Reference no: EM133685561

Assignment Overview:

In this assignment, you will work in a group of 3 to 5 students to conduct an Exploratory Data Analysis (EDA) on a comprehensive dataset. The dataset can be acquired from internal or external sources, or by merging both. You will utilize appropriate techniques, tools, and programming languages, such as Python, to perform various data procedures including data acquisition, data wrangling, and data mining to extract meaningful insights from the dataset. The final deliverables will include an EDA report and an oral presentation video to showcase your findings and analysis.

Assignment Tasks:

1. Data Acquisition:
• Identify and acquire a comprehensive dataset suitable for the EDA. You can choose from the suggested data sources provided or explore and select different datasets based on your group's common interest.
• Ensure the dataset is relevant, sufficiently large, and contains multiple variables for thorough analysis.
1. Kaggle Datasets
2. UCI Machine Learning Repository
3. Government Open Data Portals (e.g., data.gov)
4. Academic Research Databases (e.g., PubMed, IEEE Xplore)
5. Social Media APIs (e.g., Twitter, Facebook)

2. Data Wrangling:
- Preprocess the acquired dataset to handle missing values, outliers, and inconsistencies.
• Perform data cleaning tasks such as removing duplicates, standardizing formats, and transforming variables if necessary.
• Explore methods to handle categorical variables and convert them into a suitable format for analysis.

3. Data Exploration:
- Conduct initial data exploration to understand the structure, distributions, and relationships within the dataset.
• Utilize descriptive statistics and visualization techniques (e.g., histograms, box plots, scatter plots) to gain insights into individual variables and their interactions.
- Identify any patterns, trends, or anomalies present in the data.

4. Data Mining and Analysis:
- Apply appropriate data mining techniques such as clustering, classification, or regression to uncover deeper insights within the dataset.
• Utilize machine learning algorithms if applicable to predict or classify certain outcomes based on the available variables.
- Perform feature engineering if necessary to enhance the predictive power of the model.

5. EDA Report:
- Compile all findings, analysis, and visualizations into a comprehensive EDA report.
- Structure the report to include an introduction, methodology, results, discussion, and conclusion sections.
- Provide clear explanations for the steps taken, insights gained, and any challenges encountered during the analysis.
- Include visualizations and summary statistics to support your findings.

6. Oral Presentation:
- Prepare a concise oral presentation to present your EDA findings to the class.
- Highlight key insights, trends, and interesting observations discovered during the analysis.
- Use visual aids such as slides or interactive dashboards to enhance the presentation.