I am passionate about developing user-friendly software applications.
Excellent problem-solving skills and ability to perform well. Good at Chatgpt, machine learning, java, python, MySQL, C & GitHub projects
This chatbot takes symptoms from the user as input and predicts what type of disease the user may have. Depending on the type of disease, we give precautions and suggest specialist doctors in that field. For this, we have used a Sequential model to extract the symptoms from the text and KNN algorithm for predicting the type of disease.
Ogo, 2022 - Feb, 2023
•
6 bulan
Sentiment Analysis
Ogo, 2021 - Nov, 2021
•
2 bulan, 30 hari
Knowledge Solution India
Ogo, 2021 - Nov, 2021
•
2 bulan, 30 hari
I have Developed a Python Script to extract comments, reviews given by customers from the restaurant website and applied sentimental analysis. The purpose of the analysis is to build a predicted model to predict whether a review on a restaurant is positive or negative.
Ogo, 2021 - Nov, 2021
•
2 bulan, 30 hari
News Classification
Jan, 2021 - Jul, 2021
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6 bulan
YBI Foundation
Jan, 2021 - Jul, 2021
•
6 bulan
I have Successfully completed on Machine learning and have completed a project on news classifications where I have categorized the news depending on the content.
Jan, 2021 - Jul, 2021
•
6 bulan
Pendidikan
Jawaharlal Nehru Technological University
2019 - 2023
•
4 tahun
BTECH
India
2019 - 2023
•
4 tahun
Kelayakan
Smart Coder
2022
SMART INTERVIEWS
Smart Coder in Python and Java
2022
AIML
2022
Oracle
Artificial Intelligence with Machine Learning
2022
SQL
2022
Oracle
Database Programming with SQL
2022
Penerbitan
AI Enabled Legal Assistance System : A Case Study
Y. Lalitha Sri
Given a Case, finding the related prior cases and their judgements is a time consuming job a Lawyer. The Lawyer has to go through huge volumes of law books and prepare his case. An automated tool that retrieves the relevant past cases and their judgements is a very useful application for a lawyers. So, our work presents a case study tom retrieve judgements given in the past for a given factual description using LDA (https://doi.org/10.53730/ijhs.v6nS3.7553)
Pengesahan
Tepat pada masa
100%
Mengikut bajet
100%
Kadar terima
100%
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