You will get a professional Python script for data cleaning, visualization, or basic ML
Project details
Do you have raw data that needs to be transformed into actionable insights? I specialize in creating efficient, well-documented Python scripts for data cleaning, visualization, and introductory machine learning.
Whether you need to automate a repetitive spreadsheet task, visualize complex trends, or build a predictive model using Scikit-Learn, I deliver accurate and reproducible results. I pride myself on writing clean, modular code that is easy for you to understand and maintain long-term. Let’s turn your messy data into your project's biggest asset.
Whether you need to automate a repetitive spreadsheet task, visualize complex trends, or build a predictive model using Scikit-Learn, I deliver accurate and reproducible results. I pride myself on writing clean, modular code that is easy for you to understand and maintain long-term. Let’s turn your messy data into your project's biggest asset.
Machine Learning Tools
Apache Spark, Databricks Platform, Databricks MLflow, GitHub Copilot, Google Sheets, Microsoft Excel, Microsoft Power BI, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Scrapy, SQL, Tableau, TensorFlowWhat's included
| Service Tiers |
Starter
$20
|
Standard
$50
|
Advanced
$80
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 4 | 6 | 7 |
Number of Model Variations | 2 | 3 | 4 |
Number of Scenarios | 2 | 4 | 5 |
Number of Graphs/Charts | 2 | 4 | 5 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$5 - $20
1 review
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
SB
Sam B.
Jan 2, 2026
Complex Calculation Presentation for Client
About Inam
Data Scientist | ML, RAG & LLM Apps | Python, SQL & Power BI
50%
Job Success
Jamshoro, Pakistan - 2:50 pm local time
I have a strong foundation in mathematics, so I don't just run models. I understand what's happening underneath. That means I can debug issues, explain trade-offs, and build solutions that actually fit your problem instead of forcing a generic template onto it.
Here's what I can help you with:
AI & Automation: Building custom AI agents, LLM workflows (RAG pipelines with LangChain/LlamaIndex), and Python automation scripts that cut out hours of manual work.
Data Analysis & Dashboards: Interactive Power BI dashboards backed by clean SQL database design, so your reports update automatically and stay reliable as your data grows.
Python Development: Data handling and analysis with Pandas/NumPy, plus web scraping (BeautifulSoup, Selenium) when you need data the market isn't handing you directly.
API Development: Building RESTful APIs (FastAPI) so your different tools and systems can talk to each other without manual exports and imports.
Machine Learning: End-to-end model development: classification, prediction, and evaluation, using scikit-learn, PyTorch, and TensorFlow with a focus on models you can actually explain to stakeholders, not black boxes.
Recent project: I built and deployed MediFind Pakistan, a full-stack AI healthcare app with a FastAPI backend, a Random Forest model covering 41 diseases and 131 symptoms, and a Gemini-powered chatbot that works in both English and Urdu.
If you're dealing with messy data, manual reporting, or disconnected systems, let's talk about what a scalable solution looks like for you.
Steps for completing your project
After purchasing the project, send requirements so Inam can start the project.
Delivery time starts when Inam receives requirements from you.
Inam works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review & Goal Confirmation
I will review your uploaded dataset to ensure the format is correct and verify your specific goals (e.g., what you want to predict or visualize) to ensure we are aligned before I start coding.
Scripting & Data Cleaning
I will write the Python code to load your data, handle missing values, clean inconsistencies, and prepare the dataset for analysis. This ensures the foundation of the project is accurate.