vatche-t / DataAnlaysis

Data Science and Machine Learning Notebooks Collection

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:

1. Regression Analysis Notebook (Regression.ipynb)

Overview

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.

Key Concepts

• Outliers and Leverage Points in Regression
• Coefficient of Determination (R²)
• Least Squares Regression

2. MNIST Classification Notebook (MNIST.ipynb)

Overview

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.

Key Concepts

• MNIST Dataset Handling
• Image Preprocessing
• Utilization of Keras and TensorFlow

3. Central Limit Theorem Demonstration Notebook (Central_Limit_Theorem.ipynb)

Overview

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.

Key Concepts

• Central Limit Theorem
• Data Exploration and Preprocessing
• Handling Missing Data

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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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