Skills

My Skills

Tech Stack

Programming Languages:

Python

C/C++

HTML

CSS

Javascript

SQL

Frameworks and Packages:

NumPy

Pandas

Matplotlib

Seaborn

Plotly

Scikit-Learn

TensorFlow

PyTorch

Keras

NLTK

LangChain

Web Scraping

Streamlit

Flask

Hugging Face

Big Data and Other Tools:

Git/Github

MySQL

MongoDB

Hadoop

Spark

Tableau

PowerBI

Canva

Excel

Docker

AWS

AI

Machine
Learning

Deep Learning

Computer
Vision

NLP

Time Series Analysis

Data Science

Professional Certificates

My Professional Certificates

Demonstrate expertise and proficiency in a particular field..

SERVICES

What I Offer

I handle all tasks related to AI.

Machine Learning

We handle the entire machine learning model implementation process, from research to deployment and maintenance across all necessary environments. With a focus on research, development, staging, and production, we ensure seamless integration, all while streamlining the process with CI/CD pipelines.

Deep Learning

We specialize in collecting, processing, and cleaning data to create top-notch training datasets that can be used to train robust deep learning models. Our focus is on artificial neural networks, including Convolutional Neural Networks, Recurrents, and Bidirectional RNNs.

Computer Vision

We excel in a wide range of computer vision tasks, such as object detection, segmentation, classification, tracking, facial landmark detection, swapping, image restoration, and colorization using caffe models. We also have expertise in facial recognition and OCR on custom datasets, with a deep understanding of both classical and deep learning computer vision.

Data Analysis

We have the ability to manually gather and understand brief information about datasets using various tools, such as numpy, pandas, matplotlib, seaborn, SQL, Tableau, and Grafana. In addition, we are also proficient in using automated EDA libraries like dtale, sweetviz, autoviz, pandas visual analysis, dataprep, and pandas profiling report.

Data Visualization

We possess the skills to transform complex data sets into impactful and meaningful insights through the development of interactive dashboards using PowerBI and Tableau. Our expertise extends to building and monitoring dashboards with Plotly while utilizing a MySQL database, ensuring that you have real-time visibility into your data.

NLP

We're experts in developing end-to-end NLP models that prioritize text preprocessing techniques like tokenization, lemmatization, stemming, and stopwords removal to ensure computer understanding. Additionally, we utilize bag-of-words, TF-IDF, Word2Vec, and AvgWord2vec techniques for sentiment analysis.

Front End Development

With a strong understanding of HTML5, CSS3, Bootstrap5 and Javascript , I possess the skills to handle the frontend of websites across various domains. Whether it's updating an existing website or building a new one from scratch, I have the expertise needed to create an effective and engaging user interface.

Time Series Analysis

I can perform time series analysis to identify trends, patterns, and anomalies in data. I prioritize comprehensive planning and design before analyzing any time series data to ensure accurate and reliable results. I can also use time series analysis to forecast future values, make predictions, and optimize decision-making.

Backend Development

With a strong understanding of MERN, I specialize in bash command line, version control with Git and GitHub, Flask, APIs, and Databases such as MySQL and MongoDB.This expertise enables me to convert static websites into dynamic web applications with ease, and to handle deployment to ensure seamless functionality.

I know about cloud deployment

Want to see the platforms I can deploy on?

Data Visualizations

My Data Visualizations

Demonstrate expertise and proficiency in BI Tool..

Insight View for my ML model

PowerBi Demo

Data Science Projects

My Data Science Projects

real-world implementations firsthand. Take a closer look now!

Infrared

"Real world ML Research Project innovative solution leveraging Hugging Face's libraries, OpenAI's Language Model (LLM) and unsupervised machine learning techniques "

Interactive Chatbot Interface: InfraBot I spearheaded the development of an interactive chatbot interface named InfraBot. By utilizing models like text-davinci-003 and LLaMa2, InfraBot offers users an intuitive and user-friendly way to interact with complex audit processes, fraud detection methodologies, and data analysis techniques. This enhancement significantly improved user experience and accessibility. PDF and Excel Data Querying using LLM Leveraging the power of OpenAI's Language Model and Hugging Face's libraries, I engineered a robust PDF and Excel querying system. This system enables seamless extraction and processing of information from unstructured PDF documents and Excel spreadsheets. The integration of (google/flant5-xxl) further empowers efficient data analysis and validation, enhancing the overall data processing capabilities of the system. Video-to-Text Transcription with Whisper I harnessed the capabilities of OpenAI's Whisper to convert audio content from videos into accurate text transcripts. This innovation significantly improved the comprehensiveness of available data for analysis and audit purposes. The integration of accurate transcriptions has led to higher data accuracy and reliability, contributing to more informed decision-making processes. Anomaly Detection using Unsupervised Machine Learning As part of the fraud detection enhancement, I successfully implemented unsupervised machine learning algorithms to identify anomalies in financial and transactional data. By employing techniques such as Isolation Forest and DBSCAN, I created a system that autonomously detects unusual patterns and potentially fraudulent activities. This has greatly strengthened the fraud detection capabilities of the organization. Deployment and DevOps: The developed solutions were deployed on AWS EC2-ECR instances. To streamline the development and deployment process, I established continuous integration and continuous deployment (CI/CD) pipelines using GitHub Actions. This approach facilitated efficient code integration, testing, and deployment, ensuring a seamless and iterative development lifecycle. Impact: The solutions I developed have revolutionized Revoquant AI's capabilities in audit enhancement, fraud detection, and data analysis. These innovations have not only improved the accuracy and efficiency of audit processes but have also equipped the organization with powerful tools to combat fraudulent activities. The integration of advanced language models and machine learning techniques has positioned Revoquant AI at the forefront of technological advancements in the field.

Project Link

FTOSS

"Leveraged advanced Content based analytics and machine learning techniques to improve operational efficiency and drive better business outcomes"

Process Mining Tool for Process Optimization: I led the development of a process mining tool that utilized Exploratory Data Analysis (EDA) and Machine Learning algorithms. The tool focused on optimizing the entire lifecycle of a product, from purchase requisition to invoice verification. By analyzing historical data, the tool identified the "happy path" of the product life cycle – the optimal sequence of steps that led to the most efficient outcomes. This solution significantly streamlined the process, minimized delays, and improved the overall efficiency of the supply chain. Date and Capacity Planning for Machine Projects: I orchestrated date and capacity planning using a combination of data analysis techniques and machine learning algorithms. By analyzing data related to machines involved in a complex machine project, I optimized the timeline and capacity allocation. This approach ensured that the project was executed within the most efficient time frame while making the best use of available resources. The result was enhanced project delivery timelines and resource utilization. Sales Analysis and Production Planning: Leveraging Power BI, I created insightful visualizations of sales data that directly influenced production planning and inventory control decisions. By analyzing sales trends, demand patterns, and inventory levels, I provided actionable insights to reduce waste and optimize inventory management. This data-driven approach led to more accurate production planning, reduced excess inventory, and improved alignment with market demands. Impact: The solutions I developed had a profound impact on the organization's operational efficiency and business outcomes. By optimizing processes, improving decision-making, and aligning production with demand, the organization experienced reduced costs, minimized waste, and increased customer satisfaction. These achievements not only enhanced the bottom line but also positioned the organization as a leader in leveraging data-driven approaches for operational excellence. Key Techniques and Technologies Used: Exploratory Data Analysis (EDA) Machine Learning Algorithms Process Mining Data Analysis Techniques Power BI for Data Visualization Conclusion: My contributions to this project exemplify my proficiency in combining advanced analytics and machine learning techniques to address real-world challenges. The solutions I developed showcase my ability to drive operational efficiency, optimize processes, and enable data-informed decision-making to achieve substantial improvements in business outcomes.

Project Link

Inventory Detection

"Developed a smart fix using YOLOV8 Content based to help Coca-Cola tally non-Coca-Cola products in their fridges and to check and verify the accuracy of their inventory "

Title: Smart Inventory Management Solution using YOLOv8 for Non-Coca-Cola Product Detection Description: Designed and implemented an innovative inventory management solution leveraging YOLOv8, an advanced computer vision model, to enhance the accuracy and efficiency of product inventory tracking for Coca-Cola. The solution enables the real-time identification and verification of non-Coca-Cola products within Coca-Cola fridges, streamlining the inventory management process and ensuring inventory accuracy. Key Achievements: Problem Solving: Developed a novel approach to address Coca-Cola's inventory management challenge by integrating YOLOv8, a state-of-the-art object detection model. Accuracy Enhancement: Achieved remarkable accuracy in identifying and differentiating non-Coca-Cola products within Coca-Cola fridges, contributing to the precision of inventory counts and reducing errors. Real-time Monitoring: Enabled real-time monitoring of fridge contents, allowing for immediate verification of inventory accuracy and timely restocking decisions. Automated Workflow: Designed an end-to-end solution that automates the process of non-Coca-Cola product detection, minimizing manual intervention and improving operational efficiency. User-friendly Interface: Developed an intuitive user interface that provides Coca-Cola staff with a clear visualization of detected non-Coca-Cola products, aiding in rapid decision-making. Scalability: Engineered the solution with scalability in mind, ensuring it can be seamlessly integrated into various Coca-Cola locations and fridge configurations. Technical Skills Applied: Deep Learning: Utilized YOLOv8, a cutting-edge deep learning model, for object detection tasks. Computer Vision: Applied computer vision techniques to process and analyze fridge contents, extracting meaningful information for inventory management. Python Programming: Employed Python as the primary programming language for model development, integration, and user interface implementation. Data Preprocessing: Cleaned, formatted, and annotated image data to train the YOLOv8 model effectively, enhancing its accuracy and performance. Software Integration: Integrated the YOLOv8 model into Coca-Cola's existing inventory management system, ensuring a seamless and cohesive user experience. Impact: The smart inventory management solution utilizing YOLOv8 significantly improved Coca-Cola's inventory accuracy and operational efficiency. By automating non-Coca-Cola product detection and verification, the solution empowered the company to make informed restocking decisions, reduce errors in inventory counts, and optimize overall inventory management processes. This innovation aligned with Coca-Cola's commitment to leveraging technology for process enhancement and efficiency improvement. Conclusion: The development of the YOLOv8-based smart inventory management solution showcased my ability to combine deep learning, computer vision, and software engineering skills to create impactful solutions for real-world challenges. This project not only enhanced Coca-Cola's inventory accuracy but also demonstrated my expertise in developing end-to-end solutions that incorporate advanced technologies to drive operational excellence

Project Link

Time Series Forecasting

" Integrated Predictive Modeling for Inventory and Price Forecasting with Sentiment Analysis for Pricing Strategy Enhancement. "

Description: Developed a sophisticated predictive model employing a fusion of Machine Learning techniques, including Random Forest and Gradient Boosting, alongside Time Series methods such as ARIMA and LSTM, to revolutionize inventory forecasting for reverse logistics and price prediction for both B2B and B2C markets. Incorporated Sentiment Analysis techniques, including BERT, to analyze customer feedback and social media sentiments, thus refining the pricing strategy and optimizing market competitiveness. Key Achievements: Innovative Forecasting Framework: Designed a comprehensive forecasting framework that combined diverse machine learning techniques for inventory and price prediction. This enabled data-driven decision-making for both reverse logistic inventory management and pricing optimization. Time Series Mastery: Successfully applied ARIMA and LSTM time series techniques to decipher intricate patterns, trends, and anomalies within the data. This resulted in highly accurate inventory predictions and price forecasts, contributing to informed operational planning. Sentiment-Informed Pricing: Pioneered the incorporation of sentiment analysis using BERT, effectively harnessing customer feedback and social media sentiments as predictive features. Adjusted price predictions based on sentiment insights, leading to an enhanced pricing strategy that aligned with customer sentiment. Market Adaptability: Crafted a versatile solution capable of catering to both B2B and B2C markets. This adaptability showcased an understanding of distinct market dynamics and the ability to develop universally beneficial strategies. Operational Efficiency: Enabled efficient reverse logistics by precisely forecasting inventory needs. Empowered the company to streamline inventory operations, reduce wastage, and optimize supply chain management. Enhanced Pricing Strategy: By factoring in sentiment analysis, improved price predictions aligned with customer sentiment, driving better customer engagement and loyalty. Technical Skills Applied: Machine Learning Techniques: Leveraged Random Forest and Gradient Boosting algorithms to build robust predictive models for inventory and price forecasting. Time Series Analysis: Mastered ARIMA and LSTM techniques to capture temporal patterns and fluctuations, enhancing the accuracy of inventory and price predictions. Sentiment Analysis: Applied BERT-based sentiment analysis to customer feedback and social media data, transforming qualitative insights into quantitative features for pricing adjustments. Data Preprocessing: Cleaned, transformed, and normalized data to ensure optimal performance of predictive models. Python Programming: Developed the solution using Python, utilizing popular libraries such as Scikit-learn, TensorFlow, and Hugging Face Transformers for various aspects of the project. Model Evaluation: Employed rigorous evaluation techniques, including cross-validation and backtesting, to validate the performance of predictive models and sentiment analysis. Impact: The integration of diverse predictive modeling techniques with sentiment analysis led to a holistic solution that significantly improved inventory management precision and price prediction accuracy. The amalgamation of cutting-edge technologies and data-driven insights empowered the company to make well-informed decisions, minimize supply chain inefficiencies, enhance customer satisfaction, and drive profitability through optimized pricing strategies. Conclusion: The development of this integrated predictive modeling solution, coupling advanced Machine Learning and Time Series techniques with sentiment analysis, showcases my ability to create innovative strategies that effectively leverage technology to solve complex business challenges. This project underscores my expertise in predictive analytics, data-driven decision-making, and the practical application of sentiment analysis for enhancing operational efficiency and market competitiveness.

Project Link

Stock Market Prediction

"Integrated Stock Price Prediction Model Utilizing Historical Data and Financial News with ARIMA, LSTM, and Random Forest"

escription: Led the end-to-end design and development of a robust machine learning model that revolutionized stock price prediction by fusing historical data and financial news. Leveraged state-of-the-art techniques such as ARIMA, LSTM, and Random Forest, along with meticulous feature engineering. Executed thorough performance evaluation utilizing metrics such as mean squared error and accuracy, resulting in an accurate and versatile prediction tool. Key Achievements: Multifaceted Approach: Devised an innovative approach by amalgamating historical stock data with real-time financial news sentiment analysis. This comprehensive strategy harnessed the strengths of various techniques for improved predictive power. Feature Engineering Excellence: Demonstrated skill in crafting meaningful features by extracting valuable information from raw data. Engineered a blend of technical indicators, news sentiment scores, and lagged price data to enhance model performance. Hybrid Modeling Techniques: Successfully integrated ARIMA, LSTM, and Random Forest, capitalizing on ARIMA's time series analysis, LSTM's sequential data understanding, and Random Forest's ensemble capabilities. Performance Assessment: Rigorously evaluated model performance using mean squared error to measure prediction accuracy and establish a quantitative benchmark. This meticulous evaluation highlighted the model's efficacy. Predictive Accuracy: Achieved impressive accuracy in stock price prediction, contributing to informed decision-making for investors and stakeholders. Technical Skills Applied: Machine Learning Algorithms: Employed ARIMA for time series analysis, LSTM for sequential data modeling, and Random Forest for ensemble-based predictions. Feature Engineering: Engineered a rich set of features by combining technical indicators, sentiment scores from financial news, and lagged price data to capture multiple dimensions of information. Data Integration: Seamlessly integrated historical stock data and financial news using data preprocessing and transformation techniques, ensuring data compatibility for modeling. Python Programming: Utilized Python for end-to-end model development, implementing algorithms, feature engineering, and performance evaluation. Sentiment Analysis: Leveraged sentiment analysis techniques to assess financial news sentiment and incorporate it as a predictive feature. Performance Evaluation: Implemented comprehensive performance evaluation methodologies, including mean squared error and accuracy calculations, to assess model effectiveness. Impact: The creation of the integrated stock price prediction model with ARIMA, LSTM, and Random Forest substantially advanced the accuracy and comprehensiveness of stock price forecasts. By considering both historical data and real-time sentiment analysis of financial news, the model empowered investors with enhanced decision-making capabilities, leading to better financial outcomes. Conclusion: The development of this integrated stock price prediction model underscores my proficiency in blending various machine learning techniques to solve complex financial challenges. By leveraging ARIMA, LSTM, and Random Forest in conjunction with meticulous feature engineering, I demonstrated the ability to create advanced solutions that leverage both quantitative and qualitative data sources to drive accurate predictions. This project showcases my expertise in predictive modeling, feature engineering, and comprehensive performance evaluation within the domain of financial data analysis. Regenerate

Project Link

AeroMetric Length Assessment System

" Innovative Optical Measurement System for Drone-Based Pipe Length Calculation using Image Processing and Computer Vision."

Description: Led the design and development of a groundbreaking Optical Measurement System that harnessed image processing and computer vision techniques to accurately calculate the lengths of pipes captured by a drone. This pioneering solution addressed a critical challenge in the field of infrastructure inspection and measurement, facilitating precise measurements in remote and challenging environments. Key Achievements: Customized Measurement Solution: Engineered a tailor-made optical measurement system specifically designed to address the unique requirements of pipe length calculation through aerial imagery captured by drones. Image Processing Expertise: Utilized advanced image processing techniques to analyze drone-captured images, accurately identifying and delineating pipe boundaries and endpoints. Computer Vision Algorithms: Developed and implemented computer vision algorithms to extract relevant features from images, enabling accurate length calculations even in complex and varying lighting conditions. Measurement Accuracy: Achieved a high degree of measurement accuracy, demonstrating the robustness of the solution in capturing the nuances of pipe lengths and overcoming potential measurement errors. Automation and Efficiency: Designed the system to operate with minimal human intervention, automating the length measurement process and significantly reducing the time and effort required for manual measurements. Technical Skills Applied: Image Processing: Utilized image segmentation and edge detection techniques to identify pipe boundaries and endpoints, ensuring accurate length measurement. Computer Vision Algorithms: Developed custom computer vision algorithms for feature extraction and measurement calculations, optimizing the accuracy of length assessments. Python Programming: Employed Python as the primary programming language for implementing image processing, computer vision algorithms, and system integration. Drone Integration: Collaborated with drone hardware and software components to ensure seamless integration of the optical measurement system with drone-based image capture. Testing and Validation: Conducted rigorous testing and validation procedures to verify the accuracy and reliability of the measurement system under various conditions. Impact: The development of the Optical Measurement System for drone-based pipe length calculation revolutionized the field of infrastructure inspection and measurement. By enabling accurate and efficient length assessments from aerial imagery, the solution enhanced the safety and effectiveness of inspection processes in challenging environments, reducing manual labor and potential errors. Conclusion: The successful creation of the Optical Measurement System, integrating image processing and computer vision techniques for drone-based pipe length calculation, highlights my expertise in applying cutting-edge technologies to address real-world challenges. This project demonstrates my ability to design and implement innovative solutions that bridge the gap between technology and practical applications, ultimately contributing to increased efficiency and accuracy in critical infrastructure measurement tasks.

Project Link

Web Development and Designing Projects

Data Scientist from New Delhi

Hi, I am Ravi Pratap Working with Data really Excites me.

More About Me

My Skills

Programming Languages:

Python

C/C++

HTML

CSS

Javascript

SQL

Frameworks and Packages:

NumPy

Pandas

Matplotlib

Seaborn

Plotly

Scikit-Learn

TensorFlow

PyTorch

Keras

NLTK

LangChain

Web Scraping

Streamlit

Flask

Hugging Face

Big Data and Other Tools:

Git/Github

MySQL

MongoDB

Hadoop

Spark

Tableau

PowerBI

Canva

Excel

Docker

AWS

AI

Machine Learning

Deep Learning

Computer Vision

NLP

Time Series Analysis

Data Science

My Professional Certificates

SERVICES

What I Offer

I handle all tasks related to AI.

Machine Learning

Deep Learning

Computer Vision

Data Analysis

Data Visualization

NLP

Front End Development

Time Series Analysis

Backend Development

Contact Me

Contact Info

I know about cloud deployment

Want to see the platforms I can deploy on?

My Data Visualizations

My Data Science Projects

real-world implementations firsthand. Take a closer look now!

Infrared

"Real world ML Research Project innovative solution leveraging Hugging Face's libraries, OpenAI's Language Model (LLM) and unsupervised machine learning techniques "

FTOSS

"Leveraged advanced Content based analytics and machine learning techniques to improve operational efficiency and drive better business outcomes"

Inventory Detection

"Developed a smart fix using YOLOV8 Content based to help Coca-Cola tally non-Coca-Cola products in their fridges and to check and verify the accuracy of their inventory "

Time Series Forecasting

" Integrated Predictive Modeling for Inventory and Price Forecasting with Sentiment Analysis for Pricing Strategy Enhancement. "

Stock Market Prediction

"Integrated Stock Price Prediction Model Utilizing Historical Data and Financial News with ARIMA, LSTM, and Random Forest"

AeroMetric Length Assessment System

" Innovative Optical Measurement System for Drone-Based Pipe Length Calculation using Image Processing and Computer Vision."

My Web Development and Designing Projects

Please review my portfolio of web development projects.

Machine
Learning

Computer
Vision