Reference no: EM133669050

Homework

I. Question Formulation: You need to devise a question that can be answered through data analysis. This question should be of your own creation, and it should reflect your curiosity and interest.

II. Data collection: You are responsible for finding the appropriate dataset that aligns with your chosen question. Ensure that the data is clean and organized for analysis. If you don't know where to find the data set, you can use Kaggle.com It can give you more inspiration about the question formulation and data collection. You need to state where you get your data from in order to receive credits.

III. Exploratory Data Analysis: Conduct an EDA to understand the characteristics of your dataset. This step will help you gain insights and identify patterns in the data. Here are some key components of EDA I am expecting from your paper:

A. summary statistics: compute basic statistics for the dataset, such as mean, median, standard deviation, minimum, maximum, and quartiles. It provides an overview of the data's central tendencies and spread.

B. Data Visualization: Create various plots and charts to visualize the data's distribution and relationships. Common visualization tools include histograms, box plots, scatter plots, bar graphs, and line graphs.

C. Data Distribution: Examine the distribution of individual variables. This helps in identifying whether the data is normally distributed, skewed, or exhibits other patterns. Understanding the distribution can influence the choice of statistical tests and modeling techniques.

D. Correlation Analysis: Determine the relationships between variables using correlation coefficients or scatter plots. It can reveal potential associations and dependencies between variables.

E. Categorical Variables (If your data involves this type of variable and you think it is important to answer your question. If the categorical variables are not that important to answer your questions, don't worry about it.): Explore the distribution of categorical variables using frequency tables, bar charts, or pie charts.

F. Hypothesis Generation: Eventually your exploratory data analysis can lead to the formulation of hypotheses about relationships or patterns in the data to answer your question or guide further analysis.

IV. Machine Learning: Apply a machine learning algorithm to address your question. You are only required to choose one type of algorithm for this mini-project but you may have to run it multiple times with different variables, and you will decide what it is best for your result. You have the flexibility to choose from the algorithms we've learned in class, but make sure the selected algorithm is appropriate for your data. Alternatively, if you find a specific algorithm outside of your class materials that suits your needs, you are welcome to use it.

V. Project Structure: While this is a mini-project, your report should follow a structure similar to a combination of Homework. This means it should include sections for introduction, Data collection and Preprocessing, EDA, Machine Learning, Results and Discussion, and Conclusion.

VI. Data Attribution and References: In the conclusion section of your report, make sure to include a subsection titled "Data Attribution and References." In this subsection, provide a detailed list of the sources where you obtained your data, including the dataset name, the organization or website from which it was sourced, and any relevant publication or citation information. Additionally, if you consulted external research papers, articles, or resources during your project, please list these references in the same section.