EDA using Pandas Profiling | YData Profiling Part 2
🚀 Welcome back to Part 2 of our ydata_profiling video! In this video, we dive into a new case study using the Google Play Store dataset from Kaggle.
Dataset Link - https://www.kaggle.com/datasets/gauthamp10/google-playstore-apps
📊 We'll demonstrate how ydata_profiling can be used to generate a profile report for this dataset, showcasing its powerful features and capabilities.
🔍 One of the highlights of this video is the use of word clouds generated by ydata_profiling for object-type features with too many categories. These word clouds provide a visual representation of the most common words in these features, offering valuable insights into the dataset at a glance.
📈 Additionally, we'll explore a new feature called "compare," which allows us to compare the profile reports of the train and test sets of a dataset. This feature makes it easy to observe the differences in the properties of the two sets, helping us gain a deeper understanding of the dataset's characteristics.
🔥 Whether you're a data enthusiast looking to explore new datasets or a seasoned data scientist seeking innovative tools for data analysis, this video is packed with valuable information and insights.
📝 Subscribe to our channel for more tutorials on cutting-edge data analysis tools and techniques.
Видео EDA using Pandas Profiling | YData Profiling Part 2 канала Six Sigma Pro SMART
Dataset Link - https://www.kaggle.com/datasets/gauthamp10/google-playstore-apps
📊 We'll demonstrate how ydata_profiling can be used to generate a profile report for this dataset, showcasing its powerful features and capabilities.
🔍 One of the highlights of this video is the use of word clouds generated by ydata_profiling for object-type features with too many categories. These word clouds provide a visual representation of the most common words in these features, offering valuable insights into the dataset at a glance.
📈 Additionally, we'll explore a new feature called "compare," which allows us to compare the profile reports of the train and test sets of a dataset. This feature makes it easy to observe the differences in the properties of the two sets, helping us gain a deeper understanding of the dataset's characteristics.
🔥 Whether you're a data enthusiast looking to explore new datasets or a seasoned data scientist seeking innovative tools for data analysis, this video is packed with valuable information and insights.
📝 Subscribe to our channel for more tutorials on cutting-edge data analysis tools and techniques.
Видео EDA using Pandas Profiling | YData Profiling Part 2 канала Six Sigma Pro SMART
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
The A to Z of Logistic Regression | All that you need to know | Supervised Learning | Data ScienceThe A to Z of Feature Encoding | Label Encoding | One Hot Encoding | Data Preprocessing in PythonEnsemble Machine Learning Technique: VotingHands-on Hyperparameter Tuning | Grid SearchImportance of Weight Initialization in Neural Networks | Deep Learning basicsComplete guide to hands-on A/B Testing | A/B testing in Python | All that you need to knowANOVA: Behind the scenes | Uncover the logic | Hypothesis Testing | Data ScienceThe A to Z of Support Vector Machines | All you need to know | Supervised Learning | Data ScienceThe journey of a neuron | Geometric intuitionText Preprocessing | Case Conversion | Unwanted Patterns | Stopwords | Stemming | LemmatizationGetting started with Neural NetworksHands-on Text Preprocessing in Python Part 2 | Natural Language Processing basicsPython: Regular Expressions for beginnersVisual Explanation | Solved Example | Paired t test | Dependent Samples t test | Hypothesis TestingThe journey of a neuron | The PerceptronGetting the best results from your ML models | Youden's IndexHands-on Feature Selection in Python | Choose just the right features for your model | Data ScienceWhat is Point-Biserial correlation? | Theory + Hands-on | All that you need to knowConfusion Matrix | Classification Accuracy Precision Recall | Supervised Machine LearningStep-by-step guide to TF IDF | Natural Language Processing basicsThe A to Z of Linear Regression | All that you need to know | Supervised Learning | Data Science