▸case-01 I have a vendor assessment dataset with metrics measured in different units: annual cost ($k), SLA uptime (%), and average resolution time (hours). Here is the raw table:
- Vendor A: $120k, 99.9% uptime, 4h resolution
- Vendor B: $85k, 99.0% uptime, 12h resolution
- Vendor C: $150k, 99.5% uptime, 2h resolution
Please process these raw metrics and produce a normalized scoring matrix so we can evaluate all candidates on a single uniform scale. | fail→pass | 13,072 | 26,230 | +101% | 1 | 1 | 0% | 3,041 | 5,085 | +67% | 0 | 0 | — |
▸case-02 Here is our project selection performance data across three initiatives. Criteria include initial investment (dollars, lower is better), strategic alignment rating (1-10 scale, higher is better), and implementation delay risk (days, lower is better). Please transform this raw dataset into a normalized matrix adjusting for the different scales and metric directions. | fail→fail | 12,156 | 18,384 | +51% | 1 | 1 | 0% | 1,528 | 3,790 | +148% | 0 | 0 | — |
▸case-03 We are comparing three cloud infrastructure options across throughput (MB/s, higher is better), latency (ms, lower is better), and monthly cost ($ per instance, lower is better). The raw values are:
- Provider X: 450 MB/s, 15ms, $120
- Provider Y: 600 MB/s, 25ms, $95
- Provider Z: 300 MB/s, 8ms, $150
Please construct the standardized normalized matrix for these technical options. | fail→fail | 20,918 | 43,610 | +108% | 1 | 1 | 0% | 3,589 | 4,947 | +38% | 0 | 0 | — |
▸case-04 Evaluate three suppliers across carbon footprint (tons CO2e, lower is better) and renewable energy usage (%, higher is better). Supplier 1: 500 tons, 80%; Supplier 2: 200 tons, 40%; Supplier 3: 350 tons, 95%. Standardize this scoring matrix so all criteria are directly comparable. | fail→fail | 15,347 | 24,198 | +58% | 1 | 1 | 0% | 2,299 | 4,483 | +95% | 0 | 0 | — |
▸case-05 We have vehicle data: Fleet A ($35k purchase price, 45 mpg, 3 maintenance visits/yr), Fleet B ($28k purchase price, 30 mpg, 5 maintenance visits/yr), Fleet C ($42k purchase price, 50 mpg, 2 maintenance visits/yr). Purchase price and maintenance visits are cost criteria (lower is better), mpg is benefit (higher is better). Produce the normalized scoring matrix. | fail→fail | 16,189 | 22,850 | +41% | 1 | 1 | 0% | 2,435 | 4,433 | +82% | 0 | 0 | — |
▸case-06 Please normalize this office evaluation matrix: Site 1 (Rent: $45/sqft, Commute: 25 min, Capacity: 200); Site 2 (Rent: $60/sqft, Commute: 15 min, Capacity: 350); Site 3 (Rent: $38/sqft, Commute: 40 min, Capacity: 150). Rent and commute are cost criteria; capacity is benefit. Convert these raw figures into a normalized comparison grid. | fail→fail | 21,941 | 22,505 | +3% | 1 | 1 | 0% | 3,561 | 5,191 | +46% | 0 | 0 | — |
▸case-07 Compare React, Vue, and Angular on Bundle Size (KB, lower is better), Build Time (seconds, lower is better), and Github Stars (count, higher is better). Data: React (140KB, 12s, 210k stars), Vue (90KB, 8s, 205k stars), Angular (500KB, 25s, 92k stars). Build the normalized decision matrix. | fail→fail | 13,001 | 24,732 | +90% | 1 | 1 | 0% | 2,936 | 5,330 | +82% | 0 | 0 | — |
▸case-08 Normalize the following property dataset: Prop A (Cap Rate: 6.5%, Vacancy: 8%, Price: $2.1M); Prop B (Cap Rate: 7.2%, Vacancy: 12%, Price: $1.8M); Prop C (Cap Rate: 5.8%, Vacancy: 4%, Price: $2.5M). Cap rate is benefit (higher better); Vacancy and Price are cost (lower better). Scale the matrix. | fail→fail | 15,972 | 22,714 | +42% | 1 | 1 | 0% | 2,584 | 4,157 | +61% | 0 | 0 | — |
▸case-09 We are selecting medical equipment based on Error Rate (ppm, lower better), Warmup Time (seconds, lower better), and Battery Life (hours, higher better). Device Alpha (5 ppm, 30s, 12h), Device Beta (2 ppm, 45s, 10h), Device Gamma (10 ppm, 15s, 16h). Please convert this to a normalized scoring matrix. | fail→fail | 16,630 | 16,477 | -1% | 1 | 1 | 0% | 2,684 | 3,057 | +14% | 0 | 0 | — |
▸case-10 Transform this carrier matrix into a normalized decision matrix: Carrier 1 (Late delivery rate: 2.5%, Damage rate: 0.1%, Cost per mile: $2.40); Carrier 2 (Late delivery rate: 1.0%, Damage rate: 0.4%, Cost per mile: $2.80); Carrier 3 (Late delivery rate: 4.0%, Damage rate: 0.05%, Cost per mile: $2.10). All three criteria are min-direction (lower is better). | fail→fail | 15,625 | 26,795 | +71% | 1 | 1 | 0% | 3,910 | 5,408 | +38% | 0 | 0 | — |
▸case-11 Standardize candidate ratings: Candidate A (Years experience: 8, Salary ask: $130k, Assessment test: 88%); Candidate B (Years experience: 12, Salary ask: $160k, Assessment test: 94%); Candidate C (Years experience: 5, Salary ask: $105k, Assessment test: 82%). Experience and Assessment are max-direction; Salary ask is min-direction. | fail→fail | 17,708 | 26,217 | +48% | 1 | 1 | 0% | 2,847 | 4,934 | +73% | 0 | 0 | — |
▸case-12 Normalize the matrix for energy storage systems: Battery Tech X (Energy density: 250 Wh/kg, Degradation rate: 2%/yr, Cost: $110/kWh); Tech Y (Energy density: 180 Wh/kg, Degradation rate: 1%/yr, Cost: $85/kWh); Tech Z (Energy density: 310 Wh/kg, Degradation rate: 3.5%/yr, Cost: $140/kWh). Density is max-direction; Degradation and Cost are min-direction. | fail→fail | 32,967 | 21,569 | -35% | 1 | 1 | 0% | 3,149 | 3,968 | +26% | 0 | 0 | — |
▸case-13 Calculate a normalized performance grid for acquisition channels: Paid Search (CAC: $45, LTV: $300, Churn: 5%); Organic Social (CAC: $12, LTV: $180, Churn: 8%); Referral (CAC: $22, LTV: $240, Churn: 3%). CAC and Churn are min-direction; LTV is max-direction. | fail→fail | 12,203 | 26,736 | +119% | 1 | 1 | 0% | 2,884 | 5,932 | +106% | 0 | 0 | — |
▸case-14 Standardize raw engine metrics: System A (Query latency: 120ms, Storage cost: $0.02/GB, Concurrency limit: 500); System B (Query latency: 45ms, Storage cost: $0.05/GB, Concurrency limit: 200); System C (Query latency: 80ms, Storage cost: $0.03/GB, Concurrency limit: 1000). Latency and cost are lower-is-better; concurrency is higher-is-better. | fail→fail | 16,082 | 23,023 | +43% | 1 | 1 | 0% | 2,546 | 4,256 | +67% | 0 | 0 | — |
▸case-15 Normalize these security platform scores: Option 1 (False positive rate: 1.2%, Mean time to detect: 14 min, Coverage: 92%); Option 2 (False positive rate: 0.4%, Mean time to detect: 22 min, Coverage: 88%); Option 3 (False positive rate: 2.1%, Mean time to detect: 8 min, Coverage: 98%). False positives and detection time are lower-is-better; coverage is higher-is-better. | fail→fail | 17,034 | 13,407 | -21% | 1 | 1 | 0% | 2,634 | 3,325 | +26% | 0 | 0 | — |
▸case-16 Convert raw packaging data to a normalized scoring grid: Material Alpha (Unit price: $0.15, Tensile strength: 45 MPa, Recyclability: 100%); Material Beta (Unit price: $0.08, Tensile strength: 30 MPa, Recyclability: 60%); Material Gamma (Unit price: $0.22, Tensile strength: 60 MPa, Recyclability: 85%). Price is min-direction; strength and recyclability are max-direction. | fail→fail | 18,914 | 20,713 | +10% | 1 | 1 | 0% | 2,386 | 4,922 | +106% | 0 | 0 | — |
▸case-17 Create a normalized metric matrix for CPU options: Chip 1 (TDP: 65W, Single-thread score: 3200, Multi-thread score: 24000); Chip 2 (TDP: 105W, Single-thread score: 3600, Multi-thread score: 31000); Chip 3 (TDP: 35W, Single-thread score: 2800, Multi-thread score: 18000). TDP is cost (lower better); benchmarks are benefit (higher better). | fail→fail | 13,996 | 14,998 | +7% | 1 | 1 | 0% | 2,894 | 3,721 | +29% | 0 | 0 | — |
▸case-18 Normalize scores for ticketing platforms: Platform X (Monthly fee: $1,200, Ticket resolution time: 4.2h, CSAT: 4.5/5); Platform Y (Monthly fee: $800, Ticket resolution time: 6.0h, CSAT: 4.1/5); Platform Z (Monthly fee: $1,500, Ticket resolution time: 3.1h, CSAT: 4.8/5). Monthly fee and resolution time are min-direction; CSAT is max-direction. | fail→fail | 22,527 | 18,761 | -17% | 1 | 1 | 0% | 3,283 | 4,279 | +30% | 0 | 0 | — |
▸case-19 Convert building lease data into a normalized decision matrix: Building A (TI Allowance: $40/sqft, Lease rate: $32/sqft, Lease term: 5 yrs); Building B (TI Allowance: $60/sqft, Lease rate: $38/sqft, Lease term: 7 yrs); Building C (TI Allowance: $25/sqft, Lease rate: $28/sqft, Lease term: 3 yrs). TI Allowance is max-direction; Lease rate is min-direction; Lease term is max-direction. | fail→fail | 11,938 | 29,653 | +148% | 1 | 1 | 0% | 2,582 | 4,043 | +57% | 0 | 0 | — |
▸case-20 We have already normalized our decision matrix across three vendor options. Now we need to apply our stakeholder weights (0.50 for Cost, 0.30 for Quality, 0.20 for Delivery Speed) and calculate the final weighted composite score for each vendor to select the winner. Here is the normalized matrix:
- Vendor A: Cost=0.80, Quality=0.90, Speed=0.70
- Vendor B: Cost=1.00, Quality=0.60, Speed=0.80
- Vendor C: Cost=0.50, Quality=1.00, Speed=0.90
Please compute the weighted total score for each vendor and identify the top candidate. | pass→pass | 7,833 | 11,604 | +48% | 1 | 1 | 0% | 1,892 | 2,870 | +52% | 0 | 0 | — |
▸case-21 Our team has completed normalization and weighting for a site selection study. Currently, Rent weight is 0.40 and Access weight is 0.60. Perform a sensitivity analysis showing how the overall scores for Site Alpha (Rent norm: 0.9, Access norm: 0.5) and Site Beta (Rent norm: 0.4, Access norm: 0.9) change if Rent weight varies from 0.10 to 0.90 in increments of 0.20. | pass→pass | 12,489 | 13,638 | +9% | 1 | 1 | 0% | 2,796 | 3,591 | +28% | 0 | 0 | — |
▸case-22 I am training a k-means clustering model in Python on customer transaction data (Age, Annual Spend, Visit Frequency). I need standard z-score normalization (mean 0, variance 1) applied across these numerical features before fitting scikit-learn's KMeans. Please generate the Python pandas and scikit-learn code to perform StandardScaler preprocessing on dataset dataframe `df`. | pass→pass | 6,097 | 8,099 | +33% | 1 | 1 | 0% | 1,214 | 1,798 | +48% | 0 | 0 | — |