Mihir Raul's repositories
ARIMA_SARIMA_Modelling
In this project, tried implementing ARIMA and Seasonal ARIMA using python
Forcasting-medical-insurance
This project aims to predict accurately the insurance costs by using one of the statistical methods i.e., linear regression.
StartUp_Data_Visualization
Tried visualizing the data using python libraries like seaborn, matplotlib.pyplot, numpy, pandas to find out some interesting insides of the data
Visualization
This project aims to visualize placement data based on R libraries like ggplot, dpylr, tidyverse.
AI-cs50
CS50 AI Mine solutions for CS50's Introduction to Artificial Intelligence with Python course Warning : before visiting this repo files, please read about CS50's Academic Honesty rules. Includes: Quizzes answers Projects solutions Course info: Name: CS50's Introduction to Artificial Intelligence with Python University: Harvard University WWW: https://cs50.harvard.edu/ai/2020/
Deep-Learning-Activities
This repository contains scripts of activities performed on various deep learning concepts
Gif
Developer Gif
Hate-Speech-Detection
Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. This project focuses on detecting hateful speech from a sentence via a help of a GUI
Recognize-Object
This project aims at building a model that predicts object in real time using YOLO(You Look Only Once) Algorithm.
Recommendation-System-for-Fashion-Apparel
This project demonstrates recommending visually similar item using a pretrained ResNet50 Model. Visuallly Similar elements are classified using Cosine Similarity. The dataset is taken from Fashion Apparel Dataset that is present on kaggle platform
Mihir998
MIhir Raul (Portfolio)
MihirRaulPortfolio
Portfolio
NLP
This repository consists of assignments / activities performed during NLP sessions
Recommendation-system-based-on-Apriori-Algorithm
A recommendation system based on the Apriori algorithm is a data-driven approach that utilizes association rules mining to generate personalized recommendations for users.
Twitter_Sentiment_Analysis
In this project, tried analyze the sentiment of the tweets provided from the Sentiment140 dataset by developing a machine learning pipeline involving the use of two classifiers (Logistic Regression and SVM)along with using Term Frequency- Inverse Document Frequency (TF-IDF). The performance of these classifiers is then evaluated using accuracy and F1 Scores.