[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-prathimacode-hub--ML-ProjectKart":3,"tool-prathimacode-hub--ML-ProjectKart":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",157379,2,"2026-04-15T23:32:42",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":82,"stars":105,"forks":106,"last_commit_at":107,"license":108,"difficulty_score":32,"env_os":109,"env_gpu":110,"env_ram":110,"env_deps":111,"category_tags":114,"github_topics":115,"view_count":32,"oss_zip_url":80,"oss_zip_packed_at":80,"status":17,"created_at":133,"updated_at":134,"faqs":135,"releases":171},8041,"prathimacode-hub\u002FML-ProjectKart","ML-ProjectKart","🙌Kart of 234+ projects based on machine learning, deep learning, computer vision, natural language processing and all. Show your support by ✨ this repository. ","ML-ProjectKart 是一个汇聚了 234+ 个优质开源项目的资源库，专注于机器学习、深度学习、计算机视觉、自然语言处理及生成式 AI 等领域。它就像一辆装满宝藏的“购物车”，旨在为学习者提供一条从入门到精通的高效路径。\n\n对于许多渴望掌握 AI 技术但苦于缺乏系统实战案例的初学者而言，寻找高质量、结构清晰的项目往往是一大难题。ML-ProjectKart 正是为了解决这一痛点而生，它精选了一系列适合上手的项目，帮助用户通过实际操作熟悉核心算法，从而将理论知识转化为真正的工程能力。\n\n无论是刚踏入数据科学领域的学生、希望提升技能的开发者，还是对 AI 充满热情的研究人员，都能在这里找到适合自己的任务。项目覆盖了从数据分析师的数据清洗与探索，到算法工程师的模型构建与优化，甚至延伸至前后端开发及 UI 设计的全流程。此外，该仓库拥有活跃的社区氛围，明确欢迎各类贡献者参与协作，提供了详细的贡献指南，鼓励用户分享自己的独特项目或共同完善现有内容。如果你想在开源世界中历练技能，ML-ProjectKart 将是理想的起点。","\u003Cdiv align=\"center\">\n  \u003Ch1>Welcome to ML-ProjectKart👋🛒\u003C\u002Fh1>\n\u003C\u002Fdiv>\n\n\u003Cp align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_ceb1d68460b2.png\">\u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat&logo=github\">\u003C\u002Fa> \n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOpen%20Source-%F0%9F%A4%8D-Green\">\u003C\u002Fa> \n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1.svg?label=Contributions&message=Welcome&color=0059b3&style=flat-square\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fgraphs\u002Fcontributors\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcontributors-anon\u002Fprathimacode-hub\u002FML-ProjectKart\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fmaintenance\u002Fyes\u002F2025\">\u003C\u002Fa>\n\u003C\u002Fp> \n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fstargazers\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_2abcd490ea18.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fnetwork\u002Fmembers\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_bbf8aed025a9.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_444c8d6b2ac4.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues?q=is%3Aissue+is%3Aclosed\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_8853fea32f68.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fpulls\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_52b8e0115cf8.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fpulls\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_f5a314ac0435.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fpulls?q=is%3Apr+is%3Aclosed\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_83de38abdfa9.png\">\u003C\u002Fa>\n\u003C\u002Fp>\n\n\n## 📌 Repository\n\nThis kart showcases the finest collection of all projects based on machine learning, deep learning, computer vision, natural language processing and everything. Indulge in this journey of open source.\n\nThe main aim is to provide an efficient and beginner-friendly projects that would help you in mastering the ML\u002FAI algorithms and make you familiar. Turn yourself into pro with all the hands-on that got you covered.\n\n\n## 🙌 Join Here\n\nAnyone related to technology who are looking to contribute to open-source, are all invited to hop in. This place has task for everyone.\n\n| **Machine Learning** | **Deep Learning** | **Natural Language Processing** | **Computer Vision** | **Generative AI**\n\n**Data Analysts** - Frame the problem. Get, explore and prepare the data \n\n**Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Generative AI Enthusiasts** - Try different algorithms, build model, optimize the model. \n\n\u003C!--\n**Front End Designers** – Design or code the webpage designed by designers \n\n**Back End Developers** – Create backend for the model using Flask or Django \n\n**UI\u002FUX Designers** – Design dashboards, forms and webpages for the model \n-->\n\nIf you had worked on or want to initiate a unique project and want to share it with the world, you can do that through here. Go through the contributing guidelines in [CONTRIBUTING](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002FCONTRIBUTING.md)👩‍💻\n\nWhen issue is raised from your end (or) taken it from issues tab to add a project, elaborate as much as you could and as well notify us about the task you will be working.\n\nSubsequently, also go through the GitHub documentation on [creating a pull request](https:\u002F\u002Fhelp.github.com\u002Fen\u002Fgithub\u002Fcollaborating-with-issues-and-pull-requests\u002Fcreating-a-pull-request).\n\n\n## 💡Look Through The Kart Of Amazing Projects\n\n| S.No | Project Name | Description |\n| --------------- | --------------- | --------------- |\n|     01.    |  [Advertisement Click Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAdvertisement%20Click%20Prediction)  |  The goal of this project is to make a prediction model using the advertisement classification algortihms, which will predict the desired ad as per the user information.   |\n|     02.    |  [Advertisement Success Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAdvertisement%20Success%20Prediction)  |    In this project we will be working with an advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. |\n|     03.    |  [Age, Gender and Ethnicity Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAge%2C%20Gender%20and%20Ethnicity%20Prediction)  |  The goal of this project is to be able to predict age , gender and ethnicity of a person just by looking at an image of a person.   |\n|     04.    |  [Air Quality Predication](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAir%20Quality%20Prediction)  |  The goal of this project is to predict Air Quality Index (AQI ) form features as particulate matter (PM2.5 and PM10), Nitrogen Oxide (NO), Nitric Dioxide (NO2), Carbon Monoxide (CO), Sulphur Dioxide (SO2), Ozone (O3) etc.   |\n|     05.    |  [Airbnb Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAirbnb%20Price%20Prediction)  |   The goal of this project is to make a prediction model which will predict the prices of the Airbnb hotels using different parameters.  |\n|     06.    |  [Airline Passenger Satistfaction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAirline%20Passenger%20Satisfaction)  |   The aim of this project is to perform analysis of the passenger satisfaction data and data preprocessing to prepare the data and predict whether the passenger is satisfied or not with the airline services.  |\n|     07.    |  [Alpaca Identification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAlpaca%20Identification)   |   The goal of this project is to make an identification cum classification model using deep neural networks, which will identify the images of Alpaca from the user given input.   |\n|     08.    |  [Amazon Alexa Reviews](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Alexa%20Reviews)  |    This model predicts the feedback of Amazon Alexa users based on the features such as ratings, variations and verified reviews.  |\n|     09.    |  [Amazon Books Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Books%20Analysis)  |   Prediction on whether books will be fiction or non-fiction using Goodreads.  |\n|     10.    |  [Amazon Data Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Data%20Analysis)  |   The goal of this project is to predict the upvote of the customer on the basis of reviews, text and score of the cuustomer given in the dataset.  |\n|     11.    |  [Amazon Mobile Phones Reviews Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Mobile%20Phones%20Reviews%20Analysis)  |   This is one of the major techniques used to analyze customer reviews on different products and find necessary insights from reviews. The major idea is to classify reviews and determine how satisfied customers were regarding the product.  |\n|     12.    |  [Amazon Products Review Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Products%20Reviews%20Classification)  |  The goal of this project is to make a classifiation model which will classify the products review enlisted in the Amazon Inc website, so that it can help the company for their betterment, and also they can rectify their faults depending on the users' experience.   |\n|     13.    |  [American Sign Language(ASL) Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmerican%20Sign%20Language%20(ASL)%20Recognition)  |   T he aim of this project is to recognize what the person is trying to convey using different hand gestures. The dataset contains 29 classes which comprises of A to Z alphabets, nothing, space and delete hand gestures.  |\n|     14.    |  [Animate Me !](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAnimate%20Me!)  |   It is a simple OpenCV project that converts input image to a cartoon equivalent  |\n|     15.    |  [Anime Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAnime%20Recommendation%20System)  | | \n|     16.    |  [Appliances Energy Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAppliances%20Energy%20Prediction)  |   The goal of this project is to make a prediction model which will predict the enery consumption by the appliances based on the given dataset.  |\n|     17.    |  [Automated Essay Grading](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAutomated%20Essay%20Grading)  |   The goal of this project is to make a prediction model which will give scoring to student-written essays.   |\n|     18.    |  [Avito Product Analysis and Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAvito%20Product%20Analysis%20and%20Price%20Prediction)  |   Perform exploratory data analysis on the dataset of products from Avito Advertising website and develop a ML model to predict prices of the other products in the website.   |\n|     19.    |  [Balls Image Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBalls%20Image%20Classification)  |   The goal of this project is to make a deep learning model which will classify the images of different types of balls using the convoolution neural network, to be precise the MobileNet architecture.   |\n|     20.    |  [Bangladesh Premier League Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBangladesh%20Premier%20League%20Analysis)  | |\n|     21.    |  [Bank Customers Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBank%20Customers%20Prediction)  |   To predict the customers who are withdrawing their account from the bank due to some loss and other issues.   |\n|     22.  |  [Bike Crash Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBike%20Crash%20Analysis)  |    The goal of this project to analyze the dataset on various factors and depending on the various factors making of a prediction model which will predict the accident prone sights. |\n|     23.   |  [Bike Rental Demand Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBike%20Rental%20Demand%20Analysis)  |   The aim of the project is to make a forecast of the demand in Bike Rental Services. This project predicts the upcoming nature of the customer request demand.  |\n|     24.   |  [Bike Sharing Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBike%20Sharing%20Prediction)  |  The main goal of the project is to analyse the Bike Share Count in case of different seasons, weekdays, weathers, etc. and based on that train a model, to predict the number of bike users under a given circumstance.   |\n|     25.   |  [Bird Species Classification and Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBird%20Species%20Classification%20and%20Recognition)  |   The goal is to complete an end to end computer vision problem with basic parts, such as bulding an input pipeline, compose the model, data augmentation etc.   |\n|     26.   |  [Bitcoin Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBitcoin%20Price%20Prediction)  |    The main purpose of this project is to predict the price of Bitcoin. |\n|     27.   |  [Black Friday Sales - Analysis and Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBlack%20Friday%20Sales-%20Analysis%20and%20Prediction)  |     The goal of this project is to analyse and predict purchases in the black friday sales from features as age group, gender, occupation, product category etc.|\n|     28.   |  [Board Game Review Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBoard%20Game%20Review%20Prediction)  |   The goal of this project are -What are the categories of game that are the most popular?Can we build a model with the available data that predicts user rating? What factors make for the best \"modern\" board game.  |\n|     29.   |  [Body Fat Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBody%20Fat%20Prediction)  |    The goal is to predict percentage of body fat of a person. |\n|     30.   |  [Book Genre Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBook%20Genre%20Prediction)  |   The goal of this project is to make a prediction model which will predict genre of the book.   |\n|     31.   |  [Book Recommendation Systems](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBook%20Recommendation%20Systems)  |   The goal of this project is to make a recommendation system which will recommend the user a book, based on the search results given input by the users.  |\n|     32.   |  [Brain Tumor Detection and Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBrain%20Tumor%20Detection%20and%20Classification)  |   This model detects the presence of brain tumor by processing MRI scans of the patient.   |\n|     33.  |  [Brain Weight prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBrain%20Weight%20Prediction)  |   The goal of this project is to make a prediction model which will predict the weight of the human brain depending on the head size.  |\n|     34.   |  [Brazil Fires Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBrazil%20Fires%20Prediction)  |   To predict the number of the fires taken place statewise in Brazil.   |\n|     35.   |  [Breast Cancer Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBreast%20Cancer%20Prediction)  |   The goal is to predict Breast Cancer.  |\n|     36.    |  [Campus Placement Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCampus%20Placement%20Prediction)  |   The goal is to predict if a student is placed or not according to his\u002Fher percentage and other factors.  |\n|     37.    |  [Campus Recruitment - Analysis and Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCampus%20Recruitment%20-%20Analysis%20and%20Prediction)  |   The goal of this project is to analyze the factors that can effect the Campus Recruitment, and also creating a model which will predict the chances of getting placed depending on various factors.   |\n|     38.  |  [CAPTCHA Decoding](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCAPTCHA%20Decoding)  |    The goal of this project is to create a deep learning model which will recognize the captcha letters. |\n|     39.    |  [Car Brand Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCar%20Brand%20Classification)  |    This project classifies the car brand using machine learning.   |\n|     40.    |  [Car Insurance Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCar%20Insurance%20Prediction)  | | \n|     41.    |  [Car Prices Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCar%20Prices%20Prediction)  |   The main goal is to predict the price of cars with the available independent variables   |\n|     42. |  [Cartier Jewelry Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCartier%20Jewelry%20Classification)  |   The goal of this project is to make a classification model, which will classify the jewelries based on the various features.  |\n|     43.    |  [Cartoonify using KMeans](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCartoonify%20using%20KMeans)  | | \n|     44.    |  [Cats Vs Dogs Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCats%20Vs%20Dogs%20Classification)  |   The goal of this project is to classify whether the image is of a dog or a cat.    |\n|     45.    |  [Cervical Cancer Risk Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCervical%20Cancer%20Risk%20Prediction)  |  The business objective is to build a Machine Learning Prediction Model that predicts the result of Biopsy test and thereby confirming the presence\u002Fnon-presence of cervical cancer in the patients.   |\n|     46.    |  [Churn Risk Score Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FChurn%20Risk%20Score%20Prediction)  |   This project is used to predict the churn score for a website based on the related features   |\n|     47.    |  [Classification of Images using MNIST and CIFAR10](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FClassification%20of%20Images%20using%20MNIST%20and%20CIFAR10)  |   Classifying and predicting images with accuracy.   |\n|     48.    |  [Coffee Production Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCoffee%20Production%20Prediction)  |   The goal of this project is to make a prediction model which will give total production of the coffee from year 1990-2018.   |\n|     49.   |  [Colour Identification using Machine Learning](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FColour%20Identification%20using%20Machine%20Learning)  |   This project defines us the colour we have asked to see instead of showing the color object.    |\n|     50.    |  [Concrete Strength Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FConcrete%20Strength%20Prediction)  |   The aim of this project is to predict strength of the concrete from features as fly ash, blast furnamce slag, water, fine aggregate, coarse aggregate, cement, age etc.   |\n|     51.    |  [Cotton Disease Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCotton%20Disease%20Prediction)  |   This project predicts the disease of cotton tree using Transfer Learning with Resnet152V2 model.    |\n|     52.    |  [COVID19_Data-Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCOVID19_Data-Analysis)  |   The goal is to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of great help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.   |\n|     53.    |  [Covid-19 Data Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCovid-19%20Data%20Analysis)  |   The goal of this project is to analyze the situation caused due to Covid-19 pandemic using data visualization.    |\n|     54.    |  [Credit Card Fraud Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCredit%20Card%20Fraud%20Detection)  |   The aim of the project is to predict fraudulent credit card transactions using machine learning models.    |\n|     55.    |  [Crop Fertilizers Analysis and Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCrop%20Fertilizers%20Analysis%20and%20Prediction)  | | \n|     56.    |  [Crop Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCrop%20Recommendation%20System)  |   The Goal of this project is to build the recommendation model using the crop recommendation dataset.    |\n|     57.    |  [Crop Yield Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCrop%20Yield%20Prediction)  |   This dataset is focused on the different crops which can reflect the price area and other attributes from that one can predict for future price yield by using some better attributes.    |\n|     58.    |  [Cryotherapy Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCryotherapy%20Analysis)  |   In this project we are going to analyze the cryotherapy dataset and will deploy several machine learning algorithm models.    |\n|     59.    |  [Customer Income Segmentation Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCustomer%20Income%20Segmentation%20Analysis)  | | \n|     60.    |  [Customer Modelling Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCustomer%20Modeling%20Analysis)  | | \n|     61.    |  [Customer Region Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCustomer%20Region%20Classification)  |   The goal is to predict Customer Region using random forest classifier model.    |\n|     62.    |  [DNA Sequencing Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDNA%20Sequencing%20Classification)  |   In this project, we will understand how to interpret a DNA structure and how machine learning algorithms can be used to build a prediction model on DNA sequence data.    |\n|     63.    |  [Dance Form Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDance%20Form%20Classification)  |   The goal of this project is to classify the images of various dance forms and prepare a Classification model using Deep learning methods.    |\n|     64.    |  [Dandelion Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDandelion%20Recognition)  |   The goal of this project is to create classification model which will identify the dandelion from the images given to the model.    |\n|     65.    |  [Data Analysis Of Meteorological Data](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FData%20Analysis%20of%20Meteorological%20Data)  |   It analyzes the meteorological weather data of the last 10yrs from 2006 to 2016 at Finland to check an increase of global warming.   |\n|     66.    |  [DeepQ Networks](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDeepQNetworks)  | | \n|     67.    |  [Detecting Motion and Moving Objects in a Video](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDetecting%20Motion%20and%20Moving%20Objects%20in%20a%20Video)  | | \n|     68.    |  [Diabetes Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDiabetes%20Prediction)  |   The goal is to predict if a person is having Diabetes or not using logistic regression and SVM models.    |\n|     69.    |  [Diamond Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDiamond%20Price%20Prediction)  |   The goal is to predict price of Diamond.|\n|     70.    |  [Disease Symptom Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDisease%20Symptom%20Prediction)  |   The goal of this project is predict symptom of disease using the information contained in this dataset.   |\n|     71.    |  [Disneyland Reviews Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDisneyland%20Reviews%20Analysis)  |   The aim of this project is to analyse the reviews given by visitors from different countries of the world using NLP.    |\n|     72.    |  [Dog Breed Identification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDog%20Breed%20Identification)  |   This project is about predicting the breed of a dog, this can be helpful to people who are not expert in the field.    |\n|     73.    |  [Dogecoin Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDogecoin%20Price%20Prediction)  |   The goal of this project is to make a Prediction model which will predict the price of the Dogecoin in the future times depending on the previous parameters.    |\n|     74.    |  [Driver Drowsiness Detection System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDriver%20Drowsiness%20Detection%20System)  | The main purpose of this project is to awake the driver's if they fall asleep in order to avoid drowsiness related road-accidents.  |\n|     75.    |  [Dry Beans Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDry%20Beans%20Classification)  | The goal is to predict Dry Beans.  |\n|     76.    |  [Email Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmail_Classification)  |   This project classify emails as spam or not-spam on the basis of the message.    |\n|     77.    |  [Emoji Classification using OpenCV](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmoji%20Classification%20using%20OpenCV)  |   The Goal of this project is to make a model which will predict the emotion shown using the input image. And also it will classify the particular emotion shown in the given image.    |\n|     78.   |  [Emotion Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmotion%20Classification)  |   The goal of this project is to create a model which will classify different emotions based on the text provided in the dataset.  |\n|     79.    |  [Emotion Recognition using NLP](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmotion%20Recognition%20Based%20on%20NLP)  |  An Intelligent Emotion Predictor which uses NLP technique by sending text input to determine whether data is positive, negative or neutral.   |\n|     80.    |  [Employee Retention Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmployee%20Retention%20Project)  |   The project revolves around the idea to give basic modelling techniques used to classify if the employee will leave the company or not based on certain features in the datset.    |\n|     81.    |  [English Alphabet Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEnglish%20Alphabet%20Classification\u002FModel)  | The aim of this project is to recognize the computerised generated images of the English apphabets. The dataset contains 26 classes which comprises of a to z alphabets, each class containing 100 images.  |\n|     82.    |  [Exports Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FExports%20Classification)  |   The goal of this project is to build a classification model, using regression algorithms, such as, linear regression, random forest regression, decision tree regressor and many more algorithms.    |\n|     83.    |  [Face Clustering](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Clustering)  | | \n|     84.    |  [Face Detection and Blurring](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Detection%20and%20Blurring)  | | \n|     85.    |  [Face Generation using DCGAN](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Generation%20using%20DCGAN)  |   The main goal of this project is to get a generator network to generate new images of faces that look as realistic as possible using a DCGAN on a dataset of faces.    |\n|     86.    |  [Face Mask Detection Using OpenCV](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Mask%20Detection%20using%20OpenCV)  |    This is a Face Mask Detection project that uses both Haar Cascades and Caffe framework approaches for face detection and a finetuned MobileNetV2 model to detect masks on face taking real time video stream as input.   |\n|     83.    |  [Face Verification using Deep Learning](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Verification%20using%20DL)  | | \n|     84.    |  [Face Detection using PCA](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20detection%20using%20PCA)  |   Implement Face detection using Principal Component Analysis (PCA) to reduce the dimensionality of large data sets.   |\n|     85.    |  [Facial Expression Recognition using Machine Learning](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFacial%20Expression%20Recognition%20Using%20ML)  | | \n|     86.    |  [Fake Currency Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFake%20Currency%20Prediction)  |   The goal is to predict whether a given note is fake or not using machine learning models.    |\n|     87.    |  [Fake News Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFake%20News%20Detection)  |   The aim of the project is to detect whether the news is Real or Fake using different text extraction NLP techniques.    |\n|     88.    |  [Fake Job Posting Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFake%20Job%20Posting%20Prediction)  |   The goal of this project is to predict the whether the employee with jods has fake job or real.    |\n|     89.    |  [Fashion MNIST Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFashion%20MNIST%20Classification)  |   To classify various clothings into groups    |\n|     90.    |  [Fish Weight Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFish%20Weight%20Prediction)  |   The main goal of this project to predict the weight of the any fish using linear regression model.    |\n|     91.    |  [Flight Delay Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFlight%20Delay%20Prediction)  |   The main purpose of this project is to predict future delays in flights using machine learning models.    |\n|     92.    |  [Flight Fare Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFlight%20Fare%20Prediction)  |   The aim of the project is to predict the fare of different Airlines covering different routes using machine learning models.    |\n|     93.    |  [Flood Prediction]()  | | \n|     94.    |  [Flowers Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFlowers%20Recognition)  |   The goal of this project is to identify the flower name from an image uploaded by the user.    |\n|     95.    |  [Football Match Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFootball%20Match%20Prediction)  |   The goal of this project is to predict the match winner according to the prediction model.    |\n|     96.   |  [Football Team Rating Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFootball%20Team%20Rating%20Prediction)  |   The goal is to predict rating of Football Team.  |\n|     97.    |  [Forest Cover Type Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FForest%20Cover%20Type%20Classification)  |   The main goal of this project to classify the cover type of forest with the dataset using Random Forest Classification model.    |\n|     98    |  [Forest Fire Prediction ](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FForest%20Fire%20Prediction)  |  To predict the area burned in the Forest Fire.   |\n|     99    |  [Fragnance Price Prediction ](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFragnance%20Price%20Prediction)  |  To predict the price of the fragnances.   |\n|     100.  |  [Fresher's Salary Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFresher's%20Salary%20Prediction)  |   The Main Goal of this project to predict the salary of fresher students on the basis of subjects, degree_type, percentages etc.    |\n|     101.    |  [Fuel Consumption Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFuel%20Consumption%20Analysis)  |   The aim of this project is to predict the consumption depending on the gas type. It can be used to determine the effect of weather, speed or gas type on the car consumption.    |\n|     102.   |  [GOT Episodes IMDb Rating Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGOT%20Episodes%20IMDb%20Rating%20Prediction)  |  The goal of this project is to create a prediction model which will predict the IMDb ratings of the episodes of the Game of Thrones series depending on the views and season of the GOT. |\n|     103.  |  [Gender Classification Using DL](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGender%20Classification%20Using%20DL)  |  Gender classification aims to recognize a person’s gender based on the characteristics that differentiate masculinity and femininity. |\n|     104.  |  [German Traffic Sign Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGerman%20Traffic%20Sign%20Classification)  |  The aim is to automatically classify traffic signs. |\n|     105.  |  [Glass Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGlass%20Classification)  |  The aim of this project is to classify the glass type using percentage of minerals present in each class of glass.  |\n|     106.  |  [Gold Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGraduate%20Admission%20Prediction)  |  The aim of this project is to perform analysis the gold price using different EDA techniques, and eventually train different model to predict the price of gold. |\n|     107.  |  [Graduate Admission Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGlass%20Classification)  |  To predict about the Graduate Admissions from an Indian perspective.  |\n|     108.    |  [Gun Detection]()  | | \n|     109.  |  [Handwritten Digit Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHandwritten%20Digit%20Recognition)  |  The main purpose of this project is to recognize handwritten digits by humans. |\n|     110.    |  [Heart Attack Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHeart%20Attack%20Analysis)  |   The goal of this exercise is to identify the parameters that influences the heart attack and build a ML model for the prediction of heart attack.   |\n|     111.    |  [Heart Disease Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHeart%20Disease%20prediction)  |   This project predicts heart disease based on features like age, cholesterol level, blood sugar level etc.   |\n|     112.    |  [Heights and Weights Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHeights%20and%20Weights%20Prediction)  |   The goal of this project is to build the prediction model using the regression algorithms to predict height and weight.    |\n|     113.    |  [Honey Bee Pollen Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHoney%20Bee%20Pollen%20Detection)  |   The aim of this project is to classify the images of the bees to detect whether they are carrying pollen grains or not.    |\n|     114.    |  [Horses or Humans Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHorses%20or%20Humans%20Classification)  |   The aim of this project is to create a model, able to classify the horses and humans.    |\n|     115.  |  [Hotel Booking Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHotel%20Booking%20Prediction)  |  The goal is to predict the possibility of the booking ,whether the booking is successfull or not . |\n|     116.    |  [Hotel Rating Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHotel%20Rating%20Prediction)  |   The goal of this project is to build the model for the Hotel Rating Prediction using nine Machine Learning Models.    |\n|     117.    |  [House Prices Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHouse%20Price%20Prediction)  |   This project predicts the prices of houses located in the cities of the US, with the help of essential features.   |\n|     118.    |  [Human Activity Recognition using Smartphones](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHuman%20Activity%20Recognition%20using%20Smartphones)  |   The aim of this project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying using data recorded by multiple smartphone sensors.    |\n|     119.  |  [IMDB Sentiment Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIMDB%20Sentiment%20Analysis)  |The main purpose of this project is to predict the sentiment of movie reviews on IMDB.  |\n|     120.  |  [IPL Score Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIPL%20Score%20Prediction)  | The goal of this project is to predict the score of an innings in an IPL match.  |\n|     121.  |  [IPL Winner Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIPL%20Winner%20Prediction)  |  The aim of this project is to perform analysis of the IPL data and predict the winner of the IPL matches.  |\n|     122.    |  [Iris Flower Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIRIS%20Flower%20Classification)  |   The project is to classify the flowers among three 3 different types.   |\n|     123.    |  [Ice-cream Revenue Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIce%20Cream%20Revenue%20Prediction)  |   This is an icecream revenue prediction which predicts the daily revenue of icecreams depending on the temperature feature.   |\n|     124.    |  [Image Compression Using Clustering](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FImage%20Compression%20using%20Clustering)  |   This project helps in compressing the image using K-Means Clustering.   |\n|     125.    |  [Imagenet Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FImagenet%20classification)  |   ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images.    |\n|     126.    |  [Income Prediction Web App](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIncome%20Prediction%20Web%20App)  |   The Main Goal of this project to find out whether a person earn more than 50K dollar(>50K) and less than or equal to 50K dollar (\u003C=50K) based on the given presonal information about person.    |\n|     127.    |  [Insurance Claim Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FInsurance%20Claim%20Prediction)  |   The goal of this project is to classify insurance claim as 0 or 1 (if the policy holder will claim insurance or not) from features as age, number of children, BMI, residential region etc.    |\n|     128.   |  [Kidney Stone Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FKidney%20Stone%20Prediction)  |  The goal of this project is to create a prediction model which will predict the success rate of kidney stone operation based on the stone's size and type of treatment. |\n|     129.    |  [LEGO Minifigures](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FLEGO%20Minifigures)  |   This project contains the dataset of a lot of pictures of various LEGO Minifigures in different poses or with different environments for the image classification tasks.    |\n|     130.   |  [Laptop Prices Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FLaptop%20Prices%20Prediction)  |  The goal of this project is to make a Prediction model which will predict the prices of the laptops depending on various factors, such as size, company, set up and many more things! |\n|     131.    |  [License Plate Detection and Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FLicense%20Plate%20Detection%20and%20Recognition)  |   This project is to detect and identify the license plates of a vehicles.    |\n|     132.    |  [Loan Eligibility Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FLoan%20Eligibility%20Prediction)  |   The main goal of the project is to predict the eligibility of a customer for getting a loan from the company   |\n|     133.    |  [MBA Specialization Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMBA%20Specialization%20Classification)  |   The goal of this project is to find out the MBA students who will be having good scores in their MBA career based on past activities using Classification algorithms.    |\n|     134.    |  [Malaria Disease Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMalaria%20Disease%20Detection)  |   The aim of this project is to recognize whether the image of the human cell is infected with Malaria disease or not.    |\n|     135.   |  [Male & Female Eyes Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMale%20%26%20Female%20Eyes%20Classification)  | The goal of this project is to create a classification model which will classify the genders based on the eyes images as per given in the dataset. For this we are going to use different architectures of Convolution Neural Network.  |\n|     136.   |  [Mall Customer Segmentation](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMall%20Customer%20Segmentation)  | This model will segment the customers based on the parameters.  |\n|     137.   |  [Marathon Time Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMarathon%20Time%20Prediction)  | The goal of this project is to predict the time for the marathon using the given details in dataset.  |\n|     138.    |  [Marble Surface Anomaly Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMarble%20Surface%20Anomaly%20Detection)  |  The goal of this project is to make a detection model which will detect which type of marble is present. |\n|     139.    |  [Medical Cost Analysis for Smokers and Non-smokers](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMedical%20Cost%20Analysis%20for%20Smokers%20and%20Non-smokers)  |   This project consists of the fact that the average medicine charges can be changed for the smokers and non-smokers.    |\n|     140.    |  [MemPool Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMemPool%20Prediction)  |   To predict about the memepool in detail.    |\n|     141.    |  [Mobile Price Range Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMobile%20Price%20Range%20Classification)  |   In this project we do not have to predict actual price but a price range indicating how high the price is.    |\n|     142.    |  [Movie Oscar Win Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMovie%20Oscar%20Win%20Prediction)  |   The goal of this project is to make a prediction model, which will predict the chances of winning the Oscar award.    |\n|     143.    |  [Movie Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMovie%20Recommendation%20System)  |   This project will help one to recommend the movies , will provide the movies names.    |\n|     144.    |  [Mushroom Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMushroom%20Classification)  |   This project is all about deploying classification algorithms and comparing the models.    |\n|     145.    |  [Music Genre Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMusic%20Genre%20Classification)  |   The goal of this project is to create a model which will classify all the 10 genres based on the song selected.The song can be selected from the dataset or any other source.The song format must be in .wav format and for 30 sec in duration.   |\n|     146.    |  [NASA Asteroids Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNASA%20Asteroids%20Classification)  |   This project helps in classifying the NASA Asteroids and checks for features responsible for claiming whether the asteroid is hazardous.   |\n|     147.    |  [NBA-Analysis and Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNBA-Analysis%20and%20Prediction)  |   To predict number of points that were scored in the season 2013-2014 by the NBA players.   |\n|     148.    |  [Natural Images Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNatural%20Images%20Classification)  |   The goal of this project is to build the Classification Model for natural images using the Neural Networks and Deep Learning.    |\n|     149.    |  [Netflix EDA and Recommedation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNetflix%20EDA%20and%20Recommedation%20System)  |   The project aims to gain insights from detailed EDA and recommend which movie to watch next.   |\n|     150.    |  [Object Detection using OpenCV](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FOlympic%20Medal%20Prediction)  |   To understand Computer Vision Libraries like OpenCV and detect various objects using Deep Learning Models with pretrained weights.  |\n|     151.    |  [Olympic Medal Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMushroom%20Classification)  |  The goal of this project is to make a prediction model which will predict whether an athlete will win medal or not.   |\n|     152.    |  [Organ Donors Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FOrgan%20Donors%20Prediction)  |  The goal of this project is to predict donors using the information contained in this dataset.    |\n|     153.   |  [Paris Housing Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FParis%20Housing%20Classification)  |  The goal is to predict Paris Housing. |\n|     154.    |  [Parkinson's Disease Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FParkinson's%20Disease%20Prediction)  |    This project helps in finding the reasons for parkinson's disease and who are predicted to have this disease.   |\n|     155.    |  [Password Strength Classifier](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPassword%20Strength%20Classifier)  |  The goal of this project is to classify and predict the strength of the password given in the dataset. |\n|     156.   |  [Persian License Plate Characters Identification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPersian%20License%20Plate%20Characters%20Identification)  | The goal of this project is to make a detection model which will detect different characters from the Persian Number plate.  |\n|     157.    |  [Phishing Website Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPhishing%20Website%20Detection)  |   The goal of this project is to make a detection model which will detect the phishing websites depending on various factors, using machine learning algorithms.    |\n|     158.    |  [Plant Disease Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPlant%20Disease%20Prediction)  |  Goal of this project is to identify the condition of a plant by looking at the image provided. |\n|     159.    |  [Plant Seedlings Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPlant%20Seedlings%20Classification)  |   The goal of this project is to build the plant seedlings classification model. The architectures used here are, ResNet, AlexNet, Vgg, Inception, MobileNet, SqueezeNet, DenseNet to deploy the classification model.    |\n|     160.    |  [Predict Future Sales](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPredict%20Future%20Sales)  |  To predict whether the future sales.  |\n|     161.    |  [Private Companies Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPrivate%20Companies%20Prediction)  |  To predict the number of the private limited companies.   |\n|     162.    |  [Productivity Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FProductivity%20Prediction)  |   It is highly desirable among the decision makers in the garments industry to track, analyse and predict the productivity performance of the working teams in their factories.    |\n|     163.    |  [Railway Track Fault Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRailway%20Track%20Fault%20Detection)  |  To predict railway track faults.  |\n|     164.    |  [Rain Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRain%20Prediction)  |   RainTomorrow is the target variable to predict on the basis of some given Features.    |\n|     165.    |  [Reddit Tweets Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FReddit%20Tweets%20Prediction)  |  To predict about the different reddit tweets in detail.  |\n|     166.    |  [Restaurant Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRestaurant%20Recommendation%20System)  |   The goal of this project is to make a Recommendation System which will recommend the users the best restaurant that they are looking for.    |\n|     167.    |  [Resume Categorizing](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FResume%20Categorizing)  |   The goal of this project is to build the model for the Resume Categorizing using eight Machine Learning Models    |\n|     168.    |  [Road Lane Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRoad%20Lane%20Detection)  |   This project is the model for the road lane detection, which will detect the road lane from an image which will be user given.    |\n|     169.    |  [Salary Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSalary%20Prediction)  |   The goal of this project is to predict the salary based on some parameters.    |\n|     170.    |  [Salary Range Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSalary%20Range%20Classification)  |   The goal of this project is to classify salary range from features as company, job, degree etc.    |\n|     171.    |  [Sarcasm Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSarcasm%20Detection)  |   The Goal of this project is to detect Sarcasm from news headlines data set using classification algorithms and compare the algorithms to find out which one is better.    |\n|     172.    |  [Shoulder X-Ray Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FShoulder%20X-ray%20Classification)  |  The goal of this project is to create a classification model which will classify different images of shoulder x-ray and predict or, detect the type of the image! |\n|     173.    |  [Sign Language Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSign%20Language%20Prediction)  |   The sign language prediction project helps in identifying the sign language from images provided.   |\n|     174.    |  [Snapchat Filters](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSnapchat%20Filters)  |  We will build our own Snapchat Filters with the help of some libraries using Python programming language.   |\n|     175.    |  [Snapchat Witch Filter](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSnapchat%20Witch%20Filter)  | | \n|     176.    |  [Social Distancing Detector](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSocial%20Distancing%20Detector)  | | \n|     177.   |  [Social Network Influencer Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSocial%20Network%20Influencer%20Prediction)  |  The goal of the challenge is to train a machine learning model which, for a pair of individuals, predicts the human judgement on who is more influential with high accuracy.  |\n|     178.   |  [Soil Moisture Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSoil%20Moisture%20Prediction)  |  The aim of this project is to predict the moisture present in soil. |\n|     179.    |  [Solar Eclipse Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSolar%20Eclipse%20Classification)  |   The aim of this project is to classify among the main types of Solar eclipses, which are : P = Partial Eclipse, A = Annular Eclipse, T = Total Eclipse, H = Hybrid or Annular\u002FTotal Eclipse.    |\n|     180.    |  [Solar Radiation Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSolar%20Radiation%20Prediction)  |  The goal is to predict radiation of Sun.   |\n|     181.    |  [Song Genre Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSong%20Genre%20Classification)  |   The goal of this project is to build the song genre classification model using the Spotify dataset.    |\n|     182.    |  [Spam Email Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSpam%20Email%20Detection)  |   This project predicts whether the received message is spam or ham.    |\n|     183.    |  [Speech Emotion Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSpeech%20Emotion%20Recognition)  |   The goal is to predict the emotion of human while talking.    |\n|     184.  |  [Stack Overflow Questions Quality Rating Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStack%20Overflow%20Questions%20Quality%20Rating%20Prediction)  |  The gaol of this project is to make a prediction model which will predict the quality of the questions from the Stack Overflow website depending on the various factors. |\n|     185.   |  [Star Radiation Analysis and Predcition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStar%20Radiation%20Analysis%20and%20Prediction)  | The aim of the project is to analysis and predict the star radiations and perform a detailed visualizations out of the same.  |\n|     186.    |  [Stars, Galaxies and Quasars Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStars%2C%20Galaxies%20and%20Quasars%20Classification)  |   The goal of this project is to make a perfect classification model according to the data collected on stars, galaxies and quasars.    |\n|     187.    |  [Startup Profit Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStartup%20Profit%20Prediction)  |   The goal is to predict Profit of the Startups. The dataset contains data about 50 startups. It has 5 columns: “R&D Spend”, “Administration”, “Marketing Spend”, “State”, “Profit”.    |\n|     188.    |  [Stock Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStock%20Price%20Prediction)  |   The goal is to predict the price of stocks in future and make calls according to the results algorithms provide.    |\n|     189.    |  [Stocks and Crypto Research Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStocks%20and%20Crypto%20Research%20Analysis)  | | \n|     190.    |  [Stress Level Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStress%20Level%20Prediction)  |   The goal of this project is to predict stress level from features taken from the survey responses.    |\n|     191.    |  [Stroke Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStroke%20Prediction)  |   The goal of this project is to predict the rate of stroke of a person.    |\n|     192.    |  [Student Performance Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStudent%20Performance%20Prediction)  |   The goal of this project is to predict final grade of the student from features as study time, failures, free time, absenses, health status, going out,first period grade, second period grade etc.    |\n|     193.    |  [Supermarket Sales Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSupermarket%20Sales%20Prediction)  | To predict supermarket sales. |\n|     194.    |  [Terrorist Attack Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTerrorist%20Attack%20Prediction)  |   Analysis of given dataset and prediction of Terrorist attack using regression modal.   |\n|     195.     |  [Test Score Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTest%20Score%20Prediction)  |   The aim of project is to build machine learning algorithms to predict the scores of the students.|\n|     196.    |  [Tetris Object Counter](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTetris%20Object%20Counter)  |   The goal of the project is to identify all the tetris objects (tetrominoes) in the given tetris input image and return the count. It is seen that during gameplay the tetris blocks are broken and this program should be able to identify such blocks too.    |\n|     197.    |  [Text Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FText%20Classification)  | | \n|     198.     |  [Text Summarization](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FText%20Summarization)  | The goal of this project is to create a model which will summarize the articles given by the users.  |\n|     199.    |  [Titanic Survival Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTitanic%20Survival%20Prediction)  |   Use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.    |\n|     197.    |  [Tokyo Olympics Visualization Data Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTokyo%20Olympics%20Visualisation%20Data%20Analysis)  | | \n|     198.    |  [Traffic Sign Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTraffic%20Sign%20Classification)  |   The goal of this project is to make human readable traffic sign classification.    |\n|     199.    |  [Twitch Streamer Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTwitch%20Streamer%20Analysis)  |  The goal is to perform analysis and predict Followers gained per stream. |\n|     200.    |  [Twitter Sentiment Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTwitter%20Sentiment%20Analysis)  |   This project helps in analyzing the sentiment using the text data posted on twitter to check it by classifying the statements as positive, negative or neutral.   |\n|     201.    |  [U.S. Weather History Visualizations](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FU.S.%20Weather%20History%20Visualizations)  |   The goal is to analyse the 12 months data of US weather history and find the conclusion and display them via graphs.    |\n|     202.    |  [USA House Pricing Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FUSA%20House%20Pricing%20Prediction)  |   The goal of this project is to make a prediction model, which will predict the price of the houses at USA, depending on the given features.    |\n|     203.    |  [Uber Fare Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FUber%20Fare%20predictions)  |   This project helps in predicting the fare to be charged for the Uber travelling person.   |\n|     204.     |  [Vehicle Image Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FVehicle%20Image%20Classification)  |  The aim of this project is to classify a vehicle image which type of vehicle it is. |\n|     205.    |  [Vehicle and Pedestrian Tracking using OpenCV](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FVehicles%20and%20Pedestrian%20Tracking%20Using%20OpenCV)  | | \n|     206.    |  [Voice Gender Identification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FVoice%20Gender%20Identification)  | | \n|     207.    |  [Walmart Sales Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWalmart%20Sales%20Prediction)  |  The main purpose of this project is to predict the future sales at walmart. |\n|     208.     |  [Waste Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWaste%20Classification)  | The aim of this project is to recognize whether the image of the waste product is a Organic Waste or a Recyclable Waste   |\n|     209.    |  [Water Quality Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWater%20Quality%20Prediction)  |   This project indicates if water is safe for human consumption where 1 means Potable and 0 means Not potable.    |\n|     210.    |  [Web Page Phishing Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWeb%20Page%20Phishing%20Detection)  |  Many people get scammed by this Web page phishing technique. Detecting them can save people from getting scammed. Hackers usually blackmail the people to get their personal information back. Identifying this techniques can save millions.   |\n|     211.    |  [Wine Quality Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWine%20Quality%20Prediction)  |   This model predicts the quality of wine based on some features like pH, fixed acidity, citric acid etc. using SVM and Random forest algorithm.    |\n|     212.    |  [World Happiness Report Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWorld%20Happiness%20Report%20Analysis)  |  The aim of the project is to predict happiness scores and rankings and perform a detailed analysis and visualization of the training dataset and create a model. |\n|     213.    |  [World Population By Year Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWorld%20Population%20by%20Year%20analysis)  | | \n|     214.   |  [World Poverty Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWorld%20Poverty%20Analysis)  |  The goal of this project is to analyze the world poverty using dataset. |\n|     215.   |  [Yotube Video Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FYoutube%20Video%20Recommendation%20System)  |  The model will recommend the video titles on proving the search query by user. |\n|     216.    |  [Zomato Banglore Resturants Recommendation](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FZomato%20Bangalore%20Restaurants%20Recommendation%20Analysis)  |  The aim of this project is analyse a dataset and recomend the user for top restaurants in bangalore. |\n|     217.    |  [Zoo Animal Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FZoo-Animal-Classification)  |   The goal of this project is to predict the zoo animal based on some classifications using machine learning model.    |\n|     218.    |  [Body Parts Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBody%20Parts%20Classification)  | | \n|     219.    |  [Discussion Forum Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDiscussion%20Forum%20Prediction)  | | \n|     220.    |  [Mortality Rate Analysis & Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMortality%20Rate%20Analysis%20%26%20Prediction)  | | \n|     221.    |  [Named Entity Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNamed%20Entity%20Recognition)  | | \n|     222.    |  [Ramen Noodles Rating Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRamen%20Noodles%20Rating%20Analysis)  | | \n|     223.    |  [News Articles Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNews%20Articles%20Classification)  | | \n|     224.    |  [Prediction of Subject based on Question (NLP)](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPrediction%20of%20Subject%20based%20on%20Question%20(NLP))  | | \n|     225.    |  [Salt Deposits Identification & Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSalt%20Deposits%20Identification%20%26%20Prediction)  | | \n\n\n## 📝 Project Structure\n\nYour projects should contain this flow to maintain similarity across all other projects. Make sure to note these things, before you create a PR.\n\n**Dataset** - This folder would have a .csv file.\n\n**Model** - This folder would have your project file (that is .ipynb file) be it analysis or prediction. Other than project file, it should also have a **'README.md'** using this [template](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002Freadme_template.md) and **'requirements.txt'** file which would be enclosed with all needed add-ons and libraries that are included in the project. (.pkl file if available)\n\n**Images\u002FScreenshots** - This folder would have images added if applicable.\n\n\u003C!--\n**Front End** - This folder would have files related to coding and designing the webpage. \n\n**Back End** - This folder would have files regarding backend creation for the model using Flask and Django.\n\n**UI\u002FUX** - This folder would have files regarding dashboards, forms and webpages for the model.\n\nInclude README.md file for 'Front End', 'Back End' and 'UI\u002FUX' in their respective folders and elaborate briefly about how it works by showing step by step procedure using screenshots.\n-->\n\n ## 💻 Workflow:\n\n- Fork the repository\n\n- Clone your forked repository using terminal or gitbash.\n\n- Make changes to the cloned repository\n\n- Add, Commit and Push\n\n- Then in Github, in your cloned repository find the option to make a pull request \n\n> print(\"Start contributing for ML-ProjectKart\")\n\n\n\n## 🛠 Templates to Follow\n\n- [Feature request](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FISSUE_TEMPLATE\u002Ffeature_request.md)\n- [Bug Report](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FISSUE_TEMPLATE\u002Fbug_report.md)\n- [Pull Request](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FPULL_REQUEST_TEMPLATE.md)\n- [README](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002Freadme_template.md)\n\n**Note** : One should follow these templates while creating a new issue or pull request. \n\n\n## ⚙️ Things to Note\n\n* Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.\n* You can only work on issues that have been assigned to you.\n* If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.\n* If you have modified\u002Fadded code work, make sure the code compiles before submitting.\n* Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.\n* Do not update the **[README.md](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002FREADME.md).**\n\n\n ## ❄️ Open Source Programs\n\n\u003Ctable>\n\u003Ctr>\n\u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fcodepeak.technology\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_1fac59b4b78f.png\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>CodePeak 2025\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n\u003C\u002Ftd>\n\u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fcodesapiens.in\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_5aaf804d1083.png\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Code Sapiens Summer Of Code 2024\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n\u003C\u002Ftd>\n\u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fdwoc.io\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_866c1ef1a75c.jpg\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Delta Winter Of Code 2023\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n\u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fkwoc.kossiitkgp.org\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_a8c4fc849e0a.png\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Kharaghpur Winter Of Code 2023\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n \u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fhacktoberfest.digitalocean.com\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_cb4ca80fc7e5.jpg\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Hacktoberfest 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n \u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fcontribute.devincept.com\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_5d85638ba1c7.jpg\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>DevIncept Codes 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n \u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fletsgrowmore.in\u002Fsoc\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_04d0f7b7a7c3.jpg\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>LetsGrowMore SoC 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n\u003C!--\n## 📊 Leaderboard \n\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FDCP_SCORECARD.md\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_a4c0c3e45591.png\" width=\"100px;\" alt=\"\"\u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>DevIncept Codes 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FLGMSOC_SCORECARD.md\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_a4c0c3e45591.png\" width=\"100px;\" alt=\"\"\u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>LetsGrowMore SOC 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\u003C\u002Ftd>\n  \u003C\u002Ftr>  \n\u003C\u002Ftable>\n-->\n\n## ✨ Hall Of Fame   \n\nThanks go to these Wonderful People. Contributions of any kind are welcome!🚀 \n\n\u003C!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->\n\u003C!-- prettier-ignore-start -->\n\u003C!-- markdownlint-disable -->\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fgraphs\u002Fcontributors\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_a6bef93cb0dd.png\" \u002F>\n\u003C\u002Fa>\n\n\u003C!-- markdownlint-enable -->\n\u003C!-- prettier-ignore-end -->\n\u003C!-- ALL-CONTRIBUTORS-LIST:END -->\n\n\n## 📜 Code Of Conduct\n\nYou can find our Code of Conduct [here](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002FCODE_OF_CONDUCT.md).\n\n\n## 📝 License\n\nThis project follows the [Mozilla Public License 2.0](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002FLICENSE).\n\n\u003C!-- \n## ✔ Project Maintainer\n\n\u003Ctable>\n  \u003Ctr>\n\u003Ctd align=\"center\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fabhisheks008\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_6a41b52d0a96.png\" width=\"80px;\" alt=\"\"\u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Abhishek Sharma\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n-->\n\n## 😎 Project Admin\n\n\u003Ctable>\n  \u003Ctr>\n\u003Ctd align=\"center\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_c811a0512852.jpg\" width=\"100px;\" alt=\"\"\u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Prathima Kadari\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n\n![Visitor Count](https:\u002F\u002Fprofile-counter.glitch.me\u002F{prathimacode-hub}\u002Fcount.svg)\n\n\n## 🌟 Stargazers Over Time 🌟\n\n[![Stargazers over time](https:\u002F\u002Fstarchart.cc\u002Fprathimacode-hub\u002FML-ProjectKart.svg)](https:\u002F\u002Fstarchart.cc\u002Fprathimacode-hub\u002FML-ProjectKart)\n\n\n## ⭐ Give this Project a Star\n\n[![GitHub followers](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Ffollowers\u002Fprathimacode-hub.svg?label=Follow%20@prathimacode-hub&style=social)](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002F)  [![Twitter Follow](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fprathima_kadari?label=Follow%20@prathima_kadari&style=social)](https:\u002F\u002Ftwitter.com\u002Fprathima_kadari)\n\nIf you liked working on this project, do ⭐ and share this repository.\n\n🎉 🎊 😃 Happy Contributing 😃 🎊 🎉\n\n\u003C!-- \u003Csup>\u003Ckbd>***[Click Here](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002Fprathimacode-hub\u002Fblob\u002Fmain\u002FProjects\u002FOpenSource-Projects.md)***\u003C\u002Fkbd> *to view my open source projects and\u003C\u002Fsup>*  \u003Csup>\u003Ckbd>***[Get In](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002Fprathimacode-hub\u002Fblob\u002Fmain\u002FGitHub%20Projects\u002FLearning-Projects.md)***\u003C\u002Fkbd> *for learning projects.\u003C\u002Fsup>* \u003Cbr>\n\u003C\u002Ftd> -->\n\n\u003C!-- \n\u003Csup>\u003Ckbd>***[Click Here](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002Fprathimacode-hub\u002Fblob\u002Fmain\u002FGitHub%20Projects\u002FOpenSource-Projects.md)***\u003C\u002Fkbd> *to view my open source projects.\u003C\u002Fsup>* \u003Cbr> -->\n\n\n## 📬 Contact\n\nIf you want to contact me, you can reach me through the below handles.\n\n\u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fprathima-kadari\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_3f60edfd3589.png\" width=\"25\">\u003C\u002Fimg>\u003C\u002Fa>   \u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fprathima_kadari\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_d79443668a3c.png\" width=\"25\">\u003C\u002Fimg>\u003C\u002Fa>\n\n© 2025 Prathima Kadari\n\n\n[![forthebadge](https:\u002F\u002Fforthebadge.com\u002Fimages\u002Fbadges\u002Fbuilt-with-love.svg)](https:\u002F\u002Fforthebadge.com) [![forthebadge](https:\u002F\u002Fforthebadge.com\u002Fimages\u002Fbadges\u002Fbuilt-by-developers.svg)](https:\u002F\u002Fforthebadge.com) [![forthebadge](https:\u002F\u002Fforthebadge.com\u002Fimages\u002Fbadges\u002Fbuilt-with-swag.svg)](https:\u002F\u002Fforthebadge.com) \n\n","\u003Cdiv align=\"center\">\n  \u003Ch1>欢迎来到 ML-ProjectKart👋🛒\u003C\u002Fh1>\n\u003C\u002Fdiv>\n\n\u003Cp align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_ceb1d68460b2.png\">\u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat&logo=github\">\u003C\u002Fa> \n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOpen%20Source-%F0%9F%A4%8D-Green\">\u003C\u002Fa> \n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1.svg?label=Contributions&message=Welcome&color=0059b3&style=flat-square\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fgraphs\u002Fcontributors\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcontributors-anon\u002Fprathimacode-hub\u002FML-ProjectKart\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fmaintenance\u002Fyes\u002F2025\">\u003C\u002Fa>\n\u003C\u002Fp> \n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fstargazers\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_2abcd490ea18.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fnetwork\u002Fmembers\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_bbf8aed025a9.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_444c8d6b2ac4.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues?q=is%3Aissue+is%3Aclosed\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_8853fea32f68.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fpulls\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_52b8e0115cf8.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fpulls\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_f5a314ac0435.png\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fpulls?q=is%3Apr+is%3Aclosed\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_83de38abdfa9.png\">\u003C\u002Fa>\n\u003C\u002Fp>\n\n\n## 📌 仓库\n\n这个项目集合展示了基于机器学习、深度学习、计算机视觉、自然语言处理等领域的最优秀项目。快来一起探索开源之旅吧。\n\n我们的主要目标是提供高效且对初学者友好的项目，帮助你掌握机器学习和人工智能算法，并逐步熟悉相关技术。通过丰富的实践机会，助你快速成长为专业人士。\n\n\n## 🙌 加入我们\n\n所有对技术感兴趣并希望参与开源贡献的朋友，都欢迎加入！这里为每个人准备了适合的任务。\n\n| **机器学习** | **深度学习** | **自然语言处理** | **计算机视觉** | **生成式AI**\n\n**数据分析师** - 定义问题，获取、探索和准备数据。\n\n**机器学习、深度学习、计算机视觉、自然语言处理、生成式AI爱好者** - 尝试不同的算法，构建模型并进行优化。\n\n\u003C!--\n**前端设计师** – 设计或开发由设计师设计的网页界面。\n\n**后端开发者** – 使用 Flask 或 Django 为模型创建后端服务。\n\n**UI\u002FUX设计师** – 为模型设计仪表盘、表单和网页界面。\n-->\n\n如果你已经完成过某个独特的项目，或者想启动一个新项目并与大家分享，都可以通过这里实现。请仔细阅读 [CONTRIBUTING](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002FCONTRIBUTING.md) 中的贡献指南👩‍💻\n\n当你提出问题（或从问题列表中选择一个项目）时，请尽可能详细地描述你的想法，并告知我们将要承担的任务。\n\n此外，也请参考 GitHub 文档中的[如何创建拉取请求](https:\u002F\u002Fhelp.github.com\u002Fen\u002Fgithub\u002Fcollaborating-with-issues-and-pull-requests\u002Fcreating-a-pull-request)。\n\n\n## 💡 浏览精彩项目合集\n\n| S.No | Project Name | Description |\n| --------------- | --------------- | --------------- |\n|     01.    |  [Advertisement Click Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAdvertisement%20Click%20Prediction)  |  The goal of this project is to make a prediction model using the advertisement classification algortihms, which will predict the desired ad as per the user information.   |\n|     02.    |  [Advertisement Success Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAdvertisement%20Success%20Prediction)  |    In this project we will be working with an advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. |\n|     03.    |  [Age, Gender and Ethnicity Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAge%2C%20Gender%20and%20Ethnicity%20Prediction)  |  The goal of this project is to be able to predict age , gender and ethnicity of a person just by looking at an image of a person.   |\n|     04.    |  [Air Quality Predication](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAir%20Quality%20Prediction)  |  The goal of this project is to predict Air Quality Index (AQI ) form features as particulate matter (PM2.5 and PM10), Nitrogen Oxide (NO), Nitric Dioxide (NO2), Carbon Monoxide (CO), Sulphur Dioxide (SO2), Ozone (O3) etc.   |\n|     05.    |  [Airbnb Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAirbnb%20Price%20Prediction)  |   The goal of this project is to make a prediction model which will predict the prices of the Airbnb hotels using different parameters.  |\n|     06.    |  [Airline Passenger Satistfaction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAirline%20Passenger%20Satisfaction)  |   The aim of this project is to perform analysis of the passenger satisfaction data and data preprocessing to prepare the data and predict whether the passenger is satisfied or not with the airline services.  |\n|     07.    |  [Alpaca Identification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAlpaca%20Identification)   |   The goal of this project is to make an identification cum classification model using deep neural networks, which will identify the images of Alpaca from the user given input.   |\n|     08.    |  [Amazon Alexa Reviews](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Alexa%20Reviews)  |    This model predicts the feedback of Amazon Alexa users based on the features such as ratings, variations and verified reviews.  |\n|     09.    |  [Amazon Books Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Books%20Analysis)  |   Prediction on whether books will be fiction or non-fiction using Goodreads.  |\n|     10.    |  [Amazon Data Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Data%20Analysis)  |   The goal of this project is to predict the upvote of the customer on the basis of reviews, text and score of the cuustomer given in the dataset.  |\n|     11.    |  [Amazon Mobile Phones Reviews Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Mobile%20Phones%20Reviews%20Analysis)  |   This is one of the major techniques used to analyze customer reviews on different products and find necessary insights from reviews. The major idea is to classify reviews and determine how satisfied customers were regarding the product.  |\n|     12.    |  [Amazon Products Review Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmazon%20Products%20Reviews%20Classification)  |  The goal of this project is to make a classifiation model which will classify the products review enlisted in the Amazon Inc website, so that it can help the company for their betterment, and also they can rectify their faults depending on the users' experience.   |\n|     13.    |  [American Sign Language(ASL) Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAmerican%20Sign%20Language%20(ASL)%20Recognition)  |   T he aim of this project is to recognize what the person is trying to convey using different hand gestures. The dataset contains 29 classes which comprises of A to Z alphabets, nothing, space and delete hand gestures.  |\n|     14.    |  [Animate Me !](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAnimate%20Me!)  |   It is a simple OpenCV project that converts input image to a cartoon equivalent  |\n|     15.    |  [Anime Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAnime%20Recommendation%20System)  | | \n|     16.    |  [Appliances Energy Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAppliances%20Energy%20Prediction)  |   The goal of this project is to make a prediction model which will predict the enery consumption by the appliances based on the given dataset.  |\n|     17.    |  [Automated Essay Grading](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAutomated%20Essay%20Grading)  |   The goal of this project is to make a prediction model which will give scoring to student-written essays.   |\n|     18.    |  [Avito Product Analysis and Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FAvito%20Product%20Analysis%20and%20Price%20Prediction)  |   Perform exploratory data analysis on the dataset of products from Avito Advertising website and develop a ML model to predict prices of the other products in the website.   |\n|     19.    |  [Balls Image Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBalls%20Image%20Classification)  |   The goal of this project is to make a deep learning model which will classify the images of different types of balls using the convoolution neural network, to be precise the MobileNet architecture.   |\n|     20.    |  [Bangladesh Premier League Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBangladesh%20Premier%20League%20Analysis)  | |\n|     21.    |  [Bank Customers Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBank%20Customers%20Prediction)  |   To predict the customers who are withdrawing their account from the bank due to some loss and other issues.   |\n|     22.  |  [Bike Crash Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBike%20Crash%20Analysis)  |    The goal of this project to analyze the dataset on various factors and depending on the various factors making of a prediction model which will predict the accident prone sights. |\n|     23.   |  [Bike Rental Demand Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBike%20Rental%20Demand%20Analysis)  |   The aim of the project is to make a forecast of the demand in Bike Rental Services. This project predicts the upcoming nature of the customer request demand.  |\n|     24.   |  [Bike Sharing Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBike%20Sharing%20Prediction)  |  The main goal of the project is to analyse the Bike Share Count in case of different seasons, weekdays, weathers, etc. and based on that train a model, to predict the number of bike users under a given circumstance.   |\n|     25.   |  [Bird Species Classification and Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBird%20Species%20Classification%20and%20Recognition)  |   The goal is to complete an end to end computer vision problem with basic parts, such as bulding an input pipeline, compose the model, data augmentation etc.   |\n|     26.   |  [Bitcoin Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBitcoin%20Price%20Prediction)  |    The main purpose of this project is to predict the price of Bitcoin. |\n|     27.   |  [Black Friday Sales - Analysis and Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBlack%20Friday%20Sales-%20Analysis%20and%20Prediction)  |     The goal of this project is to analyse and predict purchases in the black friday sales from features as age group, gender, occupation, product category etc.|\n|     28.   |  [Board Game Review Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBoard%20Game%20Review%20Prediction)  |   The goal of this project are -What are the categories of game that are the most popular?Can we build a model with the available data that predicts user rating? What factors make for the best \"modern\" board game.  |\n|     29.   |  [Body Fat Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBody%20Fat%20Prediction)  |    The goal is to predict percentage of body fat of a person. |\n|     30.   |  [Book Genre Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBook%20Genre%20Prediction)  |   The goal of this project is to make a prediction model which will predict genre of the book.   |\n|     31.   |  [Book Recommendation Systems](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBook%20Recommendation%20Systems)  |   The goal of this project is to make a recommendation system which will recommend the user a book, based on the search results given input by the users.  |\n|     32.   |  [Brain Tumor Detection and Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBrain%20Tumor%20Detection%20and%20Classification)  |   This model detects the presence of brain tumor by processing MRI scans of the patient.   |\n|     33.  |  [Brain Weight prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBrain%20Weight%20Prediction)  |   The goal of this project is to make a prediction model which will predict the weight of the human brain depending on the head size.  |\n|     34.   |  [Brazil Fires Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBrazil%20Fires%20Prediction)  |   To predict the number of the fires taken place statewise in Brazil.   |\n|     35.   |  [Breast Cancer Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBreast%20Cancer%20Prediction)  |   The goal is to predict Breast Cancer.  |\n|     36.    |  [Campus Placement Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCampus%20Placement%20Prediction)  |   The goal is to predict if a student is placed or not according to his\u002Fher percentage and other factors.  |\n|     37.    |  [Campus Recruitment - Analysis and Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCampus%20Recruitment%20-%20Analysis%20and%20Prediction)  |   The goal of this project is to analyze the factors that can effect the Campus Recruitment, and also creating a model which will predict the chances of getting placed depending on various factors.   |\n|     38.  |  [CAPTCHA Decoding](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCAPTCHA%20Decoding)  |    The goal of this project is to create a deep learning model which will recognize the captcha letters. |\n|     39.    |  [Car Brand Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCar%20Brand%20Classification)  |    This project classifies the car brand using machine learning.   |\n|     40.    |  [Car Insurance Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCar%20Insurance%20Prediction)  | | \n|     41.    |  [Car Prices Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCar%20Prices%20Prediction)  |   The main goal is to predict the price of cars with the available independent variables   |\n|     42. |  [Cartier Jewelry Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCartier%20Jewelry%20Classification)  |   The goal of this project is to make a classification model, which will classify the jewelries based on the various features.  |\n|     43.    |  [Cartoonify using KMeans](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCartoonify%20using%20KMeans)  | | \n|     44.    |  [Cats Vs Dogs Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCats%20Vs%20Dogs%20Classification)  |   The goal of this project is to classify whether the image is of a dog or a cat.    |\n|     45.    |  [Cervical Cancer Risk Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCervical%20Cancer%20Risk%20Prediction)  |  The business objective is to build a Machine Learning Prediction Model that predicts the result of Biopsy test and thereby confirming the presence\u002Fnon-presence of cervical cancer in the patients.   |\n|     46.    |  [Churn Risk Score Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FChurn%20Risk%20Score%20Prediction)  |   This project is used to predict the churn score for a website based on the related features   |\n|     47.    |  [Classification of Images using MNIST and CIFAR10](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FClassification%20of%20Images%20using%20MNIST%20and%20CIFAR10)  |   Classifying and predicting images with accuracy.   |\n|     48.    |  [Coffee Production Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCoffee%20Production%20Prediction)  |   The goal of this project is to make a prediction model which will give total production of the coffee from year 1990-2018.   |\n|     49.   |  [Colour Identification using Machine Learning](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FColour%20Identification%20using%20Machine%20Learning)  |   This project defines us the colour we have asked to see instead of showing the color object.    |\n|     50.    |  [Concrete Strength Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FConcrete%20Strength%20Prediction)  |   The aim of this project is to predict strength of the concrete from features as fly ash, blast furnamce slag, water, fine aggregate, coarse aggregate, cement, age etc.   |\n|     51.    |  [Cotton Disease Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCotton%20Disease%20Prediction)  |   This project predicts the disease of cotton tree using Transfer Learning with Resnet152V2 model.    |\n|     52.    |  [COVID19_Data-Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCOVID19_Data-Analysis)  |   The goal is to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of great help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.   |\n|     53.    |  [Covid-19 Data Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCovid-19%20Data%20Analysis)  |   The goal of this project is to analyze the situation caused due to Covid-19 pandemic using data visualization.    |\n|     54.    |  [Credit Card Fraud Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCredit%20Card%20Fraud%20Detection)  |   The aim of the project is to predict fraudulent credit card transactions using machine learning models.    |\n|     55.    |  [Crop Fertilizers Analysis and Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCrop%20Fertilizers%20Analysis%20and%20Prediction)  | | \n|     56.    |  [Crop Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCrop%20Recommendation%20System)  |   The Goal of this project is to build the recommendation model using the crop recommendation dataset.    |\n|     57.    |  [Crop Yield Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCrop%20Yield%20Prediction)  |   This dataset is focused on the different crops which can reflect the price area and other attributes from that one can predict for future price yield by using some better attributes.    |\n|     58.    |  [Cryotherapy Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCryotherapy%20Analysis)  |   In this project we are going to analyze the cryotherapy dataset and will deploy several machine learning algorithm models.    |\n|     59.    |  [Customer Income Segmentation Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCustomer%20Income%20Segmentation%20Analysis)  | | \n|     60.    |  [Customer Modelling Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCustomer%20Modeling%20Analysis)  | | \n|     61.    |  [Customer Region Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FCustomer%20Region%20Classification)  |   The goal is to predict Customer Region using random forest classifier model.    |\n|     62.    |  [DNA Sequencing Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDNA%20Sequencing%20Classification)  |   In this project, we will understand how to interpret a DNA structure and how machine learning algorithms can be used to build a prediction model on DNA sequence data.    |\n|     63.    |  [Dance Form Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDance%20Form%20Classification)  |   The goal of this project is to classify the images of various dance forms and prepare a Classification model using Deep learning methods.    |\n|     64.    |  [Dandelion Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDandelion%20Recognition)  |   The goal of this project is to create classification model which will identify the dandelion from the images given to the model.    |\n|     65.    |  [Data Analysis Of Meteorological Data](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FData%20Analysis%20of%20Meteorological%20Data)  |   It analyzes the meteorological weather data of the last 10yrs from 2006 to 2016 at Finland to check an increase of global warming.   |\n|     66.    |  [DeepQ Networks](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDeepQNetworks)  | | \n|     67.    |  [Detecting Motion and Moving Objects in a Video](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDetecting%20Motion%20and%20Moving%20Objects%20in%20a%20Video)  | | \n|     68.    |  [Diabetes Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDiabetes%20Prediction)  |   The goal is to predict if a person is having Diabetes or not using logistic regression and SVM models.    |\n|     69.    |  [Diamond Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDiamond%20Price%20Prediction)  |   The goal is to predict price of Diamond.|\n|     70.    |  [Disease Symptom Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDisease%20Symptom%20Prediction)  |   The goal of this project is predict symptom of disease using the information contained in this dataset.   |\n|     71.    |  [Disneyland Reviews Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDisneyland%20Reviews%20Analysis)  |   The aim of this project is to analyse the reviews given by visitors from different countries of the world using NLP.    |\n|     72.    |  [Dog Breed Identification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDog%20Breed%20Identification)  |   This project is about predicting the breed of a dog, this can be helpful to people who are not expert in the field.    |\n|     73.    |  [Dogecoin Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDogecoin%20Price%20Prediction)  |   The goal of this project is to make a Prediction model which will predict the price of the Dogecoin in the future times depending on the previous parameters.    |\n|     74.    |  [Driver Drowsiness Detection System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDriver%20Drowsiness%20Detection%20System)  | The main purpose of this project is to awake the driver's if they fall asleep in order to avoid drowsiness related road-accidents.  |\n|     75.    |  [Dry Beans Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDry%20Beans%20Classification)  | The goal is to predict Dry Beans.  |\n|     76.    |  [Email Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmail_Classification)  |   This project classify emails as spam or not-spam on the basis of the message.    |\n|     77.    |  [Emoji Classification using OpenCV](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmoji%20Classification%20using%20OpenCV)  |   The Goal of this project is to make a model which will predict the emotion shown using the input image. And also it will classify the particular emotion shown in the given image.    |\n|     78.   |  [Emotion Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmotion%20Classification)  |   The goal of this project is to create a model which will classify different emotions based on the text provided in the dataset.  |\n|     79.    |  [Emotion Recognition using NLP](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmotion%20Recognition%20Based%20on%20NLP)  |  An Intelligent Emotion Predictor which uses NLP technique by sending text input to determine whether data is positive, negative or neutral.   |\n|     80.    |  [Employee Retention Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEmployee%20Retention%20Project)  |   The project revolves around the idea to give basic modelling techniques used to classify if the employee will leave the company or not based on certain features in the datset.    |\n|     81.    |  [English Alphabet Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FEnglish%20Alphabet%20Classification\u002FModel)  | The aim of this project is to recognize the computerised generated images of the English apphabets. The dataset contains 26 classes which comprises of a to z alphabets, each class containing 100 images.  |\n|     82.    |  [Exports Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FExports%20Classification)  |   The goal of this project is to build a classification model, using regression algorithms, such as, linear regression, random forest regression, decision tree regressor and many more algorithms.    |\n|     83.    |  [Face Clustering](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Clustering)  | | \n|     84.    |  [Face Detection and Blurring](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Detection%20and%20Blurring)  | | \n|     85.    |  [Face Generation using DCGAN](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Generation%20using%20DCGAN)  |   The main goal of this project is to get a generator network to generate new images of faces that look as realistic as possible using a DCGAN on a dataset of faces.    |\n|     86.    |  [Face Mask Detection Using OpenCV](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Mask%20Detection%20using%20OpenCV)  |    This is a Face Mask Detection project that uses both Haar Cascades and Caffe framework approaches for face detection and a finetuned MobileNetV2 model to detect masks on face taking real time video stream as input.   |\n|     83.    |  [Face Verification using Deep Learning](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20Verification%20using%20DL)  | | \n|     84.    |  [Face Detection using PCA](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFace%20detection%20using%20PCA)  |   Implement Face detection using Principal Component Analysis (PCA) to reduce the dimensionality of large data sets.   |\n|     85.    |  [Facial Expression Recognition using Machine Learning](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFacial%20Expression%20Recognition%20Using%20ML)  | | \n|     86.    |  [Fake Currency Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFake%20Currency%20Prediction)  |   The goal is to predict whether a given note is fake or not using machine learning models.    |\n|     87.    |  [Fake News Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFake%20News%20Detection)  |   The aim of the project is to detect whether the news is Real or Fake using different text extraction NLP techniques.    |\n|     88.    |  [Fake Job Posting Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFake%20Job%20Posting%20Prediction)  |   The goal of this project is to predict the whether the employee with jods has fake job or real.    |\n|     89.    |  [Fashion MNIST Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFashion%20MNIST%20Classification)  |   To classify various clothings into groups    |\n|     90.    |  [Fish Weight Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFish%20Weight%20Prediction)  |   The main goal of this project to predict the weight of the any fish using linear regression model.    |\n|     91.    |  [Flight Delay Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFlight%20Delay%20Prediction)  |   The main purpose of this project is to predict future delays in flights using machine learning models.    |\n|     92.    |  [Flight Fare Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFlight%20Fare%20Prediction)  |   The aim of the project is to predict the fare of different Airlines covering different routes using machine learning models.    |\n|     93.    |  [Flood Prediction]()  | | \n|     94.    |  [Flowers Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFlowers%20Recognition)  |   The goal of this project is to identify the flower name from an image uploaded by the user.    |\n|     95.    |  [Football Match Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFootball%20Match%20Prediction)  |   The goal of this project is to predict the match winner according to the prediction model.    |\n|     96.   |  [Football Team Rating Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFootball%20Team%20Rating%20Prediction)  |   The goal is to predict rating of Football Team.  |\n|     97.    |  [Forest Cover Type Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FForest%20Cover%20Type%20Classification)  |   The main goal of this project to classify the cover type of forest with the dataset using Random Forest Classification model.    |\n|     98    |  [Forest Fire Prediction ](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FForest%20Fire%20Prediction)  |  To predict the area burned in the Forest Fire.   |\n|     99    |  [Fragnance Price Prediction ](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFragnance%20Price%20Prediction)  |  To predict the price of the fragnances.   |\n|     100.  |  [Fresher's Salary Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFresher's%20Salary%20Prediction)  |   The Main Goal of this project to predict the salary of fresher students on the basis of subjects, degree_type, percentages etc.    |\n|     101.    |  [Fuel Consumption Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FFuel%20Consumption%20Analysis)  |   The aim of this project is to predict the consumption depending on the gas type. It can be used to determine the effect of weather, speed or gas type on the car consumption.    |\n|     102.   |  [GOT Episodes IMDb Rating Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGOT%20Episodes%20IMDb%20Rating%20Prediction)  |  The goal of this project is to create a prediction model which will predict the IMDb ratings of the episodes of the Game of Thrones series depending on the views and season of the GOT. |\n|     103.  |  [Gender Classification Using DL](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGender%20Classification%20Using%20DL)  |  Gender classification aims to recognize a person’s gender based on the characteristics that differentiate masculinity and femininity. |\n|     104.  |  [German Traffic Sign Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGerman%20Traffic%20Sign%20Classification)  |  The aim is to automatically classify traffic signs. |\n|     105.  |  [Glass Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGlass%20Classification)  |  The aim of this project is to classify the glass type using percentage of minerals present in each class of glass.  |\n|     106.  |  [Gold Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGraduate%20Admission%20Prediction)  |  The aim of this project is to perform analysis the gold price using different EDA techniques, and eventually train different model to predict the price of gold. |\n|     107.  |  [Graduate Admission Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FGlass%20Classification)  |  To predict about the Graduate Admissions from an Indian perspective.  |\n|     108.    |  [Gun Detection]()  | | \n|     109.  |  [Handwritten Digit Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHandwritten%20Digit%20Recognition)  |  The main purpose of this project is to recognize handwritten digits by humans. |\n|     110.    |  [Heart Attack Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHeart%20Attack%20Analysis)  |   The goal of this exercise is to identify the parameters that influences the heart attack and build a ML model for the prediction of heart attack.   |\n|     111.    |  [Heart Disease Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHeart%20Disease%20prediction)  |   This project predicts heart disease based on features like age, cholesterol level, blood sugar level etc.   |\n|     112.    |  [Heights and Weights Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHeights%20and%20Weights%20Prediction)  |   The goal of this project is to build the prediction model using the regression algorithms to predict height and weight.    |\n|     113.    |  [Honey Bee Pollen Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHoney%20Bee%20Pollen%20Detection)  |   The aim of this project is to classify the images of the bees to detect whether they are carrying pollen grains or not.    |\n|     114.    |  [Horses or Humans Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHorses%20or%20Humans%20Classification)  |   The aim of this project is to create a model, able to classify the horses and humans.    |\n|     115.  |  [Hotel Booking Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHotel%20Booking%20Prediction)  |  The goal is to predict the possibility of the booking ,whether the booking is successfull or not . |\n|     116.    |  [Hotel Rating Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHotel%20Rating%20Prediction)  |   The goal of this project is to build the model for the Hotel Rating Prediction using nine Machine Learning Models.    |\n|     117.    |  [House Prices Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHouse%20Price%20Prediction)  |   This project predicts the prices of houses located in the cities of the US, with the help of essential features.   |\n|     118.    |  [Human Activity Recognition using Smartphones](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FHuman%20Activity%20Recognition%20using%20Smartphones)  |   The aim of this project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying using data recorded by multiple smartphone sensors.    |\n|     119.  |  [IMDB Sentiment Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIMDB%20Sentiment%20Analysis)  |The main purpose of this project is to predict the sentiment of movie reviews on IMDB.  |\n|     120.  |  [IPL Score Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIPL%20Score%20Prediction)  | The goal of this project is to predict the score of an innings in an IPL match.  |\n|     121.  |  [IPL Winner Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIPL%20Winner%20Prediction)  |  The aim of this project is to perform analysis of the IPL data and predict the winner of the IPL matches.  |\n|     122.    |  [Iris Flower Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIRIS%20Flower%20Classification)  |   The project is to classify the flowers among three 3 different types.   |\n|     123.    |  [Ice-cream Revenue Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIce%20Cream%20Revenue%20Prediction)  |   This is an icecream revenue prediction which predicts the daily revenue of icecreams depending on the temperature feature.   |\n|     124.    |  [Image Compression Using Clustering](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FImage%20Compression%20using%20Clustering)  |   This project helps in compressing the image using K-Means Clustering.   |\n|     125.    |  [Imagenet Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FImagenet%20classification)  |   ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images.    |\n|     126.    |  [Income Prediction Web App](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FIncome%20Prediction%20Web%20App)  |   The Main Goal of this project to find out whether a person earn more than 50K dollar(>50K) and less than or equal to 50K dollar (\u003C=50K) based on the given presonal information about person.    |\n|     127.    |  [Insurance Claim Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FInsurance%20Claim%20Prediction)  |   The goal of this project is to classify insurance claim as 0 or 1 (if the policy holder will claim insurance or not) from features as age, number of children, BMI, residential region etc.    |\n|     128.   |  [Kidney Stone Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FKidney%20Stone%20Prediction)  |  The goal of this project is to create a prediction model which will predict the success rate of kidney stone operation based on the stone's size and type of treatment. |\n|     129.    |  [LEGO Minifigures](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FLEGO%20Minifigures)  |   This project contains the dataset of a lot of pictures of various LEGO Minifigures in different poses or with different environments for the image classification tasks.    |\n|     130.   |  [Laptop Prices Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FLaptop%20Prices%20Prediction)  |  The goal of this project is to make a Prediction model which will predict the prices of the laptops depending on various factors, such as size, company, set up and many more things! |\n|     131.    |  [License Plate Detection and Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FLicense%20Plate%20Detection%20and%20Recognition)  |   This project is to detect and identify the license plates of a vehicles.    |\n|     132.    |  [Loan Eligibility Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FLoan%20Eligibility%20Prediction)  |   The main goal of the project is to predict the eligibility of a customer for getting a loan from the company   |\n|     133.    |  [MBA Specialization Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMBA%20Specialization%20Classification)  |   The goal of this project is to find out the MBA students who will be having good scores in their MBA career based on past activities using Classification algorithms.    |\n|     134.    |  [Malaria Disease Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMalaria%20Disease%20Detection)  |   The aim of this project is to recognize whether the image of the human cell is infected with Malaria disease or not.    |\n|     135.   |  [Male & Female Eyes Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMale%20%26%20Female%20Eyes%20Classification)  | The goal of this project is to create a classification model which will classify the genders based on the eyes images as per given in the dataset. For this we are going to use different architectures of Convolution Neural Network.  |\n|     136.   |  [Mall Customer Segmentation](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMall%20Customer%20Segmentation)  | This model will segment the customers based on the parameters.  |\n|     137.   |  [Marathon Time Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMarathon%20Time%20Prediction)  | The goal of this project is to predict the time for the marathon using the given details in dataset.  |\n|     138.    |  [Marble Surface Anomaly Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMarble%20Surface%20Anomaly%20Detection)  |  The goal of this project is to make a detection model which will detect which type of marble is present. |\n|     139.    |  [Medical Cost Analysis for Smokers and Non-smokers](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMedical%20Cost%20Analysis%20for%20Smokers%20and%20Non-smokers)  |   This project consists of the fact that the average medicine charges can be changed for the smokers and non-smokers.    |\n|     140.    |  [MemPool Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMemPool%20Prediction)  |   To predict about the memepool in detail.    |\n|     141.    |  [Mobile Price Range Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMobile%20Price%20Range%20Classification)  |   In this project we do not have to predict actual price but a price range indicating how high the price is.    |\n|     142.    |  [Movie Oscar Win Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMovie%20Oscar%20Win%20Prediction)  |   The goal of this project is to make a prediction model, which will predict the chances of winning the Oscar award.    |\n|     143.    |  [Movie Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMovie%20Recommendation%20System)  |   This project will help one to recommend the movies , will provide the movies names.    |\n|     144.    |  [Mushroom Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMushroom%20Classification)  |   This project is all about deploying classification algorithms and comparing the models.    |\n|     145.    |  [Music Genre Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMusic%20Genre%20Classification)  |   The goal of this project is to create a model which will classify all the 10 genres based on the song selected.The song can be selected from the dataset or any other source.The song format must be in .wav format and for 30 sec in duration.   |\n|     146.    |  [NASA Asteroids Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNASA%20Asteroids%20Classification)  |   This project helps in classifying the NASA Asteroids and checks for features responsible for claiming whether the asteroid is hazardous.   |\n|     147.    |  [NBA-Analysis and Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNBA-Analysis%20and%20Prediction)  |   To predict number of points that were scored in the season 2013-2014 by the NBA players.   |\n|     148.    |  [Natural Images Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNatural%20Images%20Classification)  |   The goal of this project is to build the Classification Model for natural images using the Neural Networks and Deep Learning.    |\n|     149.    |  [Netflix EDA and Recommedation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNetflix%20EDA%20and%20Recommedation%20System)  |   The project aims to gain insights from detailed EDA and recommend which movie to watch next.   |\n|     150.    |  [Object Detection using OpenCV](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FOlympic%20Medal%20Prediction)  |   To understand Computer Vision Libraries like OpenCV and detect various objects using Deep Learning Models with pretrained weights.  |\n|     151.    |  [Olympic Medal Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMushroom%20Classification)  |  The goal of this project is to make a prediction model which will predict whether an athlete will win medal or not.   |\n|     152.    |  [Organ Donors Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FOrgan%20Donors%20Prediction)  |  The goal of this project is to predict donors using the information contained in this dataset.    |\n|     153.   |  [Paris Housing Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FParis%20Housing%20Classification)  |  The goal is to predict Paris Housing. |\n|     154.    |  [Parkinson's Disease Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FParkinson's%20Disease%20Prediction)  |    This project helps in finding the reasons for parkinson's disease and who are predicted to have this disease.   |\n|     155.    |  [Password Strength Classifier](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPassword%20Strength%20Classifier)  |  The goal of this project is to classify and predict the strength of the password given in the dataset. |\n|     156.   |  [Persian License Plate Characters Identification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPersian%20License%20Plate%20Characters%20Identification)  | The goal of this project is to make a detection model which will detect different characters from the Persian Number plate.  |\n|     157.    |  [Phishing Website Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPhishing%20Website%20Detection)  |   The goal of this project is to make a detection model which will detect the phishing websites depending on various factors, using machine learning algorithms.    |\n|     158.    |  [Plant Disease Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPlant%20Disease%20Prediction)  |  Goal of this project is to identify the condition of a plant by looking at the image provided. |\n|     159.    |  [Plant Seedlings Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPlant%20Seedlings%20Classification)  |   The goal of this project is to build the plant seedlings classification model. The architectures used here are, ResNet, AlexNet, Vgg, Inception, MobileNet, SqueezeNet, DenseNet to deploy the classification model.    |\n|     160.    |  [Predict Future Sales](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPredict%20Future%20Sales)  |  To predict whether the future sales.  |\n|     161.    |  [Private Companies Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPrivate%20Companies%20Prediction)  |  To predict the number of the private limited companies.   |\n|     162.    |  [Productivity Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FProductivity%20Prediction)  |   It is highly desirable among the decision makers in the garments industry to track, analyse and predict the productivity performance of the working teams in their factories.    |\n|     163.    |  [Railway Track Fault Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRailway%20Track%20Fault%20Detection)  |  To predict railway track faults.  |\n|     164.    |  [Rain Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRain%20Prediction)  |   RainTomorrow is the target variable to predict on the basis of some given Features.    |\n|     165.    |  [Reddit Tweets Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FReddit%20Tweets%20Prediction)  |  To predict about the different reddit tweets in detail.  |\n|     166.    |  [Restaurant Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRestaurant%20Recommendation%20System)  |   The goal of this project is to make a Recommendation System which will recommend the users the best restaurant that they are looking for.    |\n|     167.    |  [Resume Categorizing](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FResume%20Categorizing)  |   The goal of this project is to build the model for the Resume Categorizing using eight Machine Learning Models    |\n|     168.    |  [Road Lane Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRoad%20Lane%20Detection)  |   This project is the model for the road lane detection, which will detect the road lane from an image which will be user given.    |\n|     169.    |  [Salary Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSalary%20Prediction)  |   The goal of this project is to predict the salary based on some parameters.    |\n|     170.    |  [Salary Range Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSalary%20Range%20Classification)  |   The goal of this project is to classify salary range from features as company, job, degree etc.    |\n|     171.    |  [Sarcasm Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSarcasm%20Detection)  |   The Goal of this project is to detect Sarcasm from news headlines data set using classification algorithms and compare the algorithms to find out which one is better.    |\n|     172.    |  [Shoulder X-Ray Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FShoulder%20X-ray%20Classification)  |  The goal of this project is to create a classification model which will classify different images of shoulder x-ray and predict or, detect the type of the image! |\n|     173.    |  [Sign Language Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSign%20Language%20Prediction)  |   The sign language prediction project helps in identifying the sign language from images provided.   |\n|     174.    |  [Snapchat Filters](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSnapchat%20Filters)  |  We will build our own Snapchat Filters with the help of some libraries using Python programming language.   |\n|     175.    |  [Snapchat Witch Filter](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSnapchat%20Witch%20Filter)  | | \n|     176.    |  [Social Distancing Detector](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSocial%20Distancing%20Detector)  | | \n|     177.   |  [Social Network Influencer Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSocial%20Network%20Influencer%20Prediction)  |  The goal of the challenge is to train a machine learning model which, for a pair of individuals, predicts the human judgement on who is more influential with high accuracy.  |\n|     178.   |  [Soil Moisture Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSoil%20Moisture%20Prediction)  |  The aim of this project is to predict the moisture present in soil. |\n|     179.    |  [Solar Eclipse Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSolar%20Eclipse%20Classification)  |   The aim of this project is to classify among the main types of Solar eclipses, which are : P = Partial Eclipse, A = Annular Eclipse, T = Total Eclipse, H = Hybrid or Annular\u002FTotal Eclipse.    |\n|     180.    |  [Solar Radiation Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSolar%20Radiation%20Prediction)  |  The goal is to predict radiation of Sun.   |\n|     181.    |  [Song Genre Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSong%20Genre%20Classification)  |   The goal of this project is to build the song genre classification model using the Spotify dataset.    |\n|     182.    |  [Spam Email Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSpam%20Email%20Detection)  |   This project predicts whether the received message is spam or ham.    |\n|     183.    |  [Speech Emotion Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSpeech%20Emotion%20Recognition)  |   The goal is to predict the emotion of human while talking.    |\n|     184.  |  [Stack Overflow Questions Quality Rating Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStack%20Overflow%20Questions%20Quality%20Rating%20Prediction)  |  The gaol of this project is to make a prediction model which will predict the quality of the questions from the Stack Overflow website depending on the various factors. |\n|     185.   |  [Star Radiation Analysis and Predcition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStar%20Radiation%20Analysis%20and%20Prediction)  | The aim of the project is to analysis and predict the star radiations and perform a detailed visualizations out of the same.  |\n|     186.    |  [Stars, Galaxies and Quasars Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStars%2C%20Galaxies%20and%20Quasars%20Classification)  |   The goal of this project is to make a perfect classification model according to the data collected on stars, galaxies and quasars.    |\n|     187.    |  [Startup Profit Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStartup%20Profit%20Prediction)  |   The goal is to predict Profit of the Startups. The dataset contains data about 50 startups. It has 5 columns: “R&D Spend”, “Administration”, “Marketing Spend”, “State”, “Profit”.    |\n|     188.    |  [Stock Price Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStock%20Price%20Prediction)  |   The goal is to predict the price of stocks in future and make calls according to the results algorithms provide.    |\n|     189.    |  [Stocks and Crypto Research Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStocks%20and%20Crypto%20Research%20Analysis)  | | \n|     190.    |  [Stress Level Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStress%20Level%20Prediction)  |   The goal of this project is to predict stress level from features taken from the survey responses.    |\n|     191.    |  [Stroke Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStroke%20Prediction)  |   The goal of this project is to predict the rate of stroke of a person.    |\n|     192.    |  [Student Performance Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FStudent%20Performance%20Prediction)  |   The goal of this project is to predict final grade of the student from features as study time, failures, free time, absenses, health status, going out,first period grade, second period grade etc.    |\n|     193.    |  [Supermarket Sales Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSupermarket%20Sales%20Prediction)  | To predict supermarket sales. |\n|     194.    |  [Terrorist Attack Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTerrorist%20Attack%20Prediction)  |   Analysis of given dataset and prediction of Terrorist attack using regression modal.   |\n|     195.     |  [Test Score Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTest%20Score%20Prediction)  |   The aim of project is to build machine learning algorithms to predict the scores of the students.|\n|     196.    |  [Tetris Object Counter](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTetris%20Object%20Counter)  |   The goal of the project is to identify all the tetris objects (tetrominoes) in the given tetris input image and return the count. It is seen that during gameplay the tetris blocks are broken and this program should be able to identify such blocks too.    |\n|     197.    |  [Text Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FText%20Classification)  | | \n|     198.     |  [Text Summarization](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FText%20Summarization)  | The goal of this project is to create a model which will summarize the articles given by the users.  |\n|     199.    |  [Titanic Survival Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTitanic%20Survival%20Prediction)  |   Use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.    |\n|     197.    |  [Tokyo Olympics Visualization Data Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTokyo%20Olympics%20Visualisation%20Data%20Analysis)  | | \n|     198.    |  [Traffic Sign Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTraffic%20Sign%20Classification)  |   The goal of this project is to make human readable traffic sign classification.    |\n|     199.    |  [Twitch Streamer Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTwitch%20Streamer%20Analysis)  |  The goal is to perform analysis and predict Followers gained per stream. |\n|     200.    |  [Twitter Sentiment Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FTwitter%20Sentiment%20Analysis)  |   This project helps in analyzing the sentiment using the text data posted on twitter to check it by classifying the statements as positive, negative or neutral.   |\n|     201.    |  [U.S. Weather History Visualizations](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FU.S.%20Weather%20History%20Visualizations)  |   The goal is to analyse the 12 months data of US weather history and find the conclusion and display them via graphs.    |\n|     202.    |  [USA House Pricing Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FUSA%20House%20Pricing%20Prediction)  |   The goal of this project is to make a prediction model, which will predict the price of the houses at USA, depending on the given features.    |\n|     203.    |  [Uber Fare Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FUber%20Fare%20predictions)  |   This project helps in predicting the fare to be charged for the Uber travelling person.   |\n|     204.     |  [Vehicle Image Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FVehicle%20Image%20Classification)  |  The aim of this project is to classify a vehicle image which type of vehicle it is. |\n|     205.    |  [Vehicle and Pedestrian Tracking using OpenCV](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FVehicles%20and%20Pedestrian%20Tracking%20Using%20OpenCV)  | | \n|     206.    |  [Voice Gender Identification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FVoice%20Gender%20Identification)  | | \n|     207.    |  [Walmart Sales Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWalmart%20Sales%20Prediction)  |  The main purpose of this project is to predict the future sales at walmart. |\n|     208.     |  [Waste Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWaste%20Classification)  | The aim of this project is to recognize whether the image of the waste product is a Organic Waste or a Recyclable Waste   |\n|     209.    |  [Water Quality Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWater%20Quality%20Prediction)  |   This project indicates if water is safe for human consumption where 1 means Potable and 0 means Not potable.    |\n|     210.    |  [Web Page Phishing Detection](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWeb%20Page%20Phishing%20Detection)  |  Many people get scammed by this Web page phishing technique. Detecting them can save people from getting scammed. Hackers usually blackmail the people to get their personal information back. Identifying this techniques can save millions.   |\n|     211.    |  [Wine Quality Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWine%20Quality%20Prediction)  |   This model predicts the quality of wine based on some features like pH, fixed acidity, citric acid etc. using SVM and Random forest algorithm.    |\n|     212.    |  [World Happiness Report Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWorld%20Happiness%20Report%20Analysis)  |  The aim of the project is to predict happiness scores and rankings and perform a detailed analysis and visualization of the training dataset and create a model. |\n|     213.    |  [World Population By Year Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWorld%20Population%20by%20Year%20analysis)  | | \n|     214.   |  [World Poverty Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FWorld%20Poverty%20Analysis)  |  The goal of this project is to analyze the world poverty using dataset. |\n|     215.   |  [Yotube Video Recommendation System](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FYoutube%20Video%20Recommendation%20System)  |  The model will recommend the video titles on proving the search query by user. |\n|     216.    |  [Zomato Banglore Resturants Recommendation](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FZomato%20Bangalore%20Restaurants%20Recommendation%20Analysis)  |  The aim of this project is analyse a dataset and recomend the user for top restaurants in bangalore. |\n|     217.    |  [Zoo Animal Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FZoo-Animal-Classification)  |   The goal of this project is to predict the zoo animal based on some classifications using machine learning model.    |\n|     218.    |  [Body Parts Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FBody%20Parts%20Classification)  | | \n|     219.    |  [Discussion Forum Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FDiscussion%20Forum%20Prediction)  | | \n|     220.    |  [Mortality Rate Analysis & Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FMortality%20Rate%20Analysis%20%26%20Prediction)  | | \n|     221.    |  [Named Entity Recognition](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNamed%20Entity%20Recognition)  | | \n|     222.    |  [Ramen Noodles Rating Analysis](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FRamen%20Noodles%20Rating%20Analysis)  | | \n|     223.    |  [News Articles Classification](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FNews%20Articles%20Classification)  | | \n|     224.    |  [Prediction of Subject based on Question (NLP)](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FPrediction%20of%20Subject%20based%20on%20Question%20(NLP))  | | \n|     225.    |  [Salt Deposits Identification & Prediction](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Ftree\u002Fmain\u002FSalt%20Deposits%20Identification%20%26%20Prediction)  | |\n\n## 📝 项目结构\n\n您的项目应包含以下流程，以保持与其他项目的相似性。在创建拉取请求之前，请务必注意以下事项。\n\n**数据集** - 此文件夹将包含一个 `.csv` 文件。\n\n**模型** - 此文件夹将包含您的项目文件（即 `.ipynb` 文件），无论是分析还是预测。除了项目文件外，还应包含使用此 [模板](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002Freadme_template.md) 的 **'README.md'** 文件，以及包含项目中所需所有附加组件和库的 **'requirements.txt'** 文件。（如有 `.pkl` 文件）\n\n**图片\u002F截图** - 如果适用，此文件夹将包含图片。\n\n\u003C!--\n**前端** - 此文件夹将包含与网页编码和设计相关的文件。\n\n**后端** - 此文件夹将包含使用 Flask 和 Django 创建模型后端的相关文件。\n\n**UI\u002FUX** - 此文件夹将包含与模型的仪表盘、表单和网页相关的文件。\n\n在各自的文件夹中为“前端”、“后端”和“UI\u002FUX”包含 README.md 文件，并通过截图展示逐步操作过程，简要说明其工作原理。\n-->\n\n## 💻 工作流程：\n\n- 分支仓库\n\n- 使用终端或 GitBash 克隆您分支后的仓库。\n\n- 对克隆的仓库进行更改\n\n- 添加、提交并推送\n\n- 然后在 GitHub 中，在您克隆的仓库中找到创建拉取请求的选项\n\n> print(\"开始为 ML-ProjectKart 做贡献\")\n\n\n\n## 🛠 需遵循的模板\n\n- [功能请求](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FISSUE_TEMPLATE\u002Ffeature_request.md)\n- [错误报告](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FISSUE_TEMPLATE\u002Fbug_report.md)\n- [拉取请求](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FPULL_REQUEST_TEMPLATE.md)\n- [README](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002Freadme_template.md)\n\n**注意**：在创建新问题或拉取请求时，应遵循这些模板。\n\n\n## ⚙️ 注意事项\n\n* 请确保不要从外部来源复制代码，因为此类工作将不被认可。严禁抄袭。\n* 您只能处理已分配给您的问题。\n* 如果您想贡献算法，建议在创建拉取请求之前先创建一个新的问题，并将您的拉取请求链接到该问题。\n* 如果您修改或添加了代码，请确保在提交前代码能够成功编译。\n* 请严格使用蛇形命名法（下划线分隔）来命名文件，并将其推送到正确的文件夹中。\n* 请勿更新 **[README.md](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002FREADME.md)**。\n\n\n ## ❄️ 开源项目\n\n\u003Ctable>\n\u003Ctr>\n\u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fcodepeak.technology\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_1fac59b4b78f.png\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>CodePeak 2025\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n\u003C\u002Ftd>\n\u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fcodesapiens.in\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_5aaf804d1083.png\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Code Sapiens Summer Of Code 2024\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n\u003C\u002Ftd>\n\u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fdwoc.io\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_866c1ef1a75c.jpg\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Delta Winter Of Code 2023\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n\u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fkwoc.kossiitkgp.org\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_a8c4fc849e0a.png\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Kharaghpur Winter Of Code 2023\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n \u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fhacktoberfest.digitalocean.com\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_cb4ca80fc7e5.jpg\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>Hacktoberfest 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n \u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fcontribute.devincept.com\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_5d85638ba1c7.jpg\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>DevIncept Codes 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n \u003Ctd align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fletsgrowmore.in\u002Fsoc\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_04d0f7b7a7c3.jpg\" width=100px height=100px \u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>LetsGrowMore SoC 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\n \u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n\u003C!--\n## 📊 排行榜 \n\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FDCP_SCORECARD.md\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_a4c0c3e45591.png\" width=\"100px;\" alt=\"\"\u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>DevIncept Codes 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002F.github\u002FLGMSOC_SCORECARD.md\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_a4c0c3e45591.png\" width=\"100px;\" alt=\"\"\u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>LetsGrowMore SOC 2021\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\u003C\u002Ftd>\n  \u003C\u002Ftr>  \n\u003C\u002Ftable>\n-->\n\n## ✨ 名人堂   \n\n感谢这些杰出的人士。欢迎任何形式的贡献！🚀 \n\n\u003C!-- ALL-CONTRIBUTORS-LIST:START - 不要移除或修改这一部分 -->\n\u003C!-- prettier-ignore-start -->\n\u003C!-- markdownlint-disable -->\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fgraphs\u002Fcontributors\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_a6bef93cb0dd.png\" \u002F>\n\u003C\u002Fa>\n\n\u003C!-- markdownlint-enable -->\n\u003C!-- prettier-ignore-end -->\n\u003C!-- ALL-CONTRIBUTORS-LIST:END -->\n\n\n## 📜 行为准则\n\n您可以在此处找到我们的行为准则 [这里](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002FCODE_OF_CONDUCT.md)。\n\n\n## 📝 许可证\n\n本项目遵循 [Mozilla 公共许可证 2.0](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fblob\u002Fmain\u002FLICENSE)。\n\n\u003C!-- \n## ✔ 项目维护者\n\n\u003Ctable>\n  \u003Ctr>\n\u003Ctd align=\"center\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fabhisheks008\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_6a41b52d0a96.png\" width=\"80px;\" alt=\"\"\u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>阿比谢克·夏尔马\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n-->\n\n## 😎 项目管理员\n\n\u003Ctable>\n  \u003Ctr>\n\u003Ctd align=\"center\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_c811a0512852.jpg\" width=\"100px;\" alt=\"\"\u002F>\u003Cbr \u002F>\u003Csub>\u003Cb>普拉蒂玛·卡达里\u003C\u002Fb>\u003C\u002Fsub>\u003C\u002Fa>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n\n![访问计数](https:\u002F\u002Fprofile-counter.glitch.me\u002F{prathimacode-hub}\u002Fcount.svg)\n\n## 🌟 星光熠熠：时间线 🌟\n\n[![星光熠熠：时间线](https:\u002F\u002Fstarchart.cc\u002Fprathimacode-hub\u002FML-ProjectKart.svg)](https:\u002F\u002Fstarchart.cc\u002Fprathimacode-hub\u002FML-ProjectKart)\n\n\n## ⭐ 给这个项目点个赞吧\n\n[![GitHub 粉丝数](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Ffollowers\u002Fprathimacode-hub.svg?label=关注%20@prathimacode-hub&style=social)](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002F)  [![Twitter 关注](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fprathima_kadari?label=关注%20@prathima_kadari&style=social)](https:\u002F\u002Ftwitter.com\u002Fprathima_kadari)\n\n如果你喜欢参与这个项目，请给它点个⭐，并分享这个仓库。\n\n🎉 🎊 😃 祝你贡献愉快 😃 🎊 🎉\n\n\u003C!-- \u003Csup>\u003Ckbd>***[点击这里](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002Fprathimacode-hub\u002Fblob\u002Fmain\u002FProjects\u002FOpenSource-Projects.md)***\u003C\u002Fkbd> *查看我的开源项目，以及\u003C\u002Fsup>*  \u003Csup>\u003Ckbd>***[进入](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002Fprathimacode-hub\u002Fblob\u002Fmain\u002FGitHub%20Projects\u002FLearning-Projects.md)***\u003C\u002Fkbd> *学习项目。\u003C\u002Fsup>* \u003Cbr>\n\u003C\u002Ftd> -->\n\n\u003C!-- \n\u003Csup>\u003Ckbd>***[点击这里](https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002Fprathimacode-hub\u002Fblob\u002Fmain\u002FGitHub%20Projects\u002FOpenSource-Projects.md)***\u003C\u002Fkbd> *查看我的开源项目。\u003C\u002Fsup>* \u003Cbr> -->\n\n\n## 📬 联系方式\n\n如果你想联系我，可以通过以下方式找到我。\n\n\u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fprathima-kadari\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_3f60edfd3589.png\" width=\"25\">\u003C\u002Fimg>\u003C\u002Fa>   \u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fprathima_kadari\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_readme_d79443668a3c.png\" width=\"25\">\u003C\u002Fimg>\u003C\u002Fa>\n\n© 2025 普拉蒂玛·卡达里\n\n\n[![forthebadge](https:\u002F\u002Fforthebadge.com\u002Fimages\u002Fbadges\u002Fbuilt-with-love.svg)](https:\u002F\u002Fforthebadge.com) [![forthebadge](https:\u002F\u002Fforthebadge.com\u002Fimages\u002Fbadges\u002Fbuilt-by-developers.svg)](https:\u002F\u002Fforthebadge.com) [![forthebadge](https:\u002F\u002Fforthebadge.com\u002Fimages\u002Fbadges\u002Fbuilt-with-swag.svg)](https:\u002F\u002Fforthebadge.com)","# ML-ProjectKart 快速上手指南\n\nML-ProjectKart 是一个汇集了机器学习、深度学习、计算机视觉、自然语言处理及生成式 AI 等领域的开源项目集合。本指南将帮助你快速开始探索和使用这些项目。\n\n## 环境准备\n\n在开始之前，请确保你的开发环境满足以下基本要求：\n\n*   **操作系统**：Windows, macOS 或 Linux\n*   **Python 版本**：建议安装 Python 3.8 或更高版本\n*   **包管理工具**：pip 或 conda\n*   **代码编辑器**：VS Code, PyCharm 或 Jupyter Notebook\n*   **Git**：用于克隆仓库\n\n**前置依赖库**（大多数项目通用）：\n```bash\npip install numpy pandas matplotlib scikit-learn tensorflow torch opencv-python\n```\n> **提示**：国内用户建议使用清华源或阿里源加速安装：\n> `pip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple \u003Cpackage_name>`\n\n## 安装步骤\n\n由于 ML-ProjectKart 是一个项目合集，每个子项目可能有独立的依赖配置。以下是获取仓库并运行特定项目的通用步骤：\n\n1.  **克隆仓库**\n    打开终端，执行以下命令将项目下载到本地：\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart.git\n    cd ML-ProjectKart\n    ```\n\n2.  **选择并进入具体项目目录**\n    浏览 `ML-ProjectKart` 文件夹，找到你感兴趣的项目（例如 `Advertisement Click Prediction`），进入该目录：\n    ```bash\n    cd \"Advertisement Click Prediction\"\n    ```\n\n3.  **安装项目专属依赖**\n    检查该项目目录下是否有 `requirements.txt` 文件。如果有，请运行：\n    ```bash\n    pip install -r requirements.txt\n    ```\n    *如果没有 `requirements.txt`，请参考该项目文件夹内的 README 或代码头部注释安装特定库。*\n\n## 基本使用\n\n每个项目都是独立的，通常包含数据预处理、模型训练和预测脚本。以下以通用的运行流程为例：\n\n1.  **查看项目结构**\n    进入项目目录后，列出文件以了解入口脚本：\n    ```bash\n    ls\n    # 或 Windows 下\n    dir\n    ```\n    通常你会看到类似 `main.py`, `train.py`, `app.py` 或 `.ipynb` (Jupyter Notebook) 文件。\n\n2.  **运行示例（以 Python 脚本为例）**\n    如果项目包含 `main.py`，直接运行：\n    ```bash\n    python main.py\n    ```\n\n3.  **运行示例（以 Jupyter Notebook 为例）**\n    如果项目提供的是 Notebook 文件（如 `analysis.ipynb`），启动 Jupyter Lab 并在浏览器中打开：\n    ```bash\n    jupyter lab\n    ```\n    然后在界面中找到对应的 `.ipynb` 文件，按顺序执行单元格即可看到结果。\n\n4.  **查看结果**\n    运行结束后，模型通常会输出准确率指标，或在当前目录生成预测结果文件（如 `predictions.csv`）及可视化图表。\n\n> **注意**：部分项目可能需要手动下载数据集。请仔细阅读具体项目文件夹内的 `README.md` 说明，按照指引放置数据文件到指定目录（通常为 `data\u002F` 或 `dataset\u002F` 文件夹）。","计算机专业大三学生李明正着手准备毕业设计，他计划开发一个基于深度学习的医疗影像辅助诊断系统，但面对庞大的技术栈感到无从下手。\n\n### 没有 ML-ProjectKart 时\n- **选题迷茫**：在 GitHub 上盲目搜索关键词，结果要么过于简单（如鸢尾花分类），要么代码缺失或文档晦涩，难以找到难度适中的参考项目。\n- **技术栈割裂**：需要分别寻找数据处理、模型构建和后端部署的示例，花费数周时间拼凑碎片化代码，导致项目架构混乱。\n- **复现困难**：找到的开源项目往往缺乏详细的运行环境说明，配置依赖库时频繁报错，大量时间浪费在调试环境而非算法优化上。\n- **协作无门**：想通过参与开源提升简历含金量，却找不到对初学者友好的入门级任务，只能独自闭门造车。\n\n### 使用 ML-ProjectKart 后\n- **精准选题**：直接浏览\"Computer Vision\"和\"Deep Learning\"分类下经过筛选的 234+ 个项目，迅速锁定几个与医疗影像相关的成熟案例作为基准。\n- **全链路参考**：在一个仓库中即可找到涵盖数据预处理、模型训练到 Flask 后端部署的完整项目结构，快速搭建起系统的骨架。\n- **高效上手**：依托项目中清晰的 README 和贡献指南，顺利复现核心算法，将原本用于环境调试的时间全部投入到模型精度优化中。\n- **社区共创**：发现仓库中专门标记为\"Welcome\"的初级任务，成功提交了一个关于数据增强的改进方案，获得了社区反馈并丰富了个人作品集。\n\nML-ProjectKart 将零散的机器学习资源整合为结构化的学习路径，帮助开发者从“盲目摸索”转向“高效实战”，显著缩短了从理论到落地的周期。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fprathimacode-hub_ML-ProjectKart_62583840.png","prathimacode-hub","Prathima Kadari","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fprathimacode-hub_51925141.jpg","Chief Technology Officer👩‍💻 | Data Scientist🚀 | Keynote Speaker🎤 | Top Rated Data Science Career Coach🤝 | Community Head🏷","OptimumAI","Telangana, India","kadariprathima4@gmail.com","prathima_kadari",null,"https:\u002F\u002Fgithub.com\u002Fprathimacode-hub",[83,87,91,95,99,102],{"name":84,"color":85,"percentage":86},"Jupyter Notebook","#DA5B0B",99.7,{"name":88,"color":89,"percentage":90},"HTML","#e34c26",0.2,{"name":92,"color":93,"percentage":94},"Python","#3572A5",0.1,{"name":96,"color":97,"percentage":98},"CSS","#663399",0,{"name":100,"color":101,"percentage":98},"JavaScript","#f1e05a",{"name":103,"color":104,"percentage":98},"Procfile","#3B2F63",672,259,"2026-04-12T18:09:45","MPL-2.0","","未说明",{"notes":112,"python":110,"dependencies":113},"该仓库是一个机器学习项目合集（包含广告预测、图像分类、NLP 分析等），而非单一的可执行工具。README 中未提供统一的运行环境配置、依赖列表或安装指南。每个子项目（如'Alpaca Identification'或'Bitcoin Price Prediction'）可能拥有独立的技术栈和环境需求，用户需进入具体项目的子目录查看各自的文档。",[],[14,35,16,15],[116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132],"machine-learning-algorithms","machine-learning","algorithms-and-data-structures","algorithms","algorithms-datastructures","python3","scripts-collection","projects","beginner-friendly","contributions-welcome","computer-vision","natural-language-processing","deep-learning","open-source","hacktoberfest","python","hacktoberfest2023","2026-03-27T02:49:30.150509","2026-04-16T16:03:28.323516",[136,141,146,151,156,161,166],{"id":137,"question_zh":138,"answer_zh":139,"source_url":140},36014,"领取 Issue 后多久没有进展会被重新分配？","通常最大 PR 提交时间为 5 天。如果领取 Issue 后长时间（如超过几天）没有任何更新或回应，维护者可能会在警告后将任务重新分配给其他人。建议保持活跃并及时提交进度。","https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues\u002F61",{"id":142,"question_zh":143,"answer_zh":144,"source_url":145},36015,"如何在 Hacktoberfest 期间参与贡献并被正确识别？","在申请分配 Issue 或在消息中回复时，务必明确提及你希望作为 \"Hacktoberfest\" 参与者进行贡献。例如：\"I would like to work for this issue under hacktoberfest.\" 这样维护者在分配时会特别注意标记。","https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues\u002F424",{"id":147,"question_zh":148,"answer_zh":149,"source_url":150},36011,"提交项目时，我需要为同一个问题提交多次完整的代码吗？","不需要。针对该 Issue 提交的 PR 应符合要求并构成一个完整的项目即可。后续其他参与者可以通过不同的 Issue 提交增强功能或改进版本，无需重复上传同一份完整项目。","https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues\u002F36",{"id":152,"question_zh":153,"answer_zh":154,"source_url":155},36012,"提交 Pull Request (PR) 时有特定的格式要求吗？","是的，必须遵循项目 README 文件中提供的 PR 模板。不要只给出项目概述，请严格按照模板填写相关信息。","https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues\u002F40",{"id":157,"question_zh":158,"answer_zh":159,"source_url":160},36013,"如果无法按时完成被分配的任务，应该如何处理？","只要及时告知维护者，通常是可以接受的。你可以说明情况并主动取消分配（unassign），以便将任务开放给其他贡献者。维护者表示理解紧急事务，关键在于保持沟通。","https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues\u002F20",{"id":162,"question_zh":163,"answer_zh":164,"source_url":165},36016,"对于热门主题（如新冠数据分析），多人如何同时参与而不冲突？","维护者已创建专门的仓库或目录结构。你可以在该目录下以你的“全名”创建一个文件夹，并在其中按照标准项目结构存放你的代码和分析结果。这样允许多人针对同一主题从不同角度进行分析，且通过文件夹名称区分各自的工作。","https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues\u002F95",{"id":167,"question_zh":168,"answer_zh":169,"source_url":170},36017,"如果在 Discord 等外部渠道有查询，应该去哪里寻找回复？","如果在 GitHub Issue 上没有收到通知或回复，请检查项目的 Discord 服务器。维护者有时会建议在 Discord 上进行更快速的沟通和确认。","https:\u002F\u002Fgithub.com\u002Fprathimacode-hub\u002FML-ProjectKart\u002Fissues\u002F618",[]]