Data analysis and model training to predict whether mushrooms are poisonous or edible
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Updated
Aug 25, 2024 - Jupyter Notebook
Data analysis and model training to predict whether mushrooms are poisonous or edible
Clasification model of positive and negative reviews using NLP
Discover a comprehensive approach to constructing credit risk models. We employ various machine learning algorithms like LightGBM and CatBoost, alongside ensemble techniques for robust predictions. Our pipeline emphasizes data integrity, feature relevance, and model stability, crucial elements in credit risk assessment.
This is a triage prediction model that classifies security threat based on the level of criticality.
Water potability classifier using simple logistic regression to advanced gradient boosting algorithms
This project utilizes advanced data analysis and machine learning techniques to predict equipment failures before they occur. The goal is to detect anomalies and possible defects in equipment and processes to enable preemptive maintenance, thereby reducing downtime and costs.
Evaluating Hyperopt, Optuna, and TunedThresholdClassifierCV
We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.
The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be a fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.
This project develops an advanced predictive model to identify thyroid disease recurrence using machine learning algorithms. We used a detailed dataset with demographic, medical, and clinical features, and implemented Logistic Regression, Decision Tree, Random Forest, and CatBoost Classifier. Rigorous preprocessing and EDA were performed.
This project predicts MBTI personality types from users' recent 50 posts using NLP and ML techniques.
Bank Churn Classification
Дипломная работа физического факультета СПбГУ
bank marketing prediction for a term deposit campaign, identify non-potential customers
A Domestic violence support system for the victims, that enables users to share their thought and provides knowledge about the particular type of abuse they are going through.
ML competition submission to classify anonymous age related condition
Ad huc solution for anomaly classification of HTTP requests between service and end-user based on limited data / Решение задачи поиска аномальных HTTP запросов (их классификации) к сервису.
Credit_Card_Approval_odinschool_project
The purpose is to train a predictive model that can determine if a given customer will subscribe to a term deposit based on these various features. By analyzing historical data on successful and unsuccessful subscription outcomes, patterns can be identified which help predict future subscription behavior.
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