Pickl AI

MARCH 26, 2024

Experimentation and cross-validation help determine the dataset’s optimal ‘K’ value. Distance Metrics Distance metrics measure the similarity between data points in a dataset. Cross-Validation: Employ techniques like k-fold cross-validation to evaluate model performance and prevent overfitting.

K-nearest Neighbors 52

K-nearest Neighbors Machine Learning Machine Learning Algorithm 52

Towards AI

JULY 19, 2023

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. In our exercise, we will try to deal with this imbalance by — Using a stratified k-fold cross-validation technique to make sure our model’s aggregate metrics are not too optimistic (meaning: too good to be true!)

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Mlearning.ai

JANUARY 29, 2024

After doing all these cleaning steps data looks something like this: Features after cleaning the dataset Exploratory Data Analysis Through the data analysis we are trying to gain a deeper understanding of the values, identify patterns and trends, and visualize the distribution of the information.

Decision Trees 52

Decision Trees Data Science Exploratory Data Analysis Data Analysis 52

DataRobot

DECEMBER 20, 2021

Submit Data. After Exploratory Data Analysis is completed, you can look at your data. Just like for any other project, DataRobot will generate training pipelines and models with validation and cross-validation scores and rate them based on performance metrics. Configure Settings You Need.

Clustering 97

Clustering Deep Learning Deep Learning Exploratory Data Analysis 97

Pickl AI

AUGUST 29, 2023

Data Scientists are highly in demand across different industries for making use of the large volumes of data for analysisng and interpretation and enabling effective decision making. One of the most effective programming languages used by Data Scientists is R, that helps them to conduct data analysis and make future predictions.

Data Scientist 52

Data Scientist Clustering Data Analysis Data Analysis 52

Dataconomy

JUNE 29, 2023

They assist in data cleaning, feature scaling, and transformation, ensuring that the data is in a suitable format for model training. It is commonly used in exploratory data analysis and for presenting insights and findings.

Machine Learning 113

Machine Learning Machine Learning Deep Learning Deep Learning 113

Mlearning.ai

FEBRUARY 15, 2024

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Photo by Unsplash Earlier we saw how to collect the data and how to perform exploratory data analysis. Now comes the exciting part ….

Data Science 52

Data Science Decision Trees Exploratory Data Analysis Cross Validation 52

Mlearning.ai

MARCH 15, 2023

Data storage : Store the data in a Snowflake data warehouse by creating a data pipe between AWS and Snowflake. Data Extraction, Preprocessing & EDA : Extract & Pre-process the data using Python and perform basic Exploratory Data Analysis. The data is in good shape.

Python 52

Python AWS Exploratory Data Analysis EDA 52

DrivenData Labs

APRIL 10, 2024

Summary of approach: In the end I managed to create two submissions, both employing an ensemble of models trained across all 10-fold cross-validation (CV) splits, achieving a private leaderboard (LB) score of 0.7318.

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

DataRobot Blog

DECEMBER 20, 2022

You can understand the data and model’s behavior at any time. Once you use a training dataset, and after the Exploratory Data Analysis, DataRobot flags any data quality issues and, if significant issues are spotlighted, will automatically handle them in the modeling stage. Rapid Modeling with DataRobot AutoML.

AI 59

AI AI Cross Validation Machine Learning 59

PyImageSearch

JANUARY 23, 2023

Applying XGBoost to Our Dataset Next, we will do some exploratory data analysis and prepare the data for feeding the model. unique() # check the label distribution lblDist = sns.countplot(x='quality', data=wineDf) On Lines 33 and 34 , we read the csv file and then display the unique labels we are dealing with.

Deep Learning 107

Deep Learning Deep Learning Algorithm Decision Trees 107

Heartbeat

MAY 29, 2023

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78