PCA Insights is a data analysis project aimed at applying Principal Component Analysis (PCA) to high-dimensional datasets for dimensionality reduction, visualization, and exploration.
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Updated
Nov 26, 2024 - Jupyter Notebook
PCA Insights is a data analysis project aimed at applying Principal Component Analysis (PCA) to high-dimensional datasets for dimensionality reduction, visualization, and exploration.
Advanced Lando tooling to improve day to day work.
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Missing bridge for synccing keyboard events across applications
Log Transformation with Regular Expressions
Normalize data with interfaces.
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Python-based web scraper developed to automate the collection of posts and comments from Facebook Groups. The scraper includes authentication using session cookies, data quality checks, deduplication, standardization, and export functionality to CSV, JSON, Excel, and HTML formats.
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* Basis EDA * Handling Null/Missing Values * Handling Outliers * Handling Skewness * Handling Categorical Features * Data Normalization and Scaling * Feature Engineering *Accuracy score *Confusion matrix *Classification report
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