CSEN 5303 Section 600 Studies on Current Research Midterm Project 1 Post Date: October 20th, 2021 Due Date: November 3rd, 2021 (Late submissions are at student's own risk and will be accepted only if submitted before the final class day. It is the student's own responsibility to make sure their assignments and submissions are graded before the final class day.) Note: This can be done as a group assignment. For this assignment, create and run a Python (or R or MATLAB or any language) script to do the following: Import or load in a dataset (identify which part is the training dataset and which part is the testing dataset). Make sure the dataset is unique and different from that shown in class. Show some rows of the training and testing datasets. Make sure to show and explain the columns. Clean the dataset to remove NaNs. Show the dataset before and after removing the NaNs. If your dataset is clean from the beginning, just show that there are no NaNs. Create a figure with boxplots for any of the columns showing the distribution of data, one column plotted on x-axis and another column plotted on the y-axis. Identify outliers in the columns and mention how you choose to deal with them and why. Create the correlation matrix for the numerical columns. Create pairplots for each column with each and every other column in the dataset. Color the data points in the pairplots by the target or output variable. Create a countplot for any of the columns. Create a features engineering function or section or block of code. In the features engineering, you can drop some columns which are not relevant to the final target or output variable and create some new columns by combining existing columns or change some columns to make them more relevant. Run the features engineering code for your training and test datasets. Create an AI or ML or data science model. Fit your model to the training inputs and outputs. Calculate and print the accuracy and score for the model training and testing or provide the testing accuracy through the 5-fold cross-validation scores, along with the mean +/- std of the 5-fold cross-validation scores. Run the model for the test dataset and obtain the predictions for the target or output variable. Save the predictions to a data frame. Save the predictions to an excel sheet or csv file. Submission:
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