ICT603 Data Science Assignment Help
Assessment Overview
Assessment tasks
Assessment ID Assessment Item When due Weighting ULO#
CLO#
for MITS
1
Report – Statistical Analysis of
Business Data(Individual) (1000 Words)
Session 6
30%
1, 2
1, 2
2 Data Acquisition and Data
Part A – Session 9
Part B – Session 10
Part A –
Mining (Group) 20%
Part A – Report(1000 Part B – 1, 3 ,4 1, 2, 3
Words) 10%
Part B – Presentations Total – 30%
3 * Part A –
Data Modelling
Project (Group) Part A – Report (1500 Words)
Part B – Presentations
Session 13 (Study Week)
Part B – Session 14
(Exam Week)
Part A –
30% Part B
– 10% Total
– 40%
4, 5
1, 2, 3,
4, 5
Note: * denotes ‘Hurdle Assessment Item’ that students must achieve at least 40% in this item to pass the unit.
Referencing guides
You must reference all the sources of information you have used in your assessments. Please use the IEEE referencing style when referencing in your assessments in this unit. Refer to the library’s referencing guides for more information.
https://elearning.vit.edu.au/pluginfile.php/473840/block_html/content/VIT%20Library%20Refer encing%20-%20IEEE%20-%2007042020.pdf
Academic misconduct
VIT enforces that the integrity of its students’ academic studies follows an acceptable level of excellence. VIT will adhere to its VIT Policies, Procedures and Forms where it explains the importance of staff and student honesty in relation to academic work. It outlines the kinds of behaviours that are “academic misconduct”, including plagiarism.
Late submissions
In cases where there are no accepted mitigating circumstances as determined through VIT Policies, Procedures and Forms, late submission of assessments will lead automatically to the imposition of a penalty. Penalties will be applied as soon as the deadline is reached.
Short extensions and special consideration
Special Consideration is a request for:
Extensions of the due date for an assessment, other than an examination (e.g. assignment extension).
Special Consideration (Special Consideration in relation to a Completed assessment, including an end-of-unit Examination).
Students wishing to request Special Consideration in relation to an assessment the due date of which has not yet passed must engage in written emails to the teaching team to Request for Special Consideration as early as possible and prior to start time of the assessment due date, along with any accompanying documents, such as medical certificates.
For more information, visit VIT Policies, Procedures and Forms.
Inclusive and equitable assessment
Reasonable adjustment in assessment methods will be made to accommodate students with a documented disability or impairment. Contact the unit teaching team for more information.
Contract Cheating
Contract cheating usually involves purchasing an assignment or piece of research from anotherparty. This may be facilitated by a fellow student or friend or purchased on a website. The unauthorized use of generative Artificial Intelligence (AI) is also considered cheating. Other forms of contract cheating include paying another person to sit an exam in the student’s place.
Contract cheating warning:
By paying someone else to complete your academic work, you don’t learn as much as possible if you did the work yourself.
You are not prepared for the demands of your future employment.
You could be found guilty of academic misconduct.
Many of for pay-contract cheating companies recycle assignments despite guarantees of
Penalties for academic misconduct include suspension and exclusion.
Students in some disciplines are required to disclose any findings of guilt for academic misconduct before being accepted into certain professions (e.g., law).
You might disclose your personal and financial information unsafely, leaving yourselfopen to many risks, including possible identity theft.
You also leave yourself open to blackmail – if you pay someone else to do an assignment, they know you have engaged in fraudulent behaviour and can always blackmail you.
“original, plagiarism-free work”, so the similarity is easily detected by Turnitin.
Grades
We determine your grades to the following Grading Scheme:
Grade Percentage
A 80% – 100%
B 70% – 79%
C 60% – 69%
D 50% – 59%
F 0% – 49%
Assessment Item 1: Report – Statistical Analysis of Business Data
Overview
Assessment tasks
Assessment ID Assessment Item When due Weighting ULO#
CLO#
for MITS
1
Report – Statistical Analysis of
Business Data(Individual) (1000 Words)
Session 6
30%
1, 2
1, 2
Objective
This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to give students experience in analyzing a suitable dataset and creating different visualizations in dashboard and to improve student presentation skills relevant to the Unit of Study subject matter.
Case Study:
You are a data scientist hired by a retail company, “SmartMart,” which operates a chain of grocery stores. SmartMart has been in the market for several years and has a significant customer base. However, the company is facing challenges in optimizing its operations and maximizing profits. As a data scientist, your task is to analyze the provided dataset and identify areas where data science techniques can be applied to create business value for SmartMart.
Dataset:
You’ll need to use the below python code to generate your own artificial dataset. The dataset provided contains information on SmartMart’s sales transactions over the past year. It includes data such as:
Date and time of each transaction
Customer ID
Product ID
Quantity sold
Unit price
Total transaction amount
Store ID
Tasks:
You are tasked to apply appropriate statistical analysis techniques to extract valuable information from the dataset. This may include but is not limited to:
Descriptive statistics
Correlation analysis
Hypothesis testing
Time-series analysis
You need to:
Identify key findings and insights from your analysis that can help SmartMart make data-driven decisions to optimize its operations and increase profitability.
Present your analysis results in a clear and concise manner, including visualizations and explanations where necessary.
Provide recommendations on specific strategies or actions that SmartMart can take based on your analysis.
Deliverables:
You need to submit one report (1000 +/- 10% words) in PDF format, documenting your analysis process, findings, and recommendations containing Python code/scripts used for data analysis, along with comments explaining the code logic and methodology and relevant Visualizations (e.g., plots, charts) supporting your analysis and findings.
Note:
Please submit a single pdf report that includes screenshots of Python code along with corresponding results, as well as screenshots of visualizations
relevant/supporting your analysis. The screenshot of your python code must include your student ID and name clearly visible in the beginning of the script. You can also export the dataset as .csv file and use other software like Ms Excel for the same analysis.
Report Structure (suggestive)
Executive summary
Introduction
Analysis Process and Methodology
Findings and insights
Recommendations
Code screenshots
Conclusion
Appendix (optional
Dataset:
Use the below python code to generate dataset with 1000 rows and following 7 columns (Customer ID, Product ID, Quantity sold, Unit price, Total transaction amount, Store ID). You can use any python editor to generate the dataset.
ICT603 Data Science
Assessment 1 – Dataset generating code
Instruction- You can use this code to create your own file and
analyse the given dataset. You MUST use the given in assessment guideline to create the dataset. Refer to assessment details for more instructions.
Student ID – < ********>
Name – < ******************>
campus – < *******> Subject code – < ICT 603>
Assessment no – < Assessment 1> “””
import pandas as pd import numpy as np import random
from datetime import datetime, timedelta
# Generate 1000 random dates and times within a specific range start_date = datetime(2023, 1, 1)
end_date = datetime(2023, 12, 31)
date_times = [start_date + timedelta(seconds=random.randint(0, int((end_date – start_date).total_seconds()))) for _ in range(1000)] # Generate random customer IDs
customer_ids = [‘C’ + str(i).zfill(4) for i in range(1, 1001)]
# Generate random product IDs
product_ids = [‘P’ + str(i).zfill(3) for i in range(1, 101)] # Generate random quantities sold
quantities_sold = np.random.randint(1, 10, size=1000) # Generate random unit prices
unit_prices = np.random.uniform(1, 100, size=1000) # Calculate total transaction amounts
total_transaction_amounts = quantities_sold * unit_prices # Generate random store IDs
store_ids = [‘S’ + str(i).zfill(3) for i in range(1, 11)] # Randomly assign store IDs to transactions
store_ids = [random.choice(store_ids) for _ in range(1000)] # Create DataFrame
data = {
‘Date & Time’: date_times,
‘Customer ID’: random.choices(customer_ids, k=1000), ‘Product ID’: random.choices(product_ids, k=1000), ‘Quantity Sold’: quantities_sold,
‘Unit Price’: unit_prices,
‘Total Transaction Amount’: total_transaction_amounts, ‘Store ID’: store_ids
}
df = pd.DataFrame(data)
# Convert Date & Time column to datetime format df[‘Date & Time’] = pd.to_datetime(df[‘Date & Time’]) # Sort DataFrame by Date & Time
df = df.sort_values(by=’Date & Time’) # Reset index
df.reset_index(drop=True, inplace=True) # Print DataFrame
print(df)
df.to_csv(“ICT603_A1.csv”)
###################################################
Submission Instructions
All submissions are to be submitted through the assignment 1 Drop-boxes that will be set up in the Moodle account for this Unit of Study. Assignments not submitted through these drop boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator
Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends.
Marking Criteria/Rubric
Assessment
criteria
Professional (80%-100%) Very Good (70%-79%) Good (60%-69%)
Satisfactory (50%-
59%)
Unsatisfactory (0%- 49%)
Analysis Process and Methodology
The analysis process is
meticulously documented, including a thorough explanation of the chosen statistical techniques and their relevance to the
dataset. Themethodology is clear, logical, and
wellsupported.
The analysis process is
welldocumented, with clear explanations of the chosen statistical techniques. The
methodology is generally logical and supported by
relevant reasoning.
The analysis process is adequatelydocumented, but there may be some gaps in explaining the chosen
statistical techniques. The methodology is somewhat clear but may lack depth or coherence in reasoning.
The analysis process is somewhat
documented, with
limited explanations of the chosen statistical
techniques. The methodology is
vague orlacks clarity in reasoning.
The analysis
process ispoorly documented,
with minimal explanations of the chosen statistical
techniques. The methodology is unclear or
absent.
You will be assessed on the following marking criteria/Rubric: Total Marks: 30
Findings and Insights
Identifies key findings and insights with exceptional clarity and depth,
providing valuable and actionable insights for SmartMart’s
decisionmaking process.
Presents clear and insightful findings,
demonstrating a strong understanding of the dataset and its
implications for
SmartMart’s operations.
Identifies basic findings and insights, but may lack depth or clarity in analysis, resulting in somewhat
limited actionable insights.
Presents limited findings and
insights, withsome relevance to
SmartMart’s
operations, but
lacks depth or clear
connections to the dataset.
Fails to identify meaningful
findings or
insights, with
little relevance to SmartMart’s
operations.
Presentation and Clarity
The reportis exceptionally clear, well-organized, and
effectively communicates the analysis results and
recommendations.
Visualizationsare highly
effective and support the analysis.
The report is
wellstructured and effectively communicates the analysis results and recommendations.
Visualizationsare clear
and relevant
The report is adequately
structured and communicates the analysis results and
recommendations with some clarity. Visualizations may be somewhat unclear or lacking
in relevance.
The report lacks
clear structure and may be difficult to follow.
Communication of analysis results and
recommendations is somewhat uncleaar.
Visualisations are
limited or ineffective
The report is
poorly structured and difficult to
follow.
Communication of analysis results and
recommendation s is unclear or absent.
Visualizations are missing or
irrelevant.
Python
Code/Scripts
Python code/scripts are
welldocumented, clear, and demonstrate advanced
proficiency in data analysis techniques. Comments
thoroughly explaincode logic and methodology.
Python code/scripts are well-structured and demonstrate proficiency in data
analysis techniques. Comments provide adequate explanations of code logic and methodology.
Python code/scripts are adequately structured and demonstrate basic proficiency in data analysis techniques.
Comments may lack depth or clarity in explaining codelogic and methodology.
Python code/scripts are somewhat
disorganized or lack clarity in structure.
Demonstrates
limited proficiency in data analysis techniques.
Comments may be sparse or unclear.
Python
code/scripts are poorly structured or lack clarity.
Demonstrates minimal
proficiency in data analysis techniques.
Comments are absent or
insufficient.
Recommendations
Provides detailed and
actionable recommendations based on the analysis
findings, demonstrating a deep understanding of
SmartMart’s business needs and potential strategies for
improvement.
Offers clearand relevant recommendations based on the analysis findings, addressing SmartMart’s business needs and
suggesting potential strategies for
improvement.
Provides basic
recommendationsbased on
the analysis findings, but may lack depth or specificity in addressing SmartMart’s
businessneeds.
Offers limited
recommendations based on the analysis findings, with
minimal relevance to SmartMart’s
business needsor strategies for
improvement.
Fails to provide meaningful
recommendation s based on the
analysis findings, with little
relevance to SmartMart’s
business needs
or strategies for improvement.
Assessment Item 2: Data Acquisition and Data Mining (Group) Part A – Report and
Part B- Oral Presentation
Overview
Assessment tasks
Assessment ID Assessment Item When due Weighting ULO# CLO# for MITS
2
Data Acquisition and Data Mining(Group) Part A – Report (1000 Words)
Part B – Presentations
Part A – Session 9 Part B – Session 10 Part A – 20% Part B – 10% Total – 30%
1, 3 ,4
1, 2, 3
Assignment Overview:
In this assignment, you will work in a group of 3 to 5 students to conduct an Exploratory Data Analysis (EDA) on a comprehensive dataset. The dataset can be acquired from internal or external sources, or by merging both. You will utilize appropriate techniques, tools, and programming languages, such as Python, to perform various data procedures including data acquisition, data wrangling, and data mining to extract meaningful insights from the dataset. The final deliverables will include an EDA report and an oral presentation video to showcase your findings and analysis.
Assignment Tasks:
Data Acquisition:
Identify and acquire a comprehensive dataset suitable for the EDA. You can choose from the suggested data sources provided or explore and select different datasets based on your group’s common interest.
Ensure the dataset is relevant, sufficiently large, and contains multiple variables for thorough analysis.
Example Data Sources:
Kaggle Datasets (https://www.kaggle.com/datasets)
UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/index.php)
Government Open Data Portals (e.g., data.gov)
Academic Research Databases (e.g., PubMed, IEEE Xplore)
Social Media APIs (e.g., Twitter, Facebook)
Data Wrangling:
Preprocess the acquired dataset to handle missing values, outliers, and inconsistencies.
Perform data cleaning tasks such as removing duplicates, standardizing formats, and transforming variables if necessary.
Explore methods to handle categorical variables and convert them into a suitable format for analysis.
Note: It is mandatory that Data Wrangling operations should be incorporate in the dataset.
Data Exploration:
Conduct initial data exploration to understand the structure, distributions, and relationships within the dataset.
Utilize descriptive statistics and visualization techniques (e.g., histograms, box plots, scatter plots) to gain insights into individual variables and their interactions.
Identify any patterns, trends, or anomalies present in the data.
Data Mining and Analysis:
Apply appropriate data mining techniques such as clustering, classification, or regression to uncover deeper insights within the dataset.
Utilize machine learning algorithms if applicable to predict or classify certain outcomes based on the available variables.
Perform feature engineering if necessary to enhance the predictive power of the model.
EDA Report:
Compile all findings, analysis, and visualizations into a comprehensive EDA report.
Structure the report to include an introduction, methodology, results, discussion, and conclusion sections.
Provide clear explanations for the steps taken, insights gained, and any challenges encountered during the analysis.
Include visualizations and summary statistics to support your findings.
Oral Presentation:
Prepare a concise oral presentation to present your EDA findings to the class.
Highlight key insights, trends, and interesting observations discovered during the analysis.
Use visual aids such as slides or interactive dashboards to enhance the presentation.
Submission Guidelines:
The EDA report of 1000 words must be submitted digitally, either in PDF or Word document format. The report should include an appendix at the end containing screenshots of the Python code along with its corresponding output
The oral presentation can be delivered using presentation software (e.g., PowerPoint, Google Slides).
Ensure proper citation and referencing for any external sources or datasets used.
Please submit two files, the Report and the Oral Presentation, through the link provided in the LMS before the specified deadline.
Note: Collaboration within the group is encouraged, but each group member must contribute substantially to the analysis, report writing, and presentation. Plagiarism or unauthorized use of external sources will result in penalties.
Submission Instructions
All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodle account. Assignments not submitted through these drop-boxes will not be considered.
Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 20% per day (including weekend days).
The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in will check conference websites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re-submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission.
Your document should be a single word or pdf document containing your report
Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends.
Marking Criteria/Rubric
You will be assessed on the following marking criteria/Rubric:
Total Marks: 30
Assessment criteria
Professional (80%-
100%)
Very Good(70%-79%) Good (60%-69%)
Satisfactory (50%-
59%)
Unsatisfactory (0%-
49%)
Data Acquisition
Group acquires a highly relevant and
comprehensive dataset from a diverse rangeof sources, ensuring it contains multiple
variables for thorough analysis.
Group acquires a relevant dataset with multiple variables
suitable for analysis, demonstrating good selection from
suggested or
alternative sources.
Group acquires a
dataset, but it may lack depth or relevance in some areas,or may not contain a sufficient
number of variables for thorough analysis.
Group acquires a dataset, but it may lack relevance or
contain limited variables for
analysis.
Group fails to acquire an
appropriate
dataset, lacking relevance, depth, or variables
necessary for
analysis.
Data Wrangling
Comprehensive data wrangling techniques are applied
effectively, addressing missing values, outliers,
inconsistencies, and categorical variables.
Operations are
welldocumented and integrated
seamlessly into the
dataset.
Data wrangling
operations are performed proficiently, addressing most missing
values, outliers,
inconsistencies, and categorical variables, with adequate
documentation.
Data wrangling operations are
attempted but may lack completeness or
documentation, with
some issuesremaining unresolved.
Data wrangling efforts are minimal, leaving significant issues unaddressed, with little to no
documentation provided.
Little to no attempt is made to perform data wrangling
operations, resulting in unresolved issues and inconsistencies in the dataset.
Data Exploration
Extensive data exploration is
conducted, utilizing a wide range of
descriptive statistics and visualization techniques effectively to gain deep
insights into the dataset’s structure, distributions, and
relationships. Patterns, trends, and anomalies are identified
comprehensively.
Data exploration is conducted proficiently, utilizing descriptive
statistics and visualization techniques to gaininsights into the dataset’s
structure, distributions, and relationships. Some patterns, trends, and
anomalies are identified.
Basic dataexploration is conducted, withlimited utilization of descriptive
statistics and visualization techniques to understand the dataset’s structure, distributions, and
relationships. Some patterns or trendsmay be overlooked.
Limited data exploration is
conducted, with minimal use of
descriptive statistics and visualization techniques, resulting in shallow insights into the dataset’s
structure,
distributions, and relationships.
Important patterns or trends may be
missed.
Little to no data exploration is
conducted, resulting in a lack of
understanding of the dataset’s structure, distributions, and
relationships.
Important patterns or trends are not identified.
Data Mining and Analysis
Advanced data mining techniques are applied effectively, utilizing
appropriate algorithms to uncoverdeep insights within the dataset.
Machine learning algorithms are
implemented where applicable,
demonstrating advanced analytical skills. Feature engineering, if
necessary, is performed proficiently to enhance the predictive power of the model.
Data mining techniques are
applied proficiently, utilizing
appropriate algorithms to
uncoverinsights
within the dataset. Machine learning algorithms may be applied with
moderate success, demonstrating solid analytical skills.
Some attempts at feature engineering may be made.
Basic data mining techniques are applied, but with limited effectiveness in
uncovering insights within the dataset. Machine
learning algorithms, if applied, may lack sophistication, with minimal attempts at feature engineering.
Limited datamining techniques are
applied, with little effectiveness in uncovering insights within the dataset.
Machine learning algorithms, if applied, are
rudimentary, with no attempts at feature
engineering.
Little to no attempt is made to apply data mining
techniques, resulting in a lack of insights within the dataset.
Machine learning algorithms are not utilized, and no
attempts at feature engineering are made.
EDA Report
A comprehensive EDA report is compiled, containing detailed
findings, analysis, and visualizations. The
report is well-structured with clear sections,
providing thorough explanations for the steps taken, insights gained, and challenges
encountered duringthe analysis. Visualizations and summary statistics effectively support the findings.
An EDA report is compiled proficiently, containing
findings, analysis, and
visualizations. The report is adequately structured with clear sections,
providing explanations for the steps taken, insights gained, and challenges
encountered duringthe analysis. Visualizations and summary statistics support the findings adequately.
A basicEDA report is compiled, containing
some findings, analysis, and visualizations. The report may lack cohesion or depth in some areas, with limited explanations provided
for the steps taken, insights gained, and
challenges encountered during the analysis.
Visualizations and
summary statistics may be insufficient.
A rudimentary EDA report is compiled, containing limited findings, analysis, and visualizations.
The report lacks
structure and depth, with minimal explanations
provided for the steps taken, insights gained, and
challenges
encountered during the analysis.
Visualizations and summary statistics are lacking or
ineffective.
Little to no attempt is made to compile an EDA report,
resulting in a lackof findings, analysis, and visualizations.
The report is
incomplete or
missing key sections, with no explanations provided for the steps taken, insights gained, or challenges encountered during the analysis.
Oral Presentation
A concise oral presentation is
prepared, effectively
presenting EDA findings to the audience. Key
insights, trends,and observations are highlighted clearly,
supported by visual aids such as slides or
interactive dashboards. Presentation delivery is engaging and
demonstrates strong communication skills.
An oral presentation is prepared proficiently,
presenting EDA findings clearly to the audience. Key insights, trends, and observations are
highlighted adequately, supported by visual aids such as slides or
interactive dashboards. Presentation delivery is engaging and
demonstrates good communication skills.
A basicoral presentation is prepared, presenting EDA findings with some clarity to the audience. Key insights, trends, and observations may be overlooked or presented less effectively, with
visual aids such as slides or interactive dashboards used minimally.
Presentation delivery may lack engagement or coherence.
A rudimentary oral presentation is
prepared, lacking
clarity in presenting EDA findings to the audience. Key
insights, trends, and observations are
poorly highlighted, with minimaluse of visual aids such as
slides or interactive dashboards.
Presentation delivery
lacks engagement and coherence.
Little to no attempt is made to prepare an oral presentation, resulting in a lack of clarity in presenting EDA findings to the audience. Key
insights, trends,and observations are not highlighted
effectively, with no visual aids used.
Presentation delivery lacks
engagement and coherence.
Assessment Item 3: Data Modelling Project (Group) Part A – Report (1500 Words) and
Part B – Presentations Overview
Assessment tasks
Assessment ID Assessment Item When due Weighting ULO# CLO# for MITS
3 *
Data Modelling Project(Group) Part A – Report (1500 Words)
Part B – Presentations
Part A –
Session 13
(Study Week) Part B – Session 14
(Exam Week)
Part A – 30% Part B – 10% Total – 40%
4, 5
1, 2, 3, 4, 5
Assignment Overview:
In this assignment, you will work in a group of 3 to 5 students. In this group assessment, you will collaborate with your team members to produce a comprehensive final report summarizing the achievements of credit analysis dataset, the process of building data model(s) to fit the dataset and conducting data analysis. You will also address how the results are validated and interpreted, and provide insights and recommendations derived from your analysis. Additionally, ethical and social issues related to the project must be thoroughly addressed. You will utilize appropriate tools and languages, such as Python and Tableau, to complete this task. Your group will be required to submit a report and deliver an oral presentation.
Creating Dataset:
Use the below program to generate credit analysis dataset with 5000 customer information.
import pandas as pd import numpy as np
import random
# Set seed for reproducibility random.seed(42) # Generate sample data
num_samples = 5000
# Sample customer IDs
customer_ids = [‘C’ + str(i).zfill(4) for i in range(1, num_samples + 1)]
# Sample credit scores (ranging from 300 to 850)
credit_scores = [random.randint(300, 850) for _ in range(num_samples)]
# Sample ages (ranging from 18 to 80)
ages = [random.randint(18, 80) for _ in range(num_samples)]
# Sample income (ranging from 20000 to 200000)
income = [random.randint(20000, 200000) for _ in range(num_samples)]
# Sample loan amounts (ranging from 1000 to 100000)
loan_amounts = [random.randint(1000, 100000) for _ in range(num_samples)]
# Introduce missing values for loan amounts missing_indices = random.sample(range(num_samples), int(0.05*num_samples)) # 5% missing values for index in missing_indices: loan_amounts[index] = np.nan
# Sample loan durations (ranging from 1 to 60 months) loan_durations = [random.randint(1, 60) for _ in range(num_samples)]
# Introduce outliers for loan durations outlier_indices = random.sample(range(num_samples), int(0.02*num_samples)) # 2% outliers for index in outlier_indices:
loan_durations[index] = random.randint(120, 240) # Outliers ranging from 10 to 20 years
# Sample loan types loan_types = [‘Personal Loan’, ‘Car Loan’, ‘Home Loan’, ‘Education Loan’] loan_purposes = [random.choice(loan_types) for _ in
range(num_samples)]
# Sample employment status
employment_status = [‘Employed’, ‘Unemployed’, ‘Self-Employed’]
employment = [random.choice(employment_status) for _ in range(num_samples)]
# Sample default status
default_status = [random.choice([True, False]) for _ in range(num_samples)]
# Create DataFrame data = pd.DataFrame({
‘CustomerID’: customer_ids,
‘CreditScore’: credit_scores, ‘Age’: ages,
‘Income’: income, ‘LoanAmount’: loan_amounts,
‘LoanDurationMonths’: loan_durations, ‘LoanPurpose’: loan_purposes,
‘EmploymentStatus’: employment, ‘DefaultStatus’: default_status
})
# Display first few rows of the dataset print(data.head()) # Save DataFrame to a CSV file
data.to_csv(‘credit_analysis_dataset_with_missing_outliers.csv’, index=False)
Columns(information) in Dataset:
CustomerID: This column represents a unique identifier for each customer. It’s typically used to track individual customers within the dataset.
CreditScore: This column represents the credit score of each customer. Credit scores are numerical representations of an individual’s creditworthiness, often used by lenders to assess the risk of lending money to a borrower. Higher credit scores indicate lower credit risk.
Age: This column represents the age of each customer. Age can be an important factor in credit analysis as it may correlate with financial stability and responsibility.
Income: This column represents the income of each customer. Income is a key factor in determining creditworthiness, as it affects an individual’s ability to repay loans.
LoanAmount: This column represents the amount of the loan that each customer has applied for or obtained. It indicates the sum of money borrowed from a lender.
LoanDurationMonths: This column represents the duration of the loan in months. It indicates the length of time over which the loan is expected to be repaid.
LoanPurpose: This column represents the purpose for which the loan is taken. It could include categories such as personal loans, car loans, home loans, or education loans.
EmploymentStatus: This column represents the employment status of each customer. It indicates whether the customer is employed, unemployed, or selfemployed. Employment status is important in assessing a borrower’s ability to repay a loan.
DefaultStatus: This column represents whether the customer has defaulted on a loan. It’s a binary column where “True” indicates that the customer has defaulted, and “False” indicates that the customer has not defaulted. Default status is a critical factor in credit analysis as it reflects the risk associated with lending to a particular customer.
Task:
Data Understanding:
Describe the key features of the credit analysis dataset generated using the provided Python code.
What are the dimensions of the dataset? How many records does it contain?
Discuss the significance of each column in the dataset and how it contributes to the credit analysis process.
Are there any missing values or outliers in the dataset? If so, how do you plan to handle them before proceeding with data modeling and analysis?
Data Modeling and Analysis:
Explain the process of building data model(s) to fit the credit analysis dataset. Which techniques or algorithms did you employ for modeling? b. What metrics or criteria did you use to evaluate the performance of your data model(s)?
Provide insights into the patterns or trends observed during data analysis. How do these insights contribute to understanding customer behavior and credit risk?
Discuss any challenges or limitations encountered during the modeling and analysis phase and how you addressed them.
Validation and Interpretation:
Describe the methods used to validate the results obtained from data modeling and analysis.
How do you interpret the outcomes of your analysis in the context of credit risk assessment?
Discuss the reliability and robustness of the insights derived from the analysis.
Insights and Recommendations:
Based on your analysis, what insights can be drawn regarding customer creditworthiness and risk management?
Provide recommendations for improving the credit assessment process or mitigating credit risk based on your findings.
How do these insights and recommendations align with the objectives of the credit analysis project?
Ethical and Social Considerations:
Identify and discuss any ethical or social issues related to the collection, usage, and analysis of the credit analysis dataset.
How did your team address these ethical and social considerations throughout the project?
What measures were implemented to ensure fairness, transparency, and accountability in the analysis and decision-making process?
Oral Presentation:
Prepare a concise oral presentation to present your findings to the class.
Highlight key insights, trends, and interesting observations discovered during the analysis.
Use visual aids such as slides or interactive dashboards to enhance the presentation.
Submission Guidelines:
The Analysis report of 1500 words must be submitted digitally, either in PDF or Word document format. The report should include an appendix at the end containing screenshots of the Python code along with its corresponding output
The oral presentation can be delivered using presentation software (e.g., PowerPoint, Google Slides).
Ensure proper citation and referencing for any external sources or datasets used.
Please submit two files, the Report and the Oral Presentation, through the link provided in the LMS before the specified deadline.
Note: Collaboration within the group is encouraged, but each group member must contribute substantially to the analysis, report writing, and presentation. Plagiarism or unauthorized use of external sources will result in penalties.
