Data Cleaning, Visualization, Model Training by Machine Learning by Muhammad Umair

Data Cleaning, Visualization, Model Training by Machine Learning by Muhammad UmairData Cleaning, Visualization, Model Training by Machine Learning by Muhammad Umair

Data Cleaning, Visualization, Model Training by Machine Learning

Muhammad Umair

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About this service

Summary

I offer comprehensive services in data cleaning, visualization, and machine learning model training to transform raw data into actionable insights and predictive solutions. With a focus on accuracy, efficiency, and clear communication, I deliver clean datasets, insightful visualizations, and robust models tailored to your needs. My commitment to detail and delivering reproducible, high-quality results sets me apart in the field.

What's included

Cleaned and Preprocessed Dataset
A fully cleaned and preprocessed dataset prepared for analysis or model training, with all missing, duplicate, and inconsistent data addressed. Details: Format: Delivered in a preferred format such as CSV, Excel, or database file. Preprocessing Steps Documented: Includes a summary of cleaning processes (e.g., missing value imputation, outlier removal, normalization). Revisions: Up to 2 revisions to ensure the dataset meets project requirements.
Data Visualizations and Insights Report
A comprehensive report featuring data visualizations and insights derived from exploratory data analysis (EDA). Details: Format: PDF report or PowerPoint presentation including visualizations like bar charts, scatter plots, heatmaps, and key findings. Tools Used: Created using tools such as Python (Matplotlib/Seaborn), Tableau, or Excel, based on client preference. Visualizations Included: Up to 5 custom visualizations with insights to explain patterns and trends in the data. Revisions: Up to 2 revisions for adjusting visualizations or adding insights.
Machine Learning Model and Performance Evaluation
A trained machine learning model tailored to the client's project needs, with a detailed evaluation of its performance metrics. Details: Model Format: Delivered as a trained model file (e.g., .pkl for Python or TensorFlow model files) along with relevant code/script for reproducibility. Evaluation Metrics: Includes metrics such as accuracy, precision, recall, F1 score, or RMSE, depending on the project. Revisions: One round of revisions for fine-tuning the model parameters based on feedback.
Documentation and Codebase
Well-documented codebase and a guide explaining the workflow, including data preprocessing, visualization, and model training steps. Details: Format: Clean, commented code delivered in Python notebooks (.ipynb), and Python scripts (.py) as required. Documentation: Step-by-step instructions are provided in a PDF or markdown file for easy understanding and reproducibility. Revisions: No revisions, but post-delivery clarification support is available for 7 days.

Skills and tools

Data Scientist

ML Engineer

AI Model Developer

Matplotlib

Python

scikit-learn

seaborn

TensorFlow

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