Vacancy 🏃🏃‍♀️🏃‍♂️ Vacancy 🏃🏃‍♀️🏃‍♂️
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𝐖𝐡𝐲 𝐜𝐡𝐨𝐨𝐬𝐞 𝐮𝐬 𝐟𝐨𝐫 𝐅𝐢𝐧𝐚𝐧𝐜𝐞?

✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: हामी तपाईंको वित्तीय विश्लेषणलाई सही दिशा दिन्छौं—बजेट, नगद प्रवाह, र जोखिम मूल्याङ्कनमा विशेषज्ञता।

✅ 𝐁𝐮𝐝𝐠𝐞𝐭 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: तपाईंको वित्तीय लक्ष्यहरू हासिल गर्नका लागि ठोस र सटीक बजेट बनाउने।

✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠: सही, पारदर्शी, र समयमै वित्तीय रिपोर्टिङ्ग प्रणालीमा मद्धत गर्दछौं।

✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐜𝐲: हामी तपाईंलाई सही निवेश रणनीतिहरू छनोट गर्न र मुनाफा अधिकतम गर्न सहयोग गर्छौं।

✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐓𝐡𝐞𝐨𝐫𝐲 𝐚𝐧𝐝 𝐏𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐠𝐲: वित्तीय सिद्धान्त र व्यावहारिक तरिकामा बलियो पकड़ राख्छौं।

✅ 𝐄𝐱𝐩𝐞𝐫𝐭 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐜𝐲 𝐟𝐨𝐫 𝐁𝐢𝐧𝐝𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: हामी तपाईंको सबै वित्तीय समाधानको लागि पेशेवर मार्गदर्शन र सहायता प्रदान गर्छौं।

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𝐃𝐨𝐧’𝐭 𝐰𝐨𝐫𝐫𝐲, हामी छौं तपाईंलाई सहयोग गर्न!

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- 📝 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞: केस स्टडीमा रिसर्च कसरी गर्ने भन्ने अन्योलमा हुनुहुन्छ? हामी सहायता गर्दछौं।

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✅ 𝐅𝐫𝐞𝐞 𝐑𝐞𝐯𝐢𝐬𝐢𝐨𝐧𝐬: तपाईंको सन्तुष्टिका लागि असीमित सुधारहरू।

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✅ 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐒𝐮𝐩𝐩𝐨𝐫𝐭: तपाईंको आवश्यकताअनुसार अनुकूलित सेवा।

✅ 𝐀𝐥𝐥-𝐓𝐢𝐦𝐞 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: २४/७ सहयोग प्रदान गर्ने प्रतिबद्धता।

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CSC-40054 assessment brief

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CSC-40054 assessment brief

CSC-40054 assessment brief

Faculty of Natural Sciences

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School of Computer Science and Mathematics

Assessment Brief

Module

CSC-40054

Assessment Component

Coursework

Weighting

50%

Deadline

13:00 pm on 2nd March 2024

Module Leader

Wenjuan Zhou

Office Hours/Meeting Booking Link

Monday and Tuesday during semester 2 teaching. Email: w.zhou@keele.ac.uk to arrange a meeting.

1. What is the task for this assessment?

One report should be submitted that covers the following two tasks:

• Part I: Database management

• Part II: Data analytics

2. What is required of me in this assessment?

Guidelines

Detailed guidelines for the tasks are listed below.

Self- assessment

checklist

Make sure that you…

• The required components and weightings of each part are detailed below. Use these lists to structure and focus the content of your report.

• Use the materials discussed in the lectures and tutorials to complete the tasks.

• Ensure that your codes run correctly.

Three key pieces of

advice based on the feedback given to the previous cohort who completed this

assignment

• Read the task description thoroughly and ask for clarification.

• Read the formatting guidelines and stick to them. • Start working on your coursework early. Ensure that you understand the tasks and the submission process.

Formatting Guidelines

• Your submission should be in the form of a single zipped file. This zipped file should be named with your Student Number.

• The zipped file should contain your report in two formats MS Word and PDF. So, it should contain ONLY two files. Any other submitted file will not be marked.

• For each task, you should include the screenshot of the output after you run the code (when its execution finishes successfully). Failing to include the output screenshot will cause you to lose marks.

• The codes should be included as the appendix at the end of the report. You should clearly specify which code is related to which task. Otherwise, they will be marked zero.

• Screenshots of codes are not accepted in the appendix and will be marked zero. You should include the original code with the correct indentation so anyone can copy and paste the code into an editor and run it.

• If you have written the code, but it doesn’t run correctly, mention it in your report.

• An example of the accepted appendix is shown at the end of the coursework.

Referencing Style

Harvard referencing style

Assessment Criteria/ Markscheme:

The assessment criteria and mark scheme are listed separately below

3. What is the purpose of this assessment?

The following table shows which of the module learning outcomes are being assessed in this assignment. Use this table to help you see where and how to transfer feedback from one assignment to another. Note that your feedback may mention some of these outcomes, but that you will not receive a ‘mark’ against each one.

Module Learning Outcomes assessed

1. Evaluate available data and determine how best to analyse the information available to provide required outcomes.

2. Evaluate machine learning methods in the context of statistical analysis of data representing social or natural systems.

3. Develop advanced applications of statistical data analytics techniques using an advanced specialist programming language.

4. Assess the options of storing, managing and manipulating very large volumes of data in the context of research or business organisations.

5. Assess a range of statistical approaches and apply the correct statistical approaches to extract information from a set of data typically available in a modern business or research organisation.

Rationale

• Parts I assess your ability to access and manipulate databases

• Part II assesses your ability to critically evaluate and apply big data applications, advanced analytics, and statistical modelling techniques appropriate to different types of problems.

4. What resources might I use to get started?

Lecture slides and your completed in-class activities. However, when needed, you are expected to research and use different resources to complete the tasks.

Guidelines for Tasks

For the coursework assignment, you will be working on a dataset from a car-sharing company. The dataset contains information about the customers’ demand rate between January 2017 and August 2018. The data were collected on an hourly basis and included the time data such as date, hour, and season as well as weather data such as the weather condition, temperature, humidity, and wind speed. The ‘demand’ column represents the customer’s willingness for renting a car for a specific time. Higher demand rates show that customers are more willing to rent a car and vice versa. A complete description of the data is also shown in Table I.

IMPORTANT NOTICE: You should complete the Part I (database management) tasks using only the sqlite3 python module and SQL statements. You shouldn’t use any other python modules for these tasks. However, you can use any modules for Part II (data analytics) tasks or for importing and exporting data.

Download the dataset “CarSharing” from the KLE and complete the tasks.

Part I: Database Management (50%)

1. Create an SQLite database and import the data into a table named “CarSharing”. Create a backup table and copy the whole table into it. [5%]

2. Add a column to the CarSharing table named “temp_category”. This column should contain three string values. If the “feels-like” temperature is less than 10 then the corresponding value in the temp_category column should be “Cold”, if the feels-like temperature is between 10 and 25, the value should be “Mild”, and if the feels-like

temperature is greater than 25, then the value should be “Hot”. [5%]

3. Create another table named “temperature” by selecting the temp, temp_feel, and temp_category columns. Then drop the temp and temp_feel columns from the CarSharing table. [5%]

4. Find the distinct values of the weather column and assign a number to each value. Add another column named “weather_code” to the table containing each row’s assigned weather code. [5%]

5. Create a table called “weather” and copy the columns “weather” and “weather_code” to this table. Then drop the weather column from the CarSharing table. [5%]

6. Create a table called time with four columns containing each row’s timestamp, hour, weekday name, and month name (Hint: you can use the surftime() function for this

purpose).

[5%]

7. Assume it’s the first day you have started working at this company and your boss Linda sends you an email as follows: “

Hello, welcome to the team. I hope you enjoy working at this company. Could you please give me a report containing the following information:

a) Please tell me which date and time we had the highest demand rate in 2017. [5%]

b) Give me a table containing the name of the weekday, month, and season in which we had the highest and lowest average demand rates throughout 2017. Please include the calculated average demand values as well. [5%]

c) For the weekday selected in (b), please give me a table showing the average demand rate we had at different hours of that weekday throughout 2017. Please sort the results in descending order based on the average demand rates.

[5%]

d) Please tell me what the weather was like in 2017. Was it mostly cold, mild, or hot? Which weather condition (shown in the weather column) was the most prevalent in 2017? What was the average, highest, and lowest wind speed and humidity for each month in 2017? Please organise this information in two tables for the wind speed and humidity. Please also give me a table showing the average demand rate for each cold, mild, and hot weather in 2017 sorted in descending order based on their average demand rates. [5%]

e) Give me another table showing the information requested in (d) for the month we had the highest average demand rate in 2017 so that I can compare it with other months. [5%]

NOTICE: Full marks for task 7 will be given to solutions that use the CarSharing table after all changes in tasks 1-6 have been made to it.

Part II: Data Analytics (50%)

1. Import the CarSharing table into a CSV file and preprocess it with python. You need to drop duplicate rows and deal with null values using appropriate methods. [5%]

2. Using appropriate hypothesis testing, determine if there is a significant relationship between each column (except the timestamp column) and the demand rate. Report the tests’ results. [5%]

3. Please describe if you see any seasonal or cyclic pattern in the temp, humidity, windspeed, or demand data in 2017. Describe your answers. [5%]

4. Use an ARIMA model to predict the weekly average demand rate. Consider 30 percent of data for testing. [7.5%]

5. Use a random forest regressor and a deep neural network to predict the demand rate and report the minimum square error for each model. Which one is working better? Why? Please describe the reason. [10%]

6. Categorize the demand rate into the following two groups: demand rates greater than the average demand rate and demand rates less than the average demand rate. Use labels 1 and 2 for the first and the second groups, respectively. Now, use three different classifiers to predict the demand rates’ labels and report the accuracy of all models. Use 30 percent of data for testing. [10%]

7. Assume k is the number of clusters. Set k=2, 3, 4, and 12 and use 2 methods to cluster the temp data in 2017. Which k gives the most uniform clusters? (Clusters are called uniform when the number of samples falling into each cluster is close.) [7.5%]

NOTICE: You should use the original CarSharing dataset for Part II, not the dataset you have modified in Part II.

Guidelines for Preparing the Appendix

The following example shows a well-formatted appendix.

As you can see, PART I and PART II are clearly separated, and the code can be copied and pasted. The following steps show how you can copy your code and paste it into your report in the same way as the above example:

1. Select the code in your IDE using the mouse.

2. Use CTRL+C to copy the code.

3. Use CTRL+V to paste the code into your report.

Assessment Criteria

The criteria are adapted from the University assessment criteria and relate to all parts of this assessment.

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Vacancy 🏃🏃‍♀️🏃‍♂️ Vacancy 🏃🏃‍♀️🏃‍♂️
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𝐖𝐡𝐲 𝐜𝐡𝐨𝐨𝐬𝐞 𝐮𝐬 𝐟𝐨𝐫 𝐅𝐢𝐧𝐚𝐧𝐜𝐞?

✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: हामी तपाईंको वित्तीय विश्लेषणलाई सही दिशा दिन्छौं—बजेट, नगद प्रवाह, र जोखिम मूल्याङ्कनमा विशेषज्ञता।

✅ 𝐁𝐮𝐝𝐠𝐞𝐭 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: तपाईंको वित्तीय लक्ष्यहरू हासिल गर्नका लागि ठोस र सटीक बजेट बनाउने।

✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠: सही, पारदर्शी, र समयमै वित्तीय रिपोर्टिङ्ग प्रणालीमा मद्धत गर्दछौं।

✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐜𝐲: हामी तपाईंलाई सही निवेश रणनीतिहरू छनोट गर्न र मुनाफा अधिकतम गर्न सहयोग गर्छौं।

✅ 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐓𝐡𝐞𝐨𝐫𝐲 𝐚𝐧𝐝 𝐏𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐠𝐲: वित्तीय सिद्धान्त र व्यावहारिक तरिकामा बलियो पकड़ राख्छौं।

✅ 𝐄𝐱𝐩𝐞𝐫𝐭 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐜𝐲 𝐟𝐨𝐫 𝐁𝐢𝐧𝐝𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: हामी तपाईंको सबै वित्तीय समाधानको लागि पेशेवर मार्गदर्शन र सहायता प्रदान गर्छौं।

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𝐃𝐨𝐧’𝐭 𝐰𝐨𝐫𝐫𝐲, हामी छौं तपाईंलाई सहयोग गर्न!

𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐰𝐞 𝐜𝐚𝐧 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐲𝐨𝐮: 👉

- 📝 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞: केस स्टडीमा रिसर्च कसरी गर्ने भन्ने अन्योलमा हुनुहुन्छ? हामी सहायता गर्दछौं।

- 📊 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Data analysis मा समस्या छ? हामी तपाईंलाई सही तरिकाले guide गर्छौं।

- 🧪 𝐅𝐨𝐫𝐦𝐚𝐭𝐭𝐢𝐧𝐠 & 𝐒𝐭𝐲𝐥𝐢𝐧𝐠: Case study को formatting र styling meet गराउन मद्दत गर्छौं।

- 🔍 𝐏𝐥𝐚𝐠𝐢𝐚𝐫𝐢𝐬𝐦 𝐂𝐡𝐞𝐜𝐤: Plagiarism report प्राप्त गर्न र सुधार गर्न हामीले सहयोग गर्नेछौं।

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🌟 𝐖𝐡𝐲 𝐜𝐡𝐨𝐨𝐬𝐞 𝐮𝐬?

✅ 𝐓𝐨𝐩-𝐍𝐨𝐭𝐜𝐡 𝐐𝐮𝐚𝐥𝐢𝐭𝐲: हामी Turnitin verified, plagiarism-free, र उच्च गुणस्तरको काम प्रदान गर्छौं।

✅ 𝐅𝐚𝐬𝐭 & 𝐑𝐞𝐥𝐢𝐚𝐛𝐥𝐞: तपाईंको डेडलाइनलाई ध्यानमा राख्दै समयमै डेलिभरी।

✅ 𝐂𝐨𝐬𝐭-𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: बजेट-अनुकूल मूल्य र विशेष छुट।

✅ 𝐅𝐫𝐞𝐞 𝐑𝐞𝐯𝐢𝐬𝐢𝐨𝐧𝐬: तपाईंको सन्तुष्टिका लागि असीमित सुधारहरू।

✅ 𝐄𝐱𝐩𝐞𝐫𝐭 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞: अनुभवी विशेषज्ञहरूको सहयोग हरेक चरणमा।

✅ 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐒𝐮𝐩𝐩𝐨𝐫𝐭: तपाईंको आवश्यकताअनुसार अनुकूलित सेवा।

✅ 𝐀𝐥𝐥-𝐓𝐢𝐦𝐞 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: २४/७ सहयोग प्रदान गर्ने प्रतिबद्धता।

✅ 𝐌𝐮𝐥𝐭𝐢𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐚𝐫𝐲 𝐒𝐮𝐩𝐩𝐨𝐫𝐭: FYP, thesis, dissertation, र अन्य सबै प्रोजेक्टमा सहयोग।

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✅ 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐲: तपाईंको सफलता हाम्रो लक्ष्य हो।

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