Subject Code & Title :- COM738 Applying Data Science Principles To Build Dress Recommendation System
Assessment Type :- Assignment
Assessment Brief for COM738 – Set Exercise
1. Title: Applying Data Science principles to build dress recommendation system
Aim: To demonstrate how previous dress sales data can be used to predict future sales.
COM738 Applying Data Science Principles Assignment – UK
Note:
This data has empty spaces as missing value missing value means we don’t have any
information about the data
The given dataset contains attributes of dresses and their recommendations according to their sales sales are monitored based on alternate days. The attributes in this dataset are:
Style: Bohemia brief casual cute fashion flare novelty OL party sexy vintage work.
Price: Low, Average, Medium, High, Very-High
Rating: 1-5
Size: S, M, L, XL, Free
Season: Autumn winter Spring Summer
Neckline: O-neck backless board neck bow neck halter mandarin color open peter pan-collar ruffled scoop slash neck square collar sweet heart turn down collar V neck.
COM738 Applying Data Science Principles To Build Dress Recommendation System Assignment – UK
Sleeve Length: full half half sleeves butterfly sleeveless short three-quarter turn down null
waistline: dropped empire natural princess null.
Material: wool cotton mix etc.
Fabric Type: chiffon dobby poplin satin knitted jersey flannel corduroy etc.
Decoration: applique beading bow button cascading crystal draped embroidery feathers flowers etc.
COM738 Applying Data Science Principles Assignment – UK
Pattern type :- solid animal dot leopard etc.
Recommendation:- 0, 1
Task :-
Students will under take a data centered investigation using one of the data sets provided.
Programming language: Python and Weka
Requires you to complete a 4-page IEEE format paper which details:
1. Abstract: Provide the overview of the problem, the method methodology used and
key finding(s) and outline the contribution in a concise manner.
2.Introduction: Description of the data science problem i.e., descriptive exploratory
inferential predictive classification clustering or association analysis. Discuss data
science techniques and what is applicable to the given problem. Literature review of the data science setting context of the given problem. Problem statement and significance of work undertaken. Aims and objectives.
3.Data set description :-
A discussion on selected dataset to be used within the project.
The section should describe the source of data data collection methodology used overview of the features name and type- numeric/continuous size of the data and data quality report like completeness and data imbalance if there. Find the output class ratio.
4.Methodology :-
Description of the software tools utilised. Read the dataset in Python include the screen shot of the file reading either in main text or Appendix. Get the column names using column(). Get descriptive statistics information about the data using methods and attributes like shape info(), and describe().
5.Modelling:-
A description of modelling techniques utilised and review comparison of different classification algorithms: k-nearest neighbors (kNN) Support Vector Machine (SVM) Decision Tree (DT) Neural Network (NN) and Naïve Bayes (NB). Describe the evaluation techniques used (e.g. cross validation confusion matrix prediction accuracy).
COM738 Applying Data Science Principles Assignment – UK
6.Results:
Build the model in WEKA using the selected data set and classifiers: kNN SVM DT NN and NB. Present and describe the result.
7.Discussion:
Evaluation of the results achieved with regards to the aims and objectives of the project and discussion of the best model achieved on the selected dataset.
Discuss the significance/novelty of results.
Conclusions:- Summary of the work done.
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