代写DTS208TC Data Analytics and Visualisation Coursework 1代做Java程序

Module code and Title	DTS208TC Data Analytics and Visualisation
School Title	School of AI and Advanced Computing
Assignment Title	Coursework 1
Submission Deadline	27/Mar/2025
Final Word Count	N/A

T1 Data Preprocessing (20 marks)

Code
Result

T2. Exploratory Data Analysis (EDA) (25 marks)

T2-1: Load the CSV file; show the dimensionality, structure and summary of the dataset.

Code
Result

T2-2: Calculate the number of students whose attendance is lower than 80.

Code
Result

T2-3: Visualize the distribution of previous_scores.

Code
Result

T2-4: Calculate and visualize the number of students with different family incomes.

Codes
Result
Visualization

T2-5: Calculate and visualize the average Exam_Score of students corresponding to different Sleep_Hours.

Codes
Result
Visualization

T2-6: Analyse data visualization results of T2-5 and summarize your findings in the report.

Analysis

T3. Modelling (35 marks)

T3-1: Create a new column named ‘level’ with values 0, 1, and 2

Code
Result

T3-2: Choose 5 factors (with nomalization) and apply 1 data analytics method (e.g., kNN, logistic regression, decision tree, random forest, SVM, etc.) to predict the level value.

The method you choose
The factors you choose
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Result

T3-3: Use k-fold cross validation with k = 5 folds to evaluate performance.

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Result

T3-4: Select features (factors) and/or tune model parameters to achieve the optimal performance. Show (or plot) model performance under different feature selection and/or parameter tuning settings.

Code
Result

T3-5: Report the best prediction results (i.e., Accuracy, Precision, Recall, F1-score) and the corresponding running time.

Code
Result

T4. Evaluation and Discussion – (20 marks)

T4-1: Use one example from the given dataset and draw plots or figures to explain how the input is processed by you model to generate prediction results.

Example
Figure
Explanation

T4-2: Discuss the advantages and disadvantages of the model you choose and point out some future directions to further improve model performance.

Advantages
Disadvantages
Future Directions