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Text preprocessing involves cleaning and transforming raw text data into a format that is more suitable for analysis and machine learning

Explore GloVe word embedding for NLP in Python. Learn feature extraction, transforming words into vector representations. #GloVe #Python

Word2Vec is a NLP technique for learning word embeddings which are dense numerical representations of words in a continuous vector space

TF-IDF is a numerical representation often used in NLP and information retrieval to evaluate the importance of a word in a document

A bag-of-words is a representation of text that describes the occurrence of words within a document.

Explore Dimensionality Reduction: PCA vs LDA vs t-SNE vs UMAP in Python. Compare techniques to transform high-dimensional data.

Boost model performance with Ensemble Techniques in Python. Explore bagging, boosting, & stacking to create powerful machine learning models

Handling imbalanced classes involves applying techniques such as resampling, class weighting, and algorithm selection to mitigate the bias.

Filling missing values in a dataset is an essential step in data preprocessing to ensure the accuracy and reliability of your analysis.

Detecting and removing outliers is an important step in data analysis and can help improve the accuracy of statistical models.

Mean encoding, also known as target encoding, is a technique used to encode categorical attributes in machine learning models

Recursive Feature Elimination (RFE) is a feature selection technique used to select the most relevant features from a given dataset

Chi-square is a statistical method used to identify and select the most relevant features or variables from a dataset

The correlation matrix measures the linear relationship between pairs of features in a dataset. A correlation value ranges from -1 to 1.

Normalizing data is a common preprocessing step in machine learning. Commonly used normalization methods are Max Absolute and MinMax Scaling