Knowledge Discovery and Data Mining Assignment

In the era of data-driven decision making, knowledge discovery and data mining (KDDM) play an essential role in transforming raw data into meaningful insights. One widely adopted framework is CRISP-DM, which provides structured steps for carrying out real-world data mining projects across industries.

This assignment allows you to take on the role of a data analyst or data scientist solving a real-world problem using textual data. Your objective is to apply the CRISP-DM methodology using a real-world text dataset and demonstrate your understanding of the complete text mining and knowledge discovery process. You are expected to deliver a well-documented, functional, and insightful analytical product.

The CRISP-DM process includes:

1. Business Understanding
2. Data Understanding
3. Text Data Preparation
4. Modeling (at least two models)
5. Evaluation
6. Deployment (suggested application)

Each phase should be clearly addressed in your project report, with appropriate justifications, visuals, and insights. Emphasis should be placed on transparency, reproducibility, and the relevance of your analysis to the chosen business or application context.

You may source datasets from reliable open data repositories such as Kaggle, UCI Machine Learning Repository, data.gov.my, or other publicly accessible text-based datasets. Ensure the data you select has enough depth and variety to support meaningful analysis.

You are free to choose any domain (e.g., healthcare, retail, social media, finance, environmental science), as long as:

1. The dataset is relevant, sufficient, and manageable
2. The problem statement is well defined
3. The solution demonstrates the application of a text mining or data mining techniques (such as text classification, sentiment analysis, clustering, topic modeling, or spam detection)

Creativity, technical rigor, and clear presentation of findings will be key to achieving a high score. Ethical considerations (e.g., bias handling and responsible use of textual data) are encouraged and rewarded where appropriate.

Requirements

1. If you do not attend the walkthrough the maximum mark you can achieve for this assignment is 40%.
2. Please do not submit hand-drawn diagrams. Hand-drawn diagrams or hand-written reports will receive zero (0) marks.
3. Your submission documentation’s content should include the following items:
   • Report
   • Assessment rubric

Assessment Criteria

Report: 20%

1. Business Understanding – 10% of marks

Clearly define the business problem or application domain addressed in the project. Explain the project objectives, expected outcomes, stakeholders involved, and the relevance of the selected text dataset. Justify why the problem is important and how text mining can contribute to solving it.

2. Data Understanding – 15% of marks

Describe the selected dataset, including its source, size, attributes, and characteristics. Perform exploratory data analysis (EDA) using appropriate statistics and visualizations. Identify data quality issues, class distribution, potential challenges, and key insights obtained from the textual data.

3. Text Data Preparation – 20% of marks

Provide a complete description of all preprocessing activities performed on the text data. This may include data cleaning, tokenization, stop-word removal, stemming, lemmatization, vectorization (e.g., TF-IDF, Count Vectorizer), feature engineering, and dataset splitting. Justify the techniques selected and explain their impact on the analysis.

4. Modeling – 25% of marks

Develop and implement at least two text mining or machine learning models. Clearly describe the algorithms used, model configurations, parameter settings, and training procedures. Justify the selection of models and explain how they address the problem statement.

5. Evaluation – 20% of marks

Evaluate and compare the performance of the developed models using appropriate metrics such as Accuracy, Precision, Recall, F1-Score, Confusion Matrix, ROC-AUC, or other relevant measures. Discuss findings, strengths, limitations, and provide insights into model performance.

6. Deployment (Suggested Application) – 10% of marks

Propose a practical deployment scenario for the developed solution. Explain how the model could be integrated into a real-world application, system, or business process. Include a conceptual architecture, prototype, dashboard, web application, or workflow diagram where appropriate.

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Business understanding

The business problem selected for analysis is the classification of customer reviews of an e-commerce platform as positive or negative through the development of a sentiment analysis system. The main objectives are therefore to classify the customer reviews in an accurate way, analyse customer satisfaction trends and support the business in taking relevant decisions by utilising review insights. The expected outcome from the project is the development of a high performing text classification model, and actionable insights from customer feedback.The important stakeholders of the project are business managers, customer service team, marketing team and data analyst. The data set applied is Kaggle – amazon product reviews database.

Data understanding

The source of the dataset is Kaggle- Amazon product reviews, and the data files size is 50000 customer reviews. The attributes considered are review text, rating and sentiment and label. The findings indicate that 60% are positive reviews while 40% are negative, and the average length of the review is 85 keywords. The most frequently used keywords are quality, delivery, product, excellent. The data quality issues noted were duplicate reviews and missing values for more than 1% of the data used. The challenges identified are informal language and misspelling in the data.

Text Data preparation

The preprocessing steps to perform the analysis of the collected data to remove any kind of duplicate records, making the text useful by converting them to lowercase, removal of punctuation and numbers, TF-IDF vectorisation and train test split. This arrangement of data is important as it cleans noisy text, reduces irrelevant features and improves model accuracy.

Modeling

The model one considered is multinomial Naive Bayes and the configuration set is TF-IDF features and default smoothing (a=1). It is quite effective for faster text classification. The model 2 supports a Vector machine which has the configuration of linear kernel, and regularization parameter C=1.0. This is mainly because it is suitable in handling high dimensional text data well with high level of accuracy.

Evaluation

With respect to accuracy, the Naive Bayes showed 88% accuracy while linear SVM showed 93% accuracy. The Precision is 87% for Naive Bayes while it is 92% for linear SVM. The recall is 86% for Naive Bayes and 93% for linear SVM. The F1 score is 86% and 92% respectively while ROC-AUC is 0.89 and 0.95. The findings indicate that the best model is Linear SVM because it has higher classification accuracy, and better handling of complex text patterns.

Deployment

It is recommended to include an AI powered customer review analysis dashboard for an e-commerce website. The workflow includes customers submitting a review, the text is processed, a trained SVM model predicts the sentiment, storage of results into a database, and proper display within the dashboard using sentiment trends. The business benefits from this model are faster customer feedback analysis, improved decisions with respect to product quality, early identification of complaints by customers, and better customer service strategies.

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The application of CRISP-DM framework to the text mining project involving review analysis with data preparation, machine learning models, evaluation, deployment and business insights above revealed important findings in developing a sound model for analysing customer reviews. With our programming assignment help experts in Singapore, you can expect a similar kind of analysis supported with data. You can also have a look at the solved assignment answers for questions from National University of Singapore before deciding to go ahead with your assignment.

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