You will get A Time-Series Forecasting Model | Python, Prophet, LSTM & XGBoost
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Project details
š Know What's Coming Before It Happens. Forecast Sales, Demand & Trends.
Guessing next month's numbers from a spreadsheet formula isn't forecasting ā it's hoping. I build time-series models that learn from your historical patterns and predict what's coming.
What I forecast:
ā Sales & revenue for next week, month, or quarter
ā Demand & inventory needs to avoid stockouts
ā Website traffic & user growth trends
ā Cash flow & financial projections
ā Staffing needs based on predicted volume
How I build forecasts that work:
ā Prophet for fast, reliable business forecasting
ā LSTM for complex, non-linear patterns
ā XGBoost when external factors matter
ā Multiple models compared with real backtesting
ā Confidence intervals ā a range, not just a guess
ā Seasonality & holiday effects built in
What you get:
ā Trained model with accuracy report
ā Visual charts ā forecast vs actuals
ā Clean Python code you can rerun anytime
ā Documentation explaining the predictions
š© Contact me on Upwork before placing an order.
Guessing next month's numbers from a spreadsheet formula isn't forecasting ā it's hoping. I build time-series models that learn from your historical patterns and predict what's coming.
What I forecast:
ā Sales & revenue for next week, month, or quarter
ā Demand & inventory needs to avoid stockouts
ā Website traffic & user growth trends
ā Cash flow & financial projections
ā Staffing needs based on predicted volume
How I build forecasts that work:
ā Prophet for fast, reliable business forecasting
ā LSTM for complex, non-linear patterns
ā XGBoost when external factors matter
ā Multiple models compared with real backtesting
ā Confidence intervals ā a range, not just a guess
ā Seasonality & holiday effects built in
What you get:
ā Trained model with accuracy report
ā Visual charts ā forecast vs actuals
ā Clean Python code you can rerun anytime
ā Documentation explaining the predictions
š© Contact me on Upwork before placing an order.
Machine Learning Tools
Microsoft Excel, NumPy, pandas, Python, PyTorch, scikit-learn, SciPy, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$200
|
Standard
$600
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 5 days | 12 days | 22 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 5 | 12 | 20 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Frequently asked questions
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About Rasheshkumar Harsukhbhai
Python & AI/ML Engineer | LLM, RAG, NLP, Deep Learning & Scraping
99%
Job Success
Anand, IndiaĀ - 3:12 am 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 & Forecasting Plan
Review your historical data, identify trends and seasonality, and pick the right forecasting approach ā Prophet, LSTM, or XGBoost based on your data pattern.
Build & Backtest Models
Train multiple forecasting models, add external features if relevant, and validate accuracy using backtesting on historical data before finalizing.