machine-learning-projects

Artificial Neural Networks (ANNs) are used to forecast the stock market price. Based on that, Traders take a decision on whether to buy or sell any stock. In this blog, we will be building a forecasting technique for Amazon stock prices using 1 and 2 hidden-layer neural networks. We will evaluate and compare the performance of ANN with the traditional SVM model.

Customer churn is the term used to describe customers who might stop using the company's products or services. Netflix, telecom, or other subscription-based companies use machine learning algorithms to predict the customer churn rate. In this article, we have used a decision tree to build a machine learning model to predict customer churn rate and achieved an accuracy of 83%.

Artificial Neural Networks (ANNs) have the potential to benefit agriculture by providing accurate predictions of crop yield. Such predictions can be helpful for farmers, allowing them to estimate their profits more precisely. In this blog, we will build an ANN model for crop yield prediction and analyze the impact of rainfall, temperature, and pesticide usage on crop production.

In machine learning and data science, API (Application Programming Interface) is a powerful tool that enables seamless communication and data sharing between applications and servers. APIs are mainly used for data gathering and model deployment In data science and ML. This blog provides a step-by-step explanation of how APIs work.

Jupyter Notebook is a popular open-source IDE for data science and machine learning practitioners. It supports three programming languages: Julia, Python, and R. In this guide, we cover the installation process for Jupyter Notebook, as well as provide an overview of the basic steps for starting the server, creating notebooks, executing cells, and working with kernels.

Cancer classification is one area where machine learning can deliver a robust predictive model based on given observations to identify the cancer possibility. In this article, we have built a cancer classification model using ml to predict the presence of malignant (cancer-causing cells) or benign cells using a support vector classifier model.

In recent years, the quantity of spam emails has decreased significantly due to spam detection and filtering software. In this, We have covered these concepts: 1) Methods to segregate incoming emails into the spam or non-spam categories? 2) Steps to implement a spam classifier using the k-NN algorithm. 3) Real-life use case of Gmail, Outlook, and Yahoo.

Character recognition is a primary step in recognizing whether any text or character is present in the given image using machine learning. Google, Microsoft, and many more technical giants use optical character recognition (OCR) techniques to solve various tasks, including spam classification, automatic reply, number-plate detection, etc.

To detect whether player is genuine or false, BGMI (PUBG) uses a state-of-the-art machine learning approach to predict the presence of cheaters. It collects players' data, draws meaningful results, and categorizes cheaters into separate categories. They use a supervised learning approach to predict the occurrence of impossible events.

Machine Learning and Data Science have become vital in developing life-saving applications, such as drug discovery. Using these ML technologies, researchers can reduce the time needed to create new medicines for known and unknown diseases. In this blog post, we provide a step-by-step guide to building an application that addresses the drug discovery problem using Machine Learning in Python. To solve this regression problem, we utilized the XGBoost regressor model, which is a popular CART algorithm in the ML community.

Customer segmentation is a machine learning application that involves grouping customers based on similarities in their behaviour. This unsupervised learning technique helps companies create customer groups for targeted marketing. One way to group customers is through hierarchical clustering, which can be visualized using dendrograms. In this blog post, we will demonstrate how to implement hierarchical clustering using Python.

Prediction of wine quality can be made easy with machine learning and data science techniques. In this blog, we have discussed: 1) Why do we need a machine learning model for wine quality assessment? 2) Factors that affect wine quality 3) Various ML models to predict wine quality 4) Implementation of predicting wine quality using k-NN regressor

Learn to build a music recommendation system using the k-means algorithm. We will use the audio features from the million song data and cluster them based on their similarities. In this blog, we will be discussing these topics: 1) Methods to build a recommendation system for songs 2) Step-wise implementation 3) Ordering songs for the recommendation, etc.

In this blog, we will build an image data compressor using an unsupervised learning technique called Principal Component Analysis (PCA). We will be discussing image types and quantization, step-by-step Python code implementation for image compression using PCA, and techniques to optimize the tradeoff between compression and the number of components to retain in an image.

Sentiment analysis is a technique that comes under natural language processing(NLP) and is used to predict emotions reflected by a word or a group of words. Sentiment analysis is instrumental in brand monitoring, market research, social media monitoring, etc. This blog will discuss naive bayes to predict sentiments using their tweets.

Uber ride prices are not constant like public transport. We might have observed such variations while using the cab service. To calculate this variation, Uber uses a Machine Learning-powered Surge Pricing algorithm. In this article, we will build a machine learning model to predict the serge multiplier based on different weather conditions.

This blog will guide you through steps of detecting fraudulent transactions performed on credit cards by developing a machine learning model. Several classification algorithms can perform best and are easily deployable, like support vector machines, logistic regression, etc. In this blog, we use random forest classifier to build fraud detector.

Time series forecasting uses statistical models to predict future values using previously recorded observations. It is classified into two parts: 1) Univariate time series forecasting (Involves a single variable) 2) Multivariate time series forecasting (Involves multiple variables). Note: Time Series is a set of observations taken at a specific periodic time.

Machine learning techniques can accurately predict the house price by considering all important features. In this blog, we have discussed: 1) Why do we need machine learning models for house price prediction? 2) What are the factors that affect house prices? 3) Implementation of predicting house prices using Support Vector Regressor

In this blog, you will learn classification of movie reviews into positive and negative review categories using sentiment analysis. We have also discussed: 1) Use case of sentiment analysis? 2) Data analysis for the IMDB movie review dataset 3) Steps of text or data processing, including tokenization, lemmatization, word embedding, etc.

Machine Learning has become a tool used in almost every task that requires estimation. Companies like Cars24 and Cardekho.com uses Regression analysis to estimate the used car prices. So we need to build a model to estimate the price of used cars. The model should take car-related parameters and output a selling price.

Using machine learning, we can predict the life expectancy of a person. In this blog, we will explore parameters affecting the lifespan of individuals living in different countries and learn how life span can be estimated with the help of machine learning models. We will also focus on the application of linear regression in predicting life expectancy.

Machine learning can predict personalities based on social media usage. This is highly used in dating apps and recommendation systems. In this blog, we have discussed: 1) How personality prediction is useful? 2) Big five personality trait model 3) How ML predicts personality based on social media behavior? 4) Steps to implement personality predictor.

Experts have tried several ways to predict soil fertility using machine learning methods. But classification algorithms have proven sufficient accuracy to deal with such a problem. ML algorithms like k-NN, DTs, SVM, and random forests have been used for different case studies. In this blog, we have implemented this using gradient boosting.

Recommender System is a system that could predict the future preference for a user based on their previous behavior or by focusing on the behavior of similar users. In this blog, we have discussed: 1) What is a recommender system? 2) Their various types 3) Implementation of content-based recommender system 4) Various use cases in industry.