Saurabh Tayde

Ford

Data Scientist

June 18, 2026 – Present

PDF_Files_ChatBot_LangChain_Falcon_OpenAI

June 30, 2023 – June 30, 2023

Chatbot for Multiple PDF files is a Python program designed for interacting with multiple PDF documents. Through natural language queries, users can inquire about the PDFs and receive accurate responses based on their content. The app utilizes a language model to generate these answers.

Falcon_ChainLit_LangChain

June 27, 2023 – June 28, 2023

Chatbot in this respository is created using LangChain Framework, Open source Falcon 7B Instruct model and ChainLit. API of open source LLM is taken from Hugging Face Hub. Deployment is done using ChainLit which provides great Aesthetic UI for AI to user communication.

NLP-Specialization--Deeplearning.AI-Coursera-

October 10, 2022 – October 10, 2022

'NLP Specialization' program is offered by Deeplearning.AI on Coursera platform. In this repository, I have added notes (by Deeplearning.AI), ungraded/graded assignments of all the four courses of this specialization.

Recognition-of-Tweet-Emotion-using-Keras-Multi-Class-Text-Classification

August 7, 2022 – August 7, 2022

Created a recurrent neural network (Bidirectional LSTM) and trained it on a tweet emotion dataset to learn to recognize emotions in tweets. The dataset has thousands of tweets each classified in one of 6 emotions (joy, love, fear, surprise, sadness, anger). This is a multi class classification problem in the natural language processing domain. Used TensorFlow as machine learning framework. Projct describes how to use the Tensorflow to start performing natural language processing tasks like text classification.

Named-Entity-Recognition-using-Bi-LSTM

August 3, 2022 – August 3, 2022

In this project, used the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc. Named entity recognition is not only a standalone tool for information extraction, but it also an invaluable preprocessing step for many downstream natural language processing applications like machine translation, question answering, and text summarization.

Building-an-RFM-Recency-Frequency-Monetary-segmentation-model

April 1, 2022 – April 1, 2022

RFM segmentation enables marketers to target specific groups of consumers with communications that are far more relevant to their individual behaviours, resulting in much greater response rates and improved loyalty and customer lifetime value. RFM segmentation, like other segmentation approaches, is an effective tool to identify groups of consumers who should be treated differently. RFM stands for recency, frequency, and monetary

Comprehensive-Analysis-of-Google-play-store-Apps

April 1, 2022 – April 1, 2022

Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is an important step prior to processing and often involves reformatting data, making corrections to data, and the combining of data sets to enrich data. Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a recordset, table, or database and refers to identifying incomplete, incorrect, inaccurate, or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.

Prediction-of-Failures-in-the-Air-Pressure-System-of-Scania-Trucks-

March 31, 2022 – March 31, 2022

Scania dataset consists of data collected from heavy Scania trucks in everyday usage. The system in focus is the Air Pressure system (APS) which generates pressurised air that are utilized in various functions in a truck, such as braking and gear changes. The datasets’ positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS.

Prediction-of-steel-plates-faults-using-Machine-Learning

March 30, 2022 – March 30, 2022

Fault Diagnosis (FD) has a major importance to enhance the quality of manufacturing and to lessen the cost of product testing. Actually, quick and correct FD system helps to keep away from product quality problems and facilitates precautionary maintenance. This study evaluates the performances of three of the popular and effective data mining models to diagnose seven commonly occurring faults of the steel plate namely; Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps and Other_Faults.

Predict-the-noise-generated-by-airfoil-blades

March 30, 2022 – March 30, 2022

The noise generated by an aircraft is an efficient and environmental matter for the aerospace industry. A vital component of the total airframe noise is the airfoil self-noise, which is due to the interaction between an airfoil blade and the turbulence produced in its boundary layer and near wake. The data is fetched from UCI repository. The dataset is made up of 1,503 samples (xi), with five parameters describing the wind tunnel configuration and a dependent variable (yi) that represents the scaled sound pressure (in dB). So, Goal of this project is to predict sound pressure level and The variable to be predicted is continuous (sound pressure level).