ICT233 TUTOR-MARKED ASSIGNMENT (TMA) This assignment is price 24% of the closing designate for ICT233, Knowledge Programming. The lower-off date for this assignment is Thursday, 12 March 2026, 2355 hours. Express to College students: You are to embrace the following particulars for your submission: Direction Code, Title of the TMA, SUSS PI No., Your Name, and Submission Date. 1. Please provide solutions into the given template ICT233_TMA_JAN25_STUDENT.ipynb between the following tags: ############## Your code starts right here ############## ############### Your code ends right here ############### 2. Please invent now not alter the given code in the template, invent now not add/delete/spoil up any code cell. 3. Please print out (use print() impartial) your evaluation and insights. 4. If there is any discrepancy between the questions displayed in the template and those in the TMA paper (in pdf structure), please confer with the TMA paper (in pdf structure) because the supply of truth. Dataset Knowledge Crucial Express: Please use the dataset situated in the folder equipped with the notebook. There is never always a have to procure it from the URL, because the dataset would possibly perhaps commerce over time and impression your outcomes. Furthermore, the provided dataset involves minor modifications from the new supply for the decisions of this notebook. This dataset contains records about world electrical energy generation (https://github.com/owid/vitality-files)from various sources and countries. It’s precious for analysing world vitality trends, renewable vs non-renewable sources, and electrical energy generation patterns all through varied countries and regions. Resolution all questions Search files from 1 (74 marks) Dreams Brand dataset with files scientist mindset. Brand and operate computation logic and routines in Python. Assess use of Python handiest and Python files constructions to manufacture extract, load, and transformation operations. Assess use of Pandas dataframe to manufacture extract, load, transformation and calculation operations. Structure code in acceptable techniques (choices), looping and conditions. Assemble the supreme choice to manufacture extract, load, transformation and calculation operations on dataset. Habits visualization in an acceptable formula. Knowledge Cleaning and Exploratory Knowledge Prognosis (EDA) on World Electrical energy Skills Knowledge (a) Lacking Values Prognosis 1. Total Knowledge Completeness Summary Calculate the following: Total option of cells in the dataset: (n_columns): n_rows × n_columns Total option of lacking values: Σ (lacking values per cell) Total files completeness share: ((Total Cells – Total Lacking) / Total Cells) × 100 Total lacking fee share: 100 – Total Completeness (%) 2. Columns with Lacking Values Filter and repeat handiest the columns with lacking values (i.e., lacking rely > 0). Embody the following particulars: Column title. Depend of lacking values. Percentage of lacking values. 3. Total vs Incomplete Data Write code to: Depend and repeat the option of complete records (rows with no lacking values) and their share. Depend and repeat the option of incomplete records (rows with no less than one lacking price) and their share. 4. Distribution of Lacking Values per Checklist Write code to analyse and repeat the distribution of lacking values all through incomplete records. Particularly: Depend the option of records with 1, 2, 3, and hundreds others., lacking values. Show the implications in a readable structure. (5 marks) (b) Analyse the metadata in files/owid-vitality-metadata.csv to resolve acceptable files styles for all columns in the first dataset. Express that figuring out the metadata is important for all subsequent questions. (3 marks) (c) Which high 5 countries generate the most electrical energy in 2024 (other than regions love ‘Europe’ and country groups love ‘Excessive-earnings countries’)? (4 marks) (d) Impact a line home exhibiting the trend of total electrical energy generation of the tip 5 countries from Search files from 1(c) over time. Design TWO (2) insights from the home. (4 marks) (e) Assemble a visualization to visualise the electrical energy generation by supply for China and the US from 2010 to 2024. Provide TWO (2) insights fixed with the chart. (8 marks) (f) Assemble a visualization to visualise the electrical energy generation by supply for China and the US from 2010 to 2024, nonetheless this time normalize the knowledge by population. Provide TWO (2) insights fixed with the chart. (5 marks) (g) Assemble a visualization to visualise the electrical energy generation by supply for China and the US from 2010 to 2024, nonetheless this time normalize the knowledge by GDP. Provide TWO (2) insights fixed with the chart. (5 marks) (h) To analyse and tackle the followings: Which country (other than regions and country groups) has the top likely lower in fossil electrical energy generation per capita between 2010 and 2024? For this country, derive the replaceable vitality supply that has elevated the most in electrical energy generation per capita all during the same duration. Provide the title of the vitality supply and the prolong in generation per capita. (5 marks) (i) To analyse and manufacture the followings: Visualize nuclear electrical energy generation of all countries (other than regions and country groups) on a world plot from 1965 to 2024 the utilization of Plotly visualization library (https://plotly.com/python/choropleth-maps). Express that the parable’s minimum and most values desires to be world in wish to one year-instruct. Provide TWO (2) insights fixed with the visualization. (7 marks) (j) Assemble a visualization to present how electrical energy carbon depth (gCO₂/kWh), carbon_intensity_elec, pertains to the piece of fossil electrical energy (%), fossil_share_elec, and piece the insights on how this relationship has shifted over time. (5 marks) (k) Which countries lowered electrical energy carbon depth the most without cutting back total electrical energy request from 2010 to 2024? (5 marks) (l) Which low-carbon electrical energy technologies (nuclear, hydro, solar, wind, bioenergy, and other renewables) are most strongly connected to reductions in electrical energy carbon depth between 2010 and 2024? Exercise a sturdy regression mannequin (https://www.statsmodels.org/true/rlm.html#technicaldocumentation) with M = sm.sturdy.norms.HuberT() to sight the relationship between reductions in electrical energy carbon depth and adjustments in electrical energy generation from low-carbon technologies. Kind out the reduction in electrical energy carbon depth because the dependent variable (y) and the adjustments in generation from low-carbon technologies because the unbiased variables (X). In step with the mannequin summary, title the technologies with the largest absolute coefficient values, noting that obvious coefficients indicate that would possibly increase in generation from those technologies are connected to reductions in electrical energy carbon depth, and that p-values below 0.05 indicate statistically indispensable associations. (7 marks) (m) How unequal is access to low-carbon electrical energy all through countries with varied earnings ranges in 2024? It’s likely you’ll per chance presumably use records for countries whose names bear the observe “earnings” and visualize the distribution of low-carbon electrical energy consumption per capita all through varied lowcarbon vitality sources and earnings ranges the utilization of an acceptable home and provide the insights. (6 marks) (n) Are electrical energy programs converging globally in carbon depth? Per one year, compute the coefficient of variation (CV = customary deviation / mean) of electrical energy carbon depth all through all countries (other than regions and country groups). Impact a line home to visualise the trend of CV over time. Provide one perception fixed with the home. (5 marks) Search files from 2 (26 marks) Dreams: Brand dataset with files scientist mindset Assemble computation logic and routines in Python Habits visualization in an acceptable formula Assess use of Pandas dataframe to manufacture extract, load, transformation and calculation operations Assemble the supreme choice to manufacture extract, load, transformation and calculation operations on dataset Assess the operate and use of database ORM / SQLite the supreme choice to manufacture extract, load, transformation and calculation operations (a) Title the tip ten countries (other than regions and country groups) with the top likely receive electrical energy imports as a little bit of request, net_elec_imports_share_demand, in 2024, the utilization of: Pandas SQL (SQLite) SQLAlchemy ORM (9 marks) (b) Invent electrical energy-import-dependent countries decarbonize sooner or slower? Compare 2024 against 2010 to reach a clear and logical conclusion. Habits the evaluation the utilization of: Pandas SQL (SQLite) SQLAlchemy ORM Provide a clear and logical conclusion fixed alongside with your findings. A country is in point of fact apt as electrical energy-import-dependent if its receive electrical energy imports as a little bit of request, net_elec_imports_share_demand, is bigger than 0% in 2010. The decarbonization fee is vulnerable to be measured by carbon_intensity_elec_2010 – carbon_intensity_elec_2024 / (2024 – 2010). (17 marks) —-END OF ASSIGNMENT— Reference Source ICT233_TMA_JAN25_STUDENT.ipynb owid-vitality-metadata.csv owid-vitality-files.csv ict_233-01_v2.pdf ict_233-02_v2.pdf ict_233-03_v2.pdf ICT233_S4_V2.pdf ICT233_S5_V2.pdf ICT233_S6_V2.pdf Write My Task Obtain Dependable SUSS ICT233 Task Enhance This day Native Singapore Writers Team 100% Plagiarism-Free Essay Top Satisfaction Rate Free Revision On-Time Provide
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