Predicting Michael Jordan’s Scoring with Machine Learning
by Rishan Sanjay
I began my journey in the vast world of data with Kushagramati Analytics.
The avenues of training afforded to me by this job have been extraordinary to say the least
and the freedom to learn has been tremendously encouraging. I started off with learning Python, Pandas and Numpy and subsequently moved on to ML concepts. I decided to use an NBA dataset (up to 2017, found on Kaggle) as a part of my learning owing to my passion for the game of basketball and what better way to begin my journey in predictive analytics than predicting MJ’s scoring.
Sentiment Analysis using NLP on a Hotel Review Dataset
by Rishan Sanjay
Hotel booking companies have been amassing tremendous amounts of data. The reviews left by users are of value to the hotels but due to the volume, extracting insights is no easy task. Using Natural Language Processing (NLP) techniques we carry out a sentiment analysis based on the given review data and visualize it.Further, the data is analyzed for the negative and positive sentiments to point out Key strengths and weaknesses based
on the given reviews.
Hyperparameter Tuning in Classification Models
Flourishing companies that make the most effective use of data science create exceptional models. A model is a Machine Learning algorithm that is a combination of model data and prediction algorithm. The biggest challenge for data scientists and ML researchers is to improve the accuracy of the model using different methods and techniques.
Activation Functions in Deep Learning
In artificial neural networks(ANN), the activation function helps us to determine the output of Neural Network. They decide whether the neuron should be activated or not. It determines the output of a model, its accuracy, and computational efficiency.
How to make Multi Label predictions using Multi Label Classifiersfor text data
Rashmi P G
One of the successful implementations of Artificial Intelligence is Natural Language Processing (NLP). NLP is extensively used by industry to understand the customer sentiment and derive more insights from the data. Any successful business operates on the fundamental of its customers’ likes and dislikes, and continuously improve its quality of products and services. Analyzing the sentiment of consumers/users is most required for social media platforms like facebook and twitter to keep the posts and tweets well within the community standards.
Role of OpenCV in Image Preprocessing
by Neha V S
Open CV is a huge open-source library for Computer Vision, Machine Learning and Image Processing. It focuses on image processing, video capture and analysis including face-detection and object detection. It can identify faces, objects or even the hand-writing of a human.
How to Create a Virtual environment and Setup Django project
Django is a Web framework written in Python. A Web framework is a software that supports the development of dynamic Web sites, applications, and services. It provides a set of tools and functionalities that solves many common problems associated with Web development, such as security features, database access, sessions, template processing, URL routing, internationalization, localization, and much more.
Time Series Forecasting on COVID 19 using ARIMA
Time Series data is experimental data that has been observed at different points in time (usually evenly spaced, like once a day). For example, the data of airline ticket sales per day is a time series. However, just because a series of events has a time element does not automatically make it a time series, such as the dates of major airline disasters, which are randomly spaced and are not time series. These types of random processes are known as point process.