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+# π ML-Capsule: Hands-on ML from Basic to Advance π
- # Master Machine learning
+Welcome to **ML-Capsule**! This repository is a comprehensive collection of machine learning projects and resources, ranging from beginner to advanced levels. It covers a variety of topics, from basic machine learning concepts to deep learning, natural language processing, and much more.
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-__________________________________________________________________________
+## π Why Machine Learning?
-
+Machine learning is a technique to analyze data that automates the process of building analytical models. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
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+
-## Description
- Machine learning technique to analysis data that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
### Importance of Machine Learning
-Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.
+Machine learning is crucial because it provides enterprises with insights into customer behavior and business operational patterns, and supports the development of new products. Leading companies like Facebook, Google, and Uber integrate machine learning into their operations, making it a significant competitive differentiator.
-## π±Pre-requisites
+## π Pre-requisites
-- Python IDE : Install it by using this link [python.org](https://www.python.org/downloads/)
-- If you are new to python programming and want to have a fair knowledge before you start working on it, you can learn it in a simplified way through this [website](https://www.w3schools.com/python/)
+- **Python IDE**: Install from [python.org](https://python.org)
+- **Learn Python**: If you're new to Python, start learning from [W3Schools](https://www.w3schools.com/python/python_ml_getting_started.asp)
-## Topics
+## ποΈ Topics Covered
- ### Extracting Data
- Extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy
- * Web scrapping - Library used :->> Beautiful Soup , Which extract the data from web pages.
-
- ### Visualization
- Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed. Python offers multiple great graphing libraries that come packed with lots of different features.
- * Different types of libraries used to manipulate data in form of type of graphs and graphical representation :->> Seaborn , pandas , matplotlib etc.
-
- ### Feature selection (Variable Selection)
- the process of selecting a subset of relevant features for use in model.Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.
- * Library used for feature selection commonly :->> scikit-learn
- * Link - https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/
-
- ### Basic concepts of statistic
-A).Understand the Type of Analytics
-
+### 1. Extracting Data
+Extraction refers to methods of constructing combinations of variables to accurately describe the data.
-* Descriptive Analytics tells us what happened in the past and helps a business understand how it is performing by providing context to help stakeholders interpret information.
+- **Web Scraping**: Library used - Beautiful Soup, to extract data from web pages.
-* Diagnostic Analytics takes descriptive data a step further and helps you understand why something happened in the past.
+### 2. Visualization
+Data visualization places data in a visual context to expose patterns, trends, and correlations.
-* Predictive Analytics predicts what is most likely to happen in the future and provides companies with actionable insights based on the information.
+- **Libraries Used**: Seaborn, pandas, matplotlib
-* Prescriptive Analytics provides recommendations regarding actions that will take advantage of the predictions and guide the possible actions toward a solution
-
-B). Probability
-
-* Conditional Probability
-* Independent Events
-* Mutually Exclusive Events
-* Bayesβ Theorem
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-C). Central Tendency
- * Mean
- * Mode
- * varience
- * Skewness
- * Kurtosis:
- * Standard Deviation
-
-D). Variability
-* Range: The difference between the highest and lowest value in the dataset.
-* Percentiles β A measure that indicates the value below which a given percentage of observations in a group of observations falls.
-* Quantilesβ Values that divide the number of data points into four more or less equal parts, or quarters.
-* Interquartile Range (IQR)β A measure of statistical dispersion and variability based on dividing a data set into quartiles. IQR = Q3 β Q1
-* Variance: The average squared difference of the values from the mean to measure how spread out a set of data is relative to mean.
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-E). Relationship Between Variables
-* Causality: Relationship between two events where one event is affected by the other.
-* Covariance: A quantitative measure of the joint variability between two or more variables.
-* Correlation: Measure the relationship between two variables and ranges from -1 to 1, the normalized version of covariance.
-
-F). Probability Distribution
-* Probability Mass Function (PMF): A function that gives the probability that a discrete random variable is exactly equal to some value.
-* Probability Density Function (PDF): A function for continuous data where the value at any given sample can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample.
-* Cumulative Density Function (CDF): A function that gives the probability that a random variable is less than or equal to a certain value.
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-G). Hypothesis Testing and Statistical Significance
-* Null and Alternative Hypothesis
-* Interpretation
-* Z-Test
-* T-Test
-* ANOVA (Analysis of Variance)
-* Chi-Square Test
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-H). Regression
-* Linear Regression
- ** Assumptions of Linear Regression
-
- - Linear Relationship
- - Multivariate Normality
- - No or Little Multicollinearity
- - No or Little Autocorrelation
- - Homoscedasticity
- * Multiple Linear Regression
-
-# Data Science
-Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
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-## Why is data science important?
-In business, the goal of data science is to provide intelligence about consumers and campaigns and help companies create strong plans to engage their audience and sell their products.
-
+### 3. Feature Selection
+The process of selecting relevant features for use in a model to increase accuracy and performance.
-Data scientists must rely on creative insights using big data, the large amounts of information collected through various collection processes, like data mining.
-On an even more fundamental level, big data analytics can help brands understand the customers who ultimately help determine the long-term success of a business or initiative. In addition to targeting the right audience, data science can be used to help companies control the stories of their brands.
-Because big data is a rapidly growing field, there are constantly new tools available, and those tools need experts who can quickly learn their applications. Data scientists can help companies create a business plan to achieve goals based on research and not just intuition.
-
-Data science plays a very important role in security and fraud detection, because the massive amounts of information allow for drilling down to find slight irregularities in data that can expose weaknesses in security systems.It is a driving force between highly specialized user experiences created through personalization and customization. The analysis can be used to make customers feel seen and understood by a company.
-
+- **Library Used**: scikit-learn
+- **Learn More**: [Feature Selection](https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/)
-## What are the six major areas of data science?
-The six major areas of data science include the following:
+### 4. Basic Concepts of Statistics
+- **Analytics Types**: Descriptive, Diagnostic, Predictive, Prescriptive
+- **Probability**: Conditional, Independent Events, Bayesβ Theorem
+- **Central Tendency**: Mean, Mode, Variance, Skewness, Kurtosis, Standard Deviation
+- **Variability**: Range, Percentiles, Quantiles, IQR, Variance
+- **Relationships**: Causality, Covariance, Correlation
+- **Probability Distribution**: PMF, PDF, CDF
+- **Hypothesis Testing**: Null and Alternative Hypothesis, Z-Test, T-Test, ANOVA, Chi-Square Test
+- **Regression**: Linear Regression, Multiple Linear Regression
-* Multidisciplinary investigations. Considering large, complex systems with interconnected pieces, data scientists use varying methods to collect large amounts of data.
-* Models and methods for data. Data scientists need to rely on experience and intuition to decide which methods will work best for modeling their data, and they need to adjust those methods continuously to hone in on the insights they seek.
-* Pedagogy. It is up to data scientists to work with companies and clients to determine the best ideologies to apply while collecting and analyzing information about their customers and products.
-* Computing with data. The biggest thing that all data science projects have in common is the necessity to use tools and software to analyze the involved algorithms and statistics, because the size of the pool of information they are working with is so massive.
-* Theory. Data science theory is an evolving and sophisticated professional arena with countless applications.
-* Tool evaluation. There are many tools available for data scientists to use to manipulate and study huge quantities of data, and it's important to always evaluate their effectiveness and keep trying new ones as they become available.
-## summary
+ 
-## useful urls
-* https://www.kdnuggets.com/2020/06/8-basic-statistics-concepts.html
-* https://www.coursera.org/learn/machine-learning-with-python
-* https://www.w3schools.com/python/python_ml_getting_started.asp
-* https://www.freecodecamp.org/learn/machine-learning-with-python/
-* https://www.greatlearning.in/great-lakes-pgpdsba?&utm_source=Google&utm_medium=Search&utm_campaign=6Cities_Exact_Data_Science_Search_New_DS&adgroup_id=101317851589&campaign_id=10174480218&Keyword=data%20scientist&placement=&utm_content=c&gclid=CjwKCAjwn6GGBhADEiwAruUcKqPCvPIk1X_5mVRXj5prdpSIULnd40QgTB4kChfiFgAL1kDErGeLHRoCapUQAvD_BwE
+### 5. Data Science
-## Get Started
+- Data science is a dynamic and multidisciplinary field dedicated to extracting insights and solving complex problems through data.
-* This repo shows a good collection of Machine learning with python and data science with algorithms,projects,explanations from basic to advance level.
-* It has topics based on machine learning, deep learning, sql, natural language proccessing, object detection, classification, recommendation system,chatbots and much more.
+- **Multidisciplinary investigations** leverage knowledge from various domains, such as economics, biology, and engineering, to create comprehensive solutions by integrating diverse perspectives.
-## Take a look at existing projects
+- **Models and methods for data** are at the heart of data science, employing statistical techniques and advanced machine learning algorithms to uncover patterns, make predictions, and inform decisions.
+- **Pedagogy** in data science is concerned with the development and implementation of effective teaching practices and educational tools to ensure that learners acquire the necessary skills and knowledge.
+- **Computing with data** involves the use of computational tools and technologies for managing, processing, and analyzing large datasets, including skills in programming and database management.
+- The **theory** behind data science provides the mathematical and statistical foundations necessary for developing and applying various methods. Finally, **tool evaluation** focuses on assessing and selecting the best software, programming languages, and platforms based on performance and usability to ensure effective data analysis.
+- Together, these areas contribute to the robust and evolving nature of data science, driving innovation and informed decision-making across multiple sectors.
-
-| Content List |
- | --------------- |
-
+
-### Note:
-* Above project list will be scheduled automatically,whenever new projects add to the repo it will add in above table.
-## π Code Of Conduct:
+## Available Projects
-You can find our Code of Conduct [here](https://github.com/Niketkumardheeryan/Hands-on-ML-Basic-to-Advance-/blob/master/CODE_OF_CONDUCT.md).
+
+& many more.......
-## Have a look
-
-* Give it a π if you β€ this project.
- 
-
-* Take a look at the Existing Issues.
-* Create your own Issues, If you have new idea not listed in project.
-* Wait for the Issue to be assigned to you.
-* Fork the repository
+You can find All the Projects
+Live Project -- https://github.com/Niketkumardheeryan/ML-CaPsule
+
+## π Project Descriptions
+
+Here are some of the exciting projects featured in this repository:
+
+1. **[Alzheimer's Disease Predictor](#)**
+ A machine learning model to predict the likelihood of Alzheimer's disease based on patient data, using classification algorithms and feature selection techniques.
+
+2. **[Chatbot Using RASA](#)**
+ A conversational AI chatbot built with RASA, capable of handling various user queries and providing intelligent responses.
+
+3. **[COVID-19 Forecasting with Prophet](#)**
+ Utilize the Prophet library to forecast COVID-19 case trends and predict future outbreaks based on historical data.
+
+4. **[Fake News Detection](#)**
+ A project that uses NLP techniques to detect and classify fake news articles, employing various text processing and classification methods.
+
+5. **[Handwritten Digit Recognition](#)**
+ A deep learning model that recognizes handwritten digits using a Convolutional Neural Network (CNN) trained on the MNIST dataset.
+
+6. **[Movie Genre Classification](#)**
+ A machine learning model that predicts movie genres based on descriptions using text classification techniques and feature extraction.
+
+7. **[Employee Attrition Prediction](#)**
+ A predictive model that identifies employees at risk of leaving a company, using historical HR data and various classification algorithms.
+
+8. **[Heart Disease Prediction](#)**
+ A predictive model for diagnosing heart disease based on patient attributes, utilizing statistical and machine learning techniques to improve diagnosis accuracy.
+
+## π Summary
-
+This repository offers a rich collection of machine learning and data science projects. It includes well-documented examples, practical projects, and extensive resources to help you understand and implement various machine learning techniques.
-* Clone the repository using-
+## π Useful URLs
+- [8 Basic Statistics Concepts](https://www.kdnuggets.com/2020/06/8-basic-statistics-concepts.html)
+- [Coursera: Machine Learning with Python](https://www.coursera.org/learn/machine-learning-with-python)
+- [W3Schools: Python ML Getting Started](https://www.w3schools.com/python/python_ml_getting_started.asp)
+- [freeCodeCamp: Machine Learning with Python](https://www.freecodecamp.org/learn/machine-learning-with-python/)
+- [Great Learning: Data Science](https://www.greatlearning.in/great-lakes-pgpdsba?&utm_source=Google&utm_medium=Search&utm_campaign=6Cities_Exact_Data_Science_Search_New_DS&adgroup_id=101317851589&campaign_id=10174480218&Keyword=data%20scientist&placement=&utm_content=c&gclid=CjwKCAjwn6GGBhADEiwAruUcKqPCvPIk1X_5mVRXj5prdpSIULnd40QgTB4kChfiFgAL1kDErGeLHRoCapUQAvD_BwE)
-``` git clone https://github.com/Niketkumardheeryan/Hands-on-ML-Basic-to-Advance- ```
+## π Get Started
+This repository showcases a diverse collection of machine learning projects and data science algorithms, ranging from basic to advanced levels. It includes topics on machine learning, deep learning, SQL, NLP, object detection, classification, recommendation systems, chatbots, and much more.
-## βοΈ Contribution Guidelines
-- Have a look at [Contibuting Guidelines](https://github.com/Niketkumardheeryan/Hands-on-ML-Basic-to-Advance-/blob/master/CONTRIBUTING_GUIDELINES.md)
+
+### π Have a Look!
+Give this project a β if you love it!
+
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+
+
+
+### βοΈ Contribution Guidelines
+- Check the [Contribution Guidelines](CONTRIBUTING.md)
+- Take a look at the [Existing Issues](https://github.com/Niketkumardheeryan/Hands-on-ML-Basic-to-Advance-/issues)
+- Create your [Pull Request](https://github.com/Niketkumardheeryan/Hands-on-ML-Basic-to-Advance-/pulls)
+
+## Submitting a Pull Request
+
+To submit your contributions, follow these steps:
+
+1. **Fork the Repository**: Click the "Fork" button at the top right corner of the repository to create your own copy.
+
+2. **Clone Your Fork**: Clone your forked repository to your local machine using the following command:
+ ```bash
+ git clone https://github.com/Niketkumardheeryan/ML-CaPsule
+ ```
+
+3. **Create a Branch**: Create a new branch for your changes:
+ ```bash
+ git checkout -b my-feature
+ ```
+
+4. **Make Changes**: Make your desired changes to the codebase.
+
+5. **Commit Changes**: Commit your changes with a descriptive commit message:
+ ```bash
+ git commit -m "Add new feature"
+ ```
+
+6. **Push Changes**: Push your changes to your forked repository:
+ ```bash
+ git push origin my-feature
+ ```
+
+7. **Submit a Pull Request**: Go to your forked repository on GitHub and submit a pull request. Be sure to provide a detailed description of your changes and why they are necessary.
+
+## Project Directory Structure
+
+The project directory is organized as follows:
+
+- **Projects**: Contains subdirectories for individual projects, each with its own `README.md` file detailing project-specific information and instructions.
+- **Contributing.md**: Provides guidelines for contributing to the repository.
+- **Code_of_Conduct.md**: Outlines our community code of conduct and expectations for contributors.
+- **LICENSE**: Specifies the license under which the repository is distributed.
+
+
+### π Code of Conduct
+Please read our [Code of Conduct](CODE_OF_CONDUCT.md).
+
+
+
+### π License
+This project is licensed under the MIT License.
+
+
+
+
+Feel free to create new issues, fix bugs, and contribute to our projects. Join our community and help us build amazing machine learning solutions!
+
+Happy Coding! π©βπ»π¨βπ»
## Some awesome Contributors β¨
@@ -252,3 +418,8 @@ This project follows the [MIT License](https://choosealicense.com/licenses/mit/)
+
+## License
+
+
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+ 
+ 
