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Top 50 Data Science Projects

Top 50 Data Science Projects

What is Data Science

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Top 50 Data Science Projects are insights based on numbers, statistics, and trends from data that are used to make decisions towards achieving a specific business goal.

Top 50 Data Science Projects

  1. Student Placement Prediction using Machine Learning
  2. Text Summarization using NLP | ML
  3. Heart Disease Deduction using Big Data using ML
  4. Employee Attrition using Machine learning
  5. Smart Farming using Machine Learning Algorithm
  6. Bitcoin Price prediction using Machine Learning
  7. Churn Modelling Analysis using deep Learning | Machine learning
  8. Diabetes Prediction using Machine Learning
  9. KDD & Data Mining Approach for Finding Network Attack using ML
  10. Cyber threat Analysis on Android Apps using Machine Learning
  11. Student perform Prediction using Machine Learning
  12. Hashtag Clustering using NLP | Machine Learning
  13. Tkinter Chatbot Application using NLP
  14. Rainfall Prediction using Machine Learning
  15. Credit Card Fraud Detection using Deep Learning
  16. Fake News Detection using Machine Learning
  17. Fake Profile Identification using Machine Learning
  18. Student Feedback Classification using Random Forest with ML
  19. Liver Disease Prediction using Machine Learning
  20. Loan Approval Prediction using Machine Learning
  21. Hate Speech Detection using Machine Learning
  22. Ground Water level Prediction using Machine learning
  23. Road Accident Analysis and Classification using Machine Learning
  24. Human Activity Recognition using Machine Learning
  25. Crime Analysis using Machine Learning
  26. Intrusion Detection using Machine learning
  27. ML Model to Improve learning Process and Reduce Droupout Rates
  28. ML Based Opinion Mining Online Customer Review
  29. Detection of Distributed Service Attacks in SDN using ML
  30. Data Poison Detection using Machine Learning
  31. Detection Phishing Attacks using NLP and Machine Learning
  32. Prediction of Election Result Based on Twitter Data
  33. Ransomware Detection and Classification using Machine Learning
  34. Netflix Stock Market Prediction using Machine learning
  35. Estimation of Power consumption for Electrical Appliances using ML
  36. Crime Detection and Classification using Fuzzy logic techniques
  37. Agriculture Price Prediction of  using Machine Learning
  38.  Failure Prediction of Machineries using Machine Language
  39. Diet Recommendation System using ML
  40. Math Learning for ADHD Personality using Machine Learning
  41. Early Disease Perdition using Machine Learning
  42. Prediction and Classification of COVID-19 Death and Recovered cases using ML
  43. Hotel Review Rating Classification using NLP
  44. Election Results prediction Based on Twitter Data
  45. Arabic Natural Language Processing
  1. Student Placement Prediction using Machine Learning

The main objective of this project is to analyse previous year’s student’s historical data and predict placement possibilities of current students and aids to increase the placement percentage of the institutions using Machine Learning Algorithms.

 

  1. Text Summarization using NLP | Machine Learning

The objective of the project is to understand the concepts of natural language processing and create a tool for text summarization. The concern in automatic summarization is increasing broadly so the manual work is removed. The project concentrates on creating a tool that automatically summarizes the document.

  1. Heart Disease Deduction using Big Data using ML

In this project, we create a model to do the accurate prediction of heart disease problems in health care applications. Easier to analyse the scalable of health care big data. Less time consumption with the efficiency of data in heart disease. High performance in data maintained of heart disease prediction.

  1. Employee Attrition using Machine learning

A large number of employees work in a company. There are various factors that affect the number of employees working in a company. One essential aspect we need to consider is that we need to retain potential employees in an organization.

  1. Smart Farming using Machine Learning Algorithm

In this concept, we create Machine Learning Model for Smart Farming. Smart Farming Prediction and the recommendation can be made using Space Vector Modulation Classification and Neural Network Algorithm.

  1. Bitcoin Price prediction using Machine Learning

The Main objective of this project to predict the bitcoin using Machine Learning Algorithms. Two of the models are based on gradient boosting decision trees and one is based on long short-term memory (LSTM) recurrent neural networks. In all cases, we build investment portfolios based on the predictions and we compare their performance in terms of return on investment.

  1. Churn Modelling Analysis using deep Learning | Machine learning

Churn Analysis is one of the worldwide used analyses on Subscription Oriented Industries to analyse customer behaviours to predict the customers which are about to leave the service agreement from a company. The proposed model rst classies churn customers data using classi?cation algorithms, in which the Random Forest (RF) and Decision tree (DT) algorithm performed well with 90.44% correctly classified instances.

  1. Diabetes Prediction using Machine Learning

The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs. It can be detected by developing an accurate prediction model which will be capable of automatic separation of various accidental scenarios. The cluster will be useful to prevent accidents and develop safety measures.

  1. KDD & Data Mining Approach for Finding Network Attack using ML

The objective of the project to find the Network attacks using KDD Datas and Data Mining Approach.

Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data.

10.Cyber Threat Analysis on Android Apps using Machine Learning

To prevent malware attacks, researchers and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.? We can analyse cyber threats using two techniques, static analysis, and dynamic analysis, the most important thing is that these are the approaches to get the features that we are going to use in data science.

11.Student Performance Prediction using Machine Learning

The proposed framework focuses on merging the demographic and study related attributes with the educational psychology fields, by adding the student’s psychological characteristics. After surveying, we picked the most relevant attributes based on their rationale and correlation with the academic performance. posing users to browser-based vulnerabilities.

  1. Hashtag Clustering using NLP | Machine Learning

We apply the ML model on datasets like Twitter, Flickr, and YouTube. It will predict a similar type of hashtag with a detailed description. Unsupervised word embedding methods train with a reconstruction objective, in which the embedding is used to predict the original text.

  1. Tkinter Chatbot Application using NLP

The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.

  1. Rainfall Prediction using Machine Learning

In this paper contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. Published papers from year 2013 to 2017 from renowned online search libraries are considered for this research.

  1. Credit Card Fraud Detection using Deep learning

In the existing System, research about a case study involving credit card fraud detection, where data normalization is applied before Cluster Analysis and with results obtained from the use of Cluster Analysis and Artificial Neural Networks on fraud detection has shown that by clustering attributes neuronal inputs can be minimized.

  1. Fake News Detection using Machine Learning

The main objective is to detect fake news, which is a classic text classification problem with a straightforward proposition. It is needed to build a model that can differentiate between Real news and Fake news.

  1. Fake Profile Identification using Machine learning

This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)

  1. Student Feedback Classification using Random Forest with ML

This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)

  1. Liver Disease Prediction using Machine Learning

Liver diseases are becoming one of the most fatal diseases in several countries. Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. 

  1. Loan Approval Prediction using Machine Learning

The primary goal of this project is to extract patterns from a common loan-approved dataset, and then build a model based on these extracted patterns, in order to predict the likely loan defaulters by using classification data mining algorithms. The historical data of the customers like their age, income, loan amount, employment length etc. will be used in order to do the analysis.

  1. Hate Speech Detection using Machine Learning

This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. To Analysis of the language in the typical datasets to get hate speech by features in the ?long tail? in a dataset using Machine Learning.

  1. Ground Water level Prediction using Machine learning

Models for the prediction of water table depth were developed based on Artificial Neural Networks (ANN) with different combinations of hydrological parameters. The best combination was confirmed with factor analysis. The input parameters for groundwater level forecasting were derived using Time Series Analysis (TSA).

  1. Road Accident Analysis and Classification using Machine Learning

There is a huge impact on society due to traffic accidents where there are great costs of fatalities and injuries. In recent years, there is an increase in researches attention to determine the significant effect of the severity of the driver’s injuries which is caused due to road accidents. 

  1. Human Activity Recognition using Machine Learning

Nowadays, there is an ever-increasing migration of people to urban areas. Health care service is one of the most challenging aspects that is greatly affected by the vast influx of people to city centres. Consequently, cities around the world are investing heavily in digital transformation in an effort to provide healthier ecosystems for people. 

  1. Crime Analysis using Machine Learning

he objective of this project is to tackle a vital issue in society – Crimes. Analyzing and examining crimes happening in the world will give us a Broadview in understanding the crime regions and can be used to take necessary precautions to mitigate the crime rates.

  1. Intrusion Detection using Machine learning

Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data. An intrusion detection system (IDS) is a system that monitors and analyzes data to detect any intrusion in the system or network.

  1. ML Model to Improve learning Process and Reduce Droupout Rates

This Research to Practice Full Paper presents a systematic review of methodologies that propose ways of reducing the dropout rate in Virtual Learning Environments (VLE). This generates large amounts of data about courses and students, whose analysis requires the use of computational analytical tools. Most educational institutions claim that the greatest issue in virtual learning courses is high student dropout rates.

  1. ML Based Opinion Mining Online Customer Review

As the commercial side of the world is almost fully undergone in online platform people is trading products through the different eCommerce website. And for that reason reviewing products before buying is also a common scenario.

  1. Detection of Distributed Service Attacks in SDN using ML

A software-defined network (SDN) is a network architecture that is used to build, design the hardware components virtually. We can dynamically change the settings of network connections. In the traditional network, it’s not possible to change dynamically, because it’s a fixed connection.

  1. Data Poison Detection using Machine Learning

When no one node can produce accurate results in a reasonable amount of time, distributed machine learning (DML) can be used to train enormous datasets. However, in comparison to a non-distributed environment, this will necessarily expose more possible targets to attackers.

  1. Detection Phishing Attacks using NLP and Machine Learning

    The detection and mitigation of phishing attacks is a grand challenge due in the real world. There have been numerous studies on detecting and mitigating Phishing attacks. Phish Limiter is an effective and efficient solution to detect and mitigate phishing attacks with an accuracy of 98.39%.

  2. Prediction of Election Result Based on Twitter Data

Sentimental Analysis is a technique for teaching a computer to extract emotion from text. A text can be anything, whether a basic review, a social statement, tweets, or text messages. On digital platforms, a substantial amount of high-value and diverse social data has been accumulated. This large amount of social data might be computationally processed and analysed to learn about people’s preferences and affinities with any subject.

 34. Ransomware Detection and Classification using Machine Learning

The economic benefits and anonymity has fostered cybercriminals to perform continuous ransomware attacks in various sectors. These attacks are often delivered via phishing campaigns where a user is masqueraded with a seemingly genuine email with malicious links or attachments.

 35. Netflix Stock Market Prediction using Machine learning

Stock market prediction is the act of trying to determine the future value of a stock from social media Social media offers a robust outlet for people’s thoughts and feelings Analysis of social media is strongly related to sentiment analysis This is used to extract emotions and opinions from text Data mining methodologies like NLP, Random forest, Neural network is used for analyzing social network content and improves the average accuracy Recent analysis reveals the existence of attention-grabbing communication patterns among completely different participants of various social network platforms.

 36. Estimation of Power consumption for Electrical Appliances using ML

A non-nosy checking framework assesses the conduct of individual electric apparatuses from the estimation of the absolute family unit load request bend. The all-out burden request bend is estimated at the passageway of the electrical cable into the house. 

 37. Crime Detection and Classification using Fuzzy logic techniques

The objective of this project is to tackle a vital issue in society – Crimes. Analyzing and examining of crimes happening in the world will give us a Broadview in understanding the crime regions and can be used to take necessary precautions to mitigate the crime rates.

 38. Agriculture Price Prediction of  using Machine Learning

Agriculture creates an economic future for developing countries, the demand for modern technologies in this sector is higher. Key technologies used for this problem are Deep Learning, Machine Learning, and Visualization. 

 39. Failure Prediction of Machineries using Machine Language

Given the large deployment of high-speed railway (HSR) systems, as well as the growing popularity of highway vehicular communications systems and low-altitude flying object (LAFO) systems, wireless communications in high-mobility situations have gotten a lot of attention in recent years.

  40. Diet Recommendation System using ML

A recommendation system for patients/dieticians is a system that watches a user (patient/dietician) in a tailored approach towards remarkable or acceptable diets or food intake in a broad variety of possible options, and that produces the desired output. A patient/dietician recommendation system is carefully implemented with the goal of encouraging patients to adopt nutritional supplements, diets, and foods that are better suited to their health needs, taste, and dietary preferences.

 41. Math Learning for ADHD Personality using Machine Learning

Big Data & Data Science Projects

42.Early Disease Perdition using Machine Learning

Machine Learning techniques are used for a variety of applications. In the healthcare industry, Machine Learning plays an important role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using the Machine Learning technique the number of tests can be reduced. This reduced test plays an important role in time and performance.

43. Prediction and Classification of COVID-19 Death and Recovered cases using ML

COVID-19, Corona Virus Disease-2019, caused by a novel Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). Effective screening of this virus can enable quick and efficient diagnosis of COVID-19 can reduce the burden on the healthcare system. Detailed analysis on the provided dataset can build different and various types of machine learning algorithms, which their performance could be computed and further evaluated. In the following case, Random Forest outperformed all the other Machine Learning models like SVR, Xgboost models.

44. Hotel Review Rating Classification using NLP

Sentiment Analysis as the name suggests is a machine learning technique that allows machines to read through human emotions. Allowing machines to read and understand human emotions and extract useful insights through them is a vital resource for many businesses to grow and develop in their field.

45. Election Results prediction Based on Twitter Data

Sentiment Analysis probes public opinion on user-generated content on Web like blogs, social media or e-commerce websites. The results of Sentiment Analysis are getting much attention with marketers that they are able to evaluate the success of an advertising campaign or the attitude of people on a new product launch.

46. Arabic Natural Language Processing

Arabic is a Semitic language spoken by more than 330 million people as a native language, in an area extending from the Arabian/Persian Gulf in the East to the Atlantic Ocean in the West. Moreover, it is the language in which 1.4 billion Muslims around the world perform their daily prayers.

 

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