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https://github.com/dbarty/dataanalysisexamples


https://github.com/dbarty/dataanalysisexamples

data-analysis data-science machine-learning machinelearning matplotlib numpy pandas python python3 seaborn

Last synced: 17 days ago
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README 

          
# Data Analysis Examples



Best Practices


    

        
  1. 

            Goal Definition and Problem Understanding

            
      

                  
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      Definition of the goal: Clarify the question or problem to be solved. What is the analysis supposed to achieve? Examples: Prediction, pattern recognition, decision support.
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      Understand the business context: Understand the business requirements or scientific hypotheses behind the analysis.
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      Gather stakeholder input: Clarify requirements with the stakeholders involved (departments, management, etc.).
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            Data Collection

            
      

                  
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      Identify data sources: Determine which data sources are needed for the analysis (e.g., databases, APIs, files).
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      Collect data: Extract data from the identified sources. This can be done through queries, web scraping, APIs, or CSV uploads.
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      Document the data: Record where the data came from and what features (attributes) it contains.
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            Exploratory Data Analysis (EDA)

            
      

                  
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      Understand the data structure: Examine the data type, dimensions (rows and columns), and data distribution.
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      Calculate descriptive statistics: Compute central measures such as mean, median, standard deviation, min/max, etc.
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      Visualize the data: Create charts (e.g., bar charts, box plots, scatter plots) to identify patterns, distributions, or relationships between variables.
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      Identify correlations: Determine correlations between variables to detect possible relationships.
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      Outlier detection: Identify outliers and unusual data points that could influence the analysis.
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            Data Preprocessing

            
      

                  
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      Clean the data: Remove or correct erroneous, incomplete, or duplicate data.
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      Handle missing values: Decide whether to remove, impute, or otherwise treat missing values.
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      Handle outliers: Decide how to handle outliers (e.g., remove, winsorize, or transform them).
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    • Feature engineering to improve model performance:
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        Transformations: Apply mathematical transformations (e.g., logarithmic, square root) to smooth distributions.
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        Encode categorical data: Use one-hot encoding or label encoding for categorical data.
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        Create new features: Generate new variables, e.g., by combining existing features (e.g., creating a ratio from two variables).
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        Interaction variables: Create features that capture interactions between variables (e.g., product of two variables).
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      Adjust data formats: Convert data types (e.g., string to date) or standardize and scale numerical variables, if necessary.
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            Model Selection and Development

            
      

                  
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      Select the analysis model: Choose the appropriate model depending on the analysis goal (e.g., linear regression, decision trees, clustering, time series analysis).
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      Train-test split: Divide the data into training and test datasets to avoid overfitting.
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      Train the model: Train the model with the training data.
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      Hyperparameter tuning: Fine-tune the model to find the best parameters (e.g., using grid search or random search).
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            Model Evaluation

            
      

                  
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      Model validation: Assess the model performance using the test dataset.
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      Calculate metrics: Determine key metrics such as accuracy, F1-score, precision, recall, RMSE (Root Mean Squared Error) depending on the model type.
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      Cross-validation: Perform cross-validation to verify the robustness of the model.
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      Check for bias and variance: Ensure that the model does not suffer from overfitting or underfitting.
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            Interpretation of Results

            
      

                  
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      Understand model results: Interpret the model parameters and the relationship between features and predictions.
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      Identify important features: Determine which variables contribute most to the model.
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      Visualize the results: Create charts or graphs to visually present the results (e.g., feature importance plots, confusion matrix).
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            Conclusions and Recommendations

            
      

                  
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      Summarize insights: Summarize the key insights from the analysis in a clear and concise manner.
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      Derive recommendations: Provide concrete actions or decisions based on the analysis results.
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      Communicate results: Present the results to stakeholders in an understandable and well-structured form, e.g., in reports or presentations.
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            Model Deployment and Automation (optional)

            
      

                  
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      Model deployment: If the model is intended for real-time use, deploy it in a system that regularly provides predictions (e.g., as a web service).
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      Create data pipelines: Implement automated processes for the regular collection, processing, and analysis of new data.
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      Monitor the model: Monitor the model performance to ensure it continues to function well over time and adjust it as necessary.
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            Documentation and Maintenance

            
      

                  
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      Create documentation: Document all steps of the analysis, including data sources, data preprocessing, model selection, and results.
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      Regular updates: Keep the analysis up to date by regularly analyzing new data and improving the model.
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