PostgreSQL Queries for Resolve Issues with Consignment Measurements During Pickup
Budget: ₹600 – ₹1,500 INR
I need a skilled SQL expert particularly well-versed in PostgreSQL to assist me with my project. This involves various types of queries and is primarily focused on business analytics.
Key Tasks:
- Data Retrieval (SELECT): I need assistance with fetching and organizing relevant data from the database.
- Data Manipulation (INSERT, UPDATE, DELETE): Help is required with modifying the data as necessary.
- Database Management (CREATE, ALTER, DROP): This involves creating new tables or altering existing ones, as well as removing any unnecessary data.
The ideal freelancer for this project should have a strong background in SQL, particularly with PostgreSQL and business analytics. Experience in data manipulation and database management is crucial. Creativity and problem-solving skills are highly valued as well.
TASK 1 :- Data Analysis and Insights
1.1 Data Pre-Processing
After collecting the datasets, closely understand the consignment details data and the corresponding dimensions of length, breadth, and height for each box within the consignment
Clean the data if required- Convert units if necessary to ensure consistency in measurement (e.g., converting all dimensions to a common unit such as inches or centimeters).
Calculate the required columns e.g., density (CFT).
1.2 Exploring Consignment & Volume Details Data
Attempt univariate and bi-variate techniques to understand the data variables, try to plot relation between various column variables. Prepare a summary of all the observations and analysis figured out.
1.3 Analyzing CFT Values
Begin by calculating the CFT values for each consignment. Remember, CFT serves as a crucial variable for identifying patterns and trends across different industries and clients
Summarize the CFT values at both the client and industry levels. This involves calculating statistical parameters such as mean, maximum, percentile, and few more to understand the distribution of CFT within each category.
1.4 Using IQR techniques to detect the potential outliers
Determine industry-level upper and lower limits of CFT of different attributes like CFT, weight, volume etc using the formula mentioned below.
Compare consignment CFT values with the established industry limits, Consignments falling outside these limits are potential outliers, suggesting errors in weight or volume measurements.
TASK 2:- Model Building and Performance Evaluation
Now that we've gained valuable insights from our data analysis, it's time to translate that knowledge into actionable strategies for detecting inaccuracies in consignments. In this stage, we'll be crafting multiple logical approaches to flag potentially mis-measured consignments, ensuring the integrity of our data and processes.
2.1 Logic Development
Utilize the insights gained from data analysis to develop multiple logics for detecting consignments with inaccuracies.
Refer to the provided examples of logic as a starting point for developing your own set of criteria for flagging potentially mis-measured consignments.
Implement a binary classification approach where consignments are categorized as either correctly measured (0) or wrongly measured as (1). This way the final calculation of total number of outliers will be easier.
2.2 Logic Implementation
Implement the developed logics using appropriate programming or data analysis tools.
Test each logic to ensure its effectiveness in flagging consignments with inaccuracies in weights and dimensions.
The logic that gives the minimum number of outliers has the highest accuracy standards, making it the desired choice of logic for outlier detection.
TASK 3:- Presenting Insights and Analysis
A clear and concise presentation summarizing the project objectives, methodologies, exploratory data analysis, findings, and proposed solutions and model efficiencies.
Key Tasks:
- Data Retrieval (SELECT): I need assistance with fetching and organizing relevant data from the database.
- Data Manipulation (INSERT, UPDATE, DELETE): Help is required with modifying the data as necessary.
- Database Management (CREATE, ALTER, DROP): This involves creating new tables or altering existing ones, as well as removing any unnecessary data.
The ideal freelancer for this project should have a strong background in SQL, particularly with PostgreSQL and business analytics. Experience in data manipulation and database management is crucial. Creativity and problem-solving skills are highly valued as well.
TASK 1 :- Data Analysis and Insights
1.1 Data Pre-Processing
After collecting the datasets, closely understand the consignment details data and the corresponding dimensions of length, breadth, and height for each box within the consignment
Clean the data if required- Convert units if necessary to ensure consistency in measurement (e.g., converting all dimensions to a common unit such as inches or centimeters).
Calculate the required columns e.g., density (CFT).
1.2 Exploring Consignment & Volume Details Data
Attempt univariate and bi-variate techniques to understand the data variables, try to plot relation between various column variables. Prepare a summary of all the observations and analysis figured out.
1.3 Analyzing CFT Values
Begin by calculating the CFT values for each consignment. Remember, CFT serves as a crucial variable for identifying patterns and trends across different industries and clients
Summarize the CFT values at both the client and industry levels. This involves calculating statistical parameters such as mean, maximum, percentile, and few more to understand the distribution of CFT within each category.
1.4 Using IQR techniques to detect the potential outliers
Determine industry-level upper and lower limits of CFT of different attributes like CFT, weight, volume etc using the formula mentioned below.
Compare consignment CFT values with the established industry limits, Consignments falling outside these limits are potential outliers, suggesting errors in weight or volume measurements.
TASK 2:- Model Building and Performance Evaluation
Now that we've gained valuable insights from our data analysis, it's time to translate that knowledge into actionable strategies for detecting inaccuracies in consignments. In this stage, we'll be crafting multiple logical approaches to flag potentially mis-measured consignments, ensuring the integrity of our data and processes.
2.1 Logic Development
Utilize the insights gained from data analysis to develop multiple logics for detecting consignments with inaccuracies.
Refer to the provided examples of logic as a starting point for developing your own set of criteria for flagging potentially mis-measured consignments.
Implement a binary classification approach where consignments are categorized as either correctly measured (0) or wrongly measured as (1). This way the final calculation of total number of outliers will be easier.
2.2 Logic Implementation
Implement the developed logics using appropriate programming or data analysis tools.
Test each logic to ensure its effectiveness in flagging consignments with inaccuracies in weights and dimensions.
The logic that gives the minimum number of outliers has the highest accuracy standards, making it the desired choice of logic for outlier detection.
TASK 3:- Presenting Insights and Analysis
A clear and concise presentation summarizing the project objectives, methodologies, exploratory data analysis, findings, and proposed solutions and model efficiencies.