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Computer Science Project Customer Ask Paper

Computer Science Project Customer Ask Paper

ANSWER

Step 1: Data Collection

  • Step 2: Data Preprocessing
    • Load the dataset into a Python environment, preferably a Jupyter Notebook using Anaconda.
    • Perform data cleaning, which may include handling missing values, dealing with outliers, and ensuring data consistency.
    • Explore the dataset to gain an initial understanding of its contents.

    Step 3: Exploratory Data Analysis (EDA)

    • Conduct EDA to gain insights into the demographics and characteristics of individuals making less than and more than $50,000.
    • Create visualizations (e.g., histograms, bar plots, scatter plots) to depict distributions, correlations, and trends in the data.
    • Analyze summary statistics for key variables.

    Step 4: Feature Engineering

    • Select relevant features (columns) from the dataset that might impact an individual’s income.
    • Encode categorical variables using techniques such as one-hot encoding or label encoding.
    • Create new features if necessary, such as age groups or education categories.

    Step 5: Building a Predictive Model

    • Split the dataset into training and testing sets to evaluate model performance.
    • Choose an appropriate machine learning algorithm for regression, as your goal is to predict income.
    • Train the model using the training dataset and evaluate it using the testing dataset.
    • Fine-tune the model parameters to optimize its performance.

    Step 6: Interpretation and Insights

    • Interpret the results of your predictive model to understand which factors contribute the most to an individual’s income.
    • Provide insights to the UVW College marketing team regarding which demographics are more likely to have incomes above or below $50,000.

    Step 7: Application Development

    • Develop an application that allows the marketing team to input various demographic parameters (e.g., age, gender, education, marital status) and get predictions for individual income.
    • Make the application user-friendly and visually appealing.

    Step 8: Documentation and Reporting

    • Document all your code and analysis in a clear and organized manner.
    • Create a progress report to update the UVW College marketing team on the project’s status, including preliminary findings and any challenges encountered.

    Step 9: Final Report and Presentation

    • Prepare a final report summarizing the project, including data sources, methodology, results, and recommendations.
    • Create a presentation to communicate your findings and insights to the UVW College marketing team.

    Step 10: Review and Iteration

    • Review your work with the marketing team and gather feedback.
    • Iterate on the model or application based on their input and requirements.

    Throughout the project, make use of Python libraries such as Pandas, Matplotlib, Seaborn, and scikit-learn for data manipulation, visualization, and machine learning. Anaconda provides a convenient environment for managing these libraries.

    Remember to follow best practices in data science, including data ethics, and ensure that any personal or sensitive information is handled with care and privacy considerations.

  • Explore the dataset to gain an initial understanding of its contents.

Step 3: Exploratory Data Analysis (EDA)

  • Conduct EDA to gain insights into the demographics and characteristics of individuals making less than and more than $50,000.
  • Create visualizations (e.g., histograms, bar plots, scatter plots) to depict distributions, correlations, and trends in the data.
  • Analyze summary statistics for key variables.

Step 4: Feature Engineering

  • Select relevant features (columns) from the dataset that might impact an individual’s income.
  • Encode categorical variables using techniques such as one-hot encoding or label encoding.
  • Create new features if necessary, such as age groups or education categories.

Step 5: Building a Predictive Model

  • Split the dataset into training and testing sets to evaluate model performance.
  • Choose an appropriate machine learning algorithm for regression, as your goal is to predict income.
  • Train the model using the training dataset and evaluate it using the testing dataset.
  • Fine-tune the model parameters to optimize its performance.

Step 6: Interpretation and Insights

  • Interpret the results of your predictive model to understand which factors contribute the most to an individual’s income.
  • Provide insights to the UVW College marketing team regarding which demographics are more likely to have incomes above or below $50,000.

Step 7: Application Development

  • Develop an application that allows the marketing team to input various demographic parameters (e.g., age, gender, education, marital status) and get predictions for individual income.
  • Make the application user-friendly and visually appealing.

Step 8: Documentation and Reporting

  • Document all your code and analysis in a clear and organized manner.
  • Create a progress report to update the UVW College marketing team on the project’s status, including preliminary findings and any challenges encountered.

Step 9: Final Report and Presentation

  • Prepare a final report summarizing the project, including data sources, methodology, results, and recommendations.
  • Create a presentation to communicate your findings and insights to the UVW College marketing team.

Step 10: Review and Iteration

  • Review your work with the marketing team and gather feedback.
  • Iterate on the model or application based on their input and requirements.

Throughout the project, make use of Python libraries such as Pandas, Matplotlib, Seaborn, and scikit-learn for data manipulation, visualization, and machine learning. Anaconda provides a convenient environment for managing these libraries.

Remember to follow best practices in data science, including data ethics, and ensure that any personal or sensitive information is handled with care and privacy considerations.

Computer Science Project Customer Ask Paper

QUESTION

Description

 

 

Customer Ask

XYZ Corporation uses data to develop marketing profiles on people. These profiles are then sold to numerous companies for marketing purposes. You work at XYZ as a data analyst. You have just been given a new project working with UVW College, a local college looking to bolster enrollment. UVW has chosen a salary as a key demographic to determine criteria for marketing its degree programs. You must develop marketing profiles using data supplied by the United States Census Bureau, and you will be focusing on $50,000 as a key number for salary. There are many key variables that must be assessed for individuals making less than and more than $50,000, including age, gender, education status, marital status, occupation, etc.

For example, if the data show that the majority of individuals making less than $50,000 is under 34 years old, male, single, and has a high school diploma, the college can market to this demographic with tuition amounts, program concentrations, and even ground or online programs appropriate to this demographic.

To achieve its enrollment target, the marketing team at UVW would like to develop an application to find the factors that determine the individual’s income. One way to accomplish this is to use the United States Census Bureau data provided by the XYZ company. The marketing team wants to group the factors that can be used in the development of their proposed model/application. They also want the application to predict the income of an individual based on different values of the input parameters so that they can tailor their marketing efforts when reaching out to the individuals.

Project Dataset

You will use the following dataset to answer the customer ask:

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