Atharva Pathak's Projects
This repository consists of some basic Computer Vision & Linear Regression Projects.
Machine Learning model for Credit Card Fraud Detection
This repository describes my assignment in Data Science related to Exploratory Data Analysis in detecting the type of customers who are more likely to accept a loan with minimum defaulting rate.
Customer sentiment analysis is the process of using natural language processing (NLP) and machine learning techniques to analyze and understand the feelings, opinions, and attitudes expressed by customers in textual data, such as reviews, feedback, and social media posts.
Customer Service Chatbot Repository includes a range of features for building custom chatbots that can handle customer service queries and support requests. These features include NLP capabilities and pre-built dialog flows that can help chatbots understand and respond to customer.
This project is all about predicting salaries of Data Scientists.
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
In this capstone project, we need to create a deep learning model which can explain the contents of an image in the form of speech through caption generation with an attention mechanism on Flickr8K dataset.
This project involves building a 3D Convolutional Neural Network (CNN) to correctly recognize hand gestures by a user to control a smart TV using 3D-CNN and 2D-CNN + RNN/LSTM/GRU.
This repository uses a simple linear regression to predict house prices in US $ based upon areas in sq ft.
Lead Scoring is such a powerful metric when it comes to quantifying the lead & it is nowadays used by every CRM. In this repository, we are going to take a look at the UpGrad lead scoring case study and see how can we solve this problem through several supervised machine learning models.
This project implements Market Basket Analysis (MBA), using data mining techniques to uncover relationships between products purchased together. By analyzing transaction data, we aim to provide actionable insights to optimize marketing strategies and enhance customer experience.
Creating a possible model to detect melanoma from the dataset accurately using CNN.
Using algorithms such as collaborative filtering, content-based filtering, or hybrid methods, this recommendation engine offers personalized suggestions to users, enhancing their shopping or browsing experience.
SQL queries performed on IMDb database to provide recommendations to RSVP Movies based on insights.
Forecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models
This project in Python aims to provide a tool for estimating the size of objects in images or videos. Using computer vision techniques, the project analyzes the input media, detects objects of interest, and provides an estimation of their size based on known reference points or objects.
To ensure a better diagnosis of patients, doctors may need to look at multiple MRI scans. What if only one type of MRI needs to be done and others can be auto-generated? Generative Adversarial Networks (GANs) have been used for generating deepfakes, new fashion styles and high-resolution pictures from the low-resolution ones
Build a classification model for reducing the churn rate for a telecom company
The Battle of Neighborhoods Capstone Project for the IBM Professional Certificate on Coursera.
Twitter sentiment analysis is the process of analyzing tweets posted on the Twitter platform to determine the overall sentiment expressed within them. It involves using natural language processing (NLP) and machine learning techniques to classify tweets.