You will get A Custom ML Model | Prediction, Classification & Forecasting in Python
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Project details
š Turn Your Data Into a Model That Actually Predicts What Matters.
Every business sits on data ā sales, customers, transactions, user behavior. Most goes unused because nobody turns it into predictions. That's what I do.
I build custom ML models that predict churn, forecast demand, classify customers, detect fraud, and answer whatever question your data can solve.
What I build:
ā Prediction ā churn, LTV, conversion, price
ā Classification ā spam, fraud, sentiment, categories
ā Forecasting ā sales, demand, inventory, revenue
ā Recommendation engines for products & content
ā Anomaly detection & clustering for segmentation
How I build models that work:
ā Python with Scikit-Learn, XGBoost, LightGBM, TensorFlow
ā Feature engineering tailored to your domain
ā Multiple models compared, best one picked
ā Hyperparameter tuning with Optuna
ā Explainability with SHAP
ā Deployment as API, batch, or dashboard
What you get:
ā Trained model with evaluation report
ā Clean, documented Python code
ā Performance & feature importance charts
ā Deployment-ready code with instructions
ā Full source code ā you own everything
š© Contact me on Upwork before placing an order.
Every business sits on data ā sales, customers, transactions, user behavior. Most goes unused because nobody turns it into predictions. That's what I do.
I build custom ML models that predict churn, forecast demand, classify customers, detect fraud, and answer whatever question your data can solve.
What I build:
ā Prediction ā churn, LTV, conversion, price
ā Classification ā spam, fraud, sentiment, categories
ā Forecasting ā sales, demand, inventory, revenue
ā Recommendation engines for products & content
ā Anomaly detection & clustering for segmentation
How I build models that work:
ā Python with Scikit-Learn, XGBoost, LightGBM, TensorFlow
ā Feature engineering tailored to your domain
ā Multiple models compared, best one picked
ā Hyperparameter tuning with Optuna
ā Explainability with SHAP
ā Deployment as API, batch, or dashboard
What you get:
ā Trained model with evaluation report
ā Clean, documented Python code
ā Performance & feature importance charts
ā Deployment-ready code with instructions
ā Full source code ā you own everything
š© Contact me on Upwork before placing an order.
Machine Learning Tools
Keras, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$300
|
Standard
$800
|
Advanced
$2,000
|
|---|---|---|---|
| Delivery Time | 6 days | 14 days | 25 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 2 | 5 | 10 |
Number of Graphs/Charts | 5 | 12 | 20 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Frequently asked questions
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Aug 13, 2025
UX/UI Developer
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About Rasheshkumar Harsukhbhai
Python & AI/ML Engineer | LLM, RAG, NLP, Deep Learning & Scraping
99%
Job Success
Anand, IndiaĀ - 3:37 pm local time
I don't build demos or scripts that break in production. I build AI systems, ML models, and data pipelines that businesses run on every single day ā reliably, at scale, with real business impact.
Startups, agencies, research labs, and enterprise clients hire me to solve one core problem ā how do we turn our data into something intelligent, automated, and profitable?
Answer: Python + AI/ML + real engineering discipline.
Fine-tuned LLMs, RAG chatbots trained on your knowledge base, scrapers pulling millions of pages daily, computer vision in production, multi-agent AI crews ā I've built it, shipped it, and maintained it.
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š§ AI, LLMs, RAG & Agentic Systems :
ā¢ LLM Integration ā OpenAI GPT-4o, Claude, Gemini, LLaMA, Mistral, DeepSeek
ā¢ RAG Pipelines ā ingestion, chunking, reranking, hybrid search, HyDE
ā¢ Agentic AI ā LangChain, LangGraph, CrewAI, OpenAI Agents SDK, MCP
ā¢ Multi-Agent Workflows ā supervisor-worker, crews, human-in-the-loop
ā¢ Fine-Tuning ā LoRA, QLoRA, PEFT, DPO on custom domain data
ā¢ Vector Search ā Pinecone, Weaviate, Chroma, pgvector, FAISS, Qdrant
ā¢ Prompt Engineering, Guardrails, Structured Outputs
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š¤ Machine Learning & Deep Learning :
ā¢ Supervised, Unsupervised & Reinforcement Learning
ā¢ Classification, Regression, Clustering, Anomaly Detection
ā¢ Time-Series Forecasting (Prophet, LSTM, XGBoost)
ā¢ Recommendation Systems & Ranking Models
ā¢ Computer Vision ā YOLO, Detectron2, OCR, Segmentation
ā¢ Deep Learning ā CNNs, LSTMs, Transformers, GANs, Diffusion
ā¢ Transfer Learning, Quantization, Hyperparameter Tuning
Frameworks: TensorFlow, PyTorch, Keras, JAX, Scikit-Learn, XGBoost, LightGBM, Hugging Face
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š Natural Language Processing (NLP) :
ā¢ Text Classification, Sentiment & Emotion Analysis
ā¢ NER, Relation Extraction, Topic Modeling
ā¢ Summarization, Translation, Q&A Systems
ā¢ Document Intelligence ā PDF parsing, table extraction, OCR
ā¢ Speech ā Whisper, ElevenLabs, Deepgram
Libraries: spaCy, NLTK, Hugging Face Transformers, LangChain, LlamaIndex, Haystack
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šøļø Web Scraping & Data Extraction :
ā¢ Enterprise scrapers ā millions of pages, zero downtime
ā¢ Anti-bot bypass ā Cloudflare, DataDome, PerimeterX
ā¢ CAPTCHA solving, fingerprint spoofing, TLS fingerprinting
ā¢ Proxy rotation ā residential, datacenter, mobile IPs
ā¢ JavaScript sites, SPAs, infinite scroll, API reverse engineering
ā¢ Scheduled ETL pipelines with retry logic & monitoring
Tools: Scrapy, Selenium, Playwright, Puppeteer, BeautifulSoup, aiohttp
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āļø Backend & API Development :
ā¢ FastAPI ā high-performance async APIs with auto-docs
ā¢ Django & DRF ā full-featured web applications
ā¢ Flask ā lightweight microservices
ā¢ REST, GraphQL & gRPC APIs
ā¢ WebSockets, async programming, Celery, Redis Queue
ā¢ Auth ā JWT, OAuth 2.0, SSO, API keys
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š Data Engineering & Visualization :
ā¢ Data Cleaning, Feature Engineering, Statistical Testing
ā¢ ETL Pipelines ā Airflow, Prefect, Dagster
ā¢ Big Data ā Pandas, Dask, PySpark, Polars
ā¢ Dashboards ā Streamlit, Dash, Gradio, Plotly
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šļø Databases :
ā¢ SQL ā PostgreSQL, MySQL, ClickHouse, TimescaleDB
ā¢ NoSQL ā MongoDB, Firebase, Redis, DynamoDB
ā¢ Vector DBs ā Pinecone, Weaviate, Chroma, pgvector, Qdrant
ā¢ Warehouses ā BigQuery, Snowflake, Redshift, Databricks
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āļø Cloud, DevOps & MLOps :
ā¢ AWS (SageMaker, Bedrock, Lambda), GCP (Vertex AI), Azure ML
ā¢ Docker, Kubernetes, Terraform, CI/CD
ā¢ MLflow, W&B, DVC, BentoML for model deployment
ā¢ Monitoring ā Prometheus, Grafana, Sentry
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š” Real Problems I Solve :
ā¢ "Build a RAG chatbot trained on our 10,000 internal docs"
ā¢ "Scrape 500K product listings daily without getting blocked"
ā¢ "Fine-tune an LLM on our support tickets for auto-responses"
ā¢ "Predict customer churn 30 days before it happens"
ā¢ "Extract structured data from thousands of PDFs and invoices"
ā¢ "Build a multi-agent AI crew for our research workflow"
ā¢ "Detect defects on our assembly line with computer vision"
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š Industries: Finance, Healthcare, E-commerce, Real Estate, Marketing, Logistics, Legal, EdTech, SaaS, Manufacturing.
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ā Why Clients Trust Me :
ā¢ 8+ years of production Python, ML & AI engineering
ā¢ End-to-end delivery ā data to model to deployment to monitoring
ā¢ Deep expertise across LLMs, RAG, NLP, scraping, DL & MLOps
ā¢ Clean, tested, documented code ā not throwaway scripts
ā¢ Business-first thinking ā right questions before writing code
ā¢ Clear communication, honest timelines, long-term reliability
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š© Send me a message with your project ā let's turn your data into your competitive advantage.
Steps for completing your project
After purchasing the project, send requirements so Rasheshkumar Harsukhbhai can start the project.
Delivery time starts when Rasheshkumar Harsukhbhai receives requirements from you.
Rasheshkumar Harsukhbhai works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review & Problem Framing
Understand your business goal, explore your data, identify features and targets, and define success metrics. Deliver a modeling plan and expected outcomes for your approval.
Data Prep, Modeling & Evaluation
Clean data, engineer features, train multiple models (Scikit-Learn, XGBoost, deep learning), tune hyperparameters, and evaluate on real test sets to pick the best performer.