This collection consists of three Jupyter notebooks, each focusing on different aspects of data science and machine learning. Below is a brief overview of each notebook:
This notebook dives into regression analysis, focusing on understanding and handling outliers and leverage points and their impact on regression models. It covers:
- Identification of outliers and leverage points in regression.
- The concept of the coefficient of determination (R²) and its importance in regression analysis.
- Implementation of least squares regression in various scenarios, including with and without outliers or leverage points.
- Outliers and Leverage Points in Regression
- Coefficient of Determination (R²)
- Least Squares Regression
This notebook is centered around the MNIST dataset, a well-known dataset in machine learning for handwritten digit classification. It includes:
- Loading and preprocessing the MNIST test data.
- Importing necessary libraries from Keras and TensorFlow.
- Steps for reshaping and normalizing test images for model input.
- MNIST Dataset Handling
- Image Preprocessing
- Utilization of Keras and TensorFlow
In this notebook, the focus is on demonstrating the Central Limit Theorem (CLT), a fundamental theorem in statistics. The notebook includes:
- Loading of a dataset (FIFA2020 player data) and initial data exploration.
- Handling missing data in specific columns like 'pace' and 'dribbling'.
- Various methods for addressing missing data, such as removal, replacement, and predictive imputation.
- Central Limit Theorem
- Data Exploration and Preprocessing
- Handling Missing Data
Each notebook is a self-contained tutorial that includes both theoretical explanations and practical code examples. These notebooks are designed to provide hands-on experience with various data science and machine learning techniques.
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