15 Questions
Who is the person being thanked for their deep knowledge and keen interest in the field of Data Mining, Machine Learning, Deep Learning, and Natural Language Processing?
Supervisor Name, Lecturer
What kind of support did the supervisor provide for the project?
Endless patience, scholarly guidance, continual encouragement, constant and energetic supervision, constructive criticism, valuable advice, reading many inferior drafts, and correcting them at all stages
Who is acknowledged for their kind help in finishing the project?
Dr. Xyz Bhuiyan, Professor, and Head, Department of CSE, Daffodil International University, Dhaka
Apart from the supervisor and Dr. Xyz Bhuiyan, who else is thanked for their help in completing the project?
Other faculty members and the staff of CSE department of Daffodil International University
Who else is acknowledged for their participation in discussions while completing the course work?
Entire course mate in Daffodil International University
What divine blessing is acknowledged for making the completion of the final year project possible?
almighty God's divine blessing
Who is acknowledged for their deep knowledge and keen interest in Data Mining, Machine Learning, Deep Learning, and Natural Language Processing?
Supervisor Name
Who provided scholarly guidance, continual encouragement, and valuable advice for the completion of the project?
Supervisor Name
Who is thanked for their kind help in finishing the project, apart from the supervisor and Dr. Xyz Bhuiyan?
other faculty member and the staff of CSE department of Daffodil International University
Who is acknowledged for their constant support and patience?
parents
Match the ML algorithm with its application in ecommerce price prediction:
Linear Regression = Predicting product prices using a linear equation Decision Tree Regressor = Creating a tree-like model to predict product prices Random Forest Regressor = Using an ensemble of decision trees to predict product prices Lasso Regression = Applying regularization to improve prediction accuracy
Match the error metric with its purpose in evaluating ML models:
Mean Absolute Error (MAE) = Measuring the average magnitude of errors between predicted and actual prices Mean Squared Error (MSE) = Measuring the average of the squares of errors between predicted and actual prices Root Mean Squared Error (RMSE) = Measuring the standard deviation of the residuals Custom accuracy, precision, and F1 scores = Evaluating classification performance of Decision Tree and Random Forest models
Match the key attribute with its role in ecommerce price prediction dataset:
Product category = Categorizing products for analysis and prediction Brand = Identifying the manufacturer or producer of products Seller information = Understanding the details of the seller for pricing analysis Ratings and price history = Providing historical performance and customer feedback for products
Match the data preprocessing method with its role in preparing the dataset for ML models:
Robust data collection = Ensuring comprehensive and accurate dataset acquisition Preprocessing key attributes = Transforming and cleaning data for model input Applying standard error metrics = Evaluating model performance using statistical measures Using regularization techniques = Controlling overfitting and improving model generalization
Match the objective with its significance in the study of ML applications in ecommerce price prediction:
Comparing and evaluating ML algorithms = Determining the best approach for predicting product prices in ecommerce Providing insights for consumers and businesses = Offering valuable information to improve decision-making in online retail Employing a comprehensive dataset = Ensuring a diverse range of products for robust analysis Grounding approach in robust data collection = Ensuring accuracy and relevancy of data for ML models
Study Notes
Acknowledgments
- Dr. Xyz Bhuiyan is thanked for their deep knowledge and keen interest in Data Mining, Machine Learning, Deep Learning, and Natural Language Processing.
- The supervisor provided scholarly guidance, continual encouragement, and valuable advice for the completion of the project.
- Someone else, apart from the supervisor and Dr. Xyz Bhuiyan, is thanked for their kind help in finishing the project.
- Friends and colleagues are acknowledged for their participation in discussions while completing the course work.
- Divine blessing is acknowledged for making the completion of the final year project possible.
- Family members are acknowledged for their constant support and patience.
Machine Learning Concepts
- TBD (To be determined)
Expressing gratitude and acknowledgements for the successful completion of the final year project/internship at Daffodil International University, Dhaka. The acknowledgements include appreciation for the supervisor's guidance and expertise in the field of Data Mining, Machine Learning, Deep Learning, and Natural Language Processing.
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