Data Science and Machine Learning

Welcome to the Data Science and Machine Learning repository! This repository contains all the code I created while learning various concepts in data science and machine learning.

Table of Contents

1. Introduction
2. Folder Structure
3. Getting Started
4. Projects
5. Contributing
6. License

Introduction

This repository is designed to be a comprehensive guide for anyone interested in data science and machine learning. It includes:

• Code snippets to help you understand different concepts.
• Projects that apply the learned concepts to real-world scenarios.
• Notebooks with detailed explanations and visualizations.

Folder Structure

The repository is organized into the following subfolders:

• 01 Python for Data Analysis - NumPy: Introduction to NumPy.
• 02 Python for Data Analysis - Pandas: Introduction to Pandas.
• 03 Python for Data Analysis - Pandas with Dataset: Working with datasets using Pandas.
• 04 Data Visualization - Matplotlib: Data visualization using Matplotlib.
• 05 Data Visualization - Seaborn: Data visualization using Seaborn.
• 06 Data Visualization - Pandas Built-in Data Visualization: Using Pandas for data visualization.
• 07 Data Visualization - Plotly: Interactive plots using Plotly.
• 08 Data Visualization - Geographical Plotting: Geographical data visualization.
• 09 911 Calls Project: Analyzing 911 call data.
• 10 Finance Data Project: Financial data analysis.
• 11 Machine Learning - Linear Regression: Introduction to linear regression.
• 12 Linear Regression ECommerce Project - Mobile App or Website: ECommerce analysis using linear regression.
• 13 Logistic Regression: Introduction to logistic regression.
• 14 Logistic Regression Project - User will click on ad or not: Predicting ad clicks using logistic regression.
• 15 K Nearest Neighbors: Introduction to K-Nearest Neighbors.
• 16 KNN Project: A project using KNN.
• 17 Decision Trees and Random Forests: Introduction to decision trees and random forests.
• 18 Random Forest Project - Payback Predictor: Predicting payback using random forests.
• 19 Support Vector Machine (SVM: Introduction to SVM.
• 20 SVM - Iris Flower Project: Classifying iris flowers using SVM.
• 21 K Means Clustering: Introduction to K-Means Clustering.
• 22 K Means Clustering Project - Private or Public University: Classifying universities using K-Means Clustering.
• 23 PCA (Principal Component Analysis: Introduction to PCA.
• 24 Recommender Systems - Movie: Building a movie recommender system.
• 25 Natural Language Processing: Introduction to NLP.
• 26 NLP Project - Classify Yelp Reviews: Classifying Yelp reviews using NLP.
• 27 Big Data and Spark: Introduction to Big Data and Spark.
• 28 Deep Learning: Introduction to deep learning.
• 29 SciPy: Introduction to SciPy.

Getting Started

To get started with this repository, clone it to your local machine:

git clone https://github.com/SiddharthDhirde/Data-Science-and-Machine-Learning.git

Make sure you have the necessary Python packages installed. You can install the required packages using:

pip install -r requirements.txt

Projects

The repository contains several projects that apply the concepts learned.

Contributing

Contributions are welcome! If you have any improvements, bug fixes, or new ideas, feel free to open an issue or submit a pull request.

License

This repository is licensed under the MIT License.