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bar-chart-tutorial

This code explains how to create and customize bar charts using matplotlib library.

car-price-prediction-using-linear-regression-machine-learning-project

This is a python project for building a linear regression model that is used to predict used car prices from a given dataset using machine learning. The dataset used for this project is taken from Kaggle. For the complete video explanation, check out the following link.

customized-pie-chart-using-python

This code will help you to create and customize pie charts using python.

decision-tree-tutorial

This code explains how to create and customize decision trees in python.

deep-learning-on-amazon-alexa-reviews

This notebook will show you how to implement a deep leaning algorithm (LSTM) on the Amazon Alexa Reviews dataset

deep-learning-on-amazon-product-reviews

This project demonstrates how to perform sentiment analysis using deep learning on Amazon product reviews dataset. The dataset used for the project is obtained from Kaggle and consists of nearly 3000 reviews of amazon users regarding various amazon Alexa products like Alexa echo, Alexa dot etc. Exploratory data analysis is performed on the dataset to analyse various columns and the data is visualized using count plots and pie charts. The reviews are then processed using various methods which involve lowercase conversion, URL removal, punctuation removal, tokenization, stop word removal and stemming. The processed data is then separated into positive and negative reviews and are then visualized using Word clouds, as word clouds help to identify the most prominent/frequently used words. The processed data is then passed into a neural network, where the network learns from the data. The accuracy of the model is then measured by running the model on the test data.

deep-learning-on-imdb-movie-reviews-dataset

Implementing neural networks on the IMDB movie review dataset of 50k movie reviews

eda-on-airbnb-dataset

In this notebook, we will look at performing Exploratory Data Analysis on the Airbnb dataset available on Kaggle.

eda-on-the-big-mart-sales-data

This notebook will show you how to perform exploratory data analysis on the Big Mart Sales dataset

eda-using-python-on-the-amazon-alexa-dataset

This code explains how to perform (Exploratory Data Analysis) EDA using python on the amazon Alexa dataset.

exploratory-data-analysis-on-ford-car-price-prediction-dataset

This project demonstrates how to perform exploratory data analysis on car price prediction dataset. The dataset is available on Kaggle and consists of various attributes related to the sales price of used ford cars. We will be performing Exploratory Data Analysis to understand this data and to find out which factors affect the sales price of used Ford cars. To see the complete video explanation on this topic, check out the link in the description.

hate-speech-detection-using-machine-learning

This is a python project that is used to identify hate speech in tweets. The dataset used to train the model is available on Kaggle and consists of labelled tweets where 1 indicates hate speech tweets and 0 indicates non-hate speech tweets.

histogram-tutorial-code

This code will help you to create and customize histograms using python.

house-value-prediction-using-machine-learning-

This project is about creating a machine learning model that can predict the house value based on the given dataset. We use different machine learning algorithms such as linear regression, decision tree and random forest to train the model, and the model that gives the best performance is used to predict the house value for new data.

k-means-clustering-implementation

K means clustering implementation on the wine quality dataset

machine-learning-on-amazon-alexa-reviews-dataset

This python project explains how to implement sentiment analysis using machine learning (SVM) on amazon Alexa reviews dataset.

machine-learning-on-amazon-product-reviews

This python project implements sentiment analysis using machine learning on amazon product reviews dataset.

machine-learning-on-big-mart-sales-dataset

In this notebook, we will be performing machine learning on the Big mart sales dataset

machine-learning-on-ford-car-price-dataset

This python project compares the performance of three different machine learning classifiers on the ford car price prediction dataset.

machine-learning-on-movie-reviews-imdb-dataset---50k-reviews-

This project demonstrates how to create a sentiment analysis and machine learning model on the IMDB dataset

machine-learning-prediction-system-for-the-diabetes-dataset

The main objective of the diabetes prediction dataset is to create a machine learning prediction system to diagnostically detect whether a patient has diabetes based on the diagnostic measurements in the dataset.

medical-insurance-cost-prediction-using-linear-regression

This project explains on how to build a machine learning algorithm for calculating the medical insurance costs. Check out my video on this topic for the complete video explanation.

owl-file-for-sparql

roshanmatt

Config files for my GitHub profile.

seaborn-heatmap-tutorial

This code will help you to create and customize seaborn heatmaps using python.

supervised-machine-learning-on-iris-datatset

This python project explains on how to implement a supervised machine learning algorithm on the famous iris dataset. The different classifiers used in this project includes decision tree and k-nearest neighbors. For a complete explanation of this project, check out my video on this topic.

tokenization-tutorial-code

This repository contains the code used in my tokenization tutorial video.

twitter-sentiment-analysis-using-python-machine-learning-project-8

This project walks you on how to create a twitter sentiment analysis model using python. Twitter sentiment analysis is performed to identify the sentiments of the people towards various topics. For this project, we will be analysing the sentiment of people towards Pfizer vaccines. We will be using the data available on Kaggle to create this machine learning model. The collected tweets from Twitter will be analysed using machine learning to identify the different sentiments present in the tweets. The different sentiments identified in this project include positive sentiment, negative sentiment and neutral sentiment. We will also be using different classifiers to see which classifier gives the best model accuracy.

unsupervised-machine-learning-on-iris-dataset

This project demonstrates how to implement k-means clustering on unsupervised data. The dataset used here is the famous iris dataset. To see a complete video explanation on this topic, check out the attached link.

wine-quality-prediction-using-machine-learning

This project is about creating a machine learning algorithm that can predict the quality of wine based on the given dataset. Different machine learning algorithms such as logistic regression, decision tree and random forest are used in this project.