Data Science
Practical 1: Introduction to Python Libraries and Functions for Data Science ApplicationsPractical 2: Implementing End-to-End Data Science Workflow for Predictive Analytics ā Case Studies on Iris Flower Classification, California Housing Price Prediction, and Heart Disease PredictionPractical 3: Exploratory Data Analysis (EDA): Understanding Descriptive Statistics for Data SciencePractical 4: Statistical Analysis and Interpretation Using the Car Evaluation DatasetPractical 5: Predicting Pre-Owned Car Prices for Storm Motors ā Data Preprocessing, Feature Engineering, and Model ImplementationPractical 6: Implementing NaĆÆve Bayes Classification for Handwritten Digit Recognition Using the MNIST DatasetPractical 7: Principal Component Analysis (PCA) for Dimensionality Reduction and Wine Quality PredictionPractical 8: Implementing Various Data Filters for Enhanced Data Visualization: Range Filters (e.g., prices, temperatures, total sales), Categorical Filters (e.g., country, product type, gender), Date Filters (e.g., time-series analysis),Interactive Filters (real-time dynamic visualizations)Practical 9: Data Visualization with Matplotlib ā Creating and Customizing Graphs in PythonPractical 10: Advanced Data Visualization with Seaborn ā Statistical Plots and Insights in Python
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