Phoenix - Your AI AssistantClean, analyse and visualise your data with Python EDA
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What you get with this Offer
Turn your raw data into reliable insights through a rigorous, business-aware Exploratory Data Analysis.
I will not clean your data blindly. Every treatment will be justified by evidence, data structure and business context, while preserving the raw data.
TECHNICAL APPROACH
✓ Understand your objective and how the data was generated
✓ Validate data types, ranges, units, categories and identifier uniqueness
✓ Detect duplicates, inconsistencies and impossible values
✓ Analyse missingness and test evidence for MCAR where appropriate
✓ Flag plausible MAR or MNAR risks that cannot be confirmed from observed data alone
✓ Choose a justified treatment: deletion, median/mode, KNN, iterative or model-based imputation
✓ Investigate outliers as errors, valid rare events or business signals before taking action
✓ Use IQR, robust z-scores or multivariate methods according to the data
✓ Compare results with and without influential observations when relevant
✓ Analyse distributions, relationships, class imbalance and anomalies
✓ Check potential target leakage, time leakage and sampling bias
✓ Create up to 10 relevant visualisations
✓ Apply statistical tests only after checking assumptions
✓ Report confidence intervals, effect sizes and adjusted p-values when needed
Methods may include Pearson or Spearman correlation, t-tests, ANOVA, chi-square, Mann-Whitney or Kruskal-Wallis, depending on variable type, sample size, independence, distribution and variance assumptions.
YOU WILL RECEIVE
• Cleaned CSV or Excel dataset
• Raw-data copy and cleaning decision log
• Data-quality summary and updated data dictionary
• Documented Jupyter Notebook and Python code
• Missing-value diagnosis and justified treatment
• Outlier and sensitivity analysis
• Descriptive statistics and test results
• Up to 10 clear charts
• PDF or HTML report
• Findings, limitations and recommendations
• One revision and seven days of support
BASE OFFER SCOPE
• One CSV or Excel dataset
• Up to 50,000 rows and 30 columns
• One analysis objective
• Relevant statistical methods
For multiple datasets, surveys, time series, geospatial data, causal analysis or Machine Learning, please message me for a tailored scope.
Please provide your dataset, business context, column definitions, known business rules, questions and variables of interest. Anonymise confidential information before sharing.
I will not clean your data blindly. Every treatment will be justified by evidence, data structure and business context, while preserving the raw data.
TECHNICAL APPROACH
✓ Understand your objective and how the data was generated
✓ Validate data types, ranges, units, categories and identifier uniqueness
✓ Detect duplicates, inconsistencies and impossible values
✓ Analyse missingness and test evidence for MCAR where appropriate
✓ Flag plausible MAR or MNAR risks that cannot be confirmed from observed data alone
✓ Choose a justified treatment: deletion, median/mode, KNN, iterative or model-based imputation
✓ Investigate outliers as errors, valid rare events or business signals before taking action
✓ Use IQR, robust z-scores or multivariate methods according to the data
✓ Compare results with and without influential observations when relevant
✓ Analyse distributions, relationships, class imbalance and anomalies
✓ Check potential target leakage, time leakage and sampling bias
✓ Create up to 10 relevant visualisations
✓ Apply statistical tests only after checking assumptions
✓ Report confidence intervals, effect sizes and adjusted p-values when needed
Methods may include Pearson or Spearman correlation, t-tests, ANOVA, chi-square, Mann-Whitney or Kruskal-Wallis, depending on variable type, sample size, independence, distribution and variance assumptions.
YOU WILL RECEIVE
• Cleaned CSV or Excel dataset
• Raw-data copy and cleaning decision log
• Data-quality summary and updated data dictionary
• Documented Jupyter Notebook and Python code
• Missing-value diagnosis and justified treatment
• Outlier and sensitivity analysis
• Descriptive statistics and test results
• Up to 10 clear charts
• PDF or HTML report
• Findings, limitations and recommendations
• One revision and seven days of support
BASE OFFER SCOPE
• One CSV or Excel dataset
• Up to 50,000 rows and 30 columns
• One analysis objective
• Relevant statistical methods
For multiple datasets, surveys, time series, geospatial data, causal analysis or Machine Learning, please message me for a tailored scope.
Please provide your dataset, business context, column definitions, known business rules, questions and variables of interest. Anonymise confidential information before sharing.
What the Freelancer needs to start the work
To get started, please provide:
• Your dataset in CSV or Excel format
• A short explanation of the business or research problem
• The questions you want the analysis to answer
• A description of the columns, if their meaning is not obvious
• Any specific metrics, variables or areas you want me to investigate
• Your preferred report format: PDF, HTML or Jupyter Notebook
Please anonymise any confidential or personally identifiable information before sharing the dataset.