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advance-algorithm-stanford

Course-II of ML Specialization from Stanford Online and DeepLearning.AI taught on Coursera by Andrew Ng

alexa-reviews-sentiment-analysis

This GitHub repository presents sentiment analysis for Amazon Alexa reviews using Logistic Regression and Naive Bayes classifiers. A hybrid approach combining both models achieves a high test accuracy of 92%-94%. The code allows users to analyze their datasets and contribute to the project's further development.

caching-the-inverse-of-a-matrix-using-r

Repository for Programming Assignment 2 for R Programming on Coursera

cleaning-and-analysing-given-dataset

The project I produced for the assignment for 'Course 3: Getting and Cleaning Data, of the Data Science Specialization from Johns Hopkins University on Coursera'.

data-sharing

The Leek group guide to data sharing

eda-air-pollutant-emission

Fine particulate matter (PM2.5) is an ambient air pollutant for which there is strong evidence that it is harmful to human health. In the United States, the Environmental Protection Agency (EPA) is tasked with setting national ambient air quality standards for fine PM and for tracking the emissions of this pollutant into the atmosphere. Approximatly every 3 years, the EPA releases its database on emissions of PM2.5. This database is known as the National Emissions Inventory (NEI). You can read more information about the NEI at the EPA National Emissions Inventory web site. For each year and for each type of PM source, the NEI records how many tons of PM2.5 were emitted from that source over the course of the entire year. The data that you will use for this assignment are for 1999, 2002, 2005, and 2008.

eda_electric-power-consumption

Plotting Assignment 1 for Exploratory Data Analysis

eda_spotify

This repository contains an in-depth Exploratory Data Analysis (EDA) of the Spotify Dataset from 2022. Through insightful visualizations and statistical analysis, we gain valuable insights into trends and patterns within the music industry. Additionally, we apply clustering algorithms to group songs based on their audio features.

fish-species-prediction-cnn

This project is focused on developing an efficient and accurate solution for fish species detection using deep learning. Leveraging the MobileNetV2 convolutional neural network architecture, we aimed to classify fish species based on their images.

house-prices-kaggle

Participated in the Kaggle "Houses Price Competition" and successfully solved the challenge. Leveraged various machine learning techniques to predict house prices accurately. My solution encompasses data preprocessing, feature engineering, and ensemble models to achieve competitive results.

hunt-for-exoplanets

This project leverages machine learning techniques, including K-Nearest Neighbors (KNN) and Decision Trees, to identify exoplanets in astronomical data. By employing classification algorithms, the code sifts through vast datasets to detect potential exoplanets, aiding astronomers in their search for habitable worlds beyond our solar system.

instancefuse

MASK R-CNN: A UNIFIED FRAMEWORK FOR OBJECT INSTANCE SEGMENTATION AND BEYOND

ml-stars-galaxies-prediction

Welcome to this Repository! This repo various machine learning methods such as Linear and Logistic Regression, KNN, Random Forest, SVM & Naive Bayes for predicting stars and galaxies. The code and models provided here offer insights into celestial objects classification & you can use this as a foundation to dive into astronomical data analysis.

powerbi-walmart

python-oop

Class Notes for Object Oriented Programming - Python

r-data-cleaning

ransforming untidy data to tidy dataset using R.

rayshader_rtutorial

R Package for 2D and 3D mapping and data visualization

statstical-inference-

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

supervised-ml-stanford

Course-I of ML Specialization from Stanford Online and DeepLearning.AI taught on Coursera by Andrew Ng

translator-project.py

This repository is made for peer graded project of Python Project for AI and Development on Coursera

x-ray-pneumonia-classification

In this project, we aimed to develop a deep learning model for accurately classifying X-ray images as either pneumonia or COVID cases. The objective was to compare accuracy of different types of CNN, where we used two: DenseNet and MobileNetV2 and acheived an accuracy of something close to 54.5% and later using feature scaling it came close to 85%.