├── .circleci └── config.yml ├── .github ├── FUNDING.yml ├── ISSUE_TEMPLATE │ ├── bug_report.md │ ├── custom.md │ └── feature_request.md └── workflows │ ├── package-lock.json │ ├── package.json │ ├── release-package.yml │ └── simple-workflow.yml ├── .gitignore ├── .npmrc ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── Deep-learning ├── 0-Deep-learning.ipynb ├── ReadMe.md ├── deep-ml.png └── py-files │ └── Deep-learning.py ├── LICENSE ├── ML-Fundamental ├── 0-ML-fundamentals.ipynb ├── 0.1-ML-algorithms.ipynb ├── 0.2-Regression.ipynb ├── 0.3-Linear-reg.ipynb ├── 0.4.Linear-Parameter-estimation.ipynb ├── 1.0.Statistics.ipynb ├── 1.1-Descriptive-statistics.ipynb ├── 1.2-Inferenial-stat.ipynb ├── 4-ML-Scikit learn.ipynb ├── ML-fundamental python file │ ├── ML-algorithms.py │ ├── ML-descriptive-stat.py │ ├── ML-fundamental.py │ ├── ML-inferential-stat.py │ ├── ML-line-parameter-estimation.py │ ├── ML-line-reg.py │ ├── ML-regression.py │ └── ML-sklearn.py ├── ML-image │ ├── AI-vs-ML-vs-Deep-Learning.png │ ├── Anaconda.png │ ├── Berno-pmf.png │ ├── Binomial.png │ ├── CS-ml-deep.png │ ├── CS-ml.png │ ├── Linear-reg0.png │ ├── Linear-reg1.png │ ├── Linearity.png │ ├── ML-claasification.png │ ├── ML-classifications.png │ ├── ML1.png │ ├── Model.regresion.png │ ├── Multi-lin-reg.png │ ├── Pop-sam1.png │ ├── Pos-skew.jpeg │ ├── Possion.png │ ├── Regression1.png │ ├── Supervised-process.png │ ├── Uses-ML.png │ ├── eexpon.png │ ├── hypo1.png │ ├── independence.png │ ├── lst.png │ ├── neg-lin-reg.png │ ├── neg-skew.jpeg │ ├── normal-df.png │ ├── normal-df2.png │ ├── optimal-reg2.png │ ├── pos-lin-reg.png │ ├── python-libraries.png │ ├── rsquare1.png │ ├── snd-nd.png │ ├── snd.png │ ├── spread.png │ ├── super-alg.png │ ├── uni-dist1.png │ ├── unsuper-alg2.png │ ├── unsuper-process-png.png │ └── zero-skew.png ├── Overfitting-underfitting.ipynb ├── ReadMe.md ├── image-1.png ├── image-2.png └── image.png ├── Projects-ML ├── Example-project.md ├── ML-cycle.png ├── Py-file │ ├── Creid-risk.py │ ├── Linear-reg-py-file.py │ ├── Linear-reg.py │ ├── Monet-Carlo.py │ ├── Multiple-reg.py │ └── Proj-1-simple-line.py ├── README.md ├── image.png └── time-series-analysis-and-forecasting.ipynb ├── README.md ├── SECURITY.md ├── Statistics-fundamental ├── Boxplot_vs_PDF.svg ├── Correlaltion.png ├── Covariance.png ├── ML-Statistics.ipynb ├── ML-statistics python file │ └── ML-statistics.py.py ├── normal-distri.png └── variance.png ├── Supervised-learning ├── Advertising.csv ├── Algerian-fire-EDA.ipynb ├── Algerian_forest_fires_dataset_UPDATE.csv ├── Algerian_forest_fires_dataset_UPDATE.xlsx ├── Algerian_forst_fires_cleaned_dataset.csv ├── CarPrice.csv ├── IPL-IMB381IPL2013.csv ├── Project-1.0-simple-linear.ipynb ├── Project-1.1-Linear-reg.ipynb ├── Project-1.1.0-PCA_analysis.ipynb ├── Project-1.2-Model-training.ipynb ├── Project-1.3-Multiple-reg.ipynb ├── Project-1.3.1-Advertisment-multiple regression.ipynb ├── Project-1.4-Maximum-Likelihood-Estimation.ipynb ├── Project-1.5-Polinomial-regression.ipynb ├── Project-1.6-Advertising.ipynb ├── Project-1.7-Weight-height.ipynb ├── Project-2.0-Classification-regression.ipynb ├── Project-2.1-Logistic-project.ipynb ├── Project-2.2-Binary-logistic.ipynb ├── Project-2.3-KNN-classification.ipynb ├── Project-2.4-Decision-tree.ipynb ├── Project-2.5-Support vector.ipynb ├── Project-2.5.1-Support-vector-machine.ipynb ├── Study-dataset.csv ├── User_Data.csv ├── Weight_height.csv ├── boston_dataset.csv ├── churn.csv ├── cost-fun.png ├── curve.csv ├── dataset.csv ├── example.ipynb ├── fit-line.png ├── housing.csv ├── income_evaluation.csv ├── logistic_function_plot.png ├── ols-method.png ├── pair-plt.png └── titanic.csv ├── index.js ├── ml-data ├── BEML.csv ├── Codes │ ├── Chapter 1 - Python_Basics.pdf │ ├── Chapter 10 - Text Analytics.pdf │ ├── Chapter 2 - Descriptive Analytics.pdf │ ├── Chapter 3 - Basic Statistical Analysis.pdf │ ├── Chapter 4 - Linear Regression.pdf │ ├── Chapter 5 - Classification Problems.pdf │ ├── Chapter 6 - Advanced Machine Learning.pdf │ ├── Chapter 7 - Clustering.pdf │ ├── Chapter 8 - Forecasting.pdf │ └── Chapter 9 - Recommendation Systems.pdf ├── Country-Data.csv ├── Earnings Manipulation 220.csv ├── GLAXO.csv ├── German Credit Data.csv ├── IPL-IMB381IPL2013.csv ├── Income Data.csv ├── MBA-Salary.csv ├── auto-mpg.data ├── bank.csv ├── beer.csv ├── bollywood.csv ├── bollywoodmovies.csv ├── breakups.csv ├── con-new.csv ├── country.csv ├── curve.csv ├── customerspends.csv ├── forecast.xls ├── healthdrink.xlsx ├── hr_data.csv ├── onestop.csv ├── passport.csv ├── sentiment_train ├── snd.png ├── store.xls ├── toy_dataset.csv ├── trainingscores.csv ├── vimana.csv └── wsb.csv ├── package-lock.json └── package.json /.circleci/config.yml: -------------------------------------------------------------------------------- 1 | # Use the latest 2.1 version of CircleCI pipeline process engine. 2 | # See: https://circleci.com/docs/configuration-reference 3 | version: 2.1 4 | 5 | # Define a job to be invoked later in a workflow. 6 | # See: https://circleci.com/docs/configuration-reference/#jobs 7 | jobs: 8 | say-hello: 9 | # Specify the execution environment. You can specify an image from Docker Hub or use one of our convenience images from CircleCI's Developer Hub. 10 | # See: https://circleci.com/docs/configuration-reference/#executor-job 11 | docker: 12 | - image: cimg/base:stable 13 | # Add steps to the job 14 | # See: https://circleci.com/docs/configuration-reference/#steps 15 | steps: 16 | - checkout 17 | - run: 18 | name: "Say hello" 19 | command: "echo Hello, World!" 20 | 21 | # Orchestrate jobs using workflows 22 | # See: https://circleci.com/docs/configuration-reference/#workflows 23 | workflows: 24 | say-hello-workflow: 25 | jobs: 26 | - say-hello 27 | -------------------------------------------------------------------------------- /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: [arunsinp] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] 4 | patreon: [87801682] # Replace with a single Patreon username 5 | open_collective: # Replace with a single Open Collective username 6 | ko_fi: # Replace with a single Ko-fi username 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry 9 | liberapay: # Replace with a single Liberapay username 10 | issuehunt: # Replace with a single IssueHunt username 11 | otechie: # Replace with a single Otechie username 12 | lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry 13 | custom: ['https://www.buymeacoffee.com/arunp77] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] 14 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. 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Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /.github/workflows/package-lock.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "workflows", 3 | "version": "1.0.0", 4 | "lockfileVersion": 3, 5 | "requires": true, 6 | "packages": { 7 | "": { 8 | "name": "workflows", 9 | "version": "1.0.0", 10 | "license": "ISC" 11 | } 12 | } 13 | } 14 | -------------------------------------------------------------------------------- /.github/workflows/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "machine-learning", 3 | "version": "1.0.0", 4 | "description": "This Repository contains the Machine learning related files with fundamental theory is included in the package.", 5 | "main": "index.js", 6 | "scripts": { 7 | "test": "exit 0" 8 | }, 9 | "repository": { 10 | "type": "git", 11 | "url": "git+https://github.com/arunp77/Machine-Learning.git" 12 | }, 13 | "keywords": [ 14 | "Machine-learning" 15 | ], 16 | "author": "Arun Kumar Pandey", 17 | "license": "MIT", 18 | "bugs": { 19 | "url": "https://github.com/arunp77/Machine-Learning/issues" 20 | }, 21 | "homepage": "https://github.com/arunp77/Machine-Learning#readme", 22 | "publishConfig": { 23 | "@arunp77:registry": "https://npm.pkg.github.com/" 24 | } 25 | } 26 | -------------------------------------------------------------------------------- /.github/workflows/release-package.yml: -------------------------------------------------------------------------------- 1 | name: Node.js Package 2 | 3 | on: 4 | release: 5 | types: [created] 6 | 7 | jobs: 8 | build: 9 | runs-on: ubuntu-latest 10 | steps: 11 | - uses: actions/checkout@v4 12 | - uses: actions/setup-node@v3 13 | with: 14 | node-version: 16 15 | - run: npm ci 16 | - run: npm test 17 | 18 | publish-gpr: 19 | needs: build 20 | runs-on: ubuntu-latest 21 | permissions: 22 | packages: write 23 | contents: read 24 | steps: 25 | - uses: actions/checkout@v4 26 | - uses: actions/setup-node@v3 27 | with: 28 | node-version: 16 29 | registry-url: https://npm.pkg.github.com/ 30 | - run: npm ci 31 | - run: npm publish 32 | env: 33 | NODE_AUTH_TOKEN: ${{secrets.GITHUB_TOKEN}} 34 | -------------------------------------------------------------------------------- /.github/workflows/simple-workflow.yml: -------------------------------------------------------------------------------- 1 | # This is a basic workflow to help you get started with Actions 2 | 3 | name: CI 4 | 5 | # Controls when the workflow will run 6 | on: 7 | # Triggers the workflow on push or pull request events but only for the "main" branch 8 | push: 9 | branches: [ "main" ] 10 | pull_request: 11 | branches: [ "main" ] 12 | 13 | # Allows you to run this workflow manually from the Actions tab 14 | workflow_dispatch: 15 | 16 | # A workflow run is made up of one or more jobs that can run sequentially or in parallel 17 | jobs: 18 | # This workflow contains a single job called "build" 19 | build: 20 | # The type of runner that the job will run on 21 | runs-on: ubuntu-latest 22 | 23 | # Steps represent a sequence of tasks that will be executed as part of the job 24 | steps: 25 | # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it 26 | - uses: actions/checkout@v3 27 | 28 | # Runs a single command using the runners shell 29 | - name: Run a one-line script 30 | run: echo Hello, world! 31 | 32 | # Runs a set of commands using the runners shell 33 | - name: Run a multi-line script 34 | run: | 35 | echo Add other actions to build, 36 | echo test, and deploy your project. 37 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Node.js 2 | node_modules/ 3 | npm-debug.log 4 | yarn-error.log 5 | 6 | # macOS 7 | .DS_Store 8 | 9 | # Windows 10 | Thumbs.db 11 | Desktop.ini 12 | 13 | # Editor directories and files 14 | .vscode/ 15 | .idea/ 16 | *.suo 17 | *.ntvs* 18 | *.njsproj 19 | *.sln 20 | -------------------------------------------------------------------------------- /.npmrc: -------------------------------------------------------------------------------- 1 | @arunp77:registry=https://npm.pkg.github.com/ -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | We as members, contributors, and leaders pledge to make participation in our 6 | community a harassment-free experience for everyone, regardless of age, body 7 | size, visible or invisible disability, ethnicity, sex characteristics, gender 8 | identity and expression, level of experience, education, socio-economic status, 9 | nationality, personal appearance, race, religion, or sexual identity 10 | and orientation. 11 | 12 | We pledge to act and interact in ways that contribute to an open, welcoming, 13 | diverse, inclusive, and healthy community. 14 | 15 | ## Our Standards 16 | 17 | Examples of behavior that contributes to a positive environment for our 18 | community include: 19 | 20 | * Demonstrating empathy and kindness toward other people 21 | * Being respectful of differing opinions, viewpoints, and experiences 22 | * Giving and gracefully accepting constructive feedback 23 | * Accepting responsibility and apologizing to those affected by our mistakes, 24 | and learning from the experience 25 | * Focusing on what is best not just for us as individuals, but for the 26 | overall community 27 | 28 | Examples of unacceptable behavior include: 29 | 30 | * The use of sexualized language or imagery, and sexual attention or 31 | advances of any kind 32 | * Trolling, insulting or derogatory comments, and personal or political attacks 33 | * Public or private harassment 34 | * Publishing others' private information, such as a physical or email 35 | address, without their explicit permission 36 | * Other conduct which could reasonably be considered inappropriate in a 37 | professional setting 38 | 39 | ## Enforcement Responsibilities 40 | 41 | Community leaders are responsible for clarifying and enforcing our standards of 42 | acceptable behavior and will take appropriate and fair corrective action in 43 | response to any behavior that they deem inappropriate, threatening, offensive, 44 | or harmful. 45 | 46 | Community leaders have the right and responsibility to remove, edit, or reject 47 | comments, commits, code, wiki edits, issues, and other contributions that are 48 | not aligned to this Code of Conduct, and will communicate reasons for moderation 49 | decisions when appropriate. 50 | 51 | ## Scope 52 | 53 | This Code of Conduct applies within all community spaces, and also applies when 54 | an individual is officially representing the community in public spaces. 55 | Examples of representing our community include using an official e-mail address, 56 | posting via an official social media account, or acting as an appointed 57 | representative at an online or offline event. 58 | 59 | ## Enforcement 60 | 61 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 62 | reported to the community leaders responsible for enforcement at 63 | . 64 | All complaints will be reviewed and investigated promptly and fairly. 65 | 66 | All community leaders are obligated to respect the privacy and security of the 67 | reporter of any incident. 68 | 69 | ## Enforcement Guidelines 70 | 71 | Community leaders will follow these Community Impact Guidelines in determining 72 | the consequences for any action they deem in violation of this Code of Conduct: 73 | 74 | ### 1. Correction 75 | 76 | **Community Impact**: Use of inappropriate language or other behavior deemed 77 | unprofessional or unwelcome in the community. 78 | 79 | **Consequence**: A private, written warning from community leaders, providing 80 | clarity around the nature of the violation and an explanation of why the 81 | behavior was inappropriate. A public apology may be requested. 82 | 83 | ### 2. Warning 84 | 85 | **Community Impact**: A violation through a single incident or series 86 | of actions. 87 | 88 | **Consequence**: A warning with consequences for continued behavior. No 89 | interaction with the people involved, including unsolicited interaction with 90 | those enforcing the Code of Conduct, for a specified period of time. This 91 | includes avoiding interactions in community spaces as well as external channels 92 | like social media. Violating these terms may lead to a temporary or 93 | permanent ban. 94 | 95 | ### 3. Temporary Ban 96 | 97 | **Community Impact**: A serious violation of community standards, including 98 | sustained inappropriate behavior. 99 | 100 | **Consequence**: A temporary ban from any sort of interaction or public 101 | communication with the community for a specified period of time. No public or 102 | private interaction with the people involved, including unsolicited interaction 103 | with those enforcing the Code of Conduct, is allowed during this period. 104 | Violating these terms may lead to a permanent ban. 105 | 106 | ### 4. Permanent Ban 107 | 108 | **Community Impact**: Demonstrating a pattern of violation of community 109 | standards, including sustained inappropriate behavior, harassment of an 110 | individual, or aggression toward or disparagement of classes of individuals. 111 | 112 | **Consequence**: A permanent ban from any sort of public interaction within 113 | the community. 114 | 115 | ## Attribution 116 | 117 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], 118 | version 2.0, available at 119 | https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. 120 | 121 | Community Impact Guidelines were inspired by [Mozilla's code of conduct 122 | enforcement ladder](https://github.com/mozilla/diversity). 123 | 124 | [homepage]: https://www.contributor-covenant.org 125 | 126 | For answers to common questions about this code of conduct, see the FAQ at 127 | https://www.contributor-covenant.org/faq. Translations are available at 128 | https://www.contributor-covenant.org/translations. 129 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | ## Repository Contributor Guidelines 2 | 3 | ### Introduction 4 | 5 | Welcome to the repository for Jupyter notebooks covering machine learning, deep learning, statistics, and related topics. We appreciate your interest in contributing to this project. Before you get started, please review these guidelines to ensure a smooth and collaborative experience. 6 | 7 | ### Contribution Process 8 | 9 | 1. **Fork the Repository**: To begin contributing, fork this repository to your GitHub account. 10 | 11 | 2. **Branch Naming**: When working on a specific topic or notebook, create a new branch with a descriptive name related to the content you are working on. For example, if you are working on a notebook about regression, you could name your branch "regression-notebook." 12 | 13 | 3. **Notebook Organization**: Each notebook should be organized as follows: 14 | - Start with a clear title and brief description of the notebook's content. 15 | - Use Markdown cells for explanations and headings. 16 | - Include code cells with comments and explanations. 17 | - Ensure that code is well-documented and follows best practices. 18 | - Use Markdown to include any necessary equations or mathematical explanations. 19 | 20 | 4. **Commit Changes**: Make meaningful and atomic commits with clear messages. For example: 21 | - "Added a new section on logistic regression." 22 | - "Updated code comments for better clarity." 23 | 24 | 5. **Pull Requests**: When you're ready to contribute, open a pull request from your forked repository to the main repository. Clearly describe the purpose of your changes in the pull request description. 25 | 26 | 6. **Code Review**: Expect feedback and code review from maintainers and fellow contributors. Be responsive to comments and make necessary revisions. 27 | 28 | 7. **Testing**: Ensure that your code works correctly and does not introduce errors. If applicable, include test cases within your notebook. 29 | 30 | ### Code and Documentation Standards 31 | 32 | 1. **Coding Style**: Follow the coding style guidelines for Python and Jupyter notebooks. Consistency in code style is important for readability. If you're unsure, refer to PEP 8 for Python. 33 | 34 | 2. **Documentation**: Clearly explain the purpose and context of your notebook in Markdown cells. Include explanations of code, algorithms, and concepts. Make sure your documentation is clear and accessible to a wide audience. 35 | 36 | 3. **Use of Libraries**: When using external libraries (e.g., scikit-learn, TensorFlow), specify the version used and ensure your code is compatible with the most recent versions. 37 | 38 | ### Collaboration and Communication 39 | 40 | 1. **Collaboration**: Feel free to collaborate with others on improving existing notebooks or creating new ones. Coordination through issues and discussions is encouraged. 41 | 42 | 2. **Respectful Communication**: Be respectful and constructive in your communication with other contributors. Healthy discussions and debates are welcome. 43 | 44 | 3. **Issues**: If you encounter issues or have ideas for new notebooks, create a new issue to discuss it with the community. 45 | 46 | ### Licensing 47 | 48 | By contributing to this repository, you agree to license your work under the same license as the repository itself. Please ensure you have the necessary rights to make contributions. 49 | 50 | ### Acknowledgment 51 | 52 | We greatly appreciate your contributions to this repository and will acknowledge your work in the contributors' section. 53 | 54 | Thank you for your interest in advancing the knowledge and resources available in the field of machine learning and statistics. Happy contributing! 55 | -------------------------------------------------------------------------------- /Deep-learning/ReadMe.md: -------------------------------------------------------------------------------- 1 | # Deep Learning 2 | 3 | **Reference:** [https://arunp77.github.io/machine-learning.html](https://arunp77.github.io/machine-learning.html) 4 | 5 | - Deep Learning: 6 | - Neural Networks: 7 | - Perceptron 8 | - Multi-Layer Perceptron (MLP) 9 | - Convolution Neural Networks (CNNs): 10 | - Image Classification 11 | - Object Detection 12 | - Image Segmentation 13 | - Recurrent Neural Networks 14 | - Sequence-to-Sequence Models 15 | - Text classification 16 | - Sentiment Analysis 17 | - Long Short-term Memory (LSTM) and Gated Recurrent Units (GRU) 18 | - Time series Forecasting 19 | - Language Modeling 20 | - Generative Adversarial Networks (GANs) 21 | - Image Synthesis 22 | - Data Augmentation 23 | -------------------------------------------------------------------------------- /Deep-learning/deep-ml.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/Deep-learning/deep-ml.png -------------------------------------------------------------------------------- /Deep-learning/py-files/Deep-learning.py: -------------------------------------------------------------------------------- 1 | # %% [markdown] 2 | # # Deep Learning 3 | # 4 | # ![image.png](attachment:image.png) 5 | # 6 | # [Image reference](https://www.ait.de/en/deep-learning/) 7 | 8 | # %% [markdown] 9 | # ## Definition 10 | # Deep learning is a subfield of machine learning that involves the use of artificial neural networks with multiple layers to model and solve complex problems. 11 | # 12 | # Artificial Neural Networks (ANNs) are a type of deep learning model that is designed to simulate the way the human brain works. ANNs consist of interconnected nodes (neurons) that transmit signals and perform computations on input data to produce output values. These networks can be used for a wide range of applications, including image recognition, speech recognition, natural language processing, and more. 13 | # 14 | # ## Classification 15 | # 16 | # ANNs can be classified into several categories based on their structure and function, including: 17 | # 18 | # 1. **Feedforward neural networks:** These are neural networks that have a series of interconnected layers, where the output of each layer serves as the input for the next layer. 19 | # 20 | # 2. **Convolutional neural networks (CNNs):** CNNs are primarily used for image and video processing tasks. They consist of layers of convolutional filters that can identify patterns in the input images. 21 | # 22 | # 3. **Recurrent neural networks (RNNs):** These are neural networks that are well-suited to sequence analysis tasks, such as natural language processing or speech recognition. They use a type of neural network layer called a recurrent layer that can maintain an internal state and process inputs one at a time. 23 | # 24 | # 4. **Generative adversarial networks (GANs):** GANs are a type of network that can generate new data based on a set of input data. They consist of two networks: a generator network that creates new data, and a discriminator network that evaluates the quality of the generated data. 25 | # 26 | # 5. **Autoencoders:** Autoencoders are designed to learn a compressed representation of input data. They consist of an encoder network that compresses the input data into a low-dimensional representation, and a decoder network that can reconstruct the original input data from the compressed representation. 27 | 28 | # %% [markdown] 29 | # ![image.png](attachment:image.png) 30 | # 31 | # https://www.sciencedirect.com/science/article/pii/S2352914822000612 32 | 33 | # %% [markdown] 34 | # ![image.png](attachment:image.png) 35 | # 36 | # (simple form: https://developer.ibm.com/articles/cc-machine-learning-deep-learning-architectures/ good link) 37 | 38 | # %% [markdown] 39 | # ## Deep Learning framework 40 | # 41 | # In the context of deep learning, a framework is a software library or tool that provides a set of APIs (application programming interfaces) and abstractions to simplify the development of deep neural networks. Frameworks typically include pre-implemented building blocks for common neural network layers, such as convolutional, recurrent, and fully connected layers, as well as optimization algorithms and training routines. 42 | # 43 | # There are many popular frameworks for deep learning, some of which include: 44 | # 45 | # - **TensorFlow:** Developed by Google, it is an open-source software library for dataflow and differentiable programming across a range of tasks. 46 | # 47 | # - **PyTorch:** Developed by Facebook, it is an open-source machine learning framework used for applications such as computer vision and natural language processing. 48 | # 49 | # - **Keras:** An open-source neural network library written in Python, it runs on top of other deep learning frameworks such as TensorFlow and Theano. 50 | # 51 | # - **Caffe:** Developed by Berkeley Vision and Learning Center, it is a deep learning framework that specializes in image recognition. 52 | # 53 | # - **Theano:** Another popular open-source numerical computation library, it is used for deep learning and other mathematical computations. 54 | # 55 | # - **MXNet:** An open-source deep learning framework that is highly scalable and supports a range of programming languages including Python, R, and Julia. 56 | # 57 | # - **Chainer:** A Python-based, open-source deep learning framework that is highly flexible and allows for dynamic computation graphs. 58 | # 59 | # These frameworks provide a range of features and tools for developing and training deep neural networks, making it easier for developers and researchers to experiment with different architectures and optimize their models for specific tasks. 60 | 61 | # %% [markdown] 62 | # ## Difference between Deep learning and Machine learning 63 | # 64 | # | | Machine Learning | Deep Learning | 65 | # |-------------------------|------------------|---------------| 66 | # | Approach | Requires structure data | Does not require structure data | 67 | # | Human intervention | Requires human intervention for mistakes | Does not require human intervention for mistakes | 68 | # | Hardware | Can function on CPU | Requires GPU / Significant computing power | 69 | # | Time | Takes seconds to hours | Takes weeks | 70 | # | Uses | Forecasting, predicting and other simple applications | More complex applications like autonomus vehicles | 71 | 72 | # %% [markdown] 73 | # ## Practical uses and applications of deep learning 74 | # 75 | # Here are a few examples of the practical uses and applications of deep learning across various domains: 76 | # 77 | # 1. **Image and Object Recognition:** Deep learning has significantly improved image classification, object detection, and recognition tasks. Examples include: 78 | # 79 | # - Autonomous vehicles use deep learning algorithms to recognize and interpret objects in real-time, enabling them to navigate and make informed driving decisions. 80 | # 81 | # - Facial recognition systems, such as those used for biometric identification or security purposes, employ deep learning techniques to accurately recognize and verify individuals' faces. 82 | # 83 | # 2. **Natural Language Processing (NLP):** Deep learning has greatly advanced natural language processing tasks, allowing computers to understand and generate human language. Examples include: 84 | # 85 | # - Chatbots and virtual assistants utilize deep learning models to understand user queries, provide relevant responses, and engage in conversational interactions. 86 | # 87 | # - Machine translation systems, like Google Translate, employ deep learning to improve the accuracy and fluency of translations between different languages. 88 | # 89 | # - Sentiment analysis algorithms analyze text data from social media, customer reviews, or surveys, using deep learning models to determine the sentiment expressed in the text (e.g., positive, negative, neutral). 90 | # 91 | # 3. **Medical Diagnostics:** Deep learning has shown promising results in medical imaging analysis and disease diagnostics. Examples include: 92 | # 93 | # - Deep learning models can detect anomalies and classify medical images, such as X-rays, MRIs, or CT scans, assisting radiologists in diagnosing diseases like cancer or identifying abnormalities. 94 | # 95 | # - Deep learning algorithms have been used to predict the risk of certain diseases, such as diabetic retinopathy or cardiovascular diseases, based on patient data, enabling early detection and intervention. 96 | # 97 | # 4. **Recommendation Systems:** Deep learning models are used in recommendation systems to personalize and improve user experiences. Examples include: 98 | # 99 | # - Streaming platforms like Netflix and Spotify employ deep learning algorithms to recommend personalized movies, TV shows, or music based on a user's viewing or listening history. 100 | # 101 | # - E-commerce platforms, such as Amazon, utilize deep learning-based recommendation systems to suggest products based on a user's browsing history, purchase behavior, and similar user profiles. 102 | # 103 | # 5. **Speech Recognition:** Deep learning has significantly enhanced speech recognition accuracy and enabled voice-controlled applications. Examples include: 104 | # 105 | # - Voice assistants like Apple's Siri, Amazon's Alexa, or Google Assistant utilize deep learning models to accurately recognize and respond to spoken commands and queries. 106 | # 107 | # - Transcription services employ deep learning algorithms to convert spoken language into written text, facilitating tasks such as transcription services, voice search, or closed captioning. 108 | # 109 | # These are just a few examples showcasing the broad range of applications where deep learning has made significant advancements. The versatility and effectiveness of deep learning models have enabled breakthroughs in many fields, revolutionizing industries and improving various aspects of our lives. 110 | 111 | # %% 112 | 113 | 114 | 115 | -------------------------------------------------------------------------------- /ML-Fundamental/4-ML-Scikit learn.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "metadata": {}, 7 | "source": [ 8 | "# Machine learning & Scikit learn" 9 | ] 10 | }, 11 | { 12 | "attachments": {}, 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Machine learning is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable machines to learn from data and make predictions or decisions without being explicitly programmed. It is used in a wide range of applications, including image recognition, natural language processing, fraud detection, and recommendation systems.\n", 17 | "\n", 18 | "To get started with machine learning, there are several tools and libraries that can be used. Here are some of the most popular ones:\n", 19 | "\n", 20 | "- **Python:** Python is a popular programming language for machine learning due to its ease of use, rich libraries, and wide community support.\n", 21 | "- **NumPy:** NumPy is a library for numerical computing in Python that provides support for arrays, matrices, and linear algebra operations.\n", 22 | "- **Pandas:** Pandas is a library for data manipulation and analysis in Python that provides tools for reading and writing data, cleaning and preprocessing data, and exploring data.\n", 23 | "- **Scikit-learn:** Scikit-learn is a library for machine learning in Python that provides tools for classification, regression, clustering, dimensionality reduction, model selection, and data preprocessing.\n", 24 | "- **TensorFlow:** TensorFlow is a library for machine learning developed by Google that provides tools for building and training deep learning models.\n", 25 | "- **Keras:** Keras is a high-level API for building and training deep learning models that runs on top of TensorFlow.\n", 26 | "- **PyTorch:** PyTorch is a library for machine learning developed by Facebook that provides tools for building and training deep learning models.\n", 27 | "\n", 28 | "These are just a few of the many tools and libraries available for machine learning. The choice of tools and libraries depends on the specific application and the expertise of the user. In the present tutorial, I will discuss mainly Scikit-learn in great detailed." 29 | ] 30 | }, 31 | { 32 | "attachments": {}, 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "# Scikit-learn\n", 37 | "\n", 38 | "- **What is Scikit-learn?:** Scikit-learn (also known as sklearn) is an open-source machine learning library that provides a range of tools for implementing supervised and unsupervised learning algorithms. It is built on top of NumPy, SciPy, and Matplotlib, and is designed to integrate well with other libraries in the Python scientific computing ecosystem.\n", 39 | "\n", 40 | " Overall, scikit-learn is a powerful and user-friendly library that is widely used by data scientists and machine learning practitioners for a variety of tasks, from exploratory data analysis to building complex machine learning pipelines.\n", 41 | "\n", 42 | "- **Which applications can be implemented with the library?**\n", 43 | "\n", 44 | " With Scikit-Learn, a wide variety of AI models can be implemented, both from supervised and unsupervised learning . In general, the models can be divided into the following groups:\n", 45 | "\n", 46 | " - Classification ( Support Vector Machine , Random Forest , Decision Tree , etc.)\n", 47 | " - Regressions ( Logistic Regression , Linear Regression , etc.)\n", 48 | " - Dimension reduction ( principal component analysis , factor analysis, etc.)\n", 49 | " - Data preprocessing and visualization\n", 50 | "- **What are the advantages of scikit learn?:**\n", 51 | "\n", 52 | " Library benefits include:\n", 53 | "\n", 54 | " - simplified application of machine learning tools, data analytics and data visualization\n", 55 | " - commercial use without license fees\n", 56 | " - High degree of flexibility when fine-tuning the models\n", 57 | " - based on common and powerful data structures from Numpy\n", 58 | " - Usable in different contexts." 59 | ] 60 | }, 61 | { 62 | "attachments": {}, 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "## Some classes available in the Sklearn library\n", 67 | "\n", 68 | "Scikit-learn is a popular Python library for machine learning. It provides a wide range of machine learning algorithms and tools for data preprocessing, model selection, and evaluation. Here are some of the main classes in scikit-learn:\n", 69 | "\n", 70 | "- **Estimators:** Estimators are the main objects in scikit-learn that perform the machine learning algorithms. Each estimator is a Python class that implements a specific algorithm, such as linear regression, logistic regression, decision trees, or support vector machines. Estimators have a fit() method that takes in the training data and trains the model, and a predict() method that takes in new data and makes predictions.\n", 71 | "\n", 72 | "- **Transformers:** Transformers are objects that preprocess data before it is fed into the machine learning algorithm. Examples of transformers include data scaling, feature selection, and text preprocessing. Transformers have a fit_transform() method that takes in the training data and fits the transformer, and a transform() method that applies the transformer to new data.\n", 73 | "\n", 74 | "- **Pipelines:** Pipelines are a sequence of transformers and estimators that are combined together to form a complete machine learning workflow. Pipelines can be used to automate the process of preprocessing data and training a machine learning model. Pipelines have a fit() method that takes in the training data and trains the entire workflow, and a predict() method that takes in new data and makes predictions.\n", 75 | "\n", 76 | "- **Model Selection:** The model selection classes in scikit-learn provide tools for selecting the best model and hyperparameters for a given dataset. These classes include GridSearchCV and RandomizedSearchCV, which perform an exhaustive search over a grid of hyperparameters or a random search of hyperparameters, respectively.\n", 77 | "\n", 78 | "- **Metrics:** Metrics are used to evaluate the performance of a machine learning model. Scikit-learn provides a wide range of evaluation metrics, including accuracy, precision, recall, F1 score, and ROC curves.\n", 79 | "\n", 80 | "These are just some of the main classes in scikit-learn. Scikit-learn also provides many other useful classes and functions for machine learning, such as clustering algorithms, ensemble methods, and data loading utilities." 81 | ] 82 | }, 83 | { 84 | "cell_type": "markdown", 85 | "metadata": {}, 86 | "source": [] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [] 92 | }, 93 | { 94 | "cell_type": "markdown", 95 | "metadata": {}, 96 | "source": [] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [] 102 | }, 103 | { 104 | "attachments": {}, 105 | "cell_type": "markdown", 106 | "metadata": {}, 107 | "source": [ 108 | "# Refrences\n", 109 | "\n", 110 | "Some useful resources where you can find more information about scikit-learn and examples of how to use each class:\n", 111 | "\n", 112 | "1. Official scikit-learn documentation: https://scikit-learn.org/stable/documentation.html\n", 113 | "2. Scikit-learn tutorials: https://scikit-learn.org/stable/tutorial/index.html\n", 114 | "3. Scikit-learn examples: https://scikit-learn.org/stable/auto_examples/index.html\n", 115 | "4. Scikit-learn user guide: https://scikit-learn.org/stable/user_guide.html\n", 116 | "5. Python Data Science Handbook by Jake VanderPlas: https://jakevdp.github.io/PythonDataScienceHandbook/index.html" 117 | ] 118 | } 119 | ], 120 | "metadata": { 121 | "language_info": { 122 | "name": "python" 123 | }, 124 | "orig_nbformat": 4 125 | }, 126 | "nbformat": 4, 127 | "nbformat_minor": 2 128 | } 129 | -------------------------------------------------------------------------------- /ML-Fundamental/ML-fundamental python file/ML-regression.py: -------------------------------------------------------------------------------- 1 | # %% [markdown] 2 | # # Regression algorithms 3 | # 4 | # Regression algorithms are a type of machine learning algorithm used to predict numerical values based on input data. Regression algorithms attempt to find a relationship between the input variables and the output variable by fitting a mathematical model to the data. The goal of regression is to find a mathematical relationship between the input features and the target variable that can be used to make accurate predictions on new, unseen data. 5 | # 6 | # 7 | # 8 | # There are many different types of regression algorithms, including: 9 | # 10 | # 1. **Linear regression:** Linear regression is a simple and widely used algorithm. It assumes a linear relationship between the independent variables and the target variable. The algorithm estimates the coefficients of the linear equation that best fits the data. The equation can be of the form 11 | # 12 | # $y = mx + b$, 13 | # 14 | # where $y$ is the target variable, $x$ is the input feature, $m$ is the slope, and $b$ is the intercept. 15 | # 16 | # **Example:** applications include predicting housing prices based on features like square footage and number of bedrooms, or estimating sales based on advertising expenditure. 17 | # 18 | # 2. **Logistic regression:** Logistic regression is a popular algorithm used for binary classification problems, where the target variable has two possible outcomes (e.g., yes/no, true/false, 0/1). Despite its name, logistic regression is a classification algorithm, not a regression algorithm. It models the relationship between the independent variables (input features) and the binary target variable using the logistic function, also known as the sigmoid function. 19 | # 20 | # ![image.png](attachment:image.png) 21 | # 22 | # **Example:** predicting whether a customer will churn (i.e., stop doing business with a company) based on their demographic information and purchase history. 23 | # 24 | # 3. **Polynomial regression:** Polynomial regression is an extension of linear regression where the relationship between the variables is modeled using a polynomial equation. This allows for more flexibility in capturing nonlinear relationships between the input features and the target variable. It involves adding polynomial terms, such as $x^2$ or $x^3$, to the linear equation. Polynomial regression is useful when the data exhibits curvilinear patterns. 25 | # 26 | # **Example:** predicting the yield of a crop based on factors such as temperature, humidity, and rainfall. 27 | # 28 | # 4. **Ridge regression:** Ridge regression is a regularization technique that addresses the issue of overfitting in linear regression. It adds a penalty term to the linear regression equation to control the complexity of the model. This penalty term helps prevent the coefficients from becoming too large, reducing the model's sensitivity to the training data. Ridge regression is particularly useful when dealing with high-dimensional data or when multicollinearity (high correlation) exists among the input features. 29 | # 30 | # **Example:** predicting the price of a stock based on financial indicators such as earnings per share and price-to-earnings ratio. 31 | # 32 | # 5. **Lasso regression:** Lasso regression, similar to ridge regression, is a regularization technique used to combat overfitting. It adds a penalty term to the linear regression equation, but in this case, it uses the L1 norm of the coefficients as the penalty. Lasso regression has a feature selection property that can drive some coefficients to zero, effectively performing automatic feature selection. This makes it useful when dealing with datasets with many features or when looking to identify the most influential variables. 33 | # 34 | # **Example:** predicting the likelihood of a customer purchasing a product based on their browsing and purchase history on a website. 35 | # 36 | # 6. **Elastic Net regression:** ElasticNet regression combines both ridge and lasso regularization techniques. It adds a penalty term that is a linear combination of the L1 (lasso) and L2 (ridge) norms of the coefficients. This hybrid approach allows for feature selection while also providing stability and reducing the impact of multicollinearity. ElasticNet regression is useful when there are many correlated features and the goal is to both select relevant features and mitigate multicollinearity. 37 | # 38 | # **Example:** predicting the demand for a product based on factors such as price, advertising spend, and competitor activity. 39 | # 40 | # There are many other regression algorithms as well, and the choice of algorithm depends on the specific problem and the characteristics of the data. 41 | # 42 | # **Example:** 43 | # 44 | # - finance, 45 | # - healthcare, 46 | # - manufacturing 47 | # - Defence and space 48 | 49 | # %% [markdown] 50 | # ## Applications of Regression algorithms 51 | # 52 | # ### 1. In Finance sector: 53 | # 54 | # - **Risk management:** Regression algorithms can be used to analyze historical market data to identify patterns and trends in asset prices, which can help financial institutions to better understand and manage risks associated with their portfolios. 55 | # 56 | # - **Portfolio optimization:** Regression algorithms can be used to optimize the allocation of assets in a portfolio to maximize returns while minimizing risk. This involves using historical data to identify correlations between asset prices and building a model to predict future returns. 57 | # 58 | # - **Credit scoring:** Regression algorithms can be used to analyze borrower data such as credit scores, income, and employment history, to predict the likelihood of default on a loan. This information can be used by lenders to make more informed lending decisions. 59 | # 60 | # - **Trading strategies:** Regression algorithms can be used to analyze market data and identify profitable trading strategies. For example, a regression model could be used to predict the price of a stock based on its historical performance, and this information could be used to make buy or sell decisions. 61 | # 62 | # - **Financial forecasting:** Regression algorithms can be used to forecast financial performance metrics such as revenue, profits, and cash flow, based on historical data and other factors such as market trends and economic indicators. This information can be used by financial analysts to make informed investment recommendations. 63 | # 64 | # ### 2. In healthcare sector: 65 | # 66 | # - **Predicting patient outcomes:** Regression algorithms can be used to predict outcomes such as mortality, readmission, and length of stay for patients based on factors such as age, gender, diagnosis, and comorbidities. This information can help healthcare providers make more informed decisions about patient care and resource allocation. 67 | # 68 | # - **Predicting disease progression:** Regression algorithms can be used to predict the progression of diseases such as cancer, Alzheimer's, and Parkinson's based on biomarkers, genetic information, and other factors. This information can help with early detection and personalized treatment plans. 69 | # 70 | # - **Forecasting healthcare costs:** Regression algorithms can be used to forecast healthcare costs for individuals and populations based on factors such as age, gender, and medical history. This information can be used by insurance companies and policymakers to make more informed decisions about coverage and reimbursement. 71 | # 72 | # - **Analyzing clinical trials:** Regression algorithms can be used to analyze data from clinical trials to determine the efficacy and safety of new treatments. This information can help drug developers make decisions about which drugs to advance to the next phase of development. 73 | # 74 | # - **Predicting disease outbreaks:** Regression algorithms can be used to predict disease outbreaks based on factors such as weather patterns, population density, and vaccination rates. This information can help public health officials make decisions about resource allocation and disease prevention strategies. 75 | # 76 | # ### 3. In manufacturing sector: 77 | # 78 | # - **Quality Control:** Regression algorithms can be used to monitor the quality of manufactured products by analyzing the relationship between the input variables (such as the raw materials used, the manufacturing process parameters) and the output variables (such as the product quality metrics). This helps in identifying factors that affect the quality of the product and optimizing the manufacturing process accordingly. 79 | # 80 | # - **Predictive Maintenance:** Regression algorithms can be used to predict the remaining useful life of manufacturing equipment based on factors such as operating conditions, maintenance history, and sensor data. This helps in scheduling maintenance activities in advance, reducing downtime, and improving equipment reliability. 81 | # 82 | # - **Process Optimization:** Regression algorithms can be used to optimize the manufacturing process by analyzing the relationship between the input variables (such as the process parameters, raw materials) and the output variables (such as product yield, production rate). This helps in identifying the optimal process settings that result in the highest quality products with minimal waste. 83 | # 84 | # - **Supply Chain Management:** Regression algorithms can be used to forecast demand for raw materials and finished products based on historical sales data, economic trends, and market conditions. This helps in improving supply chain planning, reducing inventory costs, and avoiding stockouts. 85 | # 86 | # - **Root Cause Analysis:** Regression algorithms can be used to identify the root cause of defects or quality issues in the manufacturing process by analyzing the relationship between input variables and output variables. This helps in identifying the factors that contribute to defects and implementing corrective actions to prevent them from occurring in the future. 87 | # 88 | # ### 4. In Space & Defence sector: 89 | # 90 | # - **Trajectory prediction:** In the space sector, regression algorithms can be used to predict the trajectory of spacecraft, satellites, and other objects in orbit. This can help with mission planning, collision avoidance, and re-entry planning. 91 | # 92 | # - **Missile guidance:** In the defense sector, regression algorithms can be used to guide missiles to their targets. By analyzing data such as the target's speed, direction, and distance, a regression algorithm can predict the missile's trajectory and make adjustments to ensure it hits the target. 93 | # 94 | # - **Signal processing:** Regression algorithms can also be used in the analysis of signals received from space. For example, they can be used to estimate the direction of arrival of signals from space, which can help with tasks such as tracking satellites and detecting and identifying space debris. 95 | # 96 | # - **Target tracking:** In the defense sector, regression algorithms can be used to track the movement of targets such as vehicles and aircraft. By analyzing data such as the target's speed, direction, and radar signature, a regression algorithm can predict its future position and velocity, which can help with intercepting the target. 97 | # 98 | # - **Image analysis:** Regression algorithms can also be used in the analysis of images and video data from space and defense applications. For example, they can be used to estimate the size and shape of objects in images, detect anomalies, and identify targets. 99 | # 100 | # 101 | # These are just a few examples of the applications of regression algorithms in various sector. As technology advances and more data becomes available, we can expect to see even more applications of these algorithms in these fields. 102 | 103 | # %% [markdown] 104 | # ## Terminologies Related to the Regression Analysis 105 | # 106 | # 1. **Dependent variable:** The variable being predicted or explained by the regression analysis. It is also called the response variable or outcome variable. 107 | # 108 | # 2. **Independent variable:** The variable that is used to predict or explain the dependent variable. It is also called the predictor variable or explanatory variable. 109 | # 110 | # 3. **Simple linear regression:** A regression analysis that involves only one independent variable. 111 | # 112 | # 4. **Multiple linear regression:** A regression analysis that involves two or more independent variables. 113 | # 114 | # 5. **Coefficient:** The value that represents the slope of the regression line. It indicates the amount by which the dependent variable changes when the independent variable changes by one unit. 115 | # 116 | # 6. **Intercept:** The value of the dependent variable when all independent variables are set to zero. It represents the starting point of the regression line. 117 | # 118 | # 7. **Residual:** The difference between the actual value of the dependent variable and the predicted value from the regression line. 119 | # 120 | # 8. **R-squared:** A measure of how well the regression line fits the data. It represents the proportion of the variance in the dependent variable that is explained by the independent variable(s). 121 | # 122 | # 9. **Overfitting:** When a regression model is too complex and fits the training data too closely, it may not generalize well to new data. 123 | # 124 | # 10. **Underfitting:** When a regression model is too simple and does not fit the training data well enough, it may not capture the underlying relationships between the variables. 125 | # 126 | # These are some common terminologies related to regression analysis, and there may be others depending on the specific context and type of regression being used. 127 | 128 | # %% [markdown] 129 | # ## Why do we use Regression Analysis? 130 | # 131 | # Regression analysis is a statistical method used to examine the relationship between a dependent variable and one or more independent variables. It is used for a variety of purposes, including: 132 | # 133 | # 1. **Prediction:** Regression analysis can be used to predict the values of the dependent variable based on the values of the independent variables. For example, if we want to predict the sales of a product based on advertising expenditure and the size of the market, we can use regression analysis to determine the relationship between these variables and predict the sales based on the values of the independent variables. 134 | # 135 | # 2. **Hypothesis testing:** Regression analysis can be used to test hypotheses about the relationship between the dependent and independent variables. For example, we can test whether there is a significant relationship between smoking and lung cancer by using regression analysis. 136 | # 137 | # 3. **Control variables:** Regression analysis can be used to control for other variables that may affect the relationship between the dependent and independent variables. For example, if we want to examine the relationship between income and health, we may want to control for variables such as age, gender, and education. 138 | # 139 | # 4. **Forecasting:** Regression analysis can be used to forecast future trends based on historical data. For example, we can use regression analysis to forecast the demand for a product based on past sales data and other relevant variables. 140 | # 141 | # Overall, regression analysis is a useful tool for analyzing and understanding the relationship between variables and for making predictions and informed decisions based on that relationship. 142 | 143 | # %% [markdown] 144 | # # Reference 145 | # 146 | # 1. https://www.javatpoint.com/regression-analysis-in-machine-learning 147 | # 2. Machine Learning, using Python Manaranjan Pradhan | U Dinesh Kumar 148 | 149 | 150 | -------------------------------------------------------------------------------- /ML-Fundamental/ML-fundamental python file/ML-sklearn.py: -------------------------------------------------------------------------------- 1 | # %% [markdown] 2 | # # Machine learning & Scikit learn 3 | 4 | # %% [markdown] 5 | # Machine learning is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable machines to learn from data and make predictions or decisions without being explicitly programmed. It is used in a wide range of applications, including image recognition, natural language processing, fraud detection, and recommendation systems. 6 | # 7 | # To get started with machine learning, there are several tools and libraries that can be used. Here are some of the most popular ones: 8 | # 9 | # - **Python:** Python is a popular programming language for machine learning due to its ease of use, rich libraries, and wide community support. 10 | # - **NumPy:** NumPy is a library for numerical computing in Python that provides support for arrays, matrices, and linear algebra operations. 11 | # - **Pandas:** Pandas is a library for data manipulation and analysis in Python that provides tools for reading and writing data, cleaning and preprocessing data, and exploring data. 12 | # - **Scikit-learn:** Scikit-learn is a library for machine learning in Python that provides tools for classification, regression, clustering, dimensionality reduction, model selection, and data preprocessing. 13 | # - **TensorFlow:** TensorFlow is a library for machine learning developed by Google that provides tools for building and training deep learning models. 14 | # - **Keras:** Keras is a high-level API for building and training deep learning models that runs on top of TensorFlow. 15 | # - **PyTorch:** PyTorch is a library for machine learning developed by Facebook that provides tools for building and training deep learning models. 16 | # 17 | # These are just a few of the many tools and libraries available for machine learning. The choice of tools and libraries depends on the specific application and the expertise of the user. In the present tutorial, I will discuss mainly Scikit-learn in great detailed. 18 | 19 | # %% [markdown] 20 | # # Scikit-learn 21 | # 22 | # - **What is Scikit-learn?:** Scikit-learn (also known as sklearn) is an open-source machine learning library that provides a range of tools for implementing supervised and unsupervised learning algorithms. It is built on top of NumPy, SciPy, and Matplotlib, and is designed to integrate well with other libraries in the Python scientific computing ecosystem. 23 | # 24 | # Overall, scikit-learn is a powerful and user-friendly library that is widely used by data scientists and machine learning practitioners for a variety of tasks, from exploratory data analysis to building complex machine learning pipelines. 25 | # 26 | # - **Which applications can be implemented with the library?** 27 | # 28 | # With Scikit-Learn, a wide variety of AI models can be implemented, both from supervised and unsupervised learning . In general, the models can be divided into the following groups: 29 | # 30 | # - Classification ( Support Vector Machine , Random Forest , Decision Tree , etc.) 31 | # - Regressions ( Logistic Regression , Linear Regression , etc.) 32 | # - Dimension reduction ( principal component analysis , factor analysis, etc.) 33 | # - Data preprocessing and visualization 34 | # - **What are the advantages of scikit learn?:** 35 | # 36 | # Library benefits include: 37 | # 38 | # - simplified application of machine learning tools, data analytics and data visualization 39 | # - commercial use without license fees 40 | # - High degree of flexibility when fine-tuning the models 41 | # - based on common and powerful data structures from Numpy 42 | # - Usable in different contexts. 43 | 44 | # %% [markdown] 45 | # ## Some classes available in the Sklearn library 46 | # 47 | # Scikit-learn is a popular Python library for machine learning. It provides a wide range of machine learning algorithms and tools for data preprocessing, model selection, and evaluation. Here are some of the main classes in scikit-learn: 48 | # 49 | # - **Estimators:** Estimators are the main objects in scikit-learn that perform the machine learning algorithms. Each estimator is a Python class that implements a specific algorithm, such as linear regression, logistic regression, decision trees, or support vector machines. Estimators have a fit() method that takes in the training data and trains the model, and a predict() method that takes in new data and makes predictions. 50 | # 51 | # - **Transformers:** Transformers are objects that preprocess data before it is fed into the machine learning algorithm. Examples of transformers include data scaling, feature selection, and text preprocessing. Transformers have a fit_transform() method that takes in the training data and fits the transformer, and a transform() method that applies the transformer to new data. 52 | # 53 | # - **Pipelines:** Pipelines are a sequence of transformers and estimators that are combined together to form a complete machine learning workflow. Pipelines can be used to automate the process of preprocessing data and training a machine learning model. Pipelines have a fit() method that takes in the training data and trains the entire workflow, and a predict() method that takes in new data and makes predictions. 54 | # 55 | # - **Model Selection:** The model selection classes in scikit-learn provide tools for selecting the best model and hyperparameters for a given dataset. These classes include GridSearchCV and RandomizedSearchCV, which perform an exhaustive search over a grid of hyperparameters or a random search of hyperparameters, respectively. 56 | # 57 | # - **Metrics:** Metrics are used to evaluate the performance of a machine learning model. Scikit-learn provides a wide range of evaluation metrics, including accuracy, precision, recall, F1 score, and ROC curves. 58 | # 59 | # These are just some of the main classes in scikit-learn. Scikit-learn also provides many other useful classes and functions for machine learning, such as clustering algorithms, ensemble methods, and data loading utilities. 60 | 61 | # %% [markdown] 62 | # 63 | 64 | # %% [markdown] 65 | # 66 | 67 | # %% [markdown] 68 | # 69 | 70 | # %% [markdown] 71 | # 72 | 73 | # %% [markdown] 74 | # # Refrences 75 | # 76 | # Some useful resources where you can find more information about scikit-learn and examples of how to use each class: 77 | # 78 | # 1. Official scikit-learn documentation: https://scikit-learn.org/stable/documentation.html 79 | # 2. Scikit-learn tutorials: https://scikit-learn.org/stable/tutorial/index.html 80 | # 3. Scikit-learn examples: https://scikit-learn.org/stable/auto_examples/index.html 81 | # 4. Scikit-learn user guide: https://scikit-learn.org/stable/user_guide.html 82 | # 5. 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Logistic Regression 9 | 2. k-Nearest Neighbour (K-NN) 10 | 3. Support Vector Machine (SVM) 11 | 4. Decision Trees 12 | 5. Random Forest 13 | 6. Gradient Boosting 14 | - Unsupervised Learning: 15 | - Clustering: 16 | 1. K-means 17 | 2. DBSCAN 18 | 3. Hierarchical Clustering 19 | - Dimensionality Reduction 20 | 1. Principal Component Analysis (PCA) 21 | 2. t-Distributed Stochastic Neighbor Embedding (t-SNE) 22 | 3. Linear Discriminant Analysis (LDA) 23 | 4. Association Rule Learning 24 | - Reinforcement Leanring 25 | - Model Evaluation and validation: 26 | 1. Cross-Validation 27 | 2. Hyperparameter Tuning 28 | 3. Model Selection Techniques 29 | 4. Evaluation Metrics -------------------------------------------------------------------------------- /ML-Fundamental/image-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/ML-Fundamental/image-1.png -------------------------------------------------------------------------------- /ML-Fundamental/image-2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/ML-Fundamental/image-2.png -------------------------------------------------------------------------------- /ML-Fundamental/image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/ML-Fundamental/image.png -------------------------------------------------------------------------------- /Projects-ML/Example-project.md: -------------------------------------------------------------------------------- 1 | # Genral Framework of the project 2 | Let's consider a simple example where we have an ML project with two stages - development and Markdown markdown 3 | 4 | 1. Initialize your ML project: Create a new directory for your project, and initialize it using a tool like git. 5 | ```bash 6 | mkdir my-ml-project 7 | cd my-ml-project 8 | git init 9 | ``` 10 | 2. Set up a virtual environment: To keep your project's dependencies separate from your system's, you can use a virtual environment. Create a new virtual environment and activate it. 11 | ```bash 12 | python3 -m venv venv 13 | source venv/bin/activate 14 | ``` 15 | 3. Initialize a git repository: Once you've set up your virtual environment, initialize a git repository to track your project's changes. 16 | ```bash 17 | git init 18 | ``` 19 | 4. Write your ML code: Now, you can start writing your ML code. Create a new file called model.py and write your ML model code. 20 | ```python 21 | def my_ml_model(x): 22 | # Your ML model code here 23 | pass 24 | ``` 25 | 5. Commit your changes: Once you've written your ML code, commit your changes to the git repository. 26 | ```bash 27 | git add model.py 28 | git commit -m "Add my ML model" 29 | ``` 30 | 6. Prepare your code for production: Now, it's time to prepare your code for production. Write a script called train_model.py that trains your ML model using your development data. 31 | 32 | ```python 33 | import pandas as pd 34 | from model import my_ml_model 35 | 36 | # Load your development data 37 | data = pd.read_csv('data.csv') 38 | 39 | # Train your ML model 40 | my_ml_model.fit(data.drop('target', axis=1), data['target']) 41 | ``` 42 | 7. Write a script to make predictions: Create a new file called predict.py that allows you to make predictions using your trained ML model. 43 | 44 | ```python 45 | import pandas as pd 46 | from model import my_ml_model 47 | 48 | # Load your data 49 | data = pd.read_csv('data.csv') 50 | 51 | # Make predictions 52 | predictions = my_ml_model.predict(data.drop('target', axis=1)) 53 | 54 | # Print predictions 55 | print(predictions) 56 | ``` 57 | 8. Save your trained model: Train your ML model and save it to a file using the train_model.py script. 58 | ```bash 59 | python train_model.py 60 | ``` 61 | 9. Make predictions using your trained model: Now, you can use the predict.py script to make predictions using your trained ML model. 62 | ```bash 63 | python predict.py 64 | ``` 65 | 10.Prepare your model for deployment: Package your trained model, the predict.py script, and any other necessary files into a directory called model. 66 | 67 | ```bash 68 | mkdir model 69 | cp train_model.py model/ 70 | cp predict.py model/ 71 | cp -r data model/ 72 | ``` 73 | 11. Commit your changes: Finally, commit your changes to the git repository. 74 | 75 | ```bash 76 | git add model 77 | git commit -m "Prepare model for deployment" 78 | ``` 79 | 80 | 12. Write your markdown: Create a new file called README.md and write your markdown documentation. This should include information about your project, how to use it, and any necessary references. 81 | 82 | ```markdown 83 | # My ML Project 84 | 85 | This is a simple ML project that uses a hypothetical ML model. 86 | 87 | ## How to use it 88 | 89 | 1. Clone the repository. 90 | 2. Train the model using the `train_model.py` script. 91 | 3. Make predictions using the `predict.py` script. 92 | 93 | ## References 94 | 95 | - [TensorFlow](https://www.tensorflow.org/) 96 | - [scikit-learn](https://scikit-learn.org/stable/) 97 | ``` 98 | 99 | 13. Commit your changes: Finally, commit your changes to the git repository. 100 | 101 | ```bash 102 | git add README.md 103 | git commit -m "Add markdown documentation" 104 | ``` 105 | 14. Push your changes to a remote repository: Once you've finished setting up your ML project and markdown documentation, you can push your changes to a remote repository like GitHub or GitLab. 106 | 107 | ```bash 108 | git remote add origin https://github.com/username/my-ml-project.git 109 | git push -u origin master 110 | ``` 111 | 112 | -------------------------------------------------------------------------------- /Projects-ML/ML-cycle.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/Projects-ML/ML-cycle.png -------------------------------------------------------------------------------- /Projects-ML/Py-file/Multiple-reg.py: -------------------------------------------------------------------------------- 1 | # %% [markdown] 2 | # # Project-2: Auction pricing of players in the Indian premier league (IPL) 3 | # **(Multiple Linear Regression)** 4 | # 5 | # - Multiple linear regression (MLR) is a supervised learning algorithm for finding the existence of an association relationship between a dependent variable (aka response variable or outcome variable) and several independent variables (aka explanatory variables or predictor variable or features). 6 | # 7 | # - The functional form of MLR is given by: 8 | # 9 | # $Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + .... + + \beta_k X_{ki} + \epsilon_{ki}$ 10 | # 11 | # where 12 | # 13 | # $\beta_1$, $\beta_2$, $\beta_3$, .... , $\beta_k$ are partial regression coefficients. 14 | # 15 | # - X = independent variables (aka explanatory variables or predictor variable or features) 16 | # - Y = dependent variable (aka response variable or outcome variable)
17 | # 18 | # - Since the relationship between an explanatory variable and the response (outcome) variable is calculated after removing (or controlling) the effect all the other explanatory variables (features) in the model. 19 | # 20 | # - The assumptions that are made in multiple linear regression model are as follows: 21 | # 1. **Linearity:** The regression model is linear in regression parameters (b-values). 22 | # 2. **Normal distribution:** The residuals follow a normal distribution and the expected value (mean) of the residuals is zero. 23 | # 3. **Uncorrelated residuals:** In time series data, residuals are assumed to uncorrelated. 24 | # 4. **Variance of the residuals** The variance of the residuals is constant for all values of $X_i$. When the variance of the residuals is constant for different values of $X_i$, it is called homoscedasticity. A non-constant variance of residuals is called heteroscedasticity. 25 | # 5. **Correlation between independent variables:** There is no high correlation between independent variables in the model (called multi-collinearity). Multi-collinearity can destabilize the model and can result in an incorrect estimation of the regression parameters. 26 | # 27 | # The partial regressions coefficients are estimated by minimizing the sum of squared errors (SSE). 28 | 29 | # %% [markdown] 30 | # ## Objective: Predicting the SOLD PRICE (Auction Price) of Players 31 | # 32 | # The Indian Premier League (IPL) is a professional league for Twenty20 (T20) cricket championships that was started in 2008 in India. IPL was initiated by the BCCI with eight franchises comprising players from across the world. The first IPL auction was held in 2008 for ownership of the teams for 10 years, with a base price of USD 50 million. The franchises acquire players through an English auction that is con- ducted every year. However, there are several rules imposed by the IPL. For example, only international players and popular Indian players are auctioned. 33 | # 34 | # The performance of the players could be measured through several metrics. Although the IPL fol- lows the Twenty20 format of the game, it is possible that the performance of the players in the other formats of the game such as Test and One-Day matches could influence player pricing. A few players had excellent records in Test matches, but their records in Twenty20 matches were not very impressive. The performances of 130 players who played in at least one season of the IPL (2008−2011) measured through various performance metrics 35 | # 36 | # ### Data Code Description 37 | # 38 | # | Data Code | Description | 39 | # |-----------|-------------| 40 | # | AGE | Age of the player at the time of auction classified into three categories. Category 1 (L25) means the player is less than 25 years old, category 2 means that the age is between 25 and 35 years (B25− 35) and category 3 means that the age is more than 35 (A35). | 41 | # | RUNS-S | Number of runs scored by a player. | 42 | # | RUNS-C | Number of runs conceded by a player. | 43 | # | HS | Highest score by a batsman in IPL. | 44 | # | AVE-B | Average runs scored by a batsman in IPL. | 45 | # | AVE-BL | Bowling average (number of runs conceded/number of wickets taken) in IPL. | 46 | # | SR-B | Batting strike rate (ratio of the number of runs scored to the number of balls faced) in IPL. | 47 | # | SR-BL | Bowling strike rate (ratio of the number of balls bowled to the number of wickets taken) in IPL. | 48 | # | SIXERS | Number of six runs scored by a player in IPL.| 49 | # | WKTS | Number of wickets taken by a player in IPL. | 50 | # | ECON | Economy rate of a bowler (number of runs conceded by the bowler per over) in IPL. | 51 | # | CAPTAINCY EXP | Captained either a T20 team or a national team.| 52 | # | ODI-SR-B | Batting strike rate in One-Day Internationals. | 53 | # | ODI-SR-BL | Bowling strike rate in One-Day Internationals. | 54 | # | ODI-RUNS-S | Runs scored in One-Day Internationals. | 55 | # | ODI-WKTS | Wickets taken in One-Day Internationals.| 56 | # | T-RUNS-S | Runs scored in Test matches. | 57 | # | T-WKTS | Wickets taken in Test matches. | 58 | # | PLAYER-SKILL | Player’s primary skill (batsman, bowler, or allrounder). | 59 | # | COUNTRY | Country of origin of the player (AUS: Australia; IND: India; PAK: Pakistan; SA: South Africa; SL: Sri Lanka; NZ: New Zealand; WI: West Indies; OTH: Other countries). | 60 | # | YEAR-A | Year of Auction in IPL. | 61 | # | IPL TEAM | CSK: Chennai Super Kings; DC: Deccan Chargers; DD: Delhi Dare- devils; KXI: Kings XI Punjab; KKR: Kolkata Knight Riders; MI: Mumbai Indians; PWI: Pune Warriors India; RR: Rajasthan Royals; RCB: Royal Challengers Bangalore | 62 | # 63 | # *A + sign is used to indicate that the player has played for more than one team. For example, CSK+ would mean that the player has played for CSK as well as for one or more other teams. 64 | 65 | # %% [markdown] 66 | # ## Data 67 | # - Data is available at in the repository. 68 | 69 | # %% [markdown] 70 | # ## Developing Multiple Linear Regression Model Using Python 71 | 72 | # %% [markdown] 73 | # ### Importing important libraries 74 | 75 | # %% 76 | import warnings 77 | warnings.filterwarnings('ignore') 78 | 79 | import numpy as np 80 | import pandas as pd 81 | 82 | import matplotlib.pylab as plt 83 | import seaborn as sns 84 | 85 | plt.style.use('ggplot') 86 | 87 | np.set_printoptions(precision=4, linewidth=100) 88 | 89 | # %% [markdown] 90 | # ### Data file path 91 | 92 | # %% 93 | # Provide the relative path to the data file 94 | file_path = "../ml-data/IPL-IMB381IPL2013.csv" 95 | 96 | # %% [markdown] 97 | # ### Importing the data file 98 | 99 | # %% 100 | # importing the data file 101 | 102 | ipl_auction_df = pd.read_csv(file_path) 103 | 104 | # %% 105 | ipl_auction_df.info() 106 | 107 | # %% 108 | # shape of the dataframe 109 | ipl_auction_df.shape 110 | 111 | # %% [markdown] 112 | # There are 130 observations (records) and 26 columns (features) in the data, and there are no missing values. 113 | 114 | # %% 115 | # importing first 10 rows 116 | ipl_auction_df.head(5) 117 | 118 | # %% 119 | ipl_auction_df.plot.scatter(x='ODI-RUNS-S', y='BASE PRICE') 120 | 121 | # %% 122 | # displaying the initial 10 columns for the first 5 rows 123 | ipl_auction_df.iloc[0:5, 0:10] 124 | 125 | # %% [markdown] 126 | # ## Building multiple linear regression model 127 | # 128 | # - We can build a model to understand what features (`X`) of players are influencing their SOLD PRICE or predict the player’s auction prices in future. However, all columns are not features. 129 | # - For example, Sl. NO. is just a serial number and cannot be considered a feature of the player. 130 | # - We will build a model using only player’s statistics. So, BASE PRICE can also be removed. 131 | # - We will create a variable `X_feature` which will contain the list of features that we will finally use for building the model and ignore rest of the columns of the DataFrame. 132 | 133 | # %% [markdown] 134 | # ### 1. Creating a feature columns 135 | # 136 | # The following function is used for including the features in the model building. 137 | 138 | # %% 139 | # Assuming 'ipl_auction_df' is your DataFrame 140 | column_names = ipl_auction_df.columns.tolist() 141 | column_names 142 | 143 | # %% [markdown] 144 | # Not all columns are important. We select few of them to make our model for auction price. To do this, we create `X_features` list. 145 | 146 | # %% 147 | X_features = ipl_auction_df.columns 148 | 149 | # %% 150 | X_features = ['AGE', 'COUNTRY', 'PLAYING ROLE', 'T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL', 'CAPTAINCY EXP', 151 | 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C', 'WKTS', 'AVE-BL', 'ECON', 'SR-BL'] 152 | 153 | # %% [markdown] 154 | # ### 2. Encoding Categorical Features 155 | # 156 | # - Qualitative variables or categorical variables need to be encoded using dummy variables before incorporating them in the regression model. 157 | # - If a categorical variable has `n` categories (e.g., the player role in the data has four categories, namely, batsman, bowler, wicket-keeper and allrounder), then we will need `n − 1` dummy variables. So, in the case of PLAYING ROLE, we will need **three dummy variables** since there are four categories. 158 | 159 | # %% 160 | # Finding unique values of column PLAYING ROLE 161 | ipl_auction_df['PLAYING ROLE'].unique() 162 | 163 | # %% 164 | ipl_auction_df['COUNTRY'].unique() 165 | 166 | # %% [markdown] 167 | # The variable can be converted into four dummy variables. 168 | # - Set the variable value to `1` to indicate the role of the player. 169 | # - This can be done using `pd.get_dummies()` method. 170 | # - We will create dummy variables for only PLAYING ROLE to understand and then create dummy variables for the rest of the categorical variables. 171 | # 172 | 173 | # %% 174 | pd.get_dummies(ipl_auction_df['PLAYING ROLE'])[0:5] 175 | #[0:5] is a slicing operation that selects the first five rows of the resulting DataFrame 176 | 177 | # %% [markdown] 178 | # As shown in the table above, the `pd.get_dummies()` method has created four dummy variables and has already set the variables to `1` as variable value in each sample. 179 | 180 | # %% [markdown] 181 | # - We must create dummy variables for all categorical (qualitative) variables present in the dataset. 182 | 183 | # %% 184 | categorical_features = ['AGE', 'COUNTRY', 'PLAYING ROLE', 'CAPTAINCY EXP'] 185 | 186 | # %% 187 | ipl_auction_encoded_df = pd.get_dummies(ipl_auction_df[X_features], 188 | columns = categorical_features, 189 | drop_first = True) 190 | 191 | # %% 192 | ipl_auction_encoded_df.columns 193 | 194 | # %% [markdown] 195 | # - The dataset contains the new dummy variables that have been created. 196 | # - We can reassign the new features to the variable `X_features`, which we created earlier to keep track of all features that will be used to build the model finally. 197 | 198 | # %% 199 | X_features = ipl_auction_encoded_df.columns 200 | X_features 201 | 202 | # %% [markdown] 203 | # ### 3. Splitting the Dataset into Train and Validation Sets 204 | # 205 | # - Before building the model, we will split the dataset into 80:20 ratio 206 | # - The split function allows using a parameter `random_state`, which is a seed function for reproducibility of randomness. This parameter is not required to be passed. 207 | # - Setting this variable to a fixed number will make sure that the records that go into **training** and **test set** remain unchanged and hence the results can be reproduced. We will use the value 42 (it is again selected randomly). 208 | 209 | # %% 210 | # Creating feature Set(X) and Outcome Variable (Y) 211 | import statsmodels.api as sm 212 | X = sm.add_constant( ipl_auction_encoded_df ) 213 | Y = ipl_auction_df['SOLD PRICE'] 214 | 215 | # %% 216 | from sklearn.model_selection import train_test_split 217 | 218 | train_X, test_X, train_y, test_y = train_test_split(X , Y, 219 | train_size = 0.8, random_state = 42 ) 220 | 221 | # %% [markdown] 222 | # ### 4. Fitting the Model 223 | # 224 | # We will fit the model using OLS method and pass `train_y` and `train_X` as parameters. 225 | 226 | # %% 227 | ipl_model_1 = sm.OLS(train_y, train_X).fit() 228 | 229 | # %% [markdown] 230 | # Printing Estimated Parameters and Interpreting Them 231 | 232 | # %% 233 | print(ipl_model_1.params) 234 | 235 | # %% [markdown] 236 | # #### Scatter Plot 237 | 238 | # %% 239 | # Scatter plot of original data points 240 | ipl_auction_df.plot.scatter(x='Percentage in Grade 10', y='Salary', label = 'Scatter plot') 241 | 242 | # Generate predicted values using the linear regression coefficients 243 | MBA_salary = mba_salary_lm.params[0]+mba_salary_lm.params[1]*mba_salary_df['Percentage in Grade 10'] 244 | 245 | # Plot the fitted line 246 | plt.plot(mba_salary_df['Percentage in Grade 10'], MBA_salary, color='red', label='Fitted Line') 247 | 248 | # Add labels and title 249 | plt.xlabel('X') 250 | plt.ylabel('Y') 251 | plt.title('Scatter Plot with Fitted Line') 252 | 253 | # Add legend 254 | plt.legend() 255 | 256 | # Show the plot 257 | plt.show() 258 | 259 | # %% [markdown] 260 | # ### 5. Regression Model Summary Using Python 261 | # 262 | # The function `summary2()` prints the model summary which contains the information required for diagnosing a regression model 263 | 264 | # %% 265 | ipl_model_1.summary2() 266 | 267 | # %% 268 | 269 | 270 | 271 | -------------------------------------------------------------------------------- /Projects-ML/Py-file/Proj-1-simple-line.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import warnings 3 | warnings.filterwarnings('ignore') 4 | 5 | import numpy as np 6 | import pandas as pd 7 | 8 | import matplotlib.pylab as plt 9 | import seaborn as sns 10 | 11 | plt.style.use('ggplot') 12 | 13 | np.set_printoptions(precision=4, linewidth=100) 14 | 15 | # %% [markdown] 16 | # ## Objective 17 | # 18 | # - We want to study the score in a exam on the basis of study time. 19 | # 20 | # $y = m \cdot x + c$ 21 | # 22 | # - Our Data points are: ($x_i, y_i$) 23 | # - The predicted data point for a given $x$ will be $\hat{y}_i$. 24 | # 25 | # - Mean squared error function: 26 | # 27 | # $E = \frac{1}{n} \cdot \sum_{i=0}^n (y_i- \hat{y}_i)^2 = \frac{1}{n} \cdot \sum_{i=0}^n (y_i- (m\cdot x_i + b))^2$ 28 | # 29 | # - We need to calculate $m$ and $b$ by minimizing the function $E$. We can calculate the di=erivative wrt $m$ and $b$ and set it to zero. 30 | # 31 | # $\frac{\partial E}{\partial m} = - \frac{2}{n} \cdot \sum_{i=0}^n x_i (y_i- (m\cdot x_i + b))$ 32 | # 33 | # $\frac{\partial E}{\partial m} = - \frac{1}{n} \cdot \sum_{i=0}^n (y_i- (m\cdot x_i + b))$ 34 | # 35 | # If we want to improve $m$ and $b$ then all we need to do is with each itteration we say take the current values of $m$ and assign it to following equations respectively: 36 | # 37 | # New $m$ and new $b$ will be written in terms of current $m$ and current $b$, and the the direction of the steepest ascent $\partial E/\partial (a ~{\rm or}~ m)$ as: 38 | # 39 | # $m = m -\alpha \cdot \frac{\partial E}{\partial m}$ 40 | # 41 | # $b = m -\alpha \cdot \frac{\partial E}{\partial b}$ 42 | # 43 | # where $\alpha$ is a learning rate and it signifies how big or how large the steps we take. Now large is $\alpha$, faster we get to the actual optimization but for lower $\alpha$, the better the results is going to be. Beacuase we are paying attention to details much more. 44 | # 45 | 46 | # %% [markdown] 47 | # Generating a random dataset with a column of 'Study time' and 'Score'. 48 | 49 | # %% 50 | # Generating the dataset for study time and score in an exam 51 | np.random.seed(42) 52 | 53 | study_time = np.random.uniform(low=0.5, high=4.0, size=100) 54 | score = 10 * study_time + np.random.normal(loc=0, scale=5, size=100) 55 | 56 | # Create a DataFrame from the generated data 57 | df = pd.DataFrame({'studytime': study_time, 'score': score}) 58 | 59 | # Save the DataFrame to a CSV file 60 | df.to_csv('Study-dataset.csv', index=False) 61 | 62 | # Load the CSV file into a DataFrame 63 | data_df = pd.read_csv('Study-dataset.csv') 64 | data_df.head() 65 | 66 | 67 | # %% 68 | plt.scatter(data_df.studytime, data_df.score ) 69 | plt.show() 70 | 71 | # %% [markdown] 72 | # Calculating the loss function and then calculating gradients and then finding m and b by optimization of the loss function: 73 | # 74 | # ``` 75 | # def loss_function(m,b, points): 76 | # total_erro = 0 77 | # for i in range(len(points)): 78 | # x = points.iloc[i].studytime 79 | # y = points.iloc[i].Score 80 | # total_error = += (y- (m*x+b))**2 81 | # total_error / float(len(points)) 82 | # ``` 83 | 84 | # %% 85 | # loss function and then minimization 86 | 87 | 88 | # Gradient Descent Function: 89 | def gradient_descent(m_now, b_now, points, L): 90 | m_gradient = 0 91 | b_gradient = 0 92 | 93 | n = len(points) 94 | 95 | for i in range(n): 96 | x = points.iloc[i].studytime 97 | y = points.iloc[i].score 98 | 99 | m_gradient += - (2/n)*x*(y-(m_now*x+b_now)) 100 | b_gradient += - (2/n)*(y-(m_now*x+b_now)) 101 | 102 | m = m_now - m_gradient*L 103 | b = b_now - b_gradient*L 104 | 105 | return m, b 106 | 107 | ''' 108 | gradient_descent(m_now, b_now, points, L): This function takes the current values of slope (m_now), 109 | y-intercept (b_now), a DataFrame containing the data points (points), and the learning rate (L) as input. 110 | It performs gradient descent to update the values of slope and y-intercept. This function calculates the 111 | gradients of the loss function with respect to m and b for each data point and updates the values of m and b 112 | using the gradient descent update rule. Here m_gradient and b_gradient are initialized to 0. 113 | ''' 114 | 115 | # %% 116 | # Initialization: 117 | m = 0 118 | b = 0 119 | L = 0.001 120 | epochs = 300 121 | 122 | ''' 123 | The slope (m) and y-intercept (b) are initialized to 0. The learning rate (L) is set to 0.0001, 124 | and the number of epochs (iterations) is set to 300. 125 | ''' 126 | 127 | # %% 128 | # Gradient Descent Loop: 129 | for i in range(epochs): 130 | if i % 10 == 0: 131 | print(f"Epoch: {i}") 132 | m, b = gradient_descent(m,b,data_df,L) 133 | 134 | ''' 135 | This loop runs for the specified number of epochs. In each iteration, 136 | it calls the gradient_descent function to update the values of m and b. 137 | The if statement inside the loop is used to print the current epoch every 138 | 50 iterations for monitoring progress. 139 | ''' 140 | 141 | # %% 142 | print(m) 143 | print(b) 144 | 145 | # %% 146 | # Plotting the data and fitted line 147 | plt.scatter(data_df.studytime, data_df.score, color="black") 148 | plt.plot(data_df.studytime, m * data_df.studytime + b, color="red") 149 | plt.xlabel('Study Time (hours)') 150 | plt.ylabel('Score') 151 | plt.title('Linear Regression Fit') 152 | plt.show() 153 | 154 | # %% [markdown] 155 | # ## Reference 156 | # 157 | # - https://www.youtube.com/watch?v=VmbA0pi2cRQ&ab_channel=NeuralNine 158 | 159 | # %% 160 | 161 | 162 | 163 | -------------------------------------------------------------------------------- /Projects-ML/README.md: -------------------------------------------------------------------------------- 1 | # Ensuring Data Integrity and Continuity for Machine Learning Projects 2 | 3 | ## Introduction  4 | In a typical Machine Learning project, the final implemented solution should provide automated training and implementation of the selected models. This is where CI/CD comes into play: This continuous integration / continuous deploying solution provides an end-to-end pipeline that completes the cycle of a full project and ensures the model's performance. Initially, Continuous Integration and Deployment is a DevOps technique to implement an automated pipeline for production's sake by: 5 | - streamlining (rationalization) 6 | - testing 7 | - deploying/ production 8 | 9 | The DevOps field corresponds to a collection of processes that tries to reduce the development life cycle of a system by enabling the continuous delivery of high-quality software.  10 | 11 | MLOps, on the other hand, is the process of automating and industrializing machine learning applications and workflows. CI/CD represents here an automation workflow of the ML pipeline through the following operations: 12 | - building the model 13 | - testing 14 | - deploying 15 | 16 | This also prevents the data scientist to take care and worry about this process, by ensuring no human negligence and constant improvement of the model efficiency by permanent monitoring of the ML model. Any change in the model construction is thus eased and its development automated with reliable delivery. 17 | 18 | As the CI/CD workflow will automate the different steps of an ML project, let's do a quick reminder about the typical lifecycle of an ML project. 19 | 20 | A sample image 21 | 22 | - **Data preparation:** In most cases, the data is initially presented in raw form. For this reason, it is necessary to perform a few steps of preprocessing these data sets to make them usable for the modeling step. This step is generally performed by the Data Scientist or sometimes by the Data Analyst and may require the use of tools such as Apache Spark, MySQL or Python, and libraries such as Pandas or Numpy. 23 | - **Model Training:** This step led by the Data Scientist is the main focus of the project life cycle: the purpose of the model implementation is to respond to a specific problem by designing and setting the appropriate algorithm. This iteration usually requires the import of tools such as TensorFlow, PyTorch frameworks, or the library Scikit-Learn. 24 | - **Model Deploying:** Once the model is ready, the Machine Learning Engineer or the Data Engineer is intended to make it available to the customer for easy and convenient use. 25 | - **New raw data:** Although the project may be expected to be coming to an end, very often the Data Engineer receives new raw data available after these steps. They must therefore be integrated into the cycle described above to refine and improve the model performance developed previously. 26 | 27 | ## Understanding CI/CD 28 | Each phase of the project lifecycle is associated with at least one process: CI, CD or both. Let's dive into their meaning. 29 | 30 | - **Continuous Integration (CI):** CI stands for Continuous Integration. This practice gathers software engineering concepts around automating the building and testing of the model, version control, and release. By release, we here explicitly mean the action of pushing the code to the corresponding GitHub repository. 31 | - **Continuous Delivery (CD):** CD stands for Continuous Delivery or Continuous deployment. This concept represents the continuous model deployment, the formatting, and the setup of a production-like environment to allow for automated integration. Regarding the application, the CD stage includes the testing, deployment, and automated configuration of the app. 32 | 33 | ## Key Components of CI/CD: 34 | 35 | - **Version Control System (VCS):** Centralized systems like Git enable collaborative development by managing code changes. 36 | - **Automated Builds:** Tools like Jenkins or Travis CI automate the build process, creating executable code from source files. 37 | - **Automated Testing:** Automated testing frameworks (e.g., JUnit for Java) ensure code quality by identifying bugs and issues early in the development cycle. 38 | - **Deployment Automation:** Tools such as Docker and Kubernetes automate the deployment process, ensuring consistency across different environments. 39 | 40 | ## Benefits of CI/CD: 41 | 42 | - **Faster Development Cycles:** Automated processes reduce manual errors and accelerate the development lifecycle. 43 | - **Improved Code Quality:** Automated testing and continuous monitoring enhance code reliability. 44 | - **Reduced Deployment Risks:** Incremental updates and automated rollbacks minimize the impact of failed deployments. 45 | - **Enhanced Collaboration:** CI/CD fosters collaboration among development and operations teams, leading to more efficient workflows. 46 | 47 | ## CI/CD in Machine Learning: 48 | Continuous Integration and Deployment (CI/CD) has long been a common practice for the majority of software systems. By offering continuous and automatic training and application of Machine Learning models, machine learning systems may do the same thing. 49 | 50 | Machine learning applications that use CI-CD offer a complete pipeline that closes the feedback loop at every level and maintains the performance of ML models. It can also bridge the gap between scientific and engineering processes by removing obstacles between data, modeling, processing, and return. 51 | 52 | Detail of every step of the typical lifecycle management of a machine learning project and its link with CI/CD: 53 | 54 | A sample image 55 | 56 | ## Example 57 | Let's consider a simple example where we have an ML project with two stages - development and Markdown? 58 | ### **Customer Behavior Prediction Model** 59 | 60 | Imagine a scenario where an e-commerce company wants to develop a machine learning model to predict customer behavior, specifically the likelihood of a customer making a purchase. The dataset includes information about customer demographics, browsing history, past purchases, and time spent on the website. 61 | 62 | **Project struncture:** 63 | Assume the following project structure: 64 | - customer_behavior_prediction/ 65 | - src/ 66 | - model.py 67 | - tests/ 68 | - test_model.py 69 | - build.py 70 | - requirements.txt 71 | 72 | 73 | **Steps in Continuous Integration and Continuous Deployment (CI/CD) for Customer Behavior Prediction Model:** 74 | 75 | **1. Continuous Integration (CI):** 76 | 77 | * **Version Control:** 78 | - Developers work on separate branches for different features or improvements in the model. 79 | - They use a version control system (e.g., Git) to manage changes collaboratively. 80 | - **Initialize a Git repository:** ```git init``` 81 | - **Create a `.gitignore` file to exclude unnecessary files from version control:** ```touch .gitignore``` 82 | - **Add and commit the changes:** 83 | ```bash 84 | git add . 85 | git commit -m "Initial commit" 86 | ``` 87 | - **Create a GitHub repository and push your code:** 88 | ```bash 89 | git remote add origin 90 | git branch -M main 91 | git push -u origin main 92 | ``` 93 | - **Set up a virtual environment:** To keep your project's dependencies separate from your system's, you can use a virtual environment. Create a new virtual environment and activate it. 94 | ```bash 95 | python3 -m venv venv 96 | source venv/bin/activate 97 | ``` 98 | and then initialize the repor using `git init`. 99 | 100 | * **Automated Testing:** 101 | - Unit tests and integration tests are written to verify the correctness of individual model components and their interactions. 102 | - Developers commit changes regularly, triggering automated tests to identify any integration issues early in the development process. 103 | - **Install `pytest`:** `pip install pytest` 104 | - **Write a simple test in a file named `test_model.py`:** 105 | ```python 106 | # tests/test_model.py 107 | 108 | from src.model import predict_customer_behavior 109 | 110 | def test_prediction(): 111 | # Assuming predict_customer_behavior is a function in your model 112 | prediction = predict_customer_behavior(sample_input) 113 | assert prediction in [0, 1] # Adjust based on your model's output 114 | ``` 115 | - **Run the test:** `pytest` 116 | 117 | * **Build Automation:**An automated build system (e.g., Jenkins) compiles the model code, creating an executable version of the predictive model. 118 | - **Create a file named `model.py`:** 119 | ```python 120 | # src/model.py 121 | 122 | def predict_customer_behavior(input_data): 123 | # Replace this with your actual model prediction logic 124 | # For simplicity, let's assume the model predicts 1 if spending > $100, otherwise 0 125 | return 1 if input_data['spending'] > 100 else 0 126 | ``` 127 | - **Build Script (build.py):** 128 | This script will be used by continuous integration tools like Jenkins or CircleCI to build the project. 129 | ```python 130 | # build.py 131 | 132 | def build(): 133 | # Replace this with your actual build logic 134 | # In this example, we might want to install necessary dependencies 135 | # and potentially perform other build steps if needed 136 | print("Building the Customer Behavior Prediction Model...") 137 | # Add any other build steps as required 138 | ``` 139 | 140 | * **Requirements File (requirements.txt):** 141 | 142 | ```plaintext 143 | # requirements.txt 144 | 145 | numpy==1.21.0 146 | pytest==6.2.4 147 | # Add any other dependencies required for your project 148 | ``` 149 | 150 | * **Run the build script:** `python build.py` 151 | 152 | > Replace the placeholder logic in the test script (test_model.py), model script (model.py), and build script (build.py) with your actual machine learning model, test logic, and any necessary build steps. 153 | 154 | 155 | **2. Continuous Deployment (CD):** 156 | 157 | * **Staging Environment:** 158 | - Upon successful integration and testing, the model is deployed to a staging environment. 159 | - In this controlled setting, the model's performance is assessed using a subset of real data to simulate real-world conditions. 160 | 161 | * **Automated Acceptance Tests:** 162 | - Automated acceptance tests are conducted to evaluate the model's accuracy, precision, and recall in the staging environment. 163 | - These tests confirm that the model behaves as expected and meets predefined performance criteria. 164 | - Update your GitHub Actions workflow (`ci.yml`) to include acceptance tests: 165 | 166 | ```yaml 167 | # Add the following step after the 'Build model' step 168 | - name: Run acceptance tests 169 | run: python acceptance_tests.py 170 | ``` 171 | 172 | * **Deployment to Production:** 173 | - Once the model passes all tests in the staging environment, it is automatically deployed to the production environment. 174 | - The deployment process includes updating the live model with the new version, ensuring a seamless transition. 175 | - **Create a deployment script `deploy.py`:** 176 | ```python 177 | # deploy.py 178 | 179 | def deploy(): 180 | # Add your actual deployment logic here 181 | print("Deploying the model to production...") 182 | ``` 183 | - Update your GitHub Actions workflow to deploy to production after passing tests: 184 | ```yaml 185 | # Add the following step after the 'Run acceptance tests' step 186 | 187 | - name: Deploy to production 188 | run: python deploy.py 189 | ``` 190 | 191 | **3. Continuous Monitoring and Rollback:** 192 | 193 | * **Performance Monitoring:** 194 | - Continuous monitoring tools (e.g., Prometheus) track the model's performance in real-time. 195 | - Metrics such as accuracy, response time, and error rates are continuously monitored to detect any anomalies. 196 | 197 | * **Alerts and Notifications:** 198 | - Automated alert systems notify the development team of any significant deviations from expected performance. 199 | - Alerts may trigger interventions or investigations to address issues promptly. 200 | 201 | * **Automated Rollback:** 202 | - If the model's performance degrades beyond acceptable thresholds, an automated rollback mechanism is initiated. 203 | - The system reverts to the previous version of the model to maintain service reliability while the issue is investigated. 204 | 205 | **4. Automating CI/CD with GitHub Actions:** Now, let's set up GitHub Actions for automated CI. Create a ``.github/workflows/ci.yml` file: 206 | 207 | ```yaml 208 | name: CI/CD 209 | on: 210 | push: 211 | branches: 212 | - main 213 | 214 | jobs: 215 | test: 216 | runs-on: ubuntu-latest 217 | 218 | steps: 219 | - name: Checkout repository 220 | uses: actions/checkout@v2 221 | 222 | - name: Set up Python 223 | uses: actions/setup-python@v2 224 | with: 225 | python-version: 3.8 226 | 227 | - name: Install dependencies 228 | run: | 229 | pip install -r requirements.txt 230 | pip install pytest 231 | 232 | - name: Run tests 233 | run: pytest 234 | 235 | - name: Build model 236 | run: python build.py 237 | 238 | - name: Run acceptance tests 239 | run: python acceptance_tests.py 240 | 241 | - name: Deploy to production 242 | run: python deploy.py 243 | ``` 244 | This GitHub Actions workflow will be triggered on every push to the main branch, running your tests and build script. 245 | 246 | **Conclusion:** 247 | 248 | Continuous Integration and Continuous Deployment ensure that the customer behavior prediction model undergoes a seamless and controlled development, testing, and deployment process. By automating key steps and implementing continuous monitoring, the e-commerce company can maintain a reliable and high-performing predictive model, adapting to changing customer behaviors effectively. The CI/CD pipeline provides a structured framework to enhance the agility and robustness of the machine learning model in response to evolving customer dynamics. 249 | 250 | 251 | -------------------------------------------------------------------------------- /Projects-ML/image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/Projects-ML/image.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | # Table of contents 4 | 5 | - Introduction to Machine learning: 6 | - Analytics 7 | - Categories of ML algorithms 8 | - A typical ML algorithm uses the following steps 9 | - Framework for develping ML models 10 | - **Machine learning Fundamentals and application:** This repository provides a comprehensive introduction to machine learning concepts, algorithms, and applications. It covers the essential theoretical foundations of machine learning, as well as practical examples and hands-on projects to solidify your understanding. 11 | - **Deep Learning:** Delve into the world of deep learning, exploring its theoretical underpinnings and applying it to real-world problems. This folder contains notebooks and related files that guide you through the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. 12 | - **Projects-ML:** Hands-on experience is invaluable for mastering machine learning. This folder offers a collection of sample projects that apply the concepts and algorithms discussed in the other folders. These projects provide practical exposure to machine learning techniques and allow you to apply your knowledge to real-world scenarios. 13 | - **Statistical fundamentals:** Statistics plays a pivotal role in machine learning. This folder delves into the intricacies of statistics, particularly inferential and descriptive statistics. It provides detailed explanations of statistical concepts and introduces applications of statistics in machine learning. 14 | - **ML-Data:** Datasets are indispensable for machine learning projects. This folder contains the data files used in the sample projects, ensuring that you have access to the relevant data to replicate and experiment with the provided examples. 15 | - References 16 | 17 | 18 | 19 | ## Will be updating many more interesting concepts and project here soon..... 20 | 21 | 22 | # Reference 23 | 24 | Individual references are given in individual notebooks. Here are few general references that one can look at: 25 | 26 | 1. Machine learning using python, Manarajan Pradhan, U Dinesh Kumar 27 | 2. Please follow lecture series provided at [Jovian.ml](https://jovian.ai/), i.e. [Data Analysis with Python: Zero to Pandas](https://jovian.ai/learn/data-analysis-with-python-zero-to-pandas) 28 | 3. [Python roadmap](https://roadmap.sh/python/) 29 | 4. [Python tutorials at w3school](https://www.w3schools.com/python/default.asp) 30 | 5. [30 Days of Python](https://github.com/asabeneh/30-days-of-python) 31 | 6. [Python official documents](https://docs.python.org/3/tutorial/) 32 | 7. [Data science roadmaps](https://github.com/codebasics/py/blob/master/TechTopics/DataScienceRoadMap2020/data_science_roadmap_2020.md) 33 | 8. Statistics, Murray R. Spiegel, Larry J. Stephens 34 | -------------------------------------------------------------------------------- /SECURITY.md: -------------------------------------------------------------------------------- 1 | # Security Policy 2 | 3 | ## Supported Versions 4 | 5 | Currently, the security updates are applied to the following version: 6 | 7 | - Version v2.0.0.0 8 | 9 | ## Reporting a Vulnerability 10 | 11 | If you discover a security vulnerability within the supported version, please send an email to [arunp77@gmail.com](mailto:arunp77@gmail.com) with a detailed description of the vulnerability. Our team will respond to the report promptly. 12 | 13 | ## Updates and Patching 14 | 15 | Security updates for the supported version will be released as needed and will be available through the [releases](https://github.com/arunp77/Machine-Learning/releases) section of this repository. 16 | 17 | ## Responsible Disclosure 18 | 19 | We believe in responsible disclosure. We appreciate your efforts to keep our project secure and are open to working with you to address any issues. 20 | 21 | Thank you for your contributions to the security of our project. 22 | 23 | -------------------------------------------------------------------------------- /Statistics-fundamental/Correlaltion.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/Statistics-fundamental/Correlaltion.png -------------------------------------------------------------------------------- /Statistics-fundamental/Covariance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/Statistics-fundamental/Covariance.png -------------------------------------------------------------------------------- /Statistics-fundamental/normal-distri.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/Statistics-fundamental/normal-distri.png -------------------------------------------------------------------------------- /Statistics-fundamental/variance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/Statistics-fundamental/variance.png -------------------------------------------------------------------------------- /Supervised-learning/Advertising.csv: -------------------------------------------------------------------------------- 1 | "","TV","Radio","Newspaper","Sales" 2 | "1",230.1,37.8,69.2,22.1 3 | "2",44.5,39.3,45.1,10.4 4 | "3",17.2,45.9,69.3,9.3 5 | "4",151.5,41.3,58.5,18.5 6 | "5",180.8,10.8,58.4,12.9 7 | "6",8.7,48.9,75,7.2 8 | "7",57.5,32.8,23.5,11.8 9 | "8",120.2,19.6,11.6,13.2 10 | "9",8.6,2.1,1,4.8 11 | "10",199.8,2.6,21.2,10.6 12 | "11",66.1,5.8,24.2,8.6 13 | "12",214.7,24,4,17.4 14 | "13",23.8,35.1,65.9,9.2 15 | "14",97.5,7.6,7.2,9.7 16 | "15",204.1,32.9,46,19 17 | "16",195.4,47.7,52.9,22.4 18 | "17",67.8,36.6,114,12.5 19 | "18",281.4,39.6,55.8,24.4 20 | "19",69.2,20.5,18.3,11.3 21 | "20",147.3,23.9,19.1,14.6 22 | "21",218.4,27.7,53.4,18 23 | "22",237.4,5.1,23.5,12.5 24 | "23",13.2,15.9,49.6,5.6 25 | "24",228.3,16.9,26.2,15.5 26 | "25",62.3,12.6,18.3,9.7 27 | "26",262.9,3.5,19.5,12 28 | "27",142.9,29.3,12.6,15 29 | "28",240.1,16.7,22.9,15.9 30 | "29",248.8,27.1,22.9,18.9 31 | "30",70.6,16,40.8,10.5 32 | "31",292.9,28.3,43.2,21.4 33 | "32",112.9,17.4,38.6,11.9 34 | "33",97.2,1.5,30,9.6 35 | "34",265.6,20,0.3,17.4 36 | "35",95.7,1.4,7.4,9.5 37 | "36",290.7,4.1,8.5,12.8 38 | "37",266.9,43.8,5,25.4 39 | "38",74.7,49.4,45.7,14.7 40 | "39",43.1,26.7,35.1,10.1 41 | "40",228,37.7,32,21.5 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-------------------------------------------------------------------------------- /Supervised-learning/Algerian_forest_fires_dataset_UPDATE.csv: -------------------------------------------------------------------------------- 1 | Bejaia Region Dataset ,,,,,,,,,,,,, 2 | day,month,year,Temperature, RH, Ws,Rain ,FFMC,DMC,DC,ISI,BUI,FWI,Classes 3 | 1,6,2012,29,57,18,0,65.7,3.4,7.6,1.3,3.4,0.5,not fire 4 | 2,6,2012,29,61,13,1.3,64.4,4.1,7.6,1,3.9,0.4,not fire 5 | 3,6,2012,26,82,22,13.1,47.1,2.5,7.1,0.3,2.7,0.1,not fire 6 | 4,6,2012,25,89,13,2.5,28.6,1.3,6.9,0,1.7,0,not fire 7 | 5,6,2012,27,77,16,0,64.8,3,14.2,1.2,3.9,0.5,not fire 8 | 6,6,2012,31,67,14,0,82.6,5.8,22.2,3.1,7,2.5,fire 9 | 7,6,2012,33,54,13,0,88.2,9.9,30.5,6.4,10.9,7.2,fire 10 | 8,6,2012,30,73,15,0,86.6,12.1,38.3,5.6,13.5,7.1,fire 11 | 9,6,2012,25,88,13,0.2,52.9,7.9,38.8,0.4,10.5,0.3,not fire 12 | 10,6,2012,28,79,12,0,73.2,9.5,46.3,1.3,12.6,0.9,not fire 13 | 11,6,2012,31,65,14,0,84.5,12.5,54.3,4,15.8,5.6,fire 14 | 12,6,2012,26,81,19,0,84,13.8,61.4,4.8,17.7,7.1,fire 15 | 13,6,2012,27,84,21,1.2,50,6.7,17,0.5,6.7,0.2,not fire 16 | 14,6,2012,30,78,20,0.5,59,4.6,7.8,1,4.4,0.4,not fire 17 | 15,6,2012,28,80,17,3.1,49.4,3,7.4,0.4,3,0.1,not fire 18 | 16,6,2012,29,89,13,0.7,36.1,1.7,7.6,0,2.2,0,not fire 19 | 17,6,2012,30,89,16,0.6,37.3,1.1,7.8,0,1.6,0,not fire 20 | 18,6,2012,31,78,14,0.3,56.9,1.9,8,0.7,2.4,0.2,not fire 21 | 19,6,2012,31,55,16,0.1,79.9,4.5,16,2.5,5.3,1.4,not fire 22 | 20,6,2012,30,80,16,0.4,59.8,3.4,27.1,0.9,5.1,0.4,not fire 23 | 21,6,2012,30,78,14,0,81,6.3,31.6,2.6,8.4,2.2,fire 24 | 22,6,2012,31,67,17,0.1,79.1,7,39.5,2.4,9.7,2.3,not fire 25 | 23,6,2012,32,62,18,0.1,81.4,8.2,47.7,3.3,11.5,3.8,fire 26 | 24,6,2012,32,66,17,0,85.9,11.2,55.8,5.6,14.9,7.5,fire 27 | 25,6,2012,31,64,15,0,86.7,14.2,63.8,5.7,18.3,8.4,fire 28 | 26,6,2012,31,64,18,0,86.8,17.8,71.8,6.7,21.6,10.6,fire 29 | 27,6,2012,34,53,18,0,89,21.6,80.3,9.2,25.8,15,fire 30 | 28,6,2012,32,55,14,0,89.1,25.5,88.5,7.6,29.7,13.9,fire 31 | 29,6,2012,32,47,13,0.3,79.9,18.4,84.4,2.2,23.8,3.9,not fire 32 | 30,6,2012,33,50,14,0,88.7,22.9,92.8,7.2,28.3,12.9,fire 33 | 1,7,2012,29,68,19,1,59.9,2.5,8.6,1.1,2.9,0.4,not fire 34 | 2,7,2012,27,75,19,1.2,55.7,2.4,8.3,0.8,2.8,0.3,not fire 35 | 3,7,2012,32,76,20,0.7,63.1,2.6,9.2,1.3,3,0.5,not fire 36 | 4,7,2012,33,78,17,0,80.1,4.6,18.5,2.7,5.7,1.7,not fire 37 | 5,7,2012,33,66,14,0,85.9,7.6,27.9,4.8,9.1,4.9,fire 38 | 6,7,2012,32,63,14,0,87,10.9,37,5.6,12.5,6.8,fire 39 | 7,7,2012,35,64,18,0.2,80,9.7,40.4,2.8,12.1,3.2,not fire 40 | 8,7,2012,33,68,19,0,85.6,12.5,49.8,6,15.4,8,fire 41 | 9,7,2012,32,68,14,1.4,66.6,7.7,9.2,1.1,7.4,0.6,not fire 42 | 10,7,2012,33,69,13,0.7,66.6,6,9.3,1.1,5.8,0.5,not fire 43 | 11,7,2012,33,76,14,0,81.1,8.1,18.7,2.6,8.1,2.2,not fire 44 | 12,7,2012,31,75,13,0.1,75.1,7.9,27.7,1.5,9.2,0.9,not fire 45 | 13,7,2012,34,81,15,0,81.8,9.7,37.2,3,11.7,3.4,not fire 46 | 14,7,2012,34,61,13,0.6,73.9,7.8,22.9,1.4,8.4,0.8,not fire 47 | 15,7,2012,30,80,19,0.4,60.7,5.2,17,1.1,5.9,0.5,not fire 48 | 16,7,2012,28,76,21,0,72.6,7,25.5,0.7,8.3,0.4,not fire 49 | 17,7,2012,29,70,14,0,82.8,9.4,34.1,3.2,11.1,3.6,fire 50 | 18,7,2012,31,68,14,0,85.4,12.1,43.1,4.6,14.2,6,fire 51 | 19,7,2012,35,59,17,0,88.1,12,52.8,7.7,18.2,10.9,fire 52 | 20,7,2012,33,65,15,0.1,81.4,12.3,62.1,2.8,16.5,4,fire 53 | 21,7,2012,33,70,17,0,85.4,18.5,71.5,5.2,22.4,8.8,fire 54 | 22,7,2012,28,79,18,0.1,73.4,16.4,79.9,1.8,21.7,2.8,not fire 55 | 23,7,2012,27,66,22,0.4,68.2,10.5,71.3,1.8,15.4,2.1,not fire 56 | 24,7,2012,28,78,16,0.1,70,9.6,79.7,1.4,14.7,1.3,not fire 57 | 25,7,2012,31,65,18,0,84.3,12.5,88.7,4.8,18.5,7.3,fire 58 | 26,7,2012,36,53,19,0,89.2,17.1,98.6,10,23.9,15.3,fire 59 | 27,7,2012,36,48,13,0,90.3,22.2,108.5,8.7,29.4,15.3,fire 60 | 28,7,2012,33,76,15,0,86.5,24.4,117.8,5.6,32.1,11.3,fire 61 | 29,7,2012,32,73,15,0,86.6,26.7,127,5.6,35,11.9,fire 62 | 30,7,2012,31,79,15,0,85.4,28.5,136,4.7,37.4,10.7,fire 63 | 31,7,2012,35,64,17,0,87.2,31.9,145.7,6.8,41.2,15.7,fire 64 | 1,8,2012,36,45,14,0,78.8,4.8,10.2,2,4.7,0.9,not fire 65 | 2,8,2012,35,55,12,0.4,78,5.8,10,1.7,5.5,0.8,not fire 66 | 3,8,2012,35,63,14,0.3,76.6,5.7,10,1.7,5.5,0.8,not fire 67 | 4,8,2012,34,69,13,0,85,8.2,19.8,4,8.2,3.9,fire 68 | 5,8,2012,34,65,13,0,86.8,11.1,29.7,5.2,11.5,6.1,fire 69 | 6,8,2012,32,75,14,0,86.4,13,39.1,5.2,14.2,6.8,fire 70 | 7,8,2012,32,69,16,0,86.5,15.5,48.6,5.5,17.2,8,fire 71 | 8,8,2012,32,60,18,0.3,77.1,11.3,47,2.2,14.1,2.6,not fire 72 | 9,8,2012,35,59,17,0,87.4,14.8,57,6.9,17.9,9.9,fire 73 | 10,8,2012,35,55,14,0,88.9,18.6,67,7.4,21.9,11.6,fire 74 | 11,8,2012,35,63,13,0,88.9,21.7,77,7.1,25.5,12.1,fire 75 | 12,8,2012,35,51,13,0.3,81.3,15.6,75.1,2.5,20.7,4.2,not fire 76 | 13,8,2012,35,63,15,0,87,19,85.1,5.9,24.4,10.2,fire 77 | 14,8,2012,33,66,14,0,87,21.7,94.7,5.7,27.2,10.6,fire 78 | 15,8,2012,36,55,13,0.3,82.4,15.6,92.5,3.7,22,6.3,fire 79 | 16,8,2012,36,61,18,0.3,80.2,11.7,90.4,2.8,17.6,4.2,fire 80 | 17,8,2012,37,52,18,0,89.3,16,100.7,9.7,22.9,14.6,fire 81 | 18,8,2012,36,54,18,0,89.4,20,110.9,9.7,27.5,16.1,fire 82 | 19,8,2012,35,62,19,0,89.4,23.2,120.9,9.7,31.3,17.2,fire 83 | 20,8,2012,35,68,19,0,88.3,25.9,130.6,8.8,34.7,16.8,fire 84 | 21,8,2012,36,58,19,0,88.6,29.6,141.1,9.2,38.8,18.4,fire 85 | 22,8,2012,36,55,18,0,89.1,33.5,151.3,9.9,43.1,20.4,fire 86 | 23,8,2012,36,53,16,0,89.5,37.6,161.5,10.4,47.5,22.3,fire 87 | 24,8,2012,34,64,14,0,88.9,40.5,171.3,9,50.9,20.9,fire 88 | 25,8,2012,35,60,15,0,88.9,43.9,181.3,8.2,54.7,20.3,fire 89 | 26,8,2012,31,78,18,0,85.8,45.6,190.6,4.7,57.1,13.7,fire 90 | 27,8,2012,33,82,21,0,84.9,47,200.2,4.4,59.3,13.2,fire 91 | 28,8,2012,34,64,16,0,89.4,50.2,210.4,7.3,62.9,19.9,fire 92 | 29,8,2012,35,48,18,0,90.1,54.2,220.4,12.5,67.4,30.2,fire 93 | 30,8,2012,35,70,17,0.8,72.7,25.2,180.4,1.7,37.4,4.2,not fire 94 | 31,8,2012,28,80,21,16.8,52.5,8.7,8.7,0.6,8.3,0.3,not fire 95 | 1,9,2012,25,76,17,7.2,46,1.3,7.5,0.2,1.8,0.1,not fire 96 | 2,9,2012,22,86,15,10.1,30.5,0.7,7,0,1.1,0,not fire 97 | 3,9,2012,25,78,15,3.8,42.6,1.2,7.5,0.1,1.7,0,not fire 98 | 4,9,2012,29,73,17,0.1,68.4,1.9,15.7,1.4,2.9,0.5,not fire 99 | 5,9,2012,29,75,16,0,80.8,3.4,24,2.8,5.1,1.7,fire 100 | 6,9,2012,29,74,19,0.1,75.8,3.6,32.2,2.1,5.6,0.9,not fire 101 | 7,9,2012,31,71,17,0.3,69.6,3.2,30.1,1.5,5.1,0.6,not fire 102 | 8,9,2012,30,73,17,0.9,62,2.6,8.4,1.1,3,0.4,not fire 103 | 9,9,2012,30,77,15,1,56.1,2.1,8.4,0.7,2.6,0.2,not fire 104 | 10,9,2012,33,73,12,1.8,59.9,2.2,8.9,0.7,2.7,0.3,not fire 105 | 11,9,2012,30,77,21,1.8,58.5,1.9,8.4,1.1,2.4,0.3,not fire 106 | 12,9,2012,29,88,13,0,71,2.6,16.6,1.2,3.7,0.5,not fire 107 | 13,9,2012,25,86,21,4.6,40.9,1.3,7.5,0.1,1.8,0,not fire 108 | 14,9,2012,22,76,26,8.3,47.4,1.1,7,0.4,1.6,0.1,not fire 109 | 15,9,2012,24,82,15,0.4,44.9,0.9,7.3,0.2,1.4,0,not fire 110 | 16,9,2012,30,65,14,0,78.1,3.2,15.7,1.9,4.2,0.8,not fire 111 | 17,9,2012,31,52,14,0,87.7,6.4,24.3,6.2,7.7,5.9,fire 112 | 18,9,2012,32,49,11,0,89.4,9.8,33.1,6.8,11.3,7.7,fire 113 | 19,9,2012,29,57,14,0,89.3,12.5,41.3,7.8,14.2,9.7,fire 114 | 20,9,2012,28,84,18,0,83.8,13.5,49.3,4.5,16,6.3,fire 115 | 21,9,2012,31,55,11,0,87.8,16.5,57.9,5.4,19.2,8.3,fire 116 | 22,9,2012,31,50,19,0.6,77.8,10.6,41.4,2.4,12.9,2.8,not fire 117 | 23,9,2012,32,54,11,0.5,73.7,7.9,30.4,1.2,9.6,0.7,not fire 118 | 24,9,2012,29,65,19,0.6,68.3,5.5,15.2,1.5,5.8,0.7,not fire 119 | 25,9,2012,26,81,21,5.8,48.6,3,7.7,0.4,3,0.1,not fire 120 | 26,9,2012,31,54,11,0,82,6,16.3,2.5,6.2,1.7,not fire 121 | 27,9,2012,31,66,11,0,85.7,8.3,24.9,4,9,4.1,fire 122 | 28,9,2012,32,47,14,0.7,77.5,7.1,8.8,1.8,6.8,0.9,not fire 123 | 29,9,2012,26,80,16,1.8,47.4,2.9,7.7,0.3,3,0.1,not fire 124 | 30,9,2012,25,78,14,1.4,45,1.9,7.5,0.2,2.4,0.1,not fire 125 | ,,,,,,,,,,,,, 126 | Sidi-Bel Abbes Region Dataset,,,,,,,,,,,,, 127 | day,month,year,Temperature, RH, Ws,Rain ,FFMC,DMC,DC,ISI,BUI,FWI,Classes 128 | 1,6,2012,32,71,12,0.7,57.1,2.5,8.2,0.6,2.8,0.2,not fire 129 | 2,6,2012,30,73,13,4,55.7,2.7,7.8,0.6,2.9,0.2,not fire 130 | 3,6,2012,29,80,14,2,48.7,2.2,7.6,0.3,2.6,0.1,not fire 131 | 4,6,2012,30,64,14,0,79.4,5.2,15.4,2.2,5.6,1,not fire 132 | 5,6,2012,32,60,14,0.2,77.1,6,17.6,1.8,6.5,0.9,not fire 133 | 6,6,2012,35,54,11,0.1,83.7,8.4,26.3,3.1,9.3,3.1,fire 134 | 7,6,2012,35,44,17,0.2,85.6,9.9,28.9,5.4,10.7,6,fire 135 | 8,6,2012,28,51,17,1.3,71.4,7.7,7.4,1.5,7.3,0.8,not fire 136 | 9,6,2012,27,59,18,0.1,78.1,8.5,14.7,2.4,8.3,1.9,not fire 137 | 10,6,2012,30,41,15,0,89.4,13.3,22.5,8.4,13.1,10,fire 138 | 11,6,2012,31,42,21,0,90.6,18.2,30.5,13.4,18,16.7,fire 139 | 12,6,2012,27,58,17,0,88.9,21.3,37.8,8.7,21.2,12.9,fire 140 | 13,6,2012,30,52,15,2,72.3,11.4,7.8,1.4,10.9,0.9,not fire 141 | 14,6,2012,27,79,16,0.7,53.4,6.4,7.3,0.5,6.1,0.3,not fire 142 | 15,6,2012,28,90,15,0,66.8,7.2,14.7,1.2,7.1,0.6,not fire 143 | 16,6,2012,29,87,15,0.4,47.4,4.2,8,0.2,4.1,0.1,not fire 144 | 17,6,2012,31,69,17,4.7,62.2,3.9,8,1.1,3.8,0.4,not fire 145 | 18,6,2012,33,62,10,8.7,65.5,4.6,8.3,0.9,4.4,0.4,not fire 146 | 19,6,2012,32,67,14,4.5,64.6,4.4,8.2,1,4.2,0.4,not fire 147 | 20,6,2012,31,72,14,0.2,60.2,3.8,8,0.8,3.7,0.3,not fire 148 | 21,6,2012,32,55,14,0,86.2,8.3,18.4,5,8.2,4.9,fire 149 | 22,6,2012,33,46,14,1.1,78.3,8.1,8.3,1.9,7.7,1.2,not fire 150 | 23,6,2012,33,59,16,0.8,74.2,7,8.3,1.6,6.7,0.8,not fire 151 | 24,6,2012,35,68,16,0,85.3,10,17,4.9,9.9,5.3,fire 152 | 25,6,2012,34,70,16,0,86,12.8,25.6,5.4,12.7,6.7,fire 153 | 26,6,2012,36,62,16,0,87.8,16.5,34.5,7,16.4,9.5,fire 154 | 27,6,2012,36,55,15,0,89.1,20.9,43.3,8,20.8,12,fire 155 | 28,6,2012,37,37,13,0,92.5,27.2,52.4,11.7,27.1,18.4,fire 156 | 29,6,2012,37,36,13,0.6,86.2,17.9,36.7,4.8,17.8,7.2,fire 157 | 30,6,2012,34,42,15,1.7,79.7,12,8.5,2.2,11.5,2.2,not fire 158 | 1,7,2012,28,58,18,2.2,63.7,3.2,8.5,1.2,3.3,0.5,not fire 159 | 2,7,2012,33,48,16,0,87.6,7.9,17.8,6.8,7.8,6.4,fire 160 | 3,7,2012,34,56,17,0.1,84.7,9.7,27.3,4.7,10.3,5.2,fire 161 | 4,7,2012,34,58,18,0,88,13.6,36.8,8,14.1,9.9,fire 162 | 5,7,2012,34,45,18,0,90.5,18.7,46.4,11.3,18.7,15,fire 163 | 6,7,2012,35,42,15,0.3,84.7,15.5,45.1,4.3,16.7,6.3,fire 164 | 7,7,2012,38,43,13,0.5,85,13,35.4,4.1,13.7,5.2,fire 165 | 8,7,2012,35,47,18,6,80.8,9.8,9.7,3.1,9.4,3,fire 166 | 9,7,2012,36,43,15,1.9,82.3,9.4,9.9,3.2,9,3.1,fire 167 | 10,7,2012,34,51,16,3.8,77.5,8,9.5,2,7.7,1.3,not fire 168 | 11,7,2012,34,56,15,2.9,74.8,7.1,9.5,1.6,6.8,0.8,not fire 169 | 12,7,2012,36,44,13,0,90.1,12.6,19.4,8.3,12.5,9.6,fire 170 | 13,7,2012,39,45,13,0.6,85.2,11.3,10.4,4.2,10.9,4.7,fire 171 | 14,7,2012,37,37,18,0.2,88.9,12.9,14.6 9,12.5,10.4,fire , 172 | 15,7,2012,34,45,17,0,90.5,18,24.1,10.9,17.7,14.1,fire 173 | 16,7,2012,31,83,17,0,84.5,19.4,33.1,4.7,19.2,7.3,fire 174 | 17,7,2012,32,81,17,0,84.6,21.1,42.3,4.7,20.9,7.7,fire 175 | 18,7,2012,33,68,15,0,86.1,23.9,51.6,5.2,23.9,9.1,fire 176 | 19,7,2012,34,58,16,0,88.1,27.8,61.1,7.3,27.7,13,fire 177 | 20,7,2012,36,50,16,0,89.9,32.7,71,9.5,32.6,17.3,fire 178 | 21,7,2012,36,29,18,0,93.9,39.6,80.6,18.5,39.5,30,fire 179 | 22,7,2012,32,48,18,0,91.5,44.2,90.1,13.2,44,25.4,fire 180 | 23,7,2012,31,71,17,0,87.3,46.6,99,6.9,46.5,16.3,fire 181 | 24,7,2012,33,63,17,1.1,72.8,20.9,56.6,1.6,21.7,2.5,not fire 182 | 25,7,2012,39,64,9,1.2,73.8,11.7,15.9,1.1,11.4,0.7,not fire 183 | 26,7,2012,35,58,10,0.2,78.3,10.8,19.7,1.6,10.7,1,not fire 184 | 27,7,2012,29,87,18,0,80,11.8,28.3,2.8,11.8,3.2,not fire 185 | 28,7,2012,33,57,16,0,87.5,15.7,37.6,6.7,15.7,9,fire 186 | 29,7,2012,34,59,16,0,88.1,19.5,47.2,7.4,19.5,10.9,fire 187 | 30,7,2012,36,56,16,0,88.9,23.8,57.1,8.2,23.8,13.2,fire 188 | 31,7,2012,37,55,15,0,89.3,28.3,67.2,8.3,28.3,14.5,fire 189 | 1,8,2012,38,52,14,0,78.3,4.4,10.5,2,4.4,0.8,not fire 190 | 2,8,2012,40,34,14,0,93.3,10.8,21.4,13.8,10.6,13.5,fire 191 | 3,8,2012,39,33,17,0,93.7,17.1,32.1,17.2,16.9,19.5,fire 192 | 4,8,2012,38,35,15,0,93.8,23,42.7,15.7,22.9,20.9,fire 193 | 5,8,2012,34,42,17,0.1,88.3,23.6,52.5,19,23.5,12.6,fire 194 | 6,8,2012,30,54,14,3.1,70.5,11,9.1,1.3,10.5,0.8,not fire 195 | 7,8,2012,34,63,13,2.9,69.7,7.2,9.8,1.2,6.9,0.6,not fire 196 | 8,8,2012,37,56,11,0,87.4,11.2,20.2,5.2,11,5.9,fire 197 | 9,8,2012,39,43,12,0,91.7,16.5,30.9,9.6,16.4,12.7,fire 198 | 10,8,2012,39,39,15,0.2,89.3,15.8,35.4,8.2,15.8,10.7,fire 199 | 11,8,2012,40,31,15,0,94.2,22.5,46.3,16.6,22.4,21.6,fire 200 | 12,8,2012,39,21,17,0.4,93,18.4,41.5,15.5,18.4,18.8,fire 201 | 13,8,2012,35,34,16,0.2,88.3,16.9,45.1,7.5,17.5,10.5,fire 202 | 14,8,2012,37,40,13,0,91.9,22.3,55.5,10.8,22.3,15.7,fire 203 | 15,8,2012,35,46,13,0.3,83.9,16.9,54.2,3.5,19,5.5,fire 204 | 16,8,2012,40,41,10,0.1,92,22.6,65.1,9.5,24.2,14.8,fire 205 | 17,8,2012,42,24,9,0,96,30.3,76.4,15.7,30.4,24,fire 206 | 18,8,2012,37,37,14,0,94.3,35.9,86.8,16,35.9,26.3,fire 207 | 19,8,2012,35,66,15,0.1,82.7,32.7,96.8,3.3,35.5,7.7,fire 208 | 20,8,2012,36,81,15,0,83.7,34.4,107,3.8,38.1,9,fire 209 | 21,8,2012,36,71,15,0,86,36.9,117.1,5.1,41.3,12.2,fire 210 | 22,8,2012,37,53,14,0,89.5,41.1,127.5,8,45.5,18.1,fire 211 | 23,8,2012,36,43,16,0,91.2,46.1,137.7,11.5,50.2,24.5,fire 212 | 24,8,2012,35,38,15,0,92.1,51.3,147.7,12.2,54.9,26.9,fire 213 | 25,8,2012,34,40,18,0,92.1,56.3,157.5,14.3,59.5,31.1,fire 214 | 26,8,2012,33,37,16,0,92.2,61.3,167.2,13.1,64,30.3,fire 215 | 27,8,2012,36,54,14,0,91,65.9,177.3,10,68,26.1,fire 216 | 28,8,2012,35,56,14,0.4,79.2,37,166,2.1,30.6,6.1,not fire 217 | 29,8,2012,35,53,17,0.5,80.2,20.7,149.2,2.7,30.6,5.9,fire 218 | 30,8,2012,34,49,15,0,89.2,24.8,159.1,8.1,35.7,16,fire 219 | 31,8,2012,30,59,19,0,89.1,27.8,168.2,9.8,39.3,19.4,fire 220 | 1,9,2012,29,86,16,0,37.9,0.9,8.2,0.1,1.4,0,not fire 221 | 2,9,2012,28,67,19,0,75.4,2.9,16.3,2,4,0.8,not fire 222 | 3,9,2012,28,75,16,0,82.2,4.4,24.3,3.3,6,2.5,fire 223 | 4,9,2012,30,66,15,0.2,73.5,4.1,26.6,1.5,6,0.7,not fire 224 | 5,9,2012,30,58,12,4.1,66.1,4,8.4,1,3.9,0.4,not fire 225 | 6,9,2012,34,71,14,6.5,64.5,3.3,9.1,1,3.5,0.4,not fire 226 | 7,9,2012,31,62,15,0,83.3,5.8,17.7,3.8,6.4,3.2,fire 227 | 8,9,2012,30,88,14,0,82.5,6.6,26.1,3,8.1,2.7,fire 228 | 9,9,2012,30,80,15,0,83.1,7.9,34.5,3.5,10,3.7,fire 229 | 10,9,2012,29,74,15,1.1,59.5,4.7,8.2,0.8,4.6,0.3,not fire 230 | 11,9,2012,30,73,14,0,79.2,6.5,16.6,2.1,6.6,1.2,not fire 231 | 12,9,2012,31,72,14,0,84.2,8.3,25.2,3.8,9.1,3.9,fire 232 | 13,9,2012,29,49,19,0,88.6,11.5,33.4,9.1,12.4,10.3,fire 233 | 14,9,2012,28,81,15,0,84.6,12.6,41.5,4.3,14.3,5.7,fire 234 | 15,9,2012,32,51,13,0,88.7,16,50.2,6.9,17.8,9.8,fire 235 | 16,9,2012,33,26,13,0,93.9,21.2,59.2,14.2,22.4,19.3,fire 236 | 17,9,2012,34,44,12,0,92.5,25.2,63.3,11.2,26.2,17.5,fire 237 | 18,9,2012,36,33,13,0.1,90.6,25.8,77.8,9,28.2,15.4,fire 238 | 19,9,2012,29,41,8,0.1,83.9,24.9,86,2.7,28.9,5.6,fire 239 | 20,9,2012,34,58,13,0.2,79.5,18.7,88,2.1,24.4,3.8,not fire 240 | 21,9,2012,35,34,17,0,92.2,23.6,97.3,13.8,29.4,21.6,fire 241 | 22,9,2012,33,64,13,0,88.9,26.1,106.3,7.1,32.4,13.7,fire 242 | 23,9,2012,35,56,14,0,89,29.4,115.6,7.5,36,15.2,fire 243 | 24,9,2012,26,49,6,2,61.3,11.9,28.1,0.6,11.9,0.4,not fire 244 | 25,9,2012,28,70,15,0,79.9,13.8,36.1,2.4,14.1,3,not fire 245 | 26,9,2012,30,65,14,0,85.4,16,44.5,4.5,16.9,6.5,fire 246 | 27,9,2012,28,87,15,4.4,41.1,6.5,8,0.1,6.2,0,not fire 247 | 28,9,2012,27,87,29,0.5,45.9,3.5,7.9,0.4,3.4,0.2,not fire 248 | 29,9,2012,24,54,18,0.1,79.7,4.3,15.2,1.7,5.1,0.7,not fire 249 | 30,9,2012,24,64,15,0.2,67.3,3.8,16.5,1.2,4.8,0.5,not fire 250 | -------------------------------------------------------------------------------- /Supervised-learning/Algerian_forest_fires_dataset_UPDATE.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/Supervised-learning/Algerian_forest_fires_dataset_UPDATE.xlsx -------------------------------------------------------------------------------- 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Keeper,5515,1,4686,84.76,0,0,1,394,50,28.14,127.51,13,0,0,0,0,0,2008,200000,450000 12 | 11,"Bravo, DJ",2,WI,MI+,Allrounder,2200,86,2004,81.39,142,34.1,0,839,70,27.97,127.12,38,1338,47,28.47,8.12,21.11,2011,200000,200000 13 | 12,"Chanderpaul, S",3,WI,RCB,Batsman,9918,9,8778,70.74,14,52.8,1,25,16,8.33,80.64,0,0,0,0,0,0,2008,200000,200000 14 | 13,"Chawla, PP",1,IND,KXIP,Allrounder,5,3,38,65.51,32,41,0,337,24,13.48,113.09,9,1819,73,126.3,38.11,100.2,2008,125000,400000 15 | 14,"de Villiers, AB",2,SA,DD+,W. Keeper,5457,2,4998,93.19,0,0,1,1302,105,34.26,128.53,42,0,0,0,0,0,2008,200000,300000 16 | 15,"Dhawan, S",2,IND,MI+,Batsman,0,0,69,56.09,0,0,0,1540,95,31.43,122.32,36,66,4,16.5,8.25,12,2011,100000,300000 17 | 16,"Dhoni, MS",2,IND,CSK,W. 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Keeper,2648,0,2924,84.31,0,0,0,128,53,25.6,164.1,8,0,0,0,0,0,2008,150000,150000 48 | 47,Kamran Khan,1,IND,RR+,Bowler,0,0,0,0,0,0,0,3,3,3,60,0,224,9,24.89,8.48,17.78,2009,20000,24000 49 | 48,"Karthik, KD",2,IND,DD+,W. Keeper,1000,0,1008,74.5,0,0,0,1231,69,24.14,123.84,28,0,0,0,0,0,2008,200000,525000 50 | 49,"Kartik, M",2,IND,KKR+,Bowler,88,24,126,70.78,37,51.5,0,111,21,18.5,105.71,1,1013,21,48.24,7.02,41.33,2008,200000,425000 51 | 50,"Katich, SM",3,AUS,KXIP,Batsman,4188,21,1324,68.74,0,0,0,241,75,24.1,129.57,8,0,0,0,0,0,2008,200000,200000 52 | 51,"Kohli, V",1,IND,RCB,Batsman,491,0,3590,86.31,2,137,1,1639,73,28.26,119.29,49,345,4,86.25,8.84,58.5,2011,150000,1800000 53 | 52,"Kumar, P",1,IND,RCB+,Bowler,149,27,292,88.21,77,42.1,0,243,34,10.57,114.08,14,1919,53,36.21,7.73,28.11,2011,200000,800000 54 | 53,"Kumble, A",3,IND,RCB,Bowler,2506,619,938,61.06,337,43,1,35,8,11.67,74.47,0,105,2,52.5,9.55,33,2008,250000,500000 55 | 54,"Langeveldt, CK",3,SA,KKR+,Bowler,16,16,73,58.87,100,34.8,0,8,8,4,88.89,1,187,13,14.38,7.19,12,2011,100000,140000 56 | 55,"Laxman, VVS",3,IND,DC+,Batsman,8781,2,2338,71.23,0,0,1,282,52,15.67,105.62,5,0,0,0,0,0,2008,150000,375000 57 | 56,"Lee, B",2,AUS,KXI+,Bowler,1451,310,1100,82.45,377,29.2,0,103,25,11.44,121.18,6,1009,21,48.05,7.56,38.24,2008,300000,900000 58 | 57,"Maharoof, MF",2,SL,DD,Allrounder,556,25,1042,84.44,133,33.3,0,177,39,17.7,143.9,9,520,27,19.26,7.43,15.56,2008,150000,225000 59 | 58,"Malinga, SL",2,SL,MI,Bowler,275,101,327,73.81,185,31.1,0,64,17,5.82,100,4,1381,83,16.64,6.36,15.69,2008,200000,350000 60 | 59,"Mascarenhas, AD",2,ENG,RR+,Allrounder,0,0,245,95.33,13,63.2,0,74,27,8.22,101.37,1,331,19,17.42,7.01,14.95,2011,100000,100000 61 | 60,"Mathews, AD",1,SL,KKR+,Allrounder,1219,7,1447,82.59,42,43,0,376,65,25.07,123.28,12,537,15,35.8,8.2,26.33,2011,300000,950000 62 | 61,"McCullum, BB",2,NZ,KKR+,W. Keeper,3763,0,4511,89.62,0,0,1,1233,158,28.02,123.42,48,0,0,0,0,0,2008,175000,700000 63 | 62,"McDonald, AB",2,AUS,DD+,Allrounder,107,9,0,0,0,0,0,123,33,30.75,125.51,4,244,10,24.4,8.41,17.4,2011,50000,80000 64 | 63,"McGrath, GD",3,AUS,DD,Bowler,641,563,115,48.72,381,34,0,4,4,4,80,0,357,12,29.75,6.61,27,2008,350000,350000 65 | 64,Misbah-ul-Haq,3,PAK,RCB,Batsman,2173,0,2763,75.1,0,0,1,117,47,16.71,144.44,6,0,0,0,0,0,2010,100000,100000 66 | 65,"Mishra, A",2,IND,DD+,Bowler,392,43,5,27.77,19,40.1,0,186,31,10.94,102.2,3,1530,74,20.68,7.11,17.46,2011,100000,300000 67 | 66,"Mithun, A",1,IND,RCB,Bowler,120,9,51,92.72,3,60,0,32,11,8,133.33,1,435,6,72.5,9.89,44,2011,100000,260000 68 | 67,Mohammad Asif,2,PAK,DD,Bowler,141,106,34,34,46,42.1,0,3,3,1.5,50,0,296,8,37,9.25,24,2008,225000,650000 69 | 68,"Morkel, JA",2,SA,CSK,Allrounder,58,1,782,100.25,50,41.4,0,781,71,24.41,146.25,45,1899,69,27.52,8.25,20.01,2008,225000,675000 70 | 69,"Morkel, M",2,SA,RR+,Bowler,555,139,117,75.97,94,28.5,0,60,16,10,111.11,2,884,38,23.26,7.37,18.95,2011,100000,475000 71 | 70,"Muralitharan, M",3,SL,CSK+,Bowler,1261,800,674,77.56,534,35.2,0,20,6,3.33,66.67,0,1395,57,24.47,6.49,22.63,2008,250000,600000 72 | 71,"Nannes, DP",3,AUS,DD+,Bowler,0,0,1,50,1,42,0,4,3,4,30.77,0,627,24,26.13,7.17,21.92,2011,200000,650000 73 | 72,"Nayar, AM",2,IND,MI+,Allrounder,0,0,0,0,0,0,0,563,35,19.41,123.19,19,263,7,37.57,8.74,25.86,2011,50000,800000 74 | 73,"Nehra, A",2,IND,DD+,Bowler,77,44,141,57.31,157,36.6,0,38,22,19,82.61,1,1192,48,24.83,7.57,19.73,2011,200000,850000 75 | 74,"Noffke, AA",2,AUS,RCB,Allrounder,0,0,0,0,1,54,0,9,9,9,90,0,40,1,40,10,24,2010,20000,20000 76 | 75,"Ntini, M",2,SA,CSK,Bowler,699,390,199,66.77,266,32.6,0,11,11,11,61.11,0,242,7,34.57,6.91,30,2008,200000,200000 77 | 76,"Ojha, NV",2,IND,RR+,W. Keeper,0,0,1,14.28,0,0,0,960,94,22.33,117.94,50,0,0,0,0,0,2011,100000,270000 78 | 77,"Ojha, PP",1,IND,DC+,Bowler,70,62,41,43.61,20,41.7,0,10,3,1,30.3,0,1548,69,22.43,7.16,18.8,2011,200000,500000 79 | 78,"Oram, JDP",2,NZ,CSK+,Allrounder,1780,60,2377,87.16,168,39.7,0,106,41,13.25,98.15,5,327,9,36.33,9.26,23.67,2008,200000,675000 80 | 79,Pankaj Singh,2,IND,RCB+,Bowler,0,0,3,100,0,0,0,7,4,3.5,58.33,0,468,11,42.55,9.36,27.27,2011,50000,95000 81 | 80,"Patel, MM",2,IND,RR+,Bowler,60,35,74,66.07,86,36.6,0,39,23,7.8,235.49,0,1504,70,21.49,7.39,17.47,2008,100000,275000 82 | 81,"Patel, PA",2,IND,CSK+,W. 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15766609,Female,47,47000,0 208 | 15654230,Female,55,130000,1 209 | 15794566,Female,52,114000,0 210 | 15800890,Female,40,142000,1 211 | 15697424,Female,46,22000,0 212 | 15724536,Female,48,96000,1 213 | 15735878,Male,52,150000,1 214 | 15707596,Female,59,42000,0 215 | 15657163,Male,35,58000,0 216 | 15622478,Male,47,43000,0 217 | 15779529,Female,60,108000,1 218 | 15636023,Male,49,65000,0 219 | 15582066,Male,40,78000,0 220 | 15666675,Female,46,96000,0 221 | 15732987,Male,59,143000,1 222 | 15789432,Female,41,80000,0 223 | 15663161,Male,35,91000,1 224 | 15694879,Male,37,144000,1 225 | 15593715,Male,60,102000,1 226 | 15575002,Female,35,60000,0 227 | 15622171,Male,37,53000,0 228 | 15795224,Female,36,126000,1 229 | 15685346,Male,56,133000,1 230 | 15691808,Female,40,72000,0 231 | 15721007,Female,42,80000,1 232 | 15794253,Female,35,147000,1 233 | 15694453,Male,39,42000,0 234 | 15813113,Male,40,107000,1 235 | 15614187,Male,49,86000,1 236 | 15619407,Female,38,112000,0 237 | 15646227,Male,46,79000,1 238 | 15660541,Male,40,57000,0 239 | 15753874,Female,37,80000,0 240 | 15617877,Female,46,82000,0 241 | 15772073,Female,53,143000,1 242 | 15701537,Male,42,149000,1 243 | 15736228,Male,38,59000,0 244 | 15780572,Female,50,88000,1 245 | 15769596,Female,56,104000,1 246 | 15586996,Female,41,72000,0 247 | 15722061,Female,51,146000,1 248 | 15638003,Female,35,50000,0 249 | 15775590,Female,57,122000,1 250 | 15730688,Male,41,52000,0 251 | 15753102,Female,35,97000,1 252 | 15810075,Female,44,39000,0 253 | 15723373,Male,37,52000,0 254 | 15795298,Female,48,134000,1 255 | 15584320,Female,37,146000,1 256 | 15724161,Female,50,44000,0 257 | 15750056,Female,52,90000,1 258 | 15609637,Female,41,72000,0 259 | 15794493,Male,40,57000,0 260 | 15569641,Female,58,95000,1 261 | 15815236,Female,45,131000,1 262 | 15811177,Female,35,77000,0 263 | 15680587,Male,36,144000,1 264 | 15672821,Female,55,125000,1 265 | 15767681,Female,35,72000,0 266 | 15600379,Male,48,90000,1 267 | 15801336,Female,42,108000,1 268 | 15721592,Male,40,75000,0 269 | 15581282,Male,37,74000,0 270 | 15746203,Female,47,144000,1 271 | 15583137,Male,40,61000,0 272 | 15680752,Female,43,133000,0 273 | 15688172,Female,59,76000,1 274 | 15791373,Male,60,42000,1 275 | 15589449,Male,39,106000,1 276 | 15692819,Female,57,26000,1 277 | 15727467,Male,57,74000,1 278 | 15734312,Male,38,71000,0 279 | 15764604,Male,49,88000,1 280 | 15613014,Female,52,38000,1 281 | 15759684,Female,50,36000,1 282 | 15609669,Female,59,88000,1 283 | 15685536,Male,35,61000,0 284 | 15750447,Male,37,70000,1 285 | 15663249,Female,52,21000,1 286 | 15638646,Male,48,141000,0 287 | 15734161,Female,37,93000,1 288 | 15631070,Female,37,62000,0 289 | 15761950,Female,48,138000,1 290 | 15649668,Male,41,79000,0 291 | 15713912,Female,37,78000,1 292 | 15586757,Male,39,134000,1 293 | 15596522,Male,49,89000,1 294 | 15625395,Male,55,39000,1 295 | 15760570,Male,37,77000,0 296 | 15566689,Female,35,57000,0 297 | 15725794,Female,36,63000,0 298 | 15673539,Male,42,73000,1 299 | 15705298,Female,43,112000,1 300 | 15675791,Male,45,79000,0 301 | 15747043,Male,46,117000,1 302 | 15736397,Female,58,38000,1 303 | 15678201,Male,48,74000,1 304 | 15720745,Female,37,137000,1 305 | 15637593,Male,37,79000,1 306 | 15598070,Female,40,60000,0 307 | 15787550,Male,42,54000,0 308 | 15603942,Female,51,134000,0 309 | 15733973,Female,47,113000,1 310 | 15596761,Male,36,125000,1 311 | 15652400,Female,38,50000,0 312 | 15717893,Female,42,70000,0 313 | 15622585,Male,39,96000,1 314 | 15733964,Female,38,50000,0 315 | 15753861,Female,49,141000,1 316 | 15747097,Female,39,79000,0 317 | 15594762,Female,39,75000,1 318 | 15667417,Female,54,104000,1 319 | 15684861,Male,35,55000,0 320 | 15742204,Male,45,32000,1 321 | 15623502,Male,36,60000,0 322 | 15774872,Female,52,138000,1 323 | 15611191,Female,53,82000,1 324 | 15674331,Male,41,52000,0 325 | 15619465,Female,48,30000,1 326 | 15575247,Female,48,131000,1 327 | 15695679,Female,41,60000,0 328 | 15713463,Male,41,72000,0 329 | 15785170,Female,42,75000,0 330 | 15796351,Male,36,118000,1 331 | 15639576,Female,47,107000,1 332 | 15693264,Male,38,51000,0 333 | 15589715,Female,48,119000,1 334 | 15769902,Male,42,65000,0 335 | 15587177,Male,40,65000,0 336 | 15814553,Male,57,60000,1 337 | 15601550,Female,36,54000,0 338 | 15664907,Male,58,144000,1 339 | 15612465,Male,35,79000,0 340 | 15810800,Female,38,55000,0 341 | 15665760,Male,39,122000,1 342 | 15588080,Female,53,104000,1 343 | 15776844,Male,35,75000,0 344 | 15717560,Female,38,65000,0 345 | 15629739,Female,47,51000,1 346 | 15729908,Male,47,105000,1 347 | 15716781,Female,41,63000,0 348 | 15646936,Male,53,72000,1 349 | 15768151,Female,54,108000,1 350 | 15579212,Male,39,77000,0 351 | 15721835,Male,38,61000,0 352 | 15800515,Female,38,113000,1 353 | 15591279,Male,37,75000,0 354 | 15587419,Female,42,90000,1 355 | 15750335,Female,37,57000,0 356 | 15699619,Male,36,99000,1 357 | 15606472,Male,60,34000,1 358 | 15778368,Male,54,70000,1 359 | 15671387,Female,41,72000,0 360 | 15573926,Male,40,71000,1 361 | 15709183,Male,42,54000,0 362 | 15577514,Male,43,129000,1 363 | 15778830,Female,53,34000,1 364 | 15768072,Female,47,50000,1 365 | 15768293,Female,42,79000,0 366 | 15654456,Male,42,104000,1 367 | 15807525,Female,59,29000,1 368 | 15574372,Female,58,47000,1 369 | 15671249,Male,46,88000,1 370 | 15779744,Male,38,71000,0 371 | 15624755,Female,54,26000,1 372 | 15611430,Female,60,46000,1 373 | 15774744,Male,60,83000,1 374 | 15629885,Female,39,73000,0 375 | 15708791,Male,59,130000,1 376 | 15793890,Female,37,80000,0 377 | 15646091,Female,46,32000,1 378 | 15596984,Female,46,74000,0 379 | 15800215,Female,42,53000,0 380 | 15577806,Male,41,87000,1 381 | 15749381,Female,58,23000,1 382 | 15683758,Male,42,64000,0 383 | 15670615,Male,48,33000,1 384 | 15715622,Female,44,139000,1 385 | 15707634,Male,49,28000,1 386 | 15806901,Female,57,33000,1 387 | 15775335,Male,56,60000,1 388 | 15724150,Female,49,39000,1 389 | 15627220,Male,39,71000,0 390 | 15672330,Male,47,34000,1 391 | 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81,24.322385 37 | 83,25.016963 38 | 84,25.8955 39 | 84,22.8955 40 | 85,22.795205 41 | 87,24.659127 42 | 88,31.623848 43 | 88,26.623848 44 | 92,30.707012 45 | 93,37.785133 46 | 94,34.88669 47 | 95,40.011935 48 | 98,37.532318 49 | 99,44.754835 50 | 99,43.754835 51 | 100,43.0023 52 | -------------------------------------------------------------------------------- /Supervised-learning/dataset.csv: -------------------------------------------------------------------------------- 1 | studytime,score 2 | 2,111 3 | 1,107 4 | 3,26 5 | 3,63 6 | 4,7 7 | 2,10 8 | 2,68 9 | 2,55 10 | 3,46 11 | 3,87 12 | 2,96 13 | 1,95 14 | 2,118 15 | 2,93 16 | 3,75 17 | 2,110 18 | 3,100 19 | 3,19 20 | 2,20 21 | 2,74 22 | 4,92 23 | 4,66 24 | 4,10 25 | 3,43 26 | 2,106 27 | 4,30 28 | 1,38 29 | 1,115 30 | 4,66 31 | 2,76 32 | 4,120 33 | 4,6 34 | 2,99 35 | 2,92 36 | 2,115 37 | 3,117 38 | 2,114 39 | 1,36 40 | 2,118 41 | 1,16 42 | 3,77 43 | 1,91 44 | 2,14 45 | 4,105 46 | 2,71 47 | 2,14 48 | 4,119 49 | 4,68 50 | 2,76 51 | 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-------------------------------------------------------------------------------- 1 | console.log("Hello, World!"); -------------------------------------------------------------------------------- /ml-data/Codes/Chapter 1 - Python_Basics.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/ml-data/Codes/Chapter 1 - Python_Basics.pdf -------------------------------------------------------------------------------- /ml-data/Codes/Chapter 10 - Text Analytics.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/ml-data/Codes/Chapter 10 - Text Analytics.pdf -------------------------------------------------------------------------------- /ml-data/Codes/Chapter 2 - Descriptive Analytics.pdf: 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-------------------------------------------------------------------------------- 1 | Country,Age,Salary,Purchased 2 | France,44,72000,No 3 | Spain,27,48000,Yes 4 | Germany,30,54000,No 5 | Spain,38,61000,No 6 | Germany,40,,Yes 7 | France,35,58000,Yes 8 | Spain,,52000,No 9 | France,48,79000,Yes 10 | Germany,50,83000,No 11 | France,37,67000,Yes -------------------------------------------------------------------------------- /ml-data/IPL-IMB381IPL2013.csv: -------------------------------------------------------------------------------- 1 | Sl.NO.,PLAYER NAME,AGE,COUNTRY,TEAM,PLAYING ROLE,T-RUNS,T-WKTS,ODI-RUNS-S,ODI-SR-B,ODI-WKTS,ODI-SR-BL,CAPTAINCY EXP,RUNS-S,HS,AVE,SR-B,SIXERS,RUNS-C,WKTS,AVE-BL,ECON,SR-BL,AUCTION YEAR,BASE PRICE,SOLD PRICE 2 | 1,"Abdulla, YA",2,SA,KXIP,Allrounder,0,0,0,0,0,0,0,0,0,0,0,0,307,15,20.47,8.9,13.93,2009,50000,50000 3 | 2,Abdur Razzak,2,BAN,RCB,Bowler,214,18,657,71.41,185,37.6,0,0,0,0,0,0,29,0,0,14.5,0,2008,50000,50000 4 | 3,"Agarkar, AB",2,IND,KKR,Bowler,571,58,1269,80.62,288,32.9,0,167,39,18.56,121.01,5,1059,29,36.52,8.81,24.9,2008,200000,350000 5 | 4,"Ashwin, R",1,IND,CSK,Bowler,284,31,241,84.56,51,36.8,0,58,11,5.8,76.32,0,1125,49,22.96,6.23,22.14,2011,100000,850000 6 | 5,"Badrinath, S",2,IND,CSK,Batsman,63,0,79,45.93,0,0,0,1317,71,32.93,120.71,28,0,0,0,0,0,2011,100000,800000 7 | 6,"Bailey, GJ",2,AUS,CSK,Batsman,0,0,172,72.26,0,0,1,63,48,21,95.45,0,0,0,0,0,0,2009,50000,50000 8 | 7,"Balaji, L",2,IND,CSK+,Bowler,51,27,120,78.94,34,42.5,0,26,15,4.33,72.22,1,1342,52,25.81,7.98,19.4,2011,100000,500000 9 | 8,"Bollinger, DE",2,AUS,CSK,Bowler,54,50,50,92.59,62,31.3,0,21,16,21,165.88,1,693,37,18.73,7.22,15.57,2011,200000,700000 10 | 9,"Botha, J",2,SA,RR,Allrounder,83,17,609,85.77,72,53,1,335,67,30.45,114.73,3,610,19,32.11,6.85,28.11,2011,200000,950000 11 | 10,"Boucher, MV",2,SA,RCB+,W. Keeper,5515,1,4686,84.76,0,0,1,394,50,28.14,127.51,13,0,0,0,0,0,2008,200000,450000 12 | 11,"Bravo, DJ",2,WI,MI+,Allrounder,2200,86,2004,81.39,142,34.1,0,839,70,27.97,127.12,38,1338,47,28.47,8.12,21.11,2011,200000,200000 13 | 12,"Chanderpaul, S",3,WI,RCB,Batsman,9918,9,8778,70.74,14,52.8,1,25,16,8.33,80.64,0,0,0,0,0,0,2008,200000,200000 14 | 13,"Chawla, PP",1,IND,KXIP,Allrounder,5,3,38,65.51,32,41,0,337,24,13.48,113.09,9,1819,73,126.3,38.11,100.2,2008,125000,400000 15 | 14,"de Villiers, AB",2,SA,DD+,W. Keeper,5457,2,4998,93.19,0,0,1,1302,105,34.26,128.53,42,0,0,0,0,0,2008,200000,300000 16 | 15,"Dhawan, S",2,IND,MI+,Batsman,0,0,69,56.09,0,0,0,1540,95,31.43,122.32,36,66,4,16.5,8.25,12,2011,100000,300000 17 | 16,"Dhoni, MS",2,IND,CSK,W. Keeper,3509,0,6773,88.19,1,12,1,1782,70,37.13,136.45,64,0,0,0,0,0,2008,400000,1500000 18 | 17,"Dilshan, TM",2,SL,DD+,Allrounder,4722,32,6455,86.8,67,58.3,1,1077,76,28.34,117.83,24,356,5,71.2,8.07,53,2008,150000,250000 19 | 18,"Dinda, AB",2,IND,KKR+,Bowler,0,0,18,60,5,61.4,0,6,2,1,33.33,0,926,36,25.72,7.29,21.19,2011,100000,375000 20 | 19,"Dravid, RS",3,IND,RCB+,Batsman,13288,1,10889,71.24,4,46.5,1,1703,75,27.92,116.88,23,0,0,0,0,0,2011,400000,500000 21 | 20,"Duminy, J-P",2,SA,MI+,Batsman,654,11,2536,84,25,47.6,0,978,74,36.22,119.27,35,377,10,37.7,7.11,31.8,2009,300000,300000 22 | 21,"Edwards, FH",2,WI,DC,Bowler,380,157,73,45.62,60,35.6,0,4,3,4,80,0,154,5,30.8,6.6,28,2009,150000,150000 23 | 22,"Fernando, CRD",2,SL,MI,Bowler,249,97,239,60.96,187,34.7,0,4,2,0,133.33,0,298,17,17.53,7.64,13.76,2008,150000,150000 24 | 23,"Fleming, SP",3,NZ,CSK,Batsman,7172,0,8037,71.49,1,29,1,196,45,21.77,118.78,3,0,0,0,0,0,2008,350000,350000 25 | 24,"Flintoff, A",2,ENG,CSK,Allrounder,3845,226,3394,88.82,169,33.2,1,62,24,31,116.98,2,105,2,52.5,9.55,33,2009,950000,1550000 26 | 25,"Gambhir, G",2,IND,DD+,Batsman,3712,0,4819,86.17,0,0,1,2065,93,33.31,128.9,32,0,0,0,0,0,2008,220000,725000 27 | 26,"Ganguly, SC",3,IND,KKR+,Batsman,7212,32,11363,73.7,100,45.6,1,1349,91,25.45,106.81,42,363,10,36.3,7.89,27.6,2011,200000,400000 28 | 27,"Gayle, CH",2,WI,KKR+,Allrounder,6373,72,8087,83.95,156,44.4,1,1804,128,50.11,161.79,129,606,13,46.62,8.05,34.85,2008,250000,800000 29 | 28,"Gibbs, HH",3,SA,DC,Batsman,6167,0,8094,83.26,0,0,1,886,69,27.69,109.79,31,0,0,0,0,0,2008,250000,575000 30 | 29,"Gilchrist, AC",3,AUS,DC+,W. Keeper,5570,0,9619,96.94,0,0,1,1775,109,27.73,140.21,86,0,0,0,0,0,2008,300000,700000 31 | 30,"Gony, MS",2,IND,CSK+,Bowler,0,0,0,0,2,39,0,54,15,9,117.39,5,999,30,33.3,8.47,23.6,2011,50000,290000 32 | 31,Harbhajan Singh,2,IND,MI,Bowler,2164,406,1190,80.51,259,46.5,1,430,49,16.54,151.41,22,1469,54,27.2,6.85,23.83,2008,250000,850000 33 | 32,"Harris, RJ",2,AUS,DC+,Bowler,199,46,48,100,44,23.4,0,115,17,10.45,107.48,3,975,44,22.16,7.71,17.27,2011,200000,325000 34 | 33,"Hayden, ML",3,AUS,CSK,Batsman,8625,0,6133,78.96,0,0,0,1107,93,36.9,137.52,44,0,0,0,0,0,2008,225000,375000 35 | 34,"Henderson, T",3,SA,RR,Allrounder,0,0,0,0,0,0,0,11,11,5.5,68.75,1,40,1,40,6.66,36,2009,100000,650000 36 | 35,"Henriques, MC",1,AUS,KKR+,Allrounder,0,0,18,60,1,90,0,49,30,16.33,108.89,1,142,3,47.33,8.82,32.33,2011,50000,50000 37 | 36,"Hodge, BJ",3,AUS,KKR+,Batsman,503,0,575,87.51,1,66,0,1006,73,31.44,121.5,28,300,17,17.65,7.89,13.41,2011,200000,425000 38 | 37,"Hopes, JR",2,AUS,KXIP,Allrounder,0,0,1326,93.71,67,47.1,0,417,71,26.06,136.27,11,548,14,39.14,9.13,25.71,2011,200000,350000 39 | 38,"Hussey, DJ",2,AUS,KKR+,Allrounder,0,0,1488,91.4,18,36.2,0,971,71,26.24,125.78,48,345,6,57.5,8.85,39,2008,100000,625000 40 | 39,"Hussey, MEK",3,AUS,CSK,Batsman,5708,7,5262,86.97,2,117,1,958,116,39.92,120.65,25,0,0,0,0,0,2008,250000,250000 41 | 40,"Jadeja, RA",1,IND,RR+,Allrounder,0,0,860,78.61,57,46.2,0,904,48,23.18,120.86,35,750,26,28.85,7.33,23.65,2011,100000,950000 42 | 41,"Jaffer, W",2,IND,RCB,Allrounder,1944,2,10,43.47,0,0,0,130,50,16.25,107.44,3,0,0,0,0,0,2008,150000,150000 43 | 42,"Jayasuriya, ST",3,SL,MI,Allrounder,6973,98,13430,91.21,323,46,1,768,114,27.43,144.36,39,390,13,30,7.96,22.62,2008,250000,975000 44 | 43,"Jayawardena, DPMD",2,SL,KXIP+,Batsman,10440,6,10596,78.08,7,83.1,1,1471,110,30.65,128.02,33,0,0,0,0,0,2008,250000,475000 45 | 44,"Kaif, M",2,IND,RR+,Batsman,624,0,2753,72.03,0,0,0,259,34,14.39,103.6,6,0,0,0,0,0,2008,125000,675000 46 | 45,"Kallis, JH",3,SA,RCB,Allrounder,12379,276,11498,72.97,270,39.3,1,1965,89,30.7,110.95,37,1713,45,38.07,7.96,28.71,2008,225000,900000 47 | 46,Kamran Akmal,2,PAK,RR,W. Keeper,2648,0,2924,84.31,0,0,0,128,53,25.6,164.1,8,0,0,0,0,0,2008,150000,150000 48 | 47,Kamran Khan,1,IND,RR+,Bowler,0,0,0,0,0,0,0,3,3,3,60,0,224,9,24.89,8.48,17.78,2009,20000,24000 49 | 48,"Karthik, KD",2,IND,DD+,W. Keeper,1000,0,1008,74.5,0,0,0,1231,69,24.14,123.84,28,0,0,0,0,0,2008,200000,525000 50 | 49,"Kartik, M",2,IND,KKR+,Bowler,88,24,126,70.78,37,51.5,0,111,21,18.5,105.71,1,1013,21,48.24,7.02,41.33,2008,200000,425000 51 | 50,"Katich, SM",3,AUS,KXIP,Batsman,4188,21,1324,68.74,0,0,0,241,75,24.1,129.57,8,0,0,0,0,0,2008,200000,200000 52 | 51,"Kohli, V",1,IND,RCB,Batsman,491,0,3590,86.31,2,137,1,1639,73,28.26,119.29,49,345,4,86.25,8.84,58.5,2011,150000,1800000 53 | 52,"Kumar, P",1,IND,RCB+,Bowler,149,27,292,88.21,77,42.1,0,243,34,10.57,114.08,14,1919,53,36.21,7.73,28.11,2011,200000,800000 54 | 53,"Kumble, A",3,IND,RCB,Bowler,2506,619,938,61.06,337,43,1,35,8,11.67,74.47,0,105,2,52.5,9.55,33,2008,250000,500000 55 | 54,"Langeveldt, CK",3,SA,KKR+,Bowler,16,16,73,58.87,100,34.8,0,8,8,4,88.89,1,187,13,14.38,7.19,12,2011,100000,140000 56 | 55,"Laxman, VVS",3,IND,DC+,Batsman,8781,2,2338,71.23,0,0,1,282,52,15.67,105.62,5,0,0,0,0,0,2008,150000,375000 57 | 56,"Lee, B",2,AUS,KXI+,Bowler,1451,310,1100,82.45,377,29.2,0,103,25,11.44,121.18,6,1009,21,48.05,7.56,38.24,2008,300000,900000 58 | 57,"Maharoof, MF",2,SL,DD,Allrounder,556,25,1042,84.44,133,33.3,0,177,39,17.7,143.9,9,520,27,19.26,7.43,15.56,2008,150000,225000 59 | 58,"Malinga, SL",2,SL,MI,Bowler,275,101,327,73.81,185,31.1,0,64,17,5.82,100,4,1381,83,16.64,6.36,15.69,2008,200000,350000 60 | 59,"Mascarenhas, AD",2,ENG,RR+,Allrounder,0,0,245,95.33,13,63.2,0,74,27,8.22,101.37,1,331,19,17.42,7.01,14.95,2011,100000,100000 61 | 60,"Mathews, AD",1,SL,KKR+,Allrounder,1219,7,1447,82.59,42,43,0,376,65,25.07,123.28,12,537,15,35.8,8.2,26.33,2011,300000,950000 62 | 61,"McCullum, BB",2,NZ,KKR+,W. Keeper,3763,0,4511,89.62,0,0,1,1233,158,28.02,123.42,48,0,0,0,0,0,2008,175000,700000 63 | 62,"McDonald, AB",2,AUS,DD+,Allrounder,107,9,0,0,0,0,0,123,33,30.75,125.51,4,244,10,24.4,8.41,17.4,2011,50000,80000 64 | 63,"McGrath, GD",3,AUS,DD,Bowler,641,563,115,48.72,381,34,0,4,4,4,80,0,357,12,29.75,6.61,27,2008,350000,350000 65 | 64,Misbah-ul-Haq,3,PAK,RCB,Batsman,2173,0,2763,75.1,0,0,1,117,47,16.71,144.44,6,0,0,0,0,0,2010,100000,100000 66 | 65,"Mishra, A",2,IND,DD+,Bowler,392,43,5,27.77,19,40.1,0,186,31,10.94,102.2,3,1530,74,20.68,7.11,17.46,2011,100000,300000 67 | 66,"Mithun, A",1,IND,RCB,Bowler,120,9,51,92.72,3,60,0,32,11,8,133.33,1,435,6,72.5,9.89,44,2011,100000,260000 68 | 67,Mohammad Asif,2,PAK,DD,Bowler,141,106,34,34,46,42.1,0,3,3,1.5,50,0,296,8,37,9.25,24,2008,225000,650000 69 | 68,"Morkel, JA",2,SA,CSK,Allrounder,58,1,782,100.25,50,41.4,0,781,71,24.41,146.25,45,1899,69,27.52,8.25,20.01,2008,225000,675000 70 | 69,"Morkel, M",2,SA,RR+,Bowler,555,139,117,75.97,94,28.5,0,60,16,10,111.11,2,884,38,23.26,7.37,18.95,2011,100000,475000 71 | 70,"Muralitharan, M",3,SL,CSK+,Bowler,1261,800,674,77.56,534,35.2,0,20,6,3.33,66.67,0,1395,57,24.47,6.49,22.63,2008,250000,600000 72 | 71,"Nannes, DP",3,AUS,DD+,Bowler,0,0,1,50,1,42,0,4,3,4,30.77,0,627,24,26.13,7.17,21.92,2011,200000,650000 73 | 72,"Nayar, AM",2,IND,MI+,Allrounder,0,0,0,0,0,0,0,563,35,19.41,123.19,19,263,7,37.57,8.74,25.86,2011,50000,800000 74 | 73,"Nehra, A",2,IND,DD+,Bowler,77,44,141,57.31,157,36.6,0,38,22,19,82.61,1,1192,48,24.83,7.57,19.73,2011,200000,850000 75 | 74,"Noffke, AA",2,AUS,RCB,Allrounder,0,0,0,0,1,54,0,9,9,9,90,0,40,1,40,10,24,2010,20000,20000 76 | 75,"Ntini, M",2,SA,CSK,Bowler,699,390,199,66.77,266,32.6,0,11,11,11,61.11,0,242,7,34.57,6.91,30,2008,200000,200000 77 | 76,"Ojha, NV",2,IND,RR+,W. Keeper,0,0,1,14.28,0,0,0,960,94,22.33,117.94,50,0,0,0,0,0,2011,100000,270000 78 | 77,"Ojha, PP",1,IND,DC+,Bowler,70,62,41,43.61,20,41.7,0,10,3,1,30.3,0,1548,69,22.43,7.16,18.8,2011,200000,500000 79 | 78,"Oram, JDP",2,NZ,CSK+,Allrounder,1780,60,2377,87.16,168,39.7,0,106,41,13.25,98.15,5,327,9,36.33,9.26,23.67,2008,200000,675000 80 | 79,Pankaj Singh,2,IND,RCB+,Bowler,0,0,3,100,0,0,0,7,4,3.5,58.33,0,468,11,42.55,9.36,27.27,2011,50000,95000 81 | 80,"Patel, MM",2,IND,RR+,Bowler,60,35,74,66.07,86,36.6,0,39,23,7.8,235.49,0,1504,70,21.49,7.39,17.47,2008,100000,275000 82 | 81,"Patel, PA",2,IND,CSK+,W. Keeper,683,0,736,76.5,0,0,0,912,57,20.27,107.29,13,0,0,0,0,0,2008,150000,325000 83 | 82,"Pathan, IK",2,IND,KXIP+,Allrounder,1105,100,1468,78.96,165,34,0,929,60,23.82,128.31,34,1975,66,29.92,7.74,23.23,2008,200000,925000 84 | 83,"Pathan, YK",2,IND,RR+,Allrounder,0,0,810,113.6,33,45.1,0,1488,100,25.66,149.25,81,1139,36,31.64,7.2,26.36,2008,100000,475000 85 | 84,"Pietersen, KP",2,ENG,RCB+,Batsman,6654,5,4184,86.76,7,57.1,1,634,103,42.27,141.2,30,215,7,30.71,7.41,24.86,2009,1350000,1550000 86 | 85,"Pollock, SM",3,SA,MI,Allrounder,3781,421,3519,86.69,393,39.9,1,147,33,18.37,132.43,8,301,11,27.36,6.54,25,2008,200000,550000 87 | 86,"Pomersbach, LA",2,AUS,KXIP+,Batsman,0,0,0,0,0,0,0,244,79,27.11,130.48,12,0,0,0,0,0,2011,20000,50000 88 | 87,"Ponting, RT",3,AUS,KKR,Batsman,13218,5,13704,80.39,3,50,1,39,20,9.75,73.58,1,0,0,0,0,0,2008,335000,400000 89 | 88,"Powar, RR",2,IND,KXIP+,Bowler,13,6,163,62.69,34,45.1,0,67,28,22.33,104.69,1,527,13,40.54,7.42,32.77,2008,150000,170000 90 | 89,"Raina, SK",1,IND,CSK,Batsman,710,13,3525,92.71,16,61.9,0,2254,98,33.64,139.39,97,678,20,33.9,7.05,28.9,2008,125000,650000 91 | 90,"Ryder, JD",2,NZ,RCB+,Allrounder,1269,5,1100,89.72,11,34.8,0,604,86,21.57,131.88,19,303,8,37.88,7.73,29.5,2009,100000,160000 92 | 91,"Saha, WP",2,IND,KKR+,W. Keeper,74,0,4,80,0,0,0,372,59,28.62,128.28,16,0,0,0,0,0,2011,100000,100000 93 | 92,"Sangakkara, KC",2,SL,KXIP+,W. Keeper,9382,0,10472,75.75,0,0,1,1567,94,27.98,124.76,27,0,0,0,0,0,2008,250000,700000 94 | 93,"Sarwan, RR",2,WI,KXIP,Batsman,5842,23,5644,75.76,16,36.3,0,73,31,18.25,97.33,1,0,0,0,0,0,2008,225000,225000 95 | 94,"Sehwag, V",2,IND,DD,Batsman,8178,40,8090,104.68,95,45.4,1,1879,119,30.31,167.32,79,226,6,37.67,10.56,21.67,2011,400000,1800000 96 | 95,Shahid Afridi,2,PAK,DC,Allrounder,1716,48,7040,113.87,344,43.4,1,81,33,10.12,176.08,6,225,9,25,7.5,20,2008,225000,675000 97 | 96,"Sharma, I",1,IND,KKR+,Bowler,432,133,47,34.05,64,33.6,0,37,9,9.25,80.43,1,1176,36,32.67,7.63,23.61,2008,150000,950000 98 | 97,"Sharma, J",2,IND,CSK,Allrounder,0,0,35,116.66,1,150,0,36,16,9,120,2,419,12,34.92,9.88,21.33,2008,100000,225000 99 | 98,"Sharma, RG",1,IND,DC+,Batsman,0,0,1961,78.85,8,59.1,0,1975,109,31.35,129.17,82,408,14,29.14,8,21.86,2008,150000,750000 100 | 99,Shoaib Akhtar,3,PAK,KKR,Bowler,544,178,394,73.23,247,31.4,0,2,2,2,28.57,0,54,5,10.8,7.71,8.4,2008,250000,425000 101 | 100,Shoaib Malik,2,PAK,DD,Allrounder,1606,21,5253,78.37,139,47.6,1,52,24,13,110.63,0,85,2,42.5,10,25.5,2008,300000,500000 102 | 101,"Silva, LPC",2,SL,DC,Batsman,537,1,1587,70.4,1,42,0,40,23,20,153.84,1,21,0,0,21,0,2008,100000,100000 103 | 102,"Singh, RP",2,IND,DC+,Bowler,116,40,104,42.97,69,37.1,0,52,10,3.47,68.42,1,1892,74,25.57,7.75,19.78,2008,200000,875000 104 | 103,"Smith, DR",2,WI,DC+,Allrounder,320,7,925,97.26,56,44.8,0,439,87,25.82,148.81,24,338,9,37.56,8.14,27.89,2009,100000,100000 105 | 104,"Smith, GC",2,SA,RR+,Batsman,8042,8,6598,81.58,18,57,1,739,91,28.42,110.63,9,0,0,0,0,0,2008,250000,250000 106 | 105,Sohail Tanvir,2,PAK,RR,Bowler,17,5,268,94.03,55,37.4,0,36,13,12,124.13,1,266,22,12.09,6.46,11.2,2008,100000,100000 107 | 106,"Sreesanth, S",2,IND,KXIP+,Bowler,281,87,44,36.36,75,33,0,33,15,11,64.71,0,1031,35,29.46,8.25,21.43,2008,200000,625000 108 | 107,"Steyn, DW",2,SA,RCB+,Bowler,770,272,142,73.57,91,33.7,0,70,13,4.67,86.42,1,1304,59,22.1,6.58,20.15,2008,150000,325000 109 | 108,"Styris, SB",3,NZ,DC+,Allrounder,1586,20,4483,79.41,137,44.6,0,131,36,18.71,98.5,3,276,8,34.5,7.67,27,2008,175000,175000 110 | 109,"Symonds, A",3,AUS,DC+,Allrounder,1462,24,5088,92.44,133,44.6,0,974,117,36.07,129.87,41,674,20,33.7,7.7,26.35,2008,250000,1350000 111 | 110,"Taibu, T",2,ZIM,KKR,W. Keeper,1546,1,3393,67.58,2,42,1,31,15,10.33,119.23,0,0,0,0,0,0,2008,125000,125000 112 | 111,"Taylor, LRPL",2,NZ,RCB+,Batsman,2742,2,3185,81.77,0,0,1,895,81,27.97,130.28,45,24,0,0,12,0,2008,400000,1000000 113 | 112,"Tendulkar, SR",3,IND,MI,Batsman,15470,45,18426,86.23,154,52.2,1,2047,100,37.91,119.22,24,58,0,0,9.67,0,2011,400000,1800000 114 | 113,"Tiwary, MK",2,IND,DD+,Batsman,0,0,165,75.68,1,60,0,969,75,31.26,113.33,22,45,1,45,11.25,24,2008,100000,675000 115 | 114,"Tiwary, SS",1,IND,MI+,Batsman,0,0,49,87.5,0,0,0,836,42,25.33,119.6,32,0,0,0,0,0,2011,100000,1600000 116 | 115,"Tyagi, S",1,IND,CSK,Bowler,0,0,1,50,3,55,0,3,3,3,0.75,0,295,6,49.17,8.55,34.83,2011,50000,240000 117 | 116,Umar Gul,2,PAK,KKR,Bowler,541,157,368,69.04,154,32.2,0,39,24,13,205.26,5,184,12,15.33,8.17,11.2,2008,150000,150000 118 | 117,"Uthappa, RV",2,IND,RCB+,Batsman,0,0,786,91.92,0,0,0,1538,69,26.98,126.17,59,0,0,0,0,0,2008,200000,800000 119 | 118,"Vaas, WPUJC",3,SL,DC,Bowler,3089,355,2025,72.52,400,39.4,0,81,20,10.13,110.96,3,355,18,19.72,7.55,15.67,2008,200000,200000 120 | 119,Van der Merwe,2,SA,RCB+,Allrounder,0,0,39,95.12,17,41.4,0,137,35,15.22,118.1,8,427,18,23.72,6.83,20.94,2011,50000,50000 121 | 120,"Venugopal Rao, Y",2,IND,DC+,Batsman,0,0,218,60.05,0,0,0,914,71,22.29,118.24,37,321,6,53.5,9.44,34,2011,100000,700000 122 | 121,"Vettori, DL",2,NZ,DD+,Allrounder,4486,359,2105,81.93,282,45.7,1,121,29,15.13,107.08,2,878,28,31.36,6.81,27.75,2008,250000,625000 123 | 122,"Vinay Kumar, R",2,IND,RCB+,Bowler,11,1,43,43.87,28,35.3,0,217,25,9.43,104.83,5,1664,61,27.28,8.24,19.87,2011,100000,475000 124 | 123,"Warne, SK",3,AUS,RR,Bowler,3154,708,1018,72.04,293,36.3,1,198,34,9.9,92.52,6,1447,57,25.39,7.27,20.95,2008,450000,450000 125 | 124,"Warner, DA",1,AUS,DD,Batsman,483,2,876,85.79,0,0,0,1025,109,27.7,135.76,44,0,0,0,0,0,2011,200000,750000 126 | 125,"White, CL",2,AUS,RCB+,Batsman,146,5,2037,80.48,12,27.5,1,745,78,31.04,132.09,29,70,0,0,14,0,2008,100000,500000 127 | 126,"Yadav, AS",2,IND,DC,Batsman,0,0,0,0,0,0,0,49,16,9.8,125.64,2,0,0,0,0,0,2010,50000,750000 128 | 127,Younis Khan,2,PAK,RR,Batsman,6398,7,6814,75.78,3,86.6,1,3,3,3,42.85,0,0,0,0,0,0,2008,225000,225000 129 | 128,Yuvraj Singh,2,IND,KXIP+,Batsman,1775,9,8051,87.58,109,44.3,1,1237,66,26.32,131.88,67,569,23,24.74,7.02,21.13,2011,400000,1800000 130 | 129,Zaheer Khan,2,IND,MI+,Bowler,1114,288,790,73.55,278,35.4,0,99,23,9.9,91.67,1,1783,65,27.43,7.75,21.26,2008,200000,450000 131 | 130,"Zoysa, DNT",2,SL,DC,Bowler,288,64,343,95.81,108,39.4,0,11,10,11,122.22,0,99,2,49.5,9,33,2008,100000,110000 -------------------------------------------------------------------------------- /ml-data/Income Data.csv: -------------------------------------------------------------------------------- 1 | income,age 2 | 41100.0,48.75 3 | 54100.0,28.1 4 | 47800.0,46.75 5 | 19100.0,40.25 6 | 18200.0,35.8 7 | 19800.0,41.45 8 | 51500.0,31.55 9 | 39900.0,48.2 10 | 13000.0,36.8 11 | 31900.0,43.05 12 | 51700.0,29.7 13 | 56700.0,29.25 14 | 58800.0,29.65 15 | 46500.0,47.2 16 | 55000.0,28.35 17 | 18200.0,41.15 18 | 46400.0,45.15 19 | 46300.0,44.3 20 | 22100.0,46.65 21 | 53200.0,29.0 22 | 16700.0,40.2 23 | 46000.0,44.8 24 | 53200.0,29.2 25 | 41200.0,47.3 26 | 8000.0,36.5 27 | 13200.0,38.95 28 | 40600.0,43.7 29 | 12500.0,38.35 30 | 56700.0,29.85 31 | 56900.0,30.8 32 | 23900.0,38.3 33 | 40500.0,48.65 34 | 47300.0,43.3 35 | 55900.0,29.7 36 | 57100.0,28.3 37 | 6100.0,37.3 38 | 56500.0,29.35 39 | 27300.0,30.65 40 | 41700.0,47.6 41 | 52400.0,30.1 42 | 18800.0,43.55 43 | 56100.0,30.1 44 | 40200.0,45.65 45 | 53900.0,28.0 46 | 54300.0,29.0 47 | 20900.0,39.95 48 | 56100.0,28.5 49 | 17000.0,28.95 50 | 27600.0,41.35 51 | 54400.0,28.35 52 | 15700.0,40.55 53 | 55200.0,31.75 54 | 15000.0,44.15 55 | 25400.0,39.8 56 | 53300.0,28.75 57 | 41100.0,46.6 58 | 43700.0,48.1 59 | 23200.0,43.25 60 | 3200.0,39.65 61 | 50900.0,49.45 62 | 53600.0,29.15 63 | 59500.0,29.9 64 | 42100.0,46.6 65 | 11500.0,44.15 66 | 57200.0,29.25 67 | 3700.0,40.45 68 | 44600.0,44.2 69 | 54800.0,29.85 70 | 42200.0,47.85 71 | 54500.0,29.9 72 | 28000.0,37.4 73 | 30400.0,45.6 74 | 3100.0,42.6 75 | 25900.0,39.2 76 | 55300.0,30.55 77 | 45600.0,48.45 78 | 56100.0,29.4 79 | 16100.0,36.15 80 | 43300.0,46.05 81 | 45000.0,47.1 82 | 9900.000000000004,37.5 83 | 48400.0,47.15 84 | 58300.0,28.8 85 | 39500.0,45.6 86 | 41800.0,49.6 87 | 54300.0,48.4 88 | 52700.0,29.0 89 | 9600.0,42.4 90 | 44500.0,49.95 91 | 41500.0,45.15 92 | 27400.0,38.75 93 | 58500.0,30.35 94 | 8900.0,41.05 95 | 50700.0,46.1 96 | 56600.0,28.900000000000002 97 | 55300.0,28.8 98 | 39700.0,48.45 99 | 52000.0,29.85 100 | 8300.0,34.1 101 | 43300.0,43.75 102 | 56900.0,29.35 103 | 26000.0,44.4 104 | 44800.0,50.1 105 | 57000.0,28.3 106 | 49200.0,47.85 107 | 48600.0,46.2 108 | 12900.0,40.7 109 | 46200.0,47.95 110 | 54100.0,30.45 111 | 52900.0,28.15 112 | 40600.0,47.9 113 | 22200.0,40.9 114 | 53400.0,49.2 115 | 45300.0,47.3 116 | 17900.0,38.95 117 | 17000.0,38.05 118 | 40300.0,46.6 119 | 52200.0,48.35 120 | 51000.0,28.45 121 | 56900.0,29.05 122 | 52200.0,29.9 123 | 4100.0,41.0 124 | 54200.0,31.0 125 | 53700.0,29.1 126 | 56400.0,30.15 127 | 18400.0,36.75 128 | 54600.0,29.95 129 | 54700.0,29.2 130 | 53600.0,28.0 131 | 5099.999999999995,40.0 132 | 23000.0,40.55 133 | 17300.0,37.55 134 | 42200.0,42.05 135 | 10800.0,37.55 136 | 17000.0,35.55 137 | 54800.0,29.6 138 | 19400.0,40.35 139 | 42100.0,45.8 140 | 18000.0,44.9 141 | 21400.0,36.5 142 | 54800.0,29.65 143 | 40100.0,48.3 144 | 55200.0,29.1 145 | 46600.0,43.45 146 | 25800.0,40.55 147 | 17600.0,38.75 148 | 39800.0,48.05 149 | 21700.0,45.7 150 | 22900.0,36.2 151 | 54200.0,28.95 152 | 42700.0,44.15 153 | 21200.0,35.35 154 | 42700.0,45.65 155 | 57900.0,30.55 156 | 18200.0,36.4 157 | 15900.0,37.95 158 | 55000.0,28.75 159 | 56600.0,29.6 160 | 43100.0,48.95 161 | 54800.0,27.849999999999998 162 | 37900.0,50.4 163 | 49700.0,48.9 164 | 55300.0,30.5 165 | 41300.0,50.35 166 | 26200.0,41.65 167 | 52300.0,43.8 168 | 48200.0,45.2 169 | 47100.0,46.1 170 | 41500.0,43.05 171 | 22200.0,41.75 172 | 60100.0,29.45 173 | 44900.0,46.15 174 | 26300.0,44.55 175 | 55700.0,30.0 176 | 53500.0,30.7 177 | 58500.0,27.4 178 | 42200.0,45.8 179 | 56900.0,29.5 180 | 42800.0,50.4 181 | 50700.0,50.3 182 | 56100.0,28.599999999999998 183 | 25500.0,39.6 184 | 46300.0,44.35 185 | 45300.0,49.45 186 | 44300.0,47.35 187 | 41200.0,42.55 188 | 55800.0,29.15 189 | 22900.0,33.2 190 | 43200.0,45.9 191 | 25300.0,39.85 192 | 44600.0,47.4 193 | 46000.0,49.15 194 | 55600.0,27.849999999999998 195 | 54100.0,28.7 196 | 15100.0,40.2 197 | 44400.0,45.2 198 | 57700.0,29.05 199 | 16400.0,38.85 200 | 47200.0,48.35 201 | 12599.999999999995,39.3 202 | 55300.0,29.2 203 | 18800.0,39.9 204 | 49300.0,44.05 205 | 11800.0,42.35 206 | 55500.0,29.2 207 | 25100.0,43.2 208 | 38800.0,47.4 209 | 14600.0,40.3 210 | 43000.0,50.2 211 | 55500.0,29.65 212 | 45500.0,45.7 213 | 43400.0,46.85 214 | 17800.0,37.1 215 | 54000.0,28.05 216 | 54200.0,31.15 217 | 52900.0,31.15 218 | 53200.0,30.25 219 | 38200.0,47.45 220 | 25800.0,30.3 221 | 57400.0,29.8 222 | 47300.0,43.3 223 | 50200.0,30.1 224 | 56900.0,30.2 225 | 57100.0,29.85 226 | 45400.0,47.3 227 | 5600.0,37.7 228 | 16400.0,31.9 229 | 43100.0,45.0 230 | 53800.0,43.6 231 | 33800.0,39.45 232 | 40100.0,50.4 233 | 54400.0,29.45 234 | 54600.0,30.1 235 | 41300.0,45.35 236 | 54300.0,29.1 237 | 12300.0,42.45 238 | 10400.0,32.7 239 | 26300.0,30.0 240 | 40800.0,43.45 241 | 43500.0,46.65 242 | 55500.0,29.85 243 | 20000.0,42.3 244 | 23500.0,41.15 245 | 16200.0,48.05 246 | 10299.999999999996,36.9 247 | 56800.0,27.9 248 | 31900.0,42.0 249 | 13400.0,38.1 250 | 45400.0,47.8 251 | 57400.0,29.85 252 | 57100.0,29.0 253 | 47500.0,47.35 254 | 51900.0,29.0 255 | 52000.0,29.95 256 | 22300.0,40.1 257 | 39600.0,47.05 258 | 52500.0,29.55 259 | 28300.0,39.8 260 | 48600.0,46.2 261 | 49500.0,46.15 262 | 14300.0,39.2 263 | 49300.0,44.35 264 | 29000.0,36.5 265 | 16500.0,34.65 266 | 34600.0,47.85 267 | 22400.0,39.45 268 | 42000.0,46.2 269 | 55300.0,30.65 270 | 54400.0,29.25 271 | 55100.0,30.35 272 | 55400.0,29.4 273 | 48200.0,45.05 274 | 47800.0,48.15 275 | 42900.0,46.55 276 | 48600.0,46.7 277 | 52500.0,49.5 278 | 21100.0,33.35 279 | 15100.0,37.8 280 | 54900.0,27.8 281 | 49500.0,47.15 282 | 47700.0,46.8 283 | 54200.0,29.1 284 | 55500.0,28.75 285 | 56400.0,26.7 286 | 21100.0,39.4 287 | 57300.0,30.65 288 | 14100.0,35.7 289 | 13100.0,40.4 290 | 54900.0,29.6 291 | 55700.0,30.05 292 | 18300.0,39.2 293 | 26300.0,38.65 294 | 30300.0,41.55 295 | 60200.0,28.45 296 | 46900.0,46.2 297 | 38300.0,46.1 298 | 27400.0,44.15 299 | 54200.0,28.25 300 | 41800.0,44.85 301 | 38600.0,39.35 302 | -------------------------------------------------------------------------------- /ml-data/MBA-Salary.csv: -------------------------------------------------------------------------------- 1 | S. No.,Percentage in Grade 10,Salary 2 | 1,62,270000 3 | 2,76.33,200000 4 | 3,72,240000 5 | 4,60,250000 6 | 5,61,180000 7 | 6,55,300000 8 | 7,70,260000 9 | 8,68,235000 10 | 9,82.8,425000 11 | 10,59,240000 12 | 11,58,250000 13 | 12,60,180000 14 | 13,66,428000 15 | 14,83,450000 16 | 15,68,300000 17 | 16,37.33,240000 18 | 17,79,252000 19 | 18,68.4,280000 20 | 19,70,231000 21 | 20,59,224000 22 | 21,63,120000 23 | 22,50,260000 24 | 23,69,300000 25 | 24,52,120000 26 | 25,49,120000 27 | 26,64.6,250000 28 | 27,50,180000 29 | 28,74,218000 30 | 29,58,360000 31 | 30,67,150000 32 | 31,75,250000 33 | 32,60,200000 34 | 33,55,300000 35 | 34,78,330000 36 | 35,50.08,265000 37 | 36,56,340000 38 | 37,68,177600 39 | 38,52,236000 40 | 39,54,265000 41 | 40,52,200000 42 | 41,76,393000 43 | 42,64.8,360000 44 | 43,74.4,300000 45 | 44,74.5,250000 46 | 45,73.5,360000 47 | 46,57.58,180000 48 | 47,68,180000 49 | 48,69,270000 50 | 49,66,240000 51 | 50,60.8,300000 -------------------------------------------------------------------------------- /ml-data/beer.csv: -------------------------------------------------------------------------------- 1 | name,calories,sodium,alcohol,cost 2 | Budweiser,144,15,4.7,0.43 3 | Schlitz,151,19,4.9,0.43 4 | Lowenbrau,157,15,0.9,0.48 5 | Kronenbourg,170,7,5.2,0.73 6 | Heineken,152,11,5.0,0.77 7 | Old_Milwaukee,145,23,4.6,0.28 8 | Augsberger,175,24,5.5,0.40 9 | Srohs_Bohemian_Style,149,27,4.7,0.42 10 | Miller_Lite,99,10,4.3,0.43 11 | Budweiser_Light,113,8,3.7,0.40 12 | Coors,140,18,4.6,0.44 13 | Coors_Light,102,15,4.1,0.46 14 | Michelob_Light,135,11,4.2,0.50 15 | Becks,150,19,4.7,0.76 16 | Kirin,149,6,5.0,0.79 17 | Pabst_Extra_Light,68,15,2.3,0.38 18 | Hamms,139,19,4.4,0.43 19 | Heilemans_Old_Style,144,24,4.9,0.43 20 | Olympia_Goled_Light,72,6,2.9,0.46 21 | Schlitz_Light,97,7,4.2,0.47 22 | -------------------------------------------------------------------------------- /ml-data/bollywood.csv: -------------------------------------------------------------------------------- 1 | SlNo,Release Date,MovieName,ReleaseTime,Genre,Budget,BoxOfficeCollection,YoutubeViews,YoutubeLikes,YoutubeDislikes 2 | 1,18-Apr-14,2 States,LW,Romance,36,104,8576361,26622,2527 3 | 2,4-Jan-13,Table No. 21,N,Thriller ,10,12,1087320,1129,137 4 | 3,18-Jul-14,Amit Sahni Ki List,N,Comedy,10,4,572336,586,54 5 | 4,4-Jan-13,Rajdhani Express,N, Drama ,7,0.35,42626,86,19 6 | 5,4-Jul-14,Bobby Jasoos,N,Comedy,18,10.8,3113427,4512,1224 7 | 6,30-May-14,Citylights,HS, Drama ,7,35,1076591,1806,84 8 | 7,19-Sep-14,Daawat-E-Ishq,N,Comedy,30,24.6,3905050,8315,1373 9 | 8,11-Jan-13,Matru Ki Bijlee Ka Mandola,N,Comedy,33,40,2435283,4326,647 10 | 9,10-Jan-14,Dedh Ishqiya,LW,Comedy,31,27,2333067,2436,591 11 | 10,11-Jan-13,Gangoobai,N, Drama ,2,0.01,4354,1,1 12 | 11,28-Mar-14,Dishkiyaoon,N,Action ,28,6,2397647,3308,465 13 | 12,18-Jan-13,Inkaar,N,Romance,5,10.25,1077723,897,114 14 | 13,27-Jun-14,Ek Villain,HS,Romance,35,105.5,4518748,13558,1094 15 | 14,18-Jan-13,Mumbai Mirror,N,Action ,4,1.8,235767,192,164 16 | 15,8-Aug-14,Entertainment,N,Comedy,60,163,6336058,20472,2843 17 | 16,18-Jan-13,Bandook,N,Action,4,0.09,28391,8,14 18 | 17,25-Jan-13,Race 2,LW,Thriller ,60,162,7641333,12617,2138 19 | 18,6-Jun-14,Filmistaan,HS,Comedy,10,6,989096,1608,173 20 | 19,12-Sep-14,Finding Fanny,N,Comedy,15,58.9,6144142,13013,2746 21 | 20,13-Jun-14,Fugly,HS,Comedy,10,12.6,2343109,2963,556 22 | 21,1-Feb-13,David,N,Thriller ,10,3.65,839270,1542,95 23 | 22,7-Mar-14,Gulaab Gang,N,Action ,27,14.3,4687259,13219,1605 24 | 23,14-Feb-14,Gunday,N,Action,50,76.7,3503887,8383,1419 25 | 24,2-Oct-14,Haider,FS, Drama ,24,69,7048104,23590,2443 26 | 25,21-Nov-14,Happy Ending,N,Romance,48,21.5,5192338,6928,1120 27 | 26,8-Feb-13,Special 26,N, Drama ,42,103,1829417,6248,568 28 | 27,24-Oct-14,Happy New Year,LW, Drama ,125,383,6395202,37585,10269 29 | 28,8-Feb-13,ABCD _ Any Body Can Dance,N, Drama ,12,44,4042091,10609,788 30 | 29,18-Jul-14,Hate Story 2,N,Thriller,12,27.16,13798789,15708,5226 31 | 30,9-May-14,Hawaa Hawaai,HS, Drama ,11,10,764725,1377,94 32 | 31,7-Feb-14,Heartless,N,Thriller,12,1.5,4823892,5797,227 33 | 32,15-Feb-13,Murder 3,FS,Thriller,12,25,2249055,1879,747 34 | 33,23-May-14,Heropanti,HS,Romance,21,65,4005695,8189,1814 35 | 34,22-Feb-13,Kai Po Che!,N, Drama ,30,62.2,1888754,5686,512 36 | 35,21-Feb-14,Highway,N, Drama ,30,27.25,3043012,9100,971 37 | 36,22-Feb-13,Zilla Ghaziabad,N,Action,36,16,1551786,1753,619 38 | 37,6-Jun-14,Holiday,HS,Action,50,129,6841635,14182,1997 39 | 38,11-Jul-14,Humpty Sharma Ki Dulhania,N,Romance,20,130,6604595,14535,2208 40 | 39,1-Mar-13,The Attacks of 26/11,N,Thriller,30,32,1041892,1911,344 41 | 40,20-Jun-14,Humshakals,HS,Comedy,75,63.7,6280868,1,1 42 | 41,8-Mar-13,"Saheb, Biwi Aur Gangster Returns",FS, Drama ,15,22,1176596,1037,189 43 | 42,24-Jan-14,Jai Ho,N,Action,120,111,868743,2783,404 44 | 43,19-Sep-14,Khoobsurat,N,Romance,12,39.4,5454349,12548,2038 45 | 44,25-Jul-14,Kick,N,Action,100,377,18986221,101275,10066 46 | 45,15-Mar-13,3G,N,Thriller,5,5.82,1200152,1385,345 47 | 46,14-Nov-14,Kill Dil,N,Romance,35,36,2375050,4358,555 48 | 47,15-Mar-13,Mere Dad Ki Maruti,N,Comedy,5,11,569711,1226,133 49 | 48,15-Mar-13,Jolly LLB,N,Comedy,10,32.7,1741424,3024,181 50 | 49,9-May-14,Koyelaanchal,HS,Action,8,2,591838,347,31 51 | 50,2-May-14,Kya Dilli Kya Lahore,HS, Drama ,7,0.5,492344,2018,64 52 | 51,4-Jul-14,Lekar Hum Deewana Dil,N,Romance,16,2.5,3998331,2307,497 53 | 52,22-Mar-13,Aatma,N,Thriller,8,8.78,841891,997,306 54 | 53,4-Apr-14,Main Tera Hero,LW,Comedy,40,55,4903941,12657,2139 55 | 54,22-Mar-13,Sona Spa,N,Thriller,10,0.24,79430,61,112 56 | 55,29-Mar-13,Himmatwala,N,Action,50,100,2501277,1,1 57 | 56,9-May-14,Manjunath,HS, Drama ,3,1.75,370106,991,35 58 | 57,22-Aug-14,Mardaani,N, Drama ,15,36,4376650,14408,1035 59 | 58,5-Apr-13,Chashme Baddoor,N,Comedy,20,42,166066,2897,283 60 | 59,5-Sep-14,Mary Kom,N, Drama ,15,104,6086811,26560,1647 61 | 60,12-Apr-13,Nautanki Saala,LW,Comedy,9,21.5,1208841,1442,342 62 | 61,9-May-14,Mastram,HS,Comedy,2,3,3557585,3927,1364 63 | 62,12-Apr-13,Commando-A One Man Army,LW,Action,22,21,3162465,7519,562 64 | 63,19-Apr-13,Ek Thi Daayan,FS,Thriller,24,45,1837437,3296,613 65 | 64,17-Jan-14,Miss Lovely,N, Drama ,5,0.7,808439,328,95 66 | 65,26-Apr-13,Aashiqui 2,N,Romance,12,110,2926673,4944,628 67 | 66,28-Mar-14,O Teri,N,Comedy,21,3.75,1511318,3004,278 68 | 67,31-Jan-14,One by Two,N,Romance,12,2.5,1016858,1081,212 69 | 68,2-May-14,Purani Jeans,HS,Romance,10,1.1,1537063,822,229 70 | 69,3-May-13,Shootout at Wadala,N,Thriller,45,75,769365,784,126 71 | 70,7-Mar-14,Queen,N, Drama ,25,61,2981381,5339,443 72 | 71,3-May-13,Bombay Talkies,N, Drama ,6,14.3,1528538,2766,328 73 | 72,21-Mar-14,Ragini MMS 2,N,Thriller,18,50,5416213,6807,2717 74 | 73,10-May-13,Go Goa Gone,N,Comedy,16,28.2,4321162,10126,964 75 | 74,29-Aug-14,Raja Natwarlal,N,Thriller,35,29.6,4050407,8197,1155 76 | 75,25-Apr-14,Revolver Rani,N, Drama ,24,10,1744602,3013,392 77 | 76,28-Feb-14,Shaadi Ke Side Effects,N,Comedy,50,37.8,4590539,9021,1465 78 | 77,17-May-13,Aurangzeb,N, Drama ,20,23,1901503,3394,754 79 | 78,3-Jan-14,Sholay 3D,N,Action,25,11,1001670,2110,268 80 | 79,15-Aug-14,Singham Returns,LW,Action,90,200,8287587,20011,2899 81 | 80,7-Nov-14,The Shaukeens,N,Comedy,35,32.5,3395361,8395,1204 82 | 81,24-May-13,Ishkq in Paris,LW,Romance,15,2.13,630506,463,83 83 | 82,28-Nov-14,Ungli,N, Drama ,5,13.8,2763615,6907,489 84 | 83,31-May-13,Yeh Jawaani Hai Deewani,N,Romance,70,311,5123217,13826,1640 85 | 84,10-Jan-14,Yaariyan,LW,Romance,19,40,3331082,4853,767 86 | 85,7-Jun-13,Yamla Pagla Deewana 2,N,Comedy,24,55,2775792,5457,681 87 | 86,9-May-14,Yeh Hai Bakrapur,HS,Comedy,5,2,1257416,758,923 88 | 87,28-Mar-14,Youngistaan,N, Drama ,26,6.75,603710,1082,177 89 | 88,14-Jun-13,Fukrey,N,Comedy,5,36.2,227912,222,39 90 | 89,28-Nov-14,Zid,N,Thriller,20,14.05,6698987,6750,2234 91 | 90,19-Dec-14,PK,HS, Drama ,85,735,13270623,43767,5693 92 | 91,21-Jun-13,Raanjhanaa,N,Romance,35,100,5470632,12413,1018 93 | 92,5-Dec-14,Action Jackson,N,Action,90,57.45,13279636,19240,3859 94 | 93,2-Oct-14,Bang Bang,FS,Action,140,340,15889811,61805,6283 95 | 94,21-Jun-13,Shortcut Romeo,N,Thriller,15,2.58,1131573,913,255 96 | 95,12-Sep-14,Creature,N,Thriller,24,20,2876498,5844,1537 97 | 96,28-Jun-13,Ghanchakkar,N,Comedy,30,38,2248046,5108,614 98 | 97,2-Oct-13,Besharam,LW,Comedy,85,35,5319606,10253,8165 99 | 98,5-Jul-13,Lootera,N,Romance,27,28,2581449,4416,819 100 | 99,18-Jul-14,Pizza 3D,N,Thriller,10,4.35,838943,1116,153 101 | 100,16-May-14,The Xpose,HS,Thriller,16,29,3707889,3512,1292 102 | 101,5-Jul-13,Policegiri,N,Action,30,16.8,1754266,1674,420 103 | 102,12-Jul-13,Bhaag Milkha Bhaag,N, Drama ,30,164,2635390,4622,456 104 | 103,25-Apr-14,Kaanchi,N, Drama ,31,4,2360968,2807,595 105 | 104,25-Apr-14,Samrat and Co.,N,Thriller,18,2,371133,532,180 106 | 105,12-Jul-13,B.A. Pass,N, Drama ,2,5.9,2240961,842,348 107 | 106,19-Jul-13,Ramaiya Vastavaiya,N,Romance,35,30,3788488,4111,687 108 | 107,14-Mar-14,Bewakoofiyan,FS,Romance,22,14,2850376,4764,1068 109 | 108,19-Jul-13,D Day,N,Action,30,24,1939868,1804,224 110 | 109,21-Feb-14,Darr@ The Mall,N,Thriller,15,4.5,1679872,255,51 111 | 110,26-Jul-13,Issaq,N,Romance,10,5,68511,76,16 112 | 111,7-Feb-14,Hasee To Phansee,N,Romance,27,35.5,2543707,6390,975 113 | 112,26-Jul-13,Bajatey Raho,N,Comedy,5,10,462722,513,77 114 | 113,3-Jan-14,Mr Joe B Carvalho,N,Comedy,15,4,268289,139,40 115 | 114,26-Jul-13,Nasha,N,Thriller,4,8,613448,288,188 116 | 115,11-Apr-14,Bhootnath Returns,LW,Thriller,29,53,3049275,10759,657 117 | 116,9-Aug-13,Chennai Express,FS,Comedy,75,395,1882346,7954,1593 118 | 117,15-Aug-13,Once Upon Ay Time In Mumbai Dobaara!,FS,Thriller,100,80,3077135,9200,2821 119 | 118,23-Aug-13,Madras Cafe,N,Thriller,35,42.7,1709945,2974,594 120 | 119,30-Aug-13,Satyagraha,N, Drama ,50,60,1422568,2736,394 121 | 120,1-Nov-13,Krrish 3,FS,Action,115,300,23171067,72070,11888 122 | 121,7-Mar-14,Total Siyappa,N, Drama ,10,12,1719399,3656,548 123 | 122,8-Nov-13,Satya 2,FS,Thriller,15,1.1,110315,89,26 124 | 123,15-Nov-13,Rajjo,FS,Romance,12,2,1718619,1606,1083 125 | 124,22-Nov-13,Gori Tere Pyaar Mein,N,Romance,30,12.65,3799253,8559,1871 126 | 125,29-Nov-13,Bullett Raja,N,Comedy,50,40,4015674,8084,2129 127 | 126,11-Oct-13,War Chhod Na Yaar,FS,Comedy,14,7.5,40450,33,7 128 | 127,11-Oct-13,Baat Ban Gayi,FS,Comedy,4,1.5,23186,9,4 129 | 128,16-Oct-13,Boss,FS, Drama ,50,45,6464704,22977,3396 130 | 129,18-Oct-13,Shahid,FS, Drama ,6,40,1148516,3155,396 131 | 130,25-Oct-13,Mickey Virus,N,Comedy,11,6,335807,574,129 132 | 131,6-Sep-13,Zanjeer,LW,Action,75,18.2,2539431,5634,922 133 | 132,6-Sep-13,Shuddh Desi Romance,LW,Romance,25,55,5142,1,1 134 | 133,13-Sep-13,Grand Masti,LW,Comedy,35,298,1795640,3015,721 135 | 134,16-May-14,Children Of War,HS, Drama ,12,22,577569,1640,156 136 | 135,20-Sep-13,Phata Poster Nikla Hero,N, Drama ,40,34,5176897,10672,1392 137 | 136,20-Sep-13,The Lunchbox,N, Drama ,10,85,1064854,4054,285 138 | 137,9-Jan-15,Tevar,N,Action,40,56,6132407,11241,1729 139 | 138,16-Jan-15,Alone,N,Thriller,18,25,8788913,18907,2940 140 | 139,23-Jan-15,Baby,N,Action,60,125,7968156,29246,2426 141 | 140,30-Jan-15,Hawaizaada,N, Drama ,25,30.25,2368404,8619,539 142 | 141,30-Jan-15,Khamoshiyan,N,Thriller,11,14.02,3094001,4599,997 143 | 142,6-Feb-15,Shamitabh,N, Drama ,40,38,2105508,5599,677 144 | 143,13-Feb-15,Roy,FS,Romance,40,58,7687797,18974,3229 145 | 144,20-Feb-15,Badlapur,FS,Action,23,77,4550051,10602,893 146 | 145,27-Feb-15,Dum Laga Ke Haisha,N,Comedy,15,30,3250917,8185,615 147 | 146,13-Mar-15,NH10,N,Thriller,13,32.1,5592977,15464,1513 148 | 147,20-Mar-15,Dilliwali Zaalim Girlfriend,N,Comedy,32,12,2316047,4289,807 149 | 148,20-Mar-15,Hunterrr,N,Comedy,5,11.89,4674795,3706,762 150 | 149,23-May-14,Kochadaiiyaan,HS,Action,150,120,4740727,13466,2649 -------------------------------------------------------------------------------- /ml-data/bollywoodmovies.csv: -------------------------------------------------------------------------------- 1 | production_cost 2 | 601 3 | 627 4 | 330 5 | 364 6 | 562 7 | 353 8 | 583 9 | 254 10 | 528 11 | 470 12 | 125 13 | 60 14 | 101 15 | 110 16 | 60 17 | 252 18 | 281 19 | 227 20 | 484 21 | 402 22 | 408 23 | 601 24 | 593 25 | 729 26 | 402 27 | 530 28 | 708 29 | 599 30 | 439 31 | 762 32 | 292 33 | 636 34 | 444 35 | 286 36 | 636 37 | 667 38 | 252 39 | 335 40 | 457 41 | 632 -------------------------------------------------------------------------------- /ml-data/breakups.csv: -------------------------------------------------------------------------------- 1 | Before_Breakup,After_Breakup 2 | 470,408 3 | 354,439 4 | 496,321 5 | 351,437 6 | 349,335 7 | 449,344 8 | 378,318 9 | 359,492 10 | 469,531 11 | 329,417 12 | 389,358 13 | 497,391 14 | 493,398 15 | 268,394 16 | 445,508 17 | 287,399 18 | 338,345 19 | 271,341 20 | 412,326 21 | 335,467 -------------------------------------------------------------------------------- /ml-data/con-new.csv: -------------------------------------------------------------------------------- 1 | Country,Age,Salary,Purchased 2 | India,38,68000,No 3 | France,43,45000,yes 4 | Germany,30,54000,No 5 | France,48,65000,No 6 | Germany,40,,Yes 7 | India,35,58000,Yes 8 | Germany,,53000,No 9 | France,49,79000,Yes 10 | India,50,88000,No 11 | France,37,77000,Yes -------------------------------------------------------------------------------- /ml-data/country.csv: -------------------------------------------------------------------------------- 1 | Country,Corruption_Index,Gini_Index 2 | Hong Kong,77,53.7 3 | South Korea,53,30.2 4 | China,40,46.2 5 | Italy,47,32.7 6 | Mongolia,38,36.5 7 | Austria,75,27.6 8 | Norway,85,23.5 9 | UK,81,31.6 10 | Canada,82,33.7 11 | Germany,81,30.7 12 | Sweden,88,25.4 13 | Denmark,90,27.5 14 | France,69,30.1 15 | United States,74,40.8 16 | Russia ,29,40.1 17 | Portugal,62,34.2 18 | Romania,48,34 19 | Argentina,36,42.7 20 | Greece,44,34.2 21 | Thailand ,35,39.4 -------------------------------------------------------------------------------- /ml-data/curve.csv: -------------------------------------------------------------------------------- 1 | x,y 2 | 2,-1.999618 3 | 2,-1.999618 4 | 8,-3.978312 5 | 9,-1.969175 6 | 10,-0.95777 7 | 10,3.04223 8 | 11,-3.943845 9 | 14,3.11557 10 | 15,0.142095 11 | 21,-2.610555 12 | 25,2.656825 13 | 26,-0.26121 14 | 31,2.251935 15 | 31,2.251935 16 | 35,1.801555 17 | 35,-1.198445 18 | 37,-0.871723 19 | 41,-0.104375 20 | 42,4.112662 21 | 44,7.57874 22 | 47,3.361647 23 | 51,3.572515 24 | 54,10.61473 25 | 56,5.37716 26 | 58,9.196038 27 | 59,12.627275 28 | 63,12.503423 29 | 63,9.503423 30 | 64,12.01152 31 | 71,17.033895 32 | 71,17.033895 33 | 77,23.176157 34 | 77,22.176157 35 | 79,19.709455 36 | 81,24.322385 37 | 83,25.016963 38 | 84,25.8955 39 | 84,22.8955 40 | 85,22.795205 41 | 87,24.659127 42 | 88,31.623848 43 | 88,26.623848 44 | 92,30.707012 45 | 93,37.785133 46 | 94,34.88669 47 | 95,40.011935 48 | 98,37.532318 49 | 99,44.754835 50 | 99,43.754835 51 | 100,43.0023 52 | -------------------------------------------------------------------------------- /ml-data/customerspends.csv: -------------------------------------------------------------------------------- 1 | Customer,Apparel,Beauty and Healthcare 2 | 1,21.1,0.7 3 | 2,15.23,5.5 4 | 3,5.22,18.6 5 | 4,31.1,1.8 6 | 5,6.12,21.5 7 | 6,14.5,8.2 8 | 7,8.5,16.2 9 | 8,26.5,2.2 10 | 9,4.34,17.7 11 | 10,13.75,7.3 12 | 11,5.2,16.2 13 | 12,14.2,2.9 14 | 13,4.4,19.4 15 | 14,4.25,15.5 16 | 15,22.3,0.9 17 | 16,7.9,18.8 18 | 17,13.4,4.2 19 | 18,30.6,1.9 20 | 19,14.4,6.28 21 | 20,6.25,9.98 -------------------------------------------------------------------------------- /ml-data/forecast.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/ml-data/forecast.xls -------------------------------------------------------------------------------- /ml-data/healthdrink.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/ml-data/healthdrink.xlsx -------------------------------------------------------------------------------- /ml-data/onestop.csv: -------------------------------------------------------------------------------- 1 | discount_0,discount_10,discount_20 2 | 39,34,42 3 | 32,41,43 4 | 25,45,44 5 | 25,39,46 6 | 37,38,41 7 | 28,33,52 8 | 26,35,43 9 | 26,41,42 10 | 40,47,50 11 | 29,34,41 12 | 37,47,41 13 | 34,44,47 14 | 28,46,55 15 | 36,38,55 16 | 38,42,47 17 | 38,33,48 18 | 34,37,41 19 | 31,45,42 20 | 39,38,45 21 | 36,44,48 22 | 34,38,40 23 | 25,35,50 24 | 33,34,52 25 | 26,34,43 26 | 33,37,47 27 | 26,39,55 28 | 26,34,49 29 | 27,34,46 30 | 32,36,55 31 | 40,41,42 -------------------------------------------------------------------------------- /ml-data/passport.csv: -------------------------------------------------------------------------------- 1 | processing_time 2 | 16.00 3 | 16.00 4 | 30.00 5 | 37.00 6 | 25.00 7 | 22.00 8 | 19.00 9 | 35.00 10 | 27.00 11 | 32.00 12 | 34.00 13 | 28.00 14 | 24.00 15 | 35.00 16 | 24.00 17 | 21.00 18 | 32.00 19 | 29.00 20 | 24.00 21 | 35.00 22 | 28.00 23 | 29.00 24 | 18.00 25 | 31.00 26 | 28.00 27 | 33.00 28 | 32.00 29 | 24.00 30 | 25.00 31 | 22.00 32 | 21.00 33 | 27.00 34 | 41.00 35 | 23.00 36 | 23.00 37 | 16.00 38 | 24.00 39 | 38.00 40 | 26.00 41 | 28.00 -------------------------------------------------------------------------------- /ml-data/snd.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/ml-data/snd.png -------------------------------------------------------------------------------- /ml-data/store.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunp77/Machine-Learning/a75290e6472db2455bd6c750657df1208a2c34d0/ml-data/store.xls -------------------------------------------------------------------------------- /ml-data/trainingscores.csv: -------------------------------------------------------------------------------- 1 | Student,Score Before Training,Score After Training 2 | 1,30,77 3 | 2,57,79 4 | 3,97,46 5 | 4,94,44 6 | 5,WC,89 7 | 6,26,84 8 | 7,39,89 9 | 8,32,84 10 | 9,41,74 11 | 10,45,81 12 | 11,95,44 13 | 12,39,86 14 | 13,89,45 15 | 14,40,56 16 | 15,79,80 17 | 16,40,75 18 | 17,14,63 19 | 18,44,28 20 | 19,22,88 21 | 20,45,61 22 | 21,59,50 23 | 22,73,76 24 | 23,45,100 25 | 24,57,32 26 | 25,80,79 27 | 26,70,74 28 | 27,44,38 29 | 28,52,53 30 | 29,45,46 31 | 30,53,42 -------------------------------------------------------------------------------- /ml-data/vimana.csv: -------------------------------------------------------------------------------- 1 | Month,demand 2 | 1,457 3 | 2,439 4 | 3,404 5 | 4,392 6 | 5,403 7 | 6,371 8 | 7,382 9 | 8,358 10 | 9,594 11 | 10,482 12 | 11,574 13 | 12,704 14 | 13,486 15 | 14,509 16 | 15,537 17 | 16,407 18 | 17,523 19 | 18,363 20 | 19,479 21 | 20,516 22 | 21,656 23 | 22,558 24 | 23,647 25 | 24,864 26 | 25,610 27 | 26,677 28 | 27,609 29 | 28,673 30 | 29,400 31 | 30,443 32 | 31,503 33 | 32,688 34 | 33,602 35 | 34,629 36 | 35,823 37 | 36,671 38 | 37,487 -------------------------------------------------------------------------------- /ml-data/wsb.csv: -------------------------------------------------------------------------------- 1 | Month,Sale Quantity,Promotion Expenses,Competition Promotion 1,3002666,105,1 2,4401553,145,0 3,3205279,118,1 4,4245349,130,0 5,3001940,98,1 6,4377766,156,0 7,2798343,98,1 8,4303668,144,0 9,2958185,112,1 10,3623386,120,0 11,3279115,125,0 12,2843766,102,1 13,4447581,160,0 14,3675305,130,0 15,3477156,130,0 16,3720794,140,0 17,3834086,167,1 18,3888913,148,1 19,3871342,150,1 20,3679862,129,0 21,3358242,120,0 22,3361488,122,0 23,3670362,135,0 24,3123966,110,1 25,4634047,165,0 26,3772879,129,1 27,3187110,120,1 28,3093683,112,1 29,4557363,162,0 30,3816956,140,1 31,4410887,160,0 32,3694713,139,0 33,3822669,141,1 34,3689286,136,0 35,3728654,130,1 36,4732677,168,0 37,3216483,121,1 38,3453239,128,0 39,5431651,170,0 40,4241851,160,0 41,3909887,151,1 42,3216438,120,1 43,4222005,152,0 44,3621034,125,0 45,5162201,170,0 46,4627177,160,0 47,4623945,168,0 48,4599368,166,0 -------------------------------------------------------------------------------- /package-lock.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "machine-learning", 3 | "version": "1.0.0", 4 | "lockfileVersion": 3, 5 | "requires": true, 6 | "packages": { 7 | "": { 8 | "name": "machine-learning", 9 | "version": "1.0.0", 10 | "license": "MIT" 11 | } 12 | } 13 | } 14 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "machine-learning", 3 | "version": "1.0.0", 4 | "description": "This Repository contains the Machine learning related files with fundamental theory is included in the package.", 5 | "main": "index.js", 6 | "scripts": { 7 | "test": "exit 0" 8 | }, 9 | "repository": { 10 | "type": "git", 11 | "url": "git+https://github.com/arunp77/Machine-Learning.git" 12 | }, 13 | "keywords": [ 14 | "Machine-learning" 15 | ], 16 | "author": "Arun Kumar Pandey", 17 | "license": "MIT", 18 | "bugs": { 19 | "url": "https://github.com/arunp77/Machine-Learning/issues" 20 | }, 21 | "homepage": "https://github.com/arunp77/Machine-Learning#readme", 22 | "publishConfig": { 23 | "@arunp77:registry": "https://npm.pkg.github.com/" 24 | } 25 | } 26 | --------------------------------------------------------------------------------