Machine Learning techniques have been used to analyze and predict the stock prices of a given company. These systems are quite common nowadays. But how reliable are these predictions? It is a known fact that stock prices fluctuate on many factors like Economic factors, Company trends, etc. In this article, we will evaluate the reliability of these predictions by training a model on a stock price dataset and then trying to predict when the price fluctuated due to some Economic conditions. This will help us analyze the reliability of such systems when the situation deviates from the normal situation.
Quite frequently, I see Data Science being perceived as only Machine Learning. But it is quite opposite to the general belief — Data Science consists of several processes out of which Machine Learning is only a small part. In practice, data scientists spend the majority of their time collecting, preparing, and analyzing data. Below, we will discuss why this is the case and why is it important to spend more time on data collection and preparation.
The basis of a successful data science project is correctly understanding the business objective or the problem to be solved. Many a time, newbies…
Descriptive analysis of previous data helps organizations and businesses get an insight into what is going on and what will happen soon. This contributes to the growth and development of businesses by implementing better strategies and decisions.
In this project, we will be analyzing the collected data of Uber Cabs from New York City. Let us see what conclusions and findings we can dig from this data.
This analytics process is divided into four phases:-
Let us see each phase in detail.
Feedback of Product or Service is crucial to the development of any organization. What the end-users have to say about our product gives an insight on where and what to improve. This helps in product and service improvement which is the backbone to the growth of any industry.
Feedback analytics is a broad term — many methods are employed for Feedback Analytics. One such method is Sentiment Analysis. Sentiment Analysis shows what sentiment a specific sentence has. For this application, they are divided into three main categories — Positive, Negative, Neutral.
This project is divided into 4 parts — Importing…
KMeans is a Machine Learning Technique that is used for Clustering data. But what’s the difference between other ML Algorithms and KMeans? Well, KMeans is a type of Machine Learning known as Unsupervised Machine Learning. Which means, we are not labeling any data for the algorithm to learn. The algorithm itself has to find the grouping of the data provided we provide the number of groups we require. These groups are technically called as Clusters in Machine Learning.
Our Aim here is to classify notes as Fake or Genuine depending on the parameters given. These parameters are the characteristics of…
I am a Computer Science Student pursuing Bachelors in Computer Science and Engineering from Dayananda Sagar University, Bangalore.