Compare the two models from the previous two questions (weekend vs. weekdays) regarding what affects the online news popularity

We will use Online News Popularity dataset for this. Attached is a zip file OnlineNewsPopularity.zip. It contains a csv file and a text file. The former has the data and the latter has the description of the dataset.
[Regression] Here, the target variable is ‘shares,’ which refers to the number of shares. Do correlation analysis with it and other variables in the dataset to find the most relevant (related) factor (make sure this variable is continuous). Build a regression model using these variables to predict ‘shares’. Report the model.
Hint: Try ‘abs_title_sentiment_polarity’ for the most relevant factor. You should use that for regression with ‘shares’.
[Regression] Make a subset of online news that was published on weekends. Do correlation analysis with ‘shares’ and other variables in the dataset to find the two most related factors, and build a regression model using these variables to predict ‘shares’. Report the model.
[Regression] Make a subset of online news that was published on weekdays. Do correlation analysis with ‘shares’ and other variables in the dataset to find the two most related factors, and build a regression model using these variables to predict ‘shares’. Report the model.
[Model comparison] Compare the two models from the previous two questions (weekend vs. weekdays) regarding what affects the online news popularity. Report the model (provide your interpretations as you compare those two models).
[Clustering] Divide the dataset into two clusters, one having less than 1400 shares, and the other having equal to or greater than 1400 shares. Extract the two features you have from the first question, and show a 2D plot with clusters marked.
Hint: Here’s how you can create the required subsets.newsdata_low = newsdata[newsdata.sharesnewsdata_high = newsdata[newsdata.shares>=1400]We need two most relevant features to do the plotting. If you had followed the exploration process for Question-1, you should have found those two features. Try ‘title_subjectivity’ as the next one (after trying ‘abs_title_sentiment_polarity’).
[Clustering] Do clustering using two different parameters (k values) for k-means. Show the plots with clusters marked. Provide your interpretations of these two clustering methods in 2-4 sentences comparing them qualitatively.
Hint: select the two columns represented by those two factors we used in Q5.You can specify a particular point’s coordinate like:newsdata_clustering.iloc[i,0], newsdata_clustering.iloc[i,1] where i means the row index, 0 refers to 0th column (abs_title_sentiment_polarity), and 1 refers to 1st column (title_subjectivity).
[Classification] For classification, take only the two features you’ve gotten from the first question. Split the dataset into 70% for training and 30% for test using kNN. Show the resulting accuracies with three different values of k.
Hint: To get the classification accuracy, we need the correct label, ‘high’ or ‘low’ shares, for each instance. Let’s get a column and mark if a news item was popular or not. newsdata_low_extracted[‘pop’] = 0newsdata_high_extracted[‘pop’] = 1Put this data together in a dataframe.frames = [newsdata_low_extracted, newsdata_high_extracted]newsdata_classification = pd.concat(frames)Now, get our predictors and response variables.X = newsdata_classification.iloc[:,0:2] y = newsdata_classification[‘pop’]Copy your Python code as well as the outcomes.
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