{"id":20356559,"url":"https://github.com/madhurimarawat/ml-model-datasets-using-streamlits","last_synced_at":"2025-04-12T02:51:31.283Z","repository":{"id":192815871,"uuid":"686662282","full_name":"madhurimarawat/ML-Model-Datasets-Using-Streamlits","owner":"madhurimarawat","description":"This repository contains my machine learning models implementation code using streamlit in the Python programming language.","archived":false,"fork":false,"pushed_at":"2024-12-12T06:12:23.000Z","size":3754,"stargazers_count":4,"open_issues_count":0,"forks_count":4,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-25T22:35:41.733Z","etag":null,"topics":["classifier","data-visualization","decision-tree","diabetes-dataset","digits-dataset","hyperparameter-tuning","kaggle-datasets","knn","linear-regression","logistic-regression","machine-learning-algorithms","machine-learning-project","naive-bayes-algorithm","python","random-forest","regressor","streamlit-deployment","supervised-learning","svm","wine-dataset"],"latest_commit_sha":null,"homepage":"https://ml-model-datasets-using-apps-3gy37ndiancjo2nmu36sls.streamlit.app/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/madhurimarawat.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-09-03T14:41:04.000Z","updated_at":"2024-12-12T06:12:26.000Z","dependencies_parsed_at":null,"dependency_job_id":"597f7ba2-d682-4da9-a3b5-73b5fbcd098c","html_url":"https://github.com/madhurimarawat/ML-Model-Datasets-Using-Streamlits","commit_stats":null,"previous_names":["madhurimarawat/ml-model-datasets-using-streamlits"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/madhurimarawat%2FML-Model-Datasets-Using-Streamlits","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/madhurimarawat%2FML-Model-Datasets-Using-Streamlits/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/madhurimarawat%2FML-Model-Datasets-Using-Streamlits/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/madhurimarawat%2FML-Model-Datasets-Using-Streamlits/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/madhurimarawat","download_url":"https://codeload.github.com/madhurimarawat/ML-Model-Datasets-Using-Streamlits/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248509371,"owners_count":21116004,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classifier","data-visualization","decision-tree","diabetes-dataset","digits-dataset","hyperparameter-tuning","kaggle-datasets","knn","linear-regression","logistic-regression","machine-learning-algorithms","machine-learning-project","naive-bayes-algorithm","python","random-forest","regressor","streamlit-deployment","supervised-learning","svm","wine-dataset"],"created_at":"2024-11-14T23:17:02.414Z","updated_at":"2025-04-12T02:51:31.277Z","avatar_url":"https://github.com/madhurimarawat.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ML-Model-Datasets-Using-Streamlits \nThis repository contains my machine learning models implementation code using streamlit in the Python programming language.\u003cbr\u003e\u003cbr\u003e\n\n\u003ca href = \"https://ml-model-datasets-using-apps-3gy37ndiancjo2nmu36sls.streamlit.app/\"\u003e\u003cimg width=\"960\" title = \"Website Image\" alt=\"Website Image\" src=\"https://github.com/user-attachments/assets/5ad5ba7a-8227-4b7d-80a5-d043c2810383\"\u003e\u003c/a\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003ca href = \"https://ml-model-datasets-using-apps-3gy37ndiancjo2nmu36sls.streamlit.app/\"\u003e\u003cimg width=\"960\" title = \"Website Image\" alt=\"Website Image\" src=\"https://github.com/user-attachments/assets/00e15504-d1af-40bc-a260-3e484252cb32\"\u003e\u003c/a\u003e\n\n---\n\n# Mode of Execution Used \u003cimg src=\"https://th.bing.com/th/id/R.c936445e15a65dfdba20a63e14e7df39?rik=fqWqO9kKIVlK7g\u0026riu=http%3a%2f%2fassets.stickpng.com%2fimages%2f58481537cef1014c0b5e4968.png\u0026ehk=dtrTKn1QsJ3%2b2TFlSfLR%2fxHdNYHdrqqCUUs8voipcI8%3d\u0026risl=\u0026pid=ImgRaw\u0026r=0\" title=\"PyCharm\" alt=\"PyCharm\" width=\"40\" height=\"40\"\u003e\u0026nbsp;\u003cimg src=\"https://seeklogo.com/images/S/streamlit-logo-1A3B208AE4-seeklogo.com.png\" title=\"Streamlit\" alt=\"Streamlit\" width=\"40\" height=\"40\"\u003e\n\n\n\u003ch2\u003ePycharm\u003c/h2\u003e\n\n--\u003e Visit the official website of pycharm: \u003ca href=\"https://www.jetbrains.com/pycharm/\"\u003e\u003cimg src=\"https://th.bing.com/th/id/R.c936445e15a65dfdba20a63e14e7df39?rik=fqWqO9kKIVlK7g\u0026riu=http%3a%2f%2fassets.stickpng.com%2fimages%2f58481537cef1014c0b5e4968.png\u0026ehk=dtrTKn1QsJ3%2b2TFlSfLR%2fxHdNYHdrqqCUUs8voipcI8%3d\u0026risl=\u0026pid=ImgRaw\u0026r=0\" title=\"PyCharm\" alt=\"PyCharm\" width=\"40\" height=\"40\"\u003e\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Download according to the platform that will be used like Linux, Macos or Windows.\u003cbr\u003e\u003cbr\u003e\n--\u003e Two versions of Pycharm are avilable-\n1. Community version \u003cbr\u003e\u003cbr\u003e\n--\u003e Community version is open source and we can use it for free without any paid plan.\u003cbr\u003e\u003cbr\u003e\n--\u003e We can download this at the end of pycharm website.\u003cbr\u003e\u003cbr\u003e\n--\u003e After downloading community version we can directly follow the setup wizard and it will be setup.\u003cbr\u003e\u003cbr\u003e\n2.  Professional Version.\u003cbr\u003e\u003cbr\u003e\n--\u003e This is available at the top of website, we can directly download from there.\u003cbr\u003e\u003cbr\u003e\n--\u003e After downloading professional version, follow the below steps.\u003cbr\u003e\u003cbr\u003e\n--\u003e Follow the setup wizard and sign up for the free version (trial version) or else continue with the premium or paid version.\u003cbr\u003e\n\n### Using Pycharm\n--\u003e First, in pycharm we have the concept of virtual environment. In virtual environment we can install all the required libraries or frameworks.\u003cbr\u003e\u003cbr\u003e\n--\u003e Each project has its own virtual environment, so thath we can install requirements like Libraries or Framworks for that project only.\u003cbr\u003e\u003cbr\u003e\n--\u003e After this we can create a new file, various file types are available in pycharm like script files, text files and also Jupyter Notebooks.\u003cbr\u003e\u003cbr\u003e\n--\u003e After selecting the required file type, we can continue the execution of that file by saving it and using this shortcut shift+F10 (In Windows).\u003cbr\u003e\u003cbr\u003e\n--\u003e Output is given in Console while installation happens in terminal in Pycharm.\n\n## Streamlit Server\n\n--\u003e Streamlit is a python framework through which we can deploy any machine learning model and any python project with ease and without worrying about the frontend.\u003cbr\u003e\u003cbr\u003e\n--\u003e Streamlit is very user-friendly.\u003cbr\u003e\u003cbr\u003e\n--\u003e Streamlit has pre defined functions for all frontend components and we can directly use them.\u003cbr\u003e\u003cbr\u003e\n--\u003e To install streamlit in your system, just run this command-\n\n```\npip install streamlit\n```\n\n## Running Project in Streamlit Server\n\u003cp\u003eMake Sure all dependencies are already satisfied before running the app.\u003c/p\u003e\n\n1. We can Directly run streamlit app  with the following command-\u003cbr\u003e\n```\nstreamlit run app.py\n```\nwhere app.py is the name of file containing streamlit code.\u003cbr\u003e\n\nBy default, streamlit will run on port 8501.\u003cbr\u003e\n\nAlso we can execute multiple files simultaneously and it will be executed in next ports like 8502 and so on.\u003cbr\u003e\n\n2. Navigate to URL http://localhost:8501\n\nYou should be able to view the homepage of your app.\n\n🌟 Project and Models will change but this process will remain the same for all Streamlit projects.\u003cbr\u003e\n\n## Deploying using Streamlit\n\n1. Visit the official website of streamlit : \u003ca href=\"https://streamlit.io/\"\u003e\u003cimg src=\"https://seeklogo.com/images/S/streamlit-logo-1A3B208AE4-seeklogo.com.png\" title=\"Streamlit\" alt=\"Streamlit\" width=\"40\" height=\"40\"\u003e\u003c/a\u003e \u003cbr\u003e\u003cbr\u003e\n2. Now make an account with GitHub.\u003cbr\u003e\u003cbr\u003e\n3. Now add all the code in Github repository.\u003cbr\u003e\u003cbr\u003e\n4. Go to streamlit and there is an option for new deployment.\u003cbr\u003e\u003cbr\u003e\n5. Type your Github repository name and specify the file name. If you name your file as streamlit_app it will directly access it else you have to specify the path.\u003cbr\u003e\u003cbr\u003e\n6. Now also make sure you upload all your libraries and requirement name in a requirement.txt file.\u003cbr\u003e\u003cbr\u003e\n7. Version can also be mentioned like this python==3.9.\u003cbr\u003e\u003cbr\u003e\n8. When we mention version in the requirement file streamlit install all dependencies from there.\u003cbr\u003e\u003cbr\u003e\n9. If everything went well our app will be deployed on web and you can share the link and access the app from all browsers.\n\n---\n\n## About Project : HyperTuneML Platform\n\n\u003cp\u003eComplete Description about the project and resources used.\u003c/p\u003e\n\n- **Streamlit Website for Machine Learning Algorithms**: Developed a dynamic web application using Streamlit where users can interactively apply multiple supervised learning algorithms to various datasets. The app is designed to showcase **improved model accuracy** through **hyperparameter tuning** on diverse **real-world datasets**.\n  \n- **Data Visualization**: Implemented comprehensive data visualization features to illustrate the performance and results of the applied algorithms on different datasets. Visualizations include graphs, charts, and metrics to enhance understanding.\n  \n- **Hyperparameter Tuning for Enhanced Model Accuracy**: Users can select specific hyperparameters and datasets, with the app displaying the corresponding accuracy metrics. Interactive graphs visually depict the algorithm's performance, providing **practical insights** into predictive analytics.\n  \n- **Deployment**: Successfully deployed the web application using Streamlit, making it accessible online for users to utilize and explore.\n  \n- **Project Website**: Explore the project and its functionalities [HyperTuneML Platform](https://ml-model-datasets-using-apps-3gy37ndiancjo2nmu36sls.streamlit.app/).\n\n---\n\n## Algorithm Used :\n\n\u003ch2\u003eSupervised Learning\u003c/h2\u003e\n--\u003e Basically supervised learning is when we teach or train the machine using data that is well-labelled. \u003cbr\u003e\u003cbr\u003e\n--\u003e Which means some data is already tagged with the correct answer.\u003cbr\u003e\u003cbr\u003e\n--\u003e After that, the machine is provided with a new set of examples(data) so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.\u003cbr\u003e\u003cbr\u003e\n\n\u003ch3\u003ei) K-Nearest Neighbors (KNN) \u003c/h3\u003e\n\u003cbr\u003e\n--\u003e K-Nearest Neighbours is one of the most basic yet essential classification algorithms in Machine Learning.\u003cbr\u003e\u003cbr\u003e\n--\u003e It belongs to the supervised learning domain and finds intense application in pattern recognition, data mining, and intrusion detection..\u003cbr\u003e\u003cbr\u003e\n--\u003e In this algorithm,we identify category based on neighbors.\u003cbr\u003e\u003cbr\u003e\n\n\u003ch3\u003eii) Support Vector Machines (SVM) \u003c/h3\u003e\n\u003cbr\u003e\n--\u003e The main idea behind SVMs is to find a hyperplane that maximally separates the different classes in the training data. \u003cbr\u003e\u003cbr\u003e\n--\u003e This is done by finding the hyperplane that has the largest margin, which is defined as the distance between the hyperplane and the closest data points from each class. \u003cbr\u003e\u003cbr\u003e\n--\u003e Once the hyperplane is determined, new data can be classified by determining on which side of the hyperplane it falls. \u003cbr\u003e\u003cbr\u003e\n--\u003e SVMs are particularly useful when the data has many features, and/or when there is a clear margin of separation in the data.\u003cbr\u003e\u003cbr\u003e\n\n\u003ch3\u003eiii) Naive Bayes Classifiers\u003c/h3\u003e\n\u003cbr\u003e\n--\u003e Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. \u003cbr\u003e\u003cbr\u003e\n--\u003e It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.\u003cbr\u003e\u003cbr\u003e\n--\u003e The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.\u003cbr\u003e\u003cbr\u003e\n\n\u003ch3\u003eiv) Decision Tree\u003c/h3\u003e\n\u003cbr\u003e\n--\u003e It builds a flowchart-like tree structure where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.\u003cbr\u003e\u003cbr\u003e\n--\u003e It is constructed by recursively splitting the training data into subsets based on the values of the attributes until a stopping criterion is met, such as the maximum depth of the tree or the minimum number of samples required to split a node.\u003cbr\u003e\u003cbr\u003e\n--\u003e The goal is to find the attribute that maximizes the information gain or the reduction in impurity after the split.\u003cbr\u003e\u003cbr\u003e\n\n\u003ch3\u003ev) Random Forest\u003c/h3\u003e\n\u003cbr\u003e\n--\u003e It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model.\u003cbr\u003e\u003cbr\u003e\n--\u003e Instead of relying on one decision tree, the random forest takes the prediction from each tree and based on the majority votes of predictions, and it predicts the final output.\u003cbr\u003e\u003cbr\u003e\n--\u003e The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting.\u003cbr\u003e\u003cbr\u003e\n\n\u003ch3\u003evi) Linear Regression\u003c/h3\u003e\n\u003cbr\u003e\n--\u003e Regression: It predicts the continuous output variables based on the independent input variable. like the prediction of house prices based on different parameters like house age, distance from the main road, location, area, etc.\u003cbr\u003e\u003cbr\u003e\n--\u003e It computes the linear relationship between a dependent variable and one or more independent features. \u003cbr\u003e\u003cbr\u003e\n--\u003e The goal of the algorithm is to find the best linear equation that can predict the value of the dependent variable based on the independent variables.\u003cbr\u003e\n\u003cbr\u003e\n\n\u003ch3\u003evii) Logistic Regression\u003c/h3\u003e\n\u003cbr\u003e\n--\u003e Logistic regression is a supervised machine learning algorithm mainly used for classification tasks where the goal is to predict the probability that an instance of belonging to a given class or not. \u003cbr\u003e\u003cbr\u003e\n--\u003e It is a kind of statistical algorithm, which analyze the relationship between a set of independent variables and the dependent binary variables. \u003cbr\u003e\u003cbr\u003e\n--\u003e It is a powerful tool for decision-making.\u003cbr\u003e\u003cbr\u003e\n--\u003e For example email spam or not. \u003cbr\u003e\n\n---\n## Dataset Used :\n\n\u003ch2\u003eIris Dataset\u003c/h2\u003e\n--\u003e Iris Dataset is a part of sklearn library.\u003cbr\u003e\u003cbr\u003e\n--\u003e Sklearn comes loaded with datasets to practice machine learning techniques and iris is one of them. \u003cbr\u003e\u003cbr\u003e\n--\u003e Iris has 4 numerical features and a tri class target variable.\u003cbr\u003e\u003cbr\u003e\n--\u003e This dataset can be used for classification as well as clustering.\u003cbr\u003e\u003cbr\u003e\n--\u003e In this dataset, there are 4 features sepal length, sepal width, petal length and petal width and the target variable has 3 classes namely ‘setosa’, ‘versicolor’, and ‘virginica’.\u003cbr\u003e\u003cbr\u003e\n--\u003e Objective for a multiclass classifier is to predict the target class given the values for the four features.\u003cbr\u003e\u003cbr.\n--\u003e Dataset is already cleaned,no preprocessing required.\u003cbr\u003e\n\n\u003ch2\u003eBreast Cancer Dataset\u003c/h2\u003e\n--\u003e The breast cancer dataset is a classification dataset that contains 569 samples of malignant and benign tumor cells. \u003cbr\u003e\u003cbr\u003e\n--\u003e The samples are described by 30 features such as mean radius, texture, perimeter, area, smoothness, etc. \u003cbr\u003e\u003cbr\u003e\n--\u003e The target variable has 2 classes namely ‘benign’ and ‘malignant’.\u003cbr\u003e\u003cbr\u003e\n--\u003e Objective for a multiclass classifier is to predict the target class given the values for the features.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is already cleaned,no preprocessing required.\u003cbr\u003e\n\n\u003ch2\u003eWine Dataset\u003c/h2\u003e\n--\u003e The wine dataset is a classic and very easy multi-class classification dataset that is available in the sklearn library.\u003cbr\u003e\u003cbr\u003e\n--\u003e It contains 178 samples of wine with 13 features and 3 classes.\u003cbr\u003e\u003cbr\u003e\n--\u003e The goal is to predict the class of wine based on the features.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is already cleaned,no preprocessing required.\u003cbr\u003e\n\n\u003ch2\u003eDigits Dataset\u003c/h2\u003e\n--\u003e The digits dataset is a classic multi-class classification dataset that is available in the sklearn library.\u003cbr\u003e\u003cbr\u003e\n--\u003e It contains 1797 samples of digits with 10 classes.\u003cbr\u003e\u003cbr\u003e\n--\u003e The goal is to predict the class of digit based on the features.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is already cleaned,no preprocessing required.\u003cbr\u003e\n\n\u003ch2\u003eDiabetes Dataset\u003c/h2\u003e\n--\u003e The diabetes dataset is a regression dataset that is available in the sklearn library.\u003cbr\u003e\u003cbr\u003e\n--\u003e It contains 442 samples and 10 classes.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is already cleaned,no preprocessing required.\u003cbr\u003e\n\n\u003ch2\u003eNaive bayes classification data\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/himanshunakrani/naive-bayes-classification-data\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Naive bayes classification data\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Contains diabetes data for classification.\u003cbr\u003e\u003cbr\u003e\n--\u003e The dataset has 3 columns-glucose, blood pressure and diabetes and 995 entries.\u003cbr\u003e\u003cbr\u003e\n--\u003e Column glucose and blood pressure data is to classify whether the patient has diabetes or not.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is already cleaned,no preprocessing required.\u003cbr\u003e\n\n\u003ch2\u003eCars Evaluation Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/elikplim/car-evaluation-data-set\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Cars Evaluation Dataset\" alt=\"Cars Evaluation Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Contains information about cars with respect to features like Attribute Values:\u003cbr\u003e\u003cbr\u003e\n\u003ctable\u003e\n\u003ctd\u003e1. buying v-high, high, med, low \u003c/td\u003e\n\u003ctd\u003e2.maint v-high, high, med, low \u003c/td\u003e\n\u003ctd\u003e3.doors 2, 3, 4, 5-more \u003c/td\u003e\n\u003ctd\u003e4. persons 2, 4, more \u003c/td\u003e\n\u003ctd\u003e5. lug_boot small, med, big\u003c/td\u003e  \n\u003ctd\u003e6.safety low, med, high\u003c/td\u003e  \u003c/table\u003e\n--\u003e Target categories are:\u003cbr\u003e\u003cbr\u003e\n\u003ctable\u003e\n  \u003ctd\u003e1. unacc 1210 (70.023 %)\u003c/td\u003e\n  \u003ctd\u003e2. acc 384 (22.222 %)\u003c/td\u003e\n  \u003ctd\u003e3. good 69 ( 3.993 %)\u003c/td\u003e\n  \u003ctd\u003e4. v-good 65 ( 3.762 %)\u003c/td\u003e\u003c/table\u003e\n--\u003e Contains Values in string format.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is not cleaned, preprocessing is required.\u003cbr\u003e\n\n\u003ch2\u003eSalary Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/abhishek14398/salary-dataset-simple-linear-regression\n\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Salary Dataset\" alt=\"Salary Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Contains Salary data for Regression.\u003cbr\u003e\u003cbr\u003e\n--\u003e The dataset has 2 columns-Years of Experience and Salary and 30 entries.\u003cbr\u003e\u003cbr\u003e\n--\u003e Column Years of Experience is used to find regression for Salary.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is already cleaned,no preprocessing required.\u003cbr\u003e\n\n\u003ch2\u003eHeart Disease Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/rashikrahmanpritom/heart-attack-analysis-prediction-dataset\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Heart Disease Dataset\" alt=\"Heart Disease Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Contains heart disease dataset for Classification.\u003cbr\u003e\u003cbr\u003e\n--\u003e The Shape of dataset is 303 X 16.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is not cleaned, preprocessing is required.\u003cbr\u003e\n\n\u003ch2\u003eTitanic Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/vinicius150987/titanic3/data\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Titanic Dataset\" alt=\"Titanic Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Contains Titanic dataset for classification tasks (predicting survival).\u003cbr\u003e\u003cbr\u003e\n--\u003e The Shape of dataset is 1309 X 14.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is not cleaned, preprocessing is required.\n\n---\n# Libraries Used 📚 💻\n\u003cp\u003eShort Description about all libraries used.\u003c/p\u003e\nTo install python library this command is used- \u003cbr\u003e\u003cbr\u003e\n\n```\npip install library_name\n```\n\n\u003cul\u003e\n\u003cli\u003eNumPy (Numerical Python) – Enables with collection of mathematical functions\nto operate on array and matrices. \u003c/li\u003e\n  \u003cli\u003ePandas (Panel Data/ Python Data Analysis) - This library is mostly used for analyzing,\ncleaning, exploring, and manipulating data.\u003c/li\u003e\n  \u003cli\u003eMatplotlib - It is a data visualization and graphical plotting library.\u003c/li\u003e\n\u003cli\u003eScikit-learn - It is a machine learning library that enables tools for used for many other\nmachine learning algorithms such as classification, prediction, etc.\u003c/li\u003e\n  \u003cli\u003eSeaborn - It is an extension of Matplotlib library used to create more attractive and\ninformative statistical graphics.\u003c/li\u003e\n\u003c/ul\u003e\n\n---\n\n## Thanks for Visiting 😄\n\n- Drop a 🌟 if you find this repository useful.\u003cbr\u003e\u003cbr\u003e\n- If you have any doubts or suggestions, feel free to reach me.\u003cbr\u003e\u003cbr\u003e\n📫 How to reach me:  \u0026nbsp; [![Linkedin Badge](https://img.shields.io/badge/-madhurima-blue?style=flat\u0026logo=Linkedin\u0026logoColor=white)](https://www.linkedin.com/in/madhurima-rawat/) \u0026nbsp; \u0026nbsp;\n\u003ca href =\"mailto:rawatmadhurima@gmail.com\"\u003e\u003cimg src=\"https://github.com/madhurimarawat/Machine-Learning-Using-Python/assets/105432776/b6a0873a-e961-42c0-8fbf-ab65828c961a\" height=35 width=30 title=\"Mail Illustration\" alt=\"Mail Illustration📫\" \u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n- **Contribute and Discuss:** Feel free to open \u003ca href= \"https://github.com/madhurimarawat/ML-Model-Datasets-Using-Streamlits/issues\"\u003eissues 🐛\u003c/a\u003e, submit \u003ca href = \"https://github.com/madhurimarawat/ML-Model-Datasets-Using-Streamlits/pulls\"\u003epull requests 🛠️\u003c/a\u003e, or start \u003ca href = \"https://github.com/madhurimarawat/ML-Model-Datasets-Using-Streamlits/discussions\"\u003ediscussions 💬\u003c/a\u003e to help improve this repository!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmadhurimarawat%2Fml-model-datasets-using-streamlits","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmadhurimarawat%2Fml-model-datasets-using-streamlits","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmadhurimarawat%2Fml-model-datasets-using-streamlits/lists"}