CLR204 - Evaluate the Performance of Regression Algorithms/Models - Regression Algorithms/Models - Report Writing - IT Assignment Help

Assignment Task

Learning Outcomes The Subject Learning Outcomes demonstrated by successful completion of the task below include:
a) Evaluate the performance of classification and regression algorithms/models.
b) Develop linear and nonlinear models for classification and regression problems.
c) Analyse various Artificial Intelligence (AI) problems and choose the appropriate method to solve them.

Task Instructions
Multiple stages are required to complete this assessment task. Keep in mind that the target for prediction is the diameter (km) of an asteroid. Annotate the Python notebook (Blakelobato, 2020a) with any issues encountered, note any output discrepancies and any improvements you can suggest for processing the data as you work through the various stages. Your Notebook annotations will for the basis of your 500-word report and support the Python notebook code and data analysis output.
The annotations should follow the report structure.

Stage 1: Data set and Pre-Processing

1. Download the data set from Github at https://github.com/blakelobato/Predicting-Asteroid-Diameter-Dash/blob/master/model/Pred_Ast_Diam_2.csv (Blakelobato, 2020b).

2. Describe the data set (see Blakelobato, 2020d and the Centre for Near Earth Object Studies [CNEOS] glossary below) and any issues with the data upon visual inspection and using the Pandas head() function. It should be noted that pre-process and cleaning tasks have already been performed to resolve the majority of data issues. For example, the pre-processing of the database should have eliminated about half the columns by dropping duplicate columns, columns not relevant to asteroids, missing data, high cardinality columns, correlated columns and outliers in certain instances. Thus, you are not expected to undertake any major tasks in relation to cleaning the data, as this was taken into account in the data set you downloaded (Blakelobato, 2020b). However, you are encouraged to describe the data set and point out any aspects of the data worthy of further study.

Stage 2: Data Visualisation (Correlations and Two-Variable Plots)
1. Conduct an initial data exploration using only data visualisation. You should be able to review the correlations between the key attributes (see Blakelobato, 2020d) of the asteroid and your target for prediction. Using the seaborn library in Python, you can generate an annotated heatmap (see Figure 1, Basu, 2019) showing the variable correlation from most (+1) to none at all (–1). In addition to the heatmap, select at least three scatter plots to help highlight important features from the data set; for example, asteroid diameter (target variable) and absolute magnitude (H), asteroid geometric albedo and absolute magnitude (H), asteroid MOID and absolute magnitude (H) or number of observations used in the orbit fit (n_obs_used) and diameter. To gain an understanding of asteroid terms, consult the glossary available at the NASA JPL CNEOS (https://cneos.jpl.nasa.gov/glossary/).

Stage 3: Model Implementation
1. Create three models using the code and models provided by Blakelobato (2020a): Logistic Regression, Decision Tree and Random Forest.
2. Evaluate the performance metrics for each model to compare the different models. Use the table below to capture your metrics and comments. Explicitly identify the best model based on the metrics.

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