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Felhasználó avatár

Hammad R.

@hammad40241

monthly-level-two.svg
5,0
3
3,0
3,0
100%

Data Science| AI Dev | Prompt Eng | RAG | Chatbots

$15 USD / óra
・
zászlója
Pakistan (8:13 du.)
・
Ekkor csatlakozott: augusztus 24, 2023
$15 USD / óra
・
I specialize in delivering advanced solutions in data science, AI, deep learning, machine learning, natural language processing (NLP), large language models (LLM), retrieval-augmented generation (RAG), computer vision, and fine-tuning. I'm committed to driving innovation and excellence, transforming complex challenges into actionable insights. Partner with me to unlock the full potential of AI and elevate your business to new heights. Discover the future of intelligent solutions with my expertise.
Hitelesítések
Határidőre
100%
Költségvetésen belül
100%
Elfogadási arány
100%
Freelancer Data Processing Executives Pakistan
Az összes Data Processing Executives mutatása itt: Pakistan

Portfólió

Portfólió

12393948
Built an AI-powered chatbot for a leading bank to make internal processes smoother and help employees get quick answers. The chatbot is based on RAG and comes with features like: 1. Policy Assistance: Answers common questions about bank policies, leaves, perks, reimbursements, and retirement plans. 2. Leave Applications: Helps employees create leave forms in just a few clicks. 3. Claim Applications: Makes it easy to generate forms for claims like mobile, petrol, and insurance. 4. Personalized Responses: Provides replies tailored to each employee’s specific needs.
AI Banking Assistant for Employee
11350032
I developed a script to scrape emails from a client's LinkedIn account. This tool automates the extraction process, ensuring accurate and efficient data collection
Scrape Emails from Client LinkedIn
12112511
We developed an AI Document Navigator (AIDN) to help a client efficiently retrieve information from large sets of PDFs and JSON files. Our goal was to enhance document access, making it faster and more accurate with advanced AI technologies. A key challenge was handling large documents without slowing down. We optimized document splitting and retrieval processes for quick responses. We also created a context-aware retriever for more relevant information. We used OpenAI's GPT-4 for language processing, FAISS for vector storage, and implemented recursive text splitting for document chunking. We also employed robust security measures to manage environment variables.
RAG Chatbot: LangChain & Chainlit
11319414
To calculate the Customer Lifetime Value (LTV) for a U.S. firm, we first analyzed historical data to determine the Average Purchase Value (APV). This involved dividing the firm's total revenue over a specific period by the number of purchases in that period. Next, we calculated the Average Purchase Frequency Rate (APFR) by dividing the total number of purchases by the number of unique customers. Multiplying APV with APFR gave us the Customer Value. Then, we estimated the Average Customer Lifespan (ACL) by analyzing the duration of customer relationships. The LTV was calculated by multiplying Customer Value with ACL. This process provided insight into how much revenue a customer would bring over their relationship with the firm. By understanding LTV, the company can make informed decisions on customer acquisition and retention strategies.
"Calculate CLV for US sports firm"
To calculate the Customer Lifetime Value (LTV) for a U.S. firm, we first analyzed historical data to determine the Average Purchase Value (APV). This involved dividing the firm's total revenue over a specific period by the number of purchases in that period. Next, we calculated the Average Purchase Frequency Rate (APFR) by dividing the total number of purchases by the number of unique customers. Multiplying APV with APFR gave us the Customer Value. Then, we estimated the Average Customer Lifespan (ACL) by analyzing the duration of customer relationships. The LTV was calculated by multiplying Customer Value with ACL. This process provided insight into how much revenue a customer would bring over their relationship with the firm. By understanding LTV, the company can make informed decisions on customer acquisition and retention strategies.
"Calculate CLV for US sports firm"
12112538
We analyzed automotive sales data spanning from April 2007 to May 2023 across GCC countries such as Saudi Arabia, Kuwait, the United Arab Emirates, Qatar, Bahrain, and Oman. Through extensive visualization, we delved into the sales performance of various manufacturers and car segments. Our findings uncovered compelling trends and patterns in the GCC automotive industry over sixteen years. Notably, certain manufacturers consistently dominated the market, while others experienced sales fluctuations. These insights offer valuable guidance for industry stakeholders and policymakers, aiding in informed decision-making and strategic planning for the future of the automotive market in the GCC region.
Exploring Automotive Sales Trend
12112527
The AI Tax Law Assistant is a smart chatbot, trained on hundreds of PDFs covering Pakistan's tax laws. Developed with a leading government firm, this tool provides clear and quick legal help, using advanced AI to handle Pakistan's tax rules. The government firm used this assistant to make their tax advisory services faster and more accurate. The assistant has a huge knowledge base from laws, case studies, and guidelines, ensuring it understands Pakistan's tax laws well. It uses advanced language processing to understand user questions in everyday language and gives clear, simple answers. The chatbot combines information retrieval and generation to provide accurate, up-to-date responses to complex tax questions. Its easy-to-use interface allows anyone to input queries and get instant replies, making it accessible to users of all knowledge levels. The AI Tax Law Assistant offers specific advice based on different scenarios, summarizes long legal documents for quick understanding, and
AI Tax Law Assistant for Pakistan
12393948
Built an AI-powered chatbot for a leading bank to make internal processes smoother and help employees get quick answers. The chatbot is based on RAG and comes with features like: 1. Policy Assistance: Answers common questions about bank policies, leaves, perks, reimbursements, and retirement plans. 2. Leave Applications: Helps employees create leave forms in just a few clicks. 3. Claim Applications: Makes it easy to generate forms for claims like mobile, petrol, and insurance. 4. Personalized Responses: Provides replies tailored to each employee’s specific needs.
AI Banking Assistant for Employee
11350032
I developed a script to scrape emails from a client's LinkedIn account. This tool automates the extraction process, ensuring accurate and efficient data collection
Scrape Emails from Client LinkedIn
12112511
We developed an AI Document Navigator (AIDN) to help a client efficiently retrieve information from large sets of PDFs and JSON files. Our goal was to enhance document access, making it faster and more accurate with advanced AI technologies. A key challenge was handling large documents without slowing down. We optimized document splitting and retrieval processes for quick responses. We also created a context-aware retriever for more relevant information. We used OpenAI's GPT-4 for language processing, FAISS for vector storage, and implemented recursive text splitting for document chunking. We also employed robust security measures to manage environment variables.
RAG Chatbot: LangChain & Chainlit
11319414
To calculate the Customer Lifetime Value (LTV) for a U.S. firm, we first analyzed historical data to determine the Average Purchase Value (APV). This involved dividing the firm's total revenue over a specific period by the number of purchases in that period. Next, we calculated the Average Purchase Frequency Rate (APFR) by dividing the total number of purchases by the number of unique customers. Multiplying APV with APFR gave us the Customer Value. Then, we estimated the Average Customer Lifespan (ACL) by analyzing the duration of customer relationships. The LTV was calculated by multiplying Customer Value with ACL. This process provided insight into how much revenue a customer would bring over their relationship with the firm. By understanding LTV, the company can make informed decisions on customer acquisition and retention strategies.
"Calculate CLV for US sports firm"
To calculate the Customer Lifetime Value (LTV) for a U.S. firm, we first analyzed historical data to determine the Average Purchase Value (APV). This involved dividing the firm's total revenue over a specific period by the number of purchases in that period. Next, we calculated the Average Purchase Frequency Rate (APFR) by dividing the total number of purchases by the number of unique customers. Multiplying APV with APFR gave us the Customer Value. Then, we estimated the Average Customer Lifespan (ACL) by analyzing the duration of customer relationships. The LTV was calculated by multiplying Customer Value with ACL. This process provided insight into how much revenue a customer would bring over their relationship with the firm. By understanding LTV, the company can make informed decisions on customer acquisition and retention strategies.
"Calculate CLV for US sports firm"
12112538
We analyzed automotive sales data spanning from April 2007 to May 2023 across GCC countries such as Saudi Arabia, Kuwait, the United Arab Emirates, Qatar, Bahrain, and Oman. Through extensive visualization, we delved into the sales performance of various manufacturers and car segments. Our findings uncovered compelling trends and patterns in the GCC automotive industry over sixteen years. Notably, certain manufacturers consistently dominated the market, while others experienced sales fluctuations. These insights offer valuable guidance for industry stakeholders and policymakers, aiding in informed decision-making and strategic planning for the future of the automotive market in the GCC region.
Exploring Automotive Sales Trend
12112527
The AI Tax Law Assistant is a smart chatbot, trained on hundreds of PDFs covering Pakistan's tax laws. Developed with a leading government firm, this tool provides clear and quick legal help, using advanced AI to handle Pakistan's tax rules. The government firm used this assistant to make their tax advisory services faster and more accurate. The assistant has a huge knowledge base from laws, case studies, and guidelines, ensuring it understands Pakistan's tax laws well. It uses advanced language processing to understand user questions in everyday language and gives clear, simple answers. The chatbot combines information retrieval and generation to provide accurate, up-to-date responses to complex tax questions. Its easy-to-use interface allows anyone to input queries and get instant replies, making it accessible to users of all knowledge levels. The AI Tax Law Assistant offers specific advice based on different scenarios, summarizes long legal documents for quick understanding, and
AI Tax Law Assistant for Pakistan
Változtatások elmentve
5.0 · 3 Reviews
Értékelések
W
Write python function to download data from website • €8 EUR
It was an absolute pleasure to work with Hammad. Super! Looking forward to work with you again. Keep it up.
Anoop W.
@waliaanoop
•
zászlója
Kessel-Lo (Leuven), Belgium
•
25 nappal ezelőtt
Megosztás
Felhasználó avatár
Parse pdf files (Bank statements) into CSV • $10 USD
We got there with this small project. good Work Hammad
Brendan O.
@SmileBCS
•
zászlója
Belfast, United Kingdom
•
1 évvel ezelőtt
Megosztás
Felhasználó avatár
Pandas / Python help • €234 EUR
Good communication, great efforts en brilliant results
Tim B.
@timovic80
•
zászlója
Zuid-Scharwoude, Netherlands
•
1 évvel ezelőtt
Megosztás
Tapasztalat
AI Engineer
ápr., 2023 – Jelenleg is
•
1 év, 8 hónap
Habibi Group
ápr., 2023 - Jelenleg is
•
1 év, 8 hónap
- Fine-tuned Llama2, Mistral, and GPT-3.5 Turbo for domain datasets. - Engineered RAG-based chatbots for optimized interactions with PDFs, websites, and databases. - Managed vector databases (Pinecone, PgVector, Chroma) for efficient embedding storage. - Leveraged LangChain and LlamaIndex for seamless data integration. - Designed and implemented RESTful APIs using Flask, django. - Deployed RESTful APIs on AWS. - Developed ML pipelines for automated data preprocessing, training, and deployment.
zászlója
karachi, Pakistan
ápr., 2023 – Jelenleg is
•
1 év, 8 hónap
Data Scientist
márc., 2022 – Jelenleg is
•
2 év, 9 hónap
Fiverr
márc., 2022 - Jelenleg is
•
2 év, 9 hónap
I have successfully completed over 70 projects on Fiverr encompassing a wide range of areas including data science, machine learning, statistical analysis, web scraping, and computer vision.I have ascended the ranks and am proud to be a Level Two seller on Fiverr.
márc., 2022 – Jelenleg is
•
2 év, 9 hónap
Tanulmányok
Karachi University
2019 - 2023
•
4 év
Computer Science
zászlója
Pakistan
2019 - 2023
•
4 év
NED University of Engineering and Technology
2020 - 2021
•
1 év
Industrial Data Science Post Graduate Diploma
zászlója
Pakistan
2020 - 2021
•
1 év
Végzettségek
Certified Data Scientist
2021
DataQuest
--
2021
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