/DataAnlaysis

Primary LanguageJupyter Notebook

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

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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