Data Analysis: Turning Complex Data into Business Opportunities
Kedir Omer
Starting at
$
10
/hrAbout this service
Summary
I offer comprehensive data analysis services, leveraging my expertise in AI and ML and a proven track record in various sectors. My unique approach lies in transforming complex business challenges into opportunities using data.
Process
Process
Here are the steps I will follow from start to finish for a typical data analysis project:
- Understanding the Business Problem: This involves discussing with the client to understand the problem at hand and the objectives of the analysis.
- Data Collection: Depending on the problem, this could involve collecting data from various sources or using existing data provided by the client.
- Data Cleaning: This step involves cleaning the data by handling missing values, outliers, and inconsistencies.
- Exploratory Data Analysis (EDA): In this step, I perform an initial investigation on the data to discover patterns, spot anomalies, and check assumptions using summary statistics and graphical representations.
- Feature Engineering: This involves creating new features from the existing data to improve the performance of the machine learning models.
- Model Development: I develop machine learning models using the prepared dataset. This involves selecting the appropriate model, training the model, and tuning the parameters.
- Model Evaluation: The performance of the model is evaluated using appropriate metrics.
- Insights & Recommendations: Based on the results of the analysis, I provide insights and recommendations to the client.
- Presentation & Reporting: Finally, I present the findings and the data-driven recommendations to the client in an easy-to-understand format.
Each step is carried out meticulously, ensuring high-quality results that align with the client’s objectives.
What's included
Data Cleaning Report
A comprehensive report detailing the data cleaning process, including handling of missing values, outliers, and inconsistencies.
Exploratory Data Analysis (EDA) Summary
A document summarizing the key findings from the EDA, including distributions, correlations, and potential insights.
Feature Engineering Documentation
Detailed documentation of the feature engineering process, including the rationale for feature selection and the creation of new features.
Model Development Report
A report outlining the model development process, including model selection, parameter tuning, and validation methods.
Model Performance Metrics
A detailed breakdown of the model’s performance metrics, such as accuracy, precision, recall, F1 score, and AUC-ROC.
Predictive Analysis Report
A report detailing the results of the predictive analysis, including the most important features and their impact on the prediction.
Data Visualization Dashboard
An interactive dashboard displays key data visualizations and insights.
Final Presentation
A presentation summarizing the project, key findings, and recommendations.
Project Code
The complete, well-documented code for the project.
Data Dictionary
A document detailing all the variables used in the project, their definitions, and their roles in the analysis.
Skills and tools
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