Project Idea: Cancer Survival Analysis and Visualization
Objective: Analyze and visualize cancer survival rates based on a dataset containing information about cancer patients, treatment, and outcomes.
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Data Collection:
- Obtain a dataset related to cancer patients, treatment details, and survival outcomes. Public datasets are available from sources like SEER (Surveillance, Epidemiology, and End Results) program, UCI Machine Learning Repository, or other relevant medical databases.
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Data Cleaning and Preprocessing:
- Clean and preprocess the dataset, handling missing values, and ensuring data quality.
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Exploratory Data Analysis (EDA):
- Explore the dataset to understand the distribution of variables.
- Analyze demographic information, types of cancer, and treatment modalities.
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Survival Analysis:
- Utilize survival analysis techniques to calculate survival rates over time.
- Implement Kaplan-Meier estimators to visualize survival curves.
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Feature Engineering:
- Create relevant features, such as age groups, cancer stages, or treatment categories, to enhance analysis.
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Statistical Analysis:
- Perform statistical tests to identify factors that significantly impact survival rates.
- Consider Cox Proportional-Hazards model for more advanced analysis.
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Data Visualization:
- Use Python libraries (matplotlib, seaborn, Plotly) to create visually appealing plots and charts.
- Display survival curves, demographic distributions, and other relevant visualizations.
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Interactive Dashboard (Optional):
- Create an interactive dashboard using tools like Dash or Voila for users to explore the data dynamically.
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MySQL Integration:
- Store the preprocessed data in a MySQL database for efficient querying and retrieval.
- Utilize SQL queries to extract specific subsets of data for analysis.
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Report and Documentation:
- Summarize findings, insights, and visualizations in a Jupyter Notebook.
- Provide explanations for any observed trends or patterns.
- Conclusion and Recommendations:
- Conclude the analysis with a summary of key findings.
- Offer recommendations for further research or potential interventions based on the results.