parts (sample report attached).

Part one: ML Project on Python (3000 words)
- Dataset is Telecom_customer_churn.csv
- telecomChurn.ipynb is the actual code used so far (you can work on it further please feel free to add/edit the codes  etc..)
- File "ML Project Presentation and feed back" contains the presentation that was presented to the professors and at the end has the professors feedback in the last slide.
- Sample codes also provided in the file Sample Code.zip

Part two: Case Study on Spotify (2000 words):
under the pdf: MarrBernardWard_2019_26_Spotify_ArtificialIntelligence

Just answer the given questions in the pdf.
When using Jupyter, be sure to save it as an .html file when you submit it to me.
The skeleton file attached is a Jupyter file. it must be used as a guideline for how the project should be formatted. Fill in the answers in the skeleton files in Jupyter.

Just answer the given questions in the pdf.
When using Jupyter, be sure to save it as an html file when you submit it to me.
The skeleton file attached is a Jupyter file. it must be used as a guideline for how the project should be formatted. Fill in the answers in the skeleton files in Jupyter.
I cannot afford more than what I've offered. Please don't bid any higher.

Develop a predictive model and analysis write up to predict how many times a review was deemed helpful by other users. (Helpful votes).

Provide two insights, one actionable recommendation, and state highest model r-square value.

Tell the story of your analysis through:
exploratory data analysis
feature treatment and engineering
utilizing appropriate modeling techniques

Model will be assessed on:
R-Square value on unseen data (randomly seeded)
Processing speed (see coding requirements below)
Appropriateness for the problem at hand
Being submitted as a .py script

Designing Business Intelligence Reports:
In this assignment you will learn how to create new reports that will be used by different people in the organization. Business intelligence reports are very important communication tools in managerial decision-making and are targeted to variety of audiences that include accountants, finance professionals, marketers, salespeople, product managers, among others. The relevance, utility and timeliness of presented information are critical for effective and efficient decision-making. This exercise will provide you with a hands-on experience in understanding and building information-rich business reports.
Business Case:
You are the analyst at the business intelligence department of a retail, marketing and auditing consulting company and your new client is a Global Toys Corporation, one of the worlds largest toy manufacturers with operations across the globe. Few weeks ago, the company appointed a new Marketing Director, and in a recent prese

This a project that will deal with a dataset that will need to be analyzed.  It will essentially be done from scratch.  I have provided all documentation that I have been provided along with an sample paper that be used for your own reference, and a document with a suggested dataset.  If you think that you would like to do a different data set let me know.

Also let me know in your initial bid your thoughts on the provided dataset before we proceed with assigning you to it. 

Thank you.

1. Consider the training examples shown in Table 3.5 page 185 of the second Edition of the text book. Compute the Gini index for the overall collection of training examples. Compute the Gini index for the customer ID attribute. Compute the Gini index for the Geneder attribute. Compute the Gini index for the Car type attribute. Compute the Gini index for the Shirt Size attribute. Which attribute is better Gender, Car Type, or Shirt Size? Explain why Customer ID should not be used as the attribute test condition even though it has the lowest Gini index.
2. Repeat exercise (1) using entropy instead of the Gini index.
3. Use the outline of code we discussed in class to create a decision tree for the IrisDataSet which predicts the Type column using the other attributes. Create three versions of this tree: one using entropy, one using the Gini coecient, and one using the Classication error as splitting criteria. Use the rst half of the data set as the training data and the secon