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Data scientist

$30
1
Delivery in
1 day
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What you get with this Offer

Are you looking for a skilled data scientist to transform your raw data into meaningful insights? I specialize in data preprocessing, feature engineering, and machine learning model development, ensuring your data is clean, structured, and ready for accurate predictive modeling.

My Expertise Includes:
✔ Data Cleaning & Transformation – Handling missing values, outliers, and inconsistencies.
✔ Feature Engineering – Selecting, creating, and optimizing features for better model performance.
✔ Exploratory Data Analysis (EDA) – Identifying patterns, trends, and correlations within data.
✔ Machine Learning Model Development – Building and optimizing predictive models.
✔ Data Visualization – Creating insightful graphs and dashboards for better decision-making.
✔ Regression Analysis – Applying statistical methods to uncover relationships in data.

Get more with Offer Add-ons

  • I can prioritize work

    Additional 1 working day

    +$13

What the Freelancer needs to start the work

To kickstart the work efficiently, the buyer should provide the following details:

1. Project Requirements & Objectives
What is the goal of the data processing or analysis? (e.g., prediction, classification, visualization)
Any specific business problem or question to address?
2. Dataset Details
Provide the dataset (CSV, Excel, SQL, JSON, etc.) or access to the data source.
Description of the dataset (columns, data types, missing values, etc.).

3. Preprocessing & Cleaning Preferences
Any specific cleaning tasks required? (e.g., handling missing values, duplicates, outliers)
4. Desired Data Analysis & Model Requirements
Type of analysis needed (EDA, regression, classification, clustering, etc.).
Performance metrics to focus on (accuracy, RMSE, precision-recall, etc.).
5. Deliverables & Format
What should the final output be? (Charts, reports, dashboards, trained models, scripts)
Preferred format (Jupyter Notebook, Python script, Power BI dashboard, etc.).
6. Tools & Technology Preferences
Should the work be done using specific tools/libraries? (Pandas, NumPy, Scikit-learn, Power BI, etc.)
Any cloud or database integration required?
7. Timeline & Budget
Deadline for project completion.
Budget expectations (if applicable).
8. Additional Notes
Any reference materials or similar past projects?
Specific styling or reporting preferences?
Providing these details upfront ensures a smooth workflow, faster delivery, and high-quality results.