Basic Statistics and Machine Learning Algorithms.

This repository contains various data analysis and machine learning projects that include Exploratory Data Analysis (EDA), basic statistics, and the application of machine learning algorithms on multiple datasets.

Table of Contents:

-> Introduction

-> Project Structure

-> Exploratory Data Analysis (EDA)

-> Basic Statistics

-> Machine Learning Algorithms

Introduction This repository showcases my work on multiple datasets, focusing on understanding the data, extracting insights, and building predictive models. It covers a range of tasks, including data cleaning, EDA, basic statistical analysis, and the implementation of various machine learning algorithms.

Project Structure The repository is organized into folders, each representing a separate dataset and project.

Each folder contains: -> Data: The dataset used for analysis (if applicable).

-> Notebooks: Jupyter notebooks for EDA, statistics, and machine learning models.

-> Scripts: Python scripts for running the analysis.

-> Results: Outputs such as charts, model evaluation metrics, and predictions.

Datasets The datasets used in this repository vary across projects and domains. Each project folder contains information about the specific dataset used, including:

-> Source of the dataset

-> Data cleaning steps

-> Features and target variables

Exploratory Data Analysis (EDA) In the EDA section, the following analyses are typically performed:

-> Descriptive statistics

-> Visualization of distributions, correlations, and trends

-> Identifying outliers and missing values

-> Feature engineering and transformations

Basic Statistics For each project, basic statistical analysis is conducted, including:

-> Measures of central tendency (mean, median, mode)

-> Measures of dispersion (variance, standard deviation)

-> Hypothesis testing

-> Chi-Square testing

-> Correlation analysis

Machine Learning Algorithms A variety of machine learning algorithms are applied across projects, including:

#Supervised Learning:

-> Linear Regression

-> Logistic Regression

-> Decision Trees

-> Random Forest

-> Support Vector Machines (SVM)

-> k-Nearest Neighbors (k-NN)

-> XGBoost and LGBM

-> Neural Networks

-> Time Series Analysis

#Unsupervised Learning:

-> Clustering (KMeans and DBSCAN)

-> PCA (Principal Component Analysis)

-> Association Rules

-> Reccomendation Systems

For each model:

-> Data is split into training and testing sets.

-> Hyperparameter tuning is conducted (where applicable).

-> Evaluation metrics such as accuracy, precision, recall, F1 score, and AUC are reported.