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prediction-and-analysis-of-degree-of-suicidal-ideation-in-online-content-using-machine-learning

his project is an implementation of Thesis - Prediction and Analysis of Degree of Suicidal Ideation in Online Content by Noah C. Jones B.Sc., Morehouse College (2017) Submitted to the Program in Media Arts and Sciences, School of Architecture and Planning in partial fulfillment of the requirements for the degree of Master of Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 20220 This project has been completed as a mini project under Machine Learning Course under the guidance of Dr. Akshay Deepak , Assistant professor , Computer Science & Engineering Department , NIT Patna.

prediction-of-diabetes-induced-complications-using-different-machine-learning-algorithms

Bachelor of Science Final Thesis

printf

Group Project For The ALX Project With Chigozie Maduka

prodclass-thesisproject

Undergraduate Thesis Project using NLP

project-portfolio-april-2023

My Portfolio Of Data Analytics Project

python

All Algorithms implemented in Python

python-project-based-learning

Curated list of project-based tutorials

pythondataanalysis

Python data repo, jupyter notebook, python scripts and data.

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

rain-fall_data_analysis_using_data_science

Context Rainfall is very crucial things for any types of agricultural task. Climate related data is important to analyse agricultural and crop seeding related field, where those data can be used to show the predict the rainfall in different season also for different types of crops. Developed application can be found from http://ml.bigalogy.com/ Paper: http://dspace.uiu.ac.bd/handle/52243/178 Abstract Mankind have been attempting to predict the weather from prehistory. For good reason for knowing when to plant crops, when to build and when to prepare for drought and flood. In a nation such as Bangladesh being able to predict the weather, especially rainfall has never been so vitally important. The proposed research work pursues to produce prediction model on rainfall using the machine learning algorithms. The base data for this work has been collected from Bangladesh Meteorological Department. It is mainly focused on the development of models for long term rainfall prediction of Bangladesh divisions and districts (Weather Stations). Rainfall prediction is very important for the Bangladesh economy and day to day life. Scarcity or heavy - both rainfall effects rural and urban life to a great extent with the changing pattern of the climate. Unusual rainfall and long lasting rainy season is a great factor to take account into. We want to see whether too much unusual behavior is taking place another pattern resulting new clamatorial description. As agriculture is dependent on rain and heavy rainfall caused flood frequently leading to great loss to crops, rainfall is a very complex phenomenon which is dependent on various atmospheric, oceanic and geographical parameters. The relationship between these parameters and rainfall is unstable. Beside this changing behavior of clamatorial facts making the existing meteorological forecasting less usable to the users. Initially linear regression models were developed for monthly rainfall prediction of station and national level as per day month year. Here humidity, temperatures & wind parameters are used as predictors. The study is further extended by developing another popular regression analysis algorithm named Random Forest Regression. After then, few other classification algorithms have been used for model building, training and prediction. Those are Naive Bayes Classification, Decision Tree Classification (Entropy and Gini) and Random Forest Classification. In all model building and training predictor parameters were Station, Year, Month and Day. As the effect of rainfall affecting parameters is embedded in rainfall, rainfall was the label or dependent variable in these models. The developed and trained model is capable of predicting rainfall in advance for a month of a given year for a given area (for area we used here are the stations (weather parameters values are measured by Bangladesh Meteorological Department). The accuracy of rainfall estimation is above 65%. Accuracy percentage varies from algorithm to algorithm. Two regression analysis and three classification analysis models has been developed for rainfall prediction of 33 Bangladeshi weather station. Apache Spark library has been used for machine library in Scala programming language. The main idea behind the use of classification and regression analysis is to see the comparative difference between types of algorithms prediction output and the predictability along with usability. This thesis is a contribution to the effort of rainfall prediction within Bangladesh. It takes the strategy of applying machine learning models to historical weather data gathered in Bangladesh. As part of this work, a web-based software application was written using Apache Spark, Scala and HighCharts to demonstrate rainfall prediction using multiple machine learning models. Models are successively improved with the rainfall prediction accuracy. Content The given data has weather station and year wise monthly rainfall data of Bangladesh. Data is two format - 46 year (33 Weather Station) : From 1970 to 2016 Daily Rainfall Data Monthly Rainfall Data Columns: Station (Weather Station, along with Station Index) Year Month Day [For daily data file]

recommendersystems_thesis

Recommender Systems is a subject that has occupied the business and research world to a great extent. It is a widely used technology based on methods of machine learning and information retrieval. Recommender Systems, starting in 1995, have developed rapidly in terms of the variety of problems they face, the techniques they use and their practical applications. Such implementations can be found on very popular online systems such as Netflix, Amazon, Pandora and many more. The majority of Recommender Systems are based on the Collaborative Filtering technique. The Collaborative Filtering technique is a process of filtering or evaluating items using the opinions of other users and is based on the assumption that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person. Such methods have greatly occupied the research world and therefore the amount of techniques and algorithms that have been developed around this field is great. Specifically in this dissertation we conduct our study on Recommender Systems in social networks. A social network is a set of users who, in addition to interacting with objects, also develop interactions with each other. Our study and experiments are carried out in such systems where the source from which we derive information for the provision of recommendations, extends beyond the user ratings to items, to the relationships that users have developed with each other. In addition, we use techniques known as Link Prediction which are suitable for evaluating similarity between users within a graph, in order to enrich our data.

retail_churn_prediction

To predict customer churn by analyzing customer data collected from survey and customer buying behavior pattern

retail_consumer_analytics

Data-driven insights into past & predicted future consumer behavior : Customer Segmentation & Customer Churn Prediction

roadmap-to-learn-generative-ai-in-2024

scdusingka

Skin cancer is one of the most common types of human malignancy in medical sec-tor. Normally, it is being diagnosed visually starting with an initial clinical screeningand then possibly followed by dermoscopic analysis, a biopsy and histopathologicalexamination. Application of machine learning is continuously being used to deter-mine the accuracy of detecting different medical problems more effectively. A lotof new techniques have been discovered to fast forward the procedure with havinghighest percentage of accuracy. In this thesis work, we have proposed a model todetect skin cancer more effectively using image processing with convolutional neu-ral network, a part of deep learning concept under machine learning. The datasetcontains almost 3000+ images of the patients having skin diseases classified intotwo classes, malignant and benign. We have introduced CNN along with its sevendifferent architectures to find the accuracy of the images of skin cancer and per-formed a comparative analysis to find out the best architecture that suits this typeof problem. Among all the architectures Xception performed the best results withhaving almost 85.303% accuracy to determine skin cancer. Furthermore a new model was introduced which suppress the accuracy of predefined models.

sentiment-analysis-of-movie-reviews

This is a thesis report on Sentiment Analysis of movie reviews using Machine Learning algorithms such as KNN, Naive Bayes, Random Forest and SVM, and Deep Learning algorithm, BERT.

sentiment_analysis

Master Thesis UC3M

sentiment_analysis_project

code from master thesis on sentiment analysis

simple_shell

ALX Group Project with Chigozie Maduka

skin-cancer-classification

This project aims to use a convolutional neural network (CNN) to classify 7 classes of skin lesions.

skin-cancer-classification-1

CS412 - Machine Learning Course Project

skincancer

Skin cancer detection project

software-engineering-101

🧑‍💻 WELCOME TO SOFTWARE ENGINEERING 101! A complete set of road maps, tutorials, guides, exams, projects and etc. to becoming a software engineer!

software-engineering-books

Fork this repo to access these books easily

software-engineering-data-it-careers-roadmap

Descripting the Fundementals Path for Modern IT jobs

supermarket-data-analysis

This is a business intelligence project on analyzing super market data. Check out the README file for more details.

terrorist-classification

An integrated machine learning approach for classification of global terrorist activity - Yale Senior Thesis

thesis-code

Detecting fraud and corruption in international development projects using an automated machine learning model

thesis-fake-news-detection

Thesis project concerning classification of true and fake political, gossip world news using Machine Learning and Deep Learning Techniques

thesis-fraud