You will get Time Series Forecasting Project
Top Rated
Project details
Implement Time Series Forecasting to predict the Energy Demand in the Great Britain to help the Energy supplier to analysis electricity consumption for 48 settlement period ahead. This project used the historic Energy Demand data between 2009 and 2023, which is collected from the UK National Grid ESO using the API and stored in AZURE SQL SERVER with the data has 257422 records with 20 features. Gained useful insights with Exploratory Data Analysis and Performed Preprocessing to clean the dataset and Feature Engineering introduced new features to improve the prediction and understand the trend and seasonality of the Energy Demand. Trained a Time Series Analysis model, SARIMA, XGBoost, Linear Trees models, Prophet, LSTM and deep LSTM recurrent networks, using the pre-processed data to predict the future demand of the energy.
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, Databricks Platform, Google AutoML, Google Data Studio, Google Sheets, Keras, Microsoft Power BI, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, SQL, Tableau, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$250
|
Standard
$400
|
Advanced
$750
|
|---|---|---|---|
| Delivery Time | 10 days | 30 days | 70 days |
Number of Revisions | 0 | 1 | 2 |
Model Validation/Testing | - | - | |
Model Documentation | - | - | |
Data Source Connectivity | - | ||
Source Code |
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SK
Sean K.
Jul 8, 2024
You will get Credit Card Fraud Detection (Binary Classification)
Highly professional and efficient, she exceeded all expectations, and I highly recommend them to the Upwork community!
AK
Arshdeep K.
Mar 14, 2024
Vertex AI Engineer
About Anjali
Data Scientist | NLP | Open AI Assistant | GPT-4 | ChatGPT | LLMs
100%
Job Success
London, United Kingdom - 11:15 am local time
• Possess a robust academic foundation encompassing mathematical statistics, econometrics, Artificial Intelligence, machine learning, deep learning, Generative AI, and Large Language Models.
key strengths and skills:
Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow, Keras, PySpark), SQL, NoSQL (MongoDB), R, Data Analysis, Statistics, Artificial Intelligence, Machine Learning, MLOps (Docker, Kubernetes), Predictive Analysis, Deep Learning, Natural Language Processing (Spacy, NLTK), Computer Vision, Generative AI, Large Language Model (LLM) (LangChain, HuggingFace, OpenAI, LLaMa, PaLM), GPT, BERT, RAG, Time Series Analysis, Responsible AI, Data Visualization (Seaborn, Matplotlib, Plotly, PowerBI Tableau), A/B testing, JIRA, Git, Linux, AWS, AZURE, GCP, Rest API
Successfully Completed Projects:
1. Predictive Analysis
2. Time Series Forecasting
3. Clustering
4. Text Classification, Document Classification
5. Sentiment Analysis, Aspect Based Sentiment Analysis
6. Custom Named Entity Recognition
7. Question Answering System
8. Topic Modeling
9. LLM Chatbot using RAG and LangChain
10. Text Summarization
11. Nueral Style transfer using Generative AI
12. GPT-3 for intent detection.
13. Object Detection
14. Object Recognition
15. Credit Card Fraud Detection
16. Classification
Languages: Python, R, SQL.
ML Libraries: Pandas, Scikit learn, Spacy, Tensorflow, , Pytorch, Transformers
Machine Learning (Classification, Regression, clustering, Decision Trees, K-Means Clustering, hierarchical clustering),
Deep Learning (DNN, CNN, RNN, Transfer learning, AlexNet, LeNet, VGG, Resnet, Inception, MobileNet, YOLO, Transpose CNN, U-Net, LSTM, LLM, GenAI, RAG),
Statistical Methods (Predictive analysis, Hypothesis Testing and Confidence Intervals, Principal Component Analysis, LDA and Dimensionality Reduction),
Programming Languages and tools (Python, Java, HTML and CSS, TypeScript, Tailwindcss, Nextjs),
Version Control Tools (Git),
Python Libraries (Sklearn, Scipy, Statsmodel, Pandas, NumPy, Seaborn, Matplotlib, Selenium, BS4, NLTK, TensorFlow, Keras, shap, Boto3, Flask, FastAPI, Streamlit),
Scripting Language (Unix, Ubuntu, Fedora),
Database Language (SQL, MySQL, Postgres SQL, SQL Server),
Data Reporting Tool (Excel, GCP Data Studio), PowerBI, Tableau
Cloud Tools (AWS (sagemaker, s3, Lex, Lamda), GCP (Bigquery, Dataproc, Dataflow, Vertex AI, cloud SQL, etc.), AZURE)
CERTIFICATION
• Machine Learning in Production Specialization (MLOps) - Deeplearing.ai
• Deep Learning Specialization - Deeplearning.ai
• Machine Learning - Stanford university
• Natural Language Processing Specialization - Deeplearing.ai
• Organizational Leadership Specialization Northwestern University
• Generative Adversarial Networks (GANs) Specialization - Deeplearing.ai
• Generative AI Fundamentals Specialization - IBM
• TensorFlow: Advanced Techniques Specialization
• AI Applications in Marketing and Finance
GitHub: github.com/AnjaliDharmik
Steps for completing your project
After purchasing the project, send requirements so Anjali can start the project.
Delivery time starts when Anjali receives requirements from you.
Anjali works on your project following the steps below.
Revisions may occur after the delivery date.
Data Collection
Gather historical time series data relevant to the problem at hand. Ensure that the data covers a sufficiently long period to capture patterns and trends.
Cleaning and Imputation
Clean the data by handling missing values, outliers, and any inconsistencies. Impute missing values using appropriate techniques, ensuring data integrity.