There are 0 repository under cross-validation-score topic.
Using scikit-learn RandomizedSearchCV and cross_val_score for ML Nested Cross Validation
I developed a sophisticated ML model using LLMs to predict user preferences in chatbot interactions.implemented a comprehensive data preprocessing pipeline,including feature extraction and encoding,to optimize performance. conducted extensive hyperparameter tuning and evaluation, enhancing accuracy and in AI-driven conversational systems.
Using various supervised learning estimators in Sci-Kit Learn to get the best prediction accuracy if possible for the pima indians dataset.
Predicts early diabetes risk using SMOTE for balancing and KNN for classification.
Classifies e-commerce user intent to purchase using ML models on session data.
Built Random Forest classifier from scratch on top of Scikit Learn decision trees. Using Scikit Learn to create data cleaning pipelines, perform grid searches for hyper parameter tuning, and decision tree modeling
Iris dataset
This is a Kaggle Dataset where we classify the cars using their various features. Here I used plotly to visualize the Accuracy Scores. Also I used CrossValScore to get More accurate Accuracy Score.
Exploring a music dataset by examining correlations between numerical variables, running a principal component analysis for dimensionality reduction and finally fitting both scikit learn Decision Tree Classification and Logistic Regression models to compare their performance.
This project analyzes and predicts apartment rental prices in Manhattan using machine learning techniques. The dataset is sourced from StreetEasy and contains various features about rental listings, such as the number of bedrooms, bathrooms, square footage, amenities, and proximity to the subway.
In this project, I have developed a Machine Learning model to predict whether users will click on ads. By analyzing various characteristics of users who click on ads, we can gain valuable insights and optimize ad campaigns for better engagement.
A Linear Regression project to analyze and predict sales based on TV, Radio, and Newspaper advertisement budgets.
K Nearest Neighbours in Python
Model-Validation-Methods
Machine learning model which can predict the strength of a mixture for given composition of ingredients like cement, slag, ash, water, superplastic, coarse aggregates, fine aggregates, age.
Project compares three regression models for predicting the amount of gold recovered from gold ore in order to optimize gold production and eliminate unprofitable parameters. Data provided by Zyfra.
This project focuses on detecting and classifying brain tumors (NonDemented, Very Mild Demented, Mild Demented, Moderate Demented)
In this classification i got up with best model for the iris flower data set and i used to code and made my own classification model. tried to visualize dataset and perform train test split as test data and train data by this i came up on Understanding the Evaluation metrics and accuracy score which given Understanding the hands on implementation of AI concepts theoretically learned.
Study Project for Yandex Practicum
GridSearchCV, RandomSearchCV For Model optimization and Saving/Loading the model
GridSearchCV For Model optimization
Overcoming overfitting and underfitting
A machine learning project that predicts car prices based on a dataset.
Prediction Model, Bias and Uncertainty
Zyfra is engaged in developing efficient solutions for heavy industry. as a Data Scientist we should be able to predict the amount of gold extracted or recovered from gold ore.
Model to predict the amount of gold extracted from gold mineral.
This folder contains project assignments that solve the problem of a case based on a dataset with hypothesis testing, Supervised and Unsupervised material.
Create a prototype for a machine learning model to predict the amount of gold recovered from gold ore.
A model which can predict if the customer will pay the loan or not.
Different types of supervised learning models used for classification problem. Included cross validation for finding hyperparameters whenever necessaruy.