Improving breast cancer prediction through fuzzy rule-based reasoning.
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Updated
Apr 21, 2024 - R
Improving breast cancer prediction through fuzzy rule-based reasoning.
Say NO to Myocardial Infarcation! 💖 (BC2406)
Prediction of Heart Attacks Using Different algorithms in R.
Leverage external data and non-traditional methods to accurately assess and shortlist candidates with the relevant skillsets, experience and psycho-emotional traits, and match them with relevant job openings to drive operational efficiency and improve accuracy in the matching process
🌳 Predicting a wildfire's severity using classification models
Leverage external data and non-traditional methods to accurately assess and shortlist candidates with the relevant skillsets, experience and psycho-emotional traits, and match them with relevant job openings to drive operational efficiency and improve accuracy in the matching process
Final Project for Higher Diploma in Data Analytics at National College of Ireland
The data at hand is of flight satisfaction survey along with the customer flight information, the task at hand is to build a model that predicts satisfaction/dissatisfaction given the various attributes
The objective of the project is to create a machine learning model. We are doing a supervised learning and our aim is to do predictive analysis to predict housing price.
A data set of 30000 records and 24 variables containing information on defaults, demographic factors, credit data, delinquency, repayment and billed amounts of a credit card client in Taiwan from April 2005 to September 2005. The objective was to apply statistical, data visualization and Machine learning techniques (supervised and unsupervised) …
It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Here, the aim is to analyze the dataset and detect the fradulent transactions.
The objective of the project is to create a machine learning model. We are doing a supervised learning and our aim is to do predictive analysis to predict median housing price.
Statistical analysis on calcium concentration during dialysis
Initial text mining exercise was performed on a dataset of Shark tank episodes with 495 entrepreneurs making their pitch to VCs. Used that to build multiple models (CART, Random Forest, Logistic Regression) to predict keywords which have an impact on striking a deal.
The objective of this exercise was to build a model using a Supervised learning technique to figure out profitable segments to target for cross-selling personal loans. A Pilot campaign data of 20000 customers was used which included several demographic and behavioral variables. The Model was further validated and a deployment strategy was recomm…
Develop a logistic regression model and a CART model to predict diabetes outcome
Build a model that will help them identify the potential customers who have a higher probability of purchasing the loan.
Multivariate linear regression, CART and Random Forest dataset analysis
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