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This project implements a computer vision-based fall detection system using deep learning techniques. The system can detect whether a person has fallen in images or video frames.
This study aims to explore the adoption of Google Play store Apps Data Analysis and Prediction models of the Google Play store apps data using Exploratory Data Analysis (EDA) which include the Data Analysis Techniques and implementation of Machine Learning methodologies to explore some critical analysis and insights using Python.
This repo consists the implementation of Ant-Colony-Optimization algorithm written in python and helps to find optminal path in travelling salesman problem
This repository contains two machine learning projects that demonstrate my expertise in developing, tuning, and evaluating machine learning models from scratch. Each notebook showcases a different approach to solving real-world data-driven problems, from simple models to more advanced techniques.
This project predicts Titanic survivors using classification models. It includes data cleaning, pre-processing, exploratory data analysis (EDA), categorical feature conversion, model building, and evaluation. Python libraries like Pandas, NumPy, Matplotlib, and Seaborn are used to analyze and predict survival outcomes.
Data Dash is a marketing consulting company that specializes in the mining of and analysis of social media data. Compiled a dataset of possible influencers, used Tweepy (Twitter) API and Vader Sentiment analysis to produce a list of potential spokespersons that have the most potential as influencers based on a combination of number of followers,…
This project focuses on analyzing caste-based hate crimes using Python for data manipulation, Jupyter Notebook for exploratory data analysis (EDA), and Streamlit for creating an interactive web application to present insights.
This demonstrates how to use decision trees for classification and understand the foundations of ensemble methods like random forests and boosted trees. Part of Week 4 from the Machine Learning Specialization (Course 2) by Andrew Ng.