Machine Learning Experiments Repository
Welcome to the Machine Learning Experiments repository! This repository hosts a collection of hands-on machine learning experiments involving various supervised learning models. These experiments aim to provide practical insights into the world of machine learning and its applications.
This Repo Consists of Implementation of Basic Supervised Learning Algorithms using Python...!
Experiments Overview
1. Random Dataset Generation
Experiment with generating synthetic datasets to simulate different scenarios.
2. Descriptive Statistics
Explore the fundamental statistical properties of datasets. Learn how to calculate and interpret measures like mean, median, variance, and more.
3. Exploring Data
Dive into data exploration techniques. Uncover patterns, relationships, and anomalies within datasets to guide preprocessing and modeling.
4. Visualizing the Data
Visualize data using graphs and plots. Understand the power of visual representation in gaining insights from complex datasets.
5. Preprocessing the Data
Learn preprocessing techniques to clean, transform, and prepare data for modeling. Handle missing values, categorical variables, and more.
Building Models
Implement and evaluate different supervised learning models on real-world datasets. Understand the strengths and weaknesses of each algorithm.
1. Naive Bayes
Implement the Naive Bayes algorithm for classification tasks. Explore its probabilistic approach and learn how it's used in various domains.
2. Linear Regression
Understand linear regression, a fundamental algorithm for regression tasks. Learn how to fit a linear model to data and make predictions.
3. Logistic Regression
Dive into logistic regression, a staple for binary classification. Discover how it models the probability of a binary outcome.
4. k-Nearest Neighbors (kNN)
Explore the intuitive kNN algorithm for classification tasks. Understand how it leverages proximity to make predictions.
5. Decision Tree
Implement decision trees, a versatile algorithm for classification and regression. Learn how trees make sequential decisions to classify or predict.
Datasets
This repository includes various datasets used in the experiments. They cover diverse domains and are carefully selected to showcase different aspects of machine learning applications.
Usage
Feel free to explore the experiments and datasets in this repository. Each experiment is organized in its respective directory and includes detailed instructions on usage.