Kindly use the following link to view all datasets related to the project: GitHub Behailu Developer Mode. Dev:- Source Data.
Given a dataset named Netflix. Connect to the dataset and perform the following tasks for data modeling. Analyze the dataset by cleaning and modifying it, thereby drawing relevant insights.
Extract, clean, and analyze data from an Excel file, establish relationships in the data model, and generate insights on "Total Content" and "Runtime Hours." Determine the most prevalent content for the top 5 genres, count released content annually by type, and provide production country and runtime details. Calculate the overall runtime, count of movies and TV shows, and average IMDb rating. Integrate the Netflix logo and a brief introduction to Netflix.
ABC is a leading restaurant inspection firm with a global presence. Based on the region, it has identified the city where the facility is located. There are inspectors assigned to each region. The region has several facilities. Restaurant inspection data is compiled based on identified violations, and sanitary grades of A, B, and C are assigned. It needs to create a report that includes visuals with data presented region-wise and add roles to ensure users can view the data based on their region.
Develop a comprehensive "Restaurant Inspections Report" focusing on critical metrics like inspections, violations, and sanitation grades.
Tailor the report for two roles: Lead Inspector (regions 1–10) and Chief Inspector (all regions 1–16).
Inspection Volume by Facility Region: Display the number of inspections per facility region (Region Codes 1–16). Enable clear visualization for easy regional analysis.
Violations Analysis: Showcase the number of violations by facility region. Provide a breakdown of violation descriptions for each region. Include % of total violations to highlight relative severity.
Sanitation Grades: Incorporate grade and sanitation grade information for each facility. Create a visually appealing, sanitation-grade distribution across regions.
Quicken Loans is a leading mortgage lending company serving clients nationwide. The company processes thousands of loan applications each year, tracking them from the initial submission stage through approval, closing, and funding. Each loan follows defined milestones, with amounts and volumes recorded by loan type and status. This project aims to create an interactive and insightful report that visualizes key metrics over time, enabling stakeholders to monitor performance, identify trends, and inform more informed decision-making.
Develop a comprehensive "Loan Performance Dashboard" focusing on critical metrics like loan applications, approvals, closings, fundings, and volumes over time.
Track and display trends for each stage in the loan process, from funding application, with the ability to analyze both counts and dollar volumes.
Loan Applications Over Time: Display the total number of loan applications submitted over a selected period to understand demand trends.
Loan Approvals Over Time: Display the number of approved loans over time, allowing for the evaluation of approval rate changes and operational efficiency.
Funded Loans Over Time: Visualize the number of subsidized loans, providing insight into final loan conversions.
Loan Volume Metrics: Present application volume, approved volume, closed volume, and funded volume over time to track financial flow through the loan lifecycle.
Milestone Status: Display the distribution of loans across various milestone statuses, highlighting any bottlenecks or delays in processing.
Amount by Loan Type: Break down total loan amounts by type to understand product mix and market demand.
Amount by Milestone Status: Display total amounts corresponding to each milestone, enabling financial assessment at each process stage.