You will get a top-notch end-to-end project from EDA > VISUALIZATION > MODELING >
Project details
You will get the opportunity to choose what specifications you want. We will be able to converse verbally at any point. I will give you a top-notch end-to-end service with detailed documentation. Additionally, you will get the opportunity to learn in any project you are interested to learn.
Machine Learning Tools
Keras, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$25
|
Standard
$50
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 2 | 3 | 5 |
Number of Scenarios | 2 | 3 | 5 |
Number of Graphs/Charts | 5 | 10 | 15 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$5 - $25About Ayomide
Machine Learning | Data Science | Generative AI | Statistics | SQL
Lagos, Nigeria - 12:08 pm local time
Confident and dedicated tech enthusiast, Seeking to apply data science expertise in
developing actionable insights and predictive models to address business and daily life
challenges. Aim to enhance skills in machine learning, data visualization, and statistical
analysis for informed decision-making. Aspire to collaborate with diverse teams, contributing
to innovative projects that drive organizational growth and efficiency. Committed to
continuous learning and also blending with emerging trends to deliver impactful solutions
and contribute positively to any organization I belong.
Steps for completing your project
After purchasing the project, send requirements so Ayomide can start the project.
Delivery time starts when Ayomide receives requirements from you.
Ayomide works on your project following the steps below.
Revisions may occur after the delivery date.
Review client specifications
Analyze the requirements and any datasets or documents shared by the client. Clarify project goals and expected outputs.
Data preprocessing and exploration
Clean the dataset, handle missing values, encode features, and perform basic exploratory data analysis to understand patterns and insights.