shikhararyan

Annotating-multiple-PDFs-

This project aims to automate the process of highlighting specific keywords within PDF documents. Leveraging the PyMuPDF library (fitz module), along with pandas for handling keyword data stored in CSV format, the script systematically searches through each page of the PDF documents, identifies the keywords, and highlights them.

shikhararyan

Heart-attack-risk-Prediction-Model---Random-Forest

This notebook explores and analyzes the Heart Disease UCI dataset using Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn. It includes data visualization, feature engineering, model building using Random Forest Classifier, and evaluation of the model's performance in predicting the presence or absence of heart disease.

Bank_loan_Customer_calls

This project involves the analysis of two datasets: a portfolio dataset containing information about loan portfolios, and a communication history dataset containing information about communication interactions with customers. The aim of the project is to gain insights into customer behavior and loan portfolio trends.

web-scraping---store-details-geolocation

This project involves scraping the location details of V-Mart retail stores including address, area, contact number, coordinates, timing, and directions from the official V-Mart website. It utilizes web scraping techniques with Python libraries such as requests, BeautifulSoup, and pandas.

Online-Payment-Fraud-Detection-Model-EDA-Classification

This project implements an online payment fraud detection system, combining exploratory data analysis (EDA) and classification using a decision tree classifier. The model achieves high accuracy in identifying fraudulent transactions, providing valuable insights for secure online transactions.

Car_selling_price_predictor

This project develops a machine learning model to predict car selling prices based on various features. It includes data preprocessing, exploratory data analysis (EDA), and model training using Random Forest Regressor and Linear Regression algorithms. The Random Forest model achieves higher accuracy and is saved for future use.

Text-Classification-Transformer-Model-

This sentiment analysis model utilizes a Transformer architecture to classify text sentiment into positive, negative, or neutral categories with high accuracy. It preprocesses text data, trains the model on the IMDB dataset, and effectively predicts sentiment based on user input.

Spotify_streams_prediction_model

This project builds a predictive model for music track popularity using machine learning. It also conducts Exploratory Data Analysis (EDA) to understand the dataset's patterns and correlations.

Text-Generation-and-summarization-using-Pipelines-

This project demonstrates sentiment analysis and text generation tasks using Hugging Face's NLP framework. It showcases the preprocessing of text data, sentiment analysis with pipelines, and text generation using the GPT-2 model on a dataset of news articles. It serves as a tutorial for implementing NLP tasks efficiently with Hugging Face's tools.

ChatBot-GPT-3.5-Turbo-Based-

Personal Chatbot Powered by GPT-3.5" is a Flask web application that utilizes OpenAI's powerful GPT-3.5 model to create a conversational chatbot experience. Users can interact with the chatbot in natural language, receiving responses based on the context of the conversation.

WEB_Scrapping-basic-technique

This assignment involves web scraping data from a Singapore government website using Python libraries like BeautifulSoup and requests. The scraped data includes names, citizenships, and dates of birth of individuals mentioned in a table on the website. After scraping the data, it is processed and saved into a CSV file for further analysis or usage.

Biometric-based-Voting-machine-

This project is a Voting Machine application built with Python and MySQL. It verifies voters' Aadhar card numbers, checks their eligibility to vote, and integrates with a finger recognition module for authentication. The user-friendly interface is created using Tkinter, ensuring smooth interaction for voters.

Recommendation-system-Collaboratve-Filtering-

Collaborative Filtering based Recommendation System: This repository contains the implementation of a collaborative filtering-based recommendation system for book recommendations. It utilizes user-item interactions to suggest books that users might be interested in, enhancing personalized user experiences in the domain of book recommendation.

lead-has-potention-or-not-

hackerearth_basics_of_input_output_in_python

this code repository for beginners in python