{"id":20505121,"url":"https://github.com/dhrupad17/codeclause_stock_market_prediction","last_synced_at":"2026-04-15T20:32:06.984Z","repository":{"id":112540349,"uuid":"606752295","full_name":"dhrupad17/CodeClause_Stock_Market_Prediction","owner":"dhrupad17","description":"This is My Task 1 as a Data science Intern at CodeClause","archived":false,"fork":false,"pushed_at":"2023-02-26T13:32:20.000Z","size":372,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-05T20:57:25.959Z","etag":null,"topics":["codeclause","codeclause-task","datascience","jupyter-notebook","python3"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dhrupad17.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-02-26T13:15:57.000Z","updated_at":"2023-05-27T14:12:10.000Z","dependencies_parsed_at":null,"dependency_job_id":"f46c9471-bb47-49fa-bc68-f30328b94492","html_url":"https://github.com/dhrupad17/CodeClause_Stock_Market_Prediction","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dhrupad17/CodeClause_Stock_Market_Prediction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dhrupad17%2FCodeClause_Stock_Market_Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dhrupad17%2FCodeClause_Stock_Market_Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dhrupad17%2FCodeClause_Stock_Market_Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dhrupad17%2FCodeClause_Stock_Market_Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dhrupad17","download_url":"https://codeload.github.com/dhrupad17/CodeClause_Stock_Market_Prediction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dhrupad17%2FCodeClause_Stock_Market_Prediction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31859278,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-15T15:24:51.572Z","status":"ssl_error","status_checked_at":"2026-04-15T15:24:39.138Z","response_time":63,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["codeclause","codeclause-task","datascience","jupyter-notebook","python3"],"created_at":"2024-11-15T19:43:57.674Z","updated_at":"2026-04-15T20:32:06.969Z","avatar_url":"https://github.com/dhrupad17.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📈 Stock Market Prediction 📊\n\n## Description\n\nIn this project I have attempted to implement machine learning approach to predict stock prices(close). Machine learning is effectively implemented in forecasting stock\nprices. The objective is to predict the stock prices in order to make more informed and accurate investment decisions. We propose a stock price prediction system that integrates mathematical functions, machine learning, and other external factors for the purpose ofachieving better stock prediction accuracy and issuing profitable trades. \n\n``LSTMs`` are very powerful in sequence prediction problems because they’re able to store past information.This is important in my case because the previous price of a stock is crucial in predicting its future price. While predicting the actual price of a stock is an uphill climb, I have builta model that will predict whether the price will go up or down.\n\n\n### Dataset:-\n\nhttps://www.kaggle.com/datasets/akshaydattatraykhare/nsetataglobal\n\n### Overview:-\n\n![image](https://user-images.githubusercontent.com/91726340/221412777-b4d04475-179f-4b81-9346-f1ddd410d59b.png)\n![image](https://user-images.githubusercontent.com/91726340/221412821-90b5a716-9e81-4df8-b84f-c6eef2d7d398.png)\n![image](https://user-images.githubusercontent.com/91726340/221412835-04e00a51-3567-4a69-9756-4eccf6181255.png)\n\n### Thank You\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdhrupad17%2Fcodeclause_stock_market_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdhrupad17%2Fcodeclause_stock_market_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdhrupad17%2Fcodeclause_stock_market_prediction/lists"}