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- Choose a topic: Select a topic that interests you and has relevant data available.
- Define the problem: Clearly articulate the question you want to answer or the problem you want to solve.
- Collect data: Gather relevant data from various sources, such as databases, APIs, or public datasets.
- Clean and preprocess data: Handle missing values, outliers, and inconsistencies to ensure data quality.
- Summarize data: Calculate descriptive statistics (mean, median, mode, standard deviation).
- Visualize data: Create charts Phone Number graphs, and visualizations to understand data patterns.
- Identify relationships: Look for correlations and dependencies between variables.
- Statistical analysis: Use statistical methods like hypothesis testing, regression analysis, or ANOVA.
- Machine learning: Apply machine learning algorithms for tasks like classification, regression, or clustering.
- Data mining: Discover patterns and relationships in large datasets.
- Select models: Choose appropriate models based on your problem and data.
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