{"id":27128247,"url":"https://github.com/jiteshshelke/codsoft","last_synced_at":"2025-08-12T21:08:31.013Z","repository":{"id":215789845,"uuid":"739773005","full_name":"JiteshShelke/CODSOFT","owner":"JiteshShelke","description":"A repository showcasing three machine learning projects—Titanic Survival Prediction, Movie Rating Prediction, and Iris Flower Classification—completed during CodSoft's Data Science Internship. 🚀","archived":false,"fork":false,"pushed_at":"2025-03-30T18:02:58.000Z","size":1007,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-07T18:57:29.775Z","etag":null,"topics":["codsoft","codsoftinternship","data-analysis","data-science","linear-regression","logistic-regression","machine-learning","machine-learning-algorithms","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JiteshShelke.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2024-01-06T13:57:26.000Z","updated_at":"2025-03-31T11:03:08.000Z","dependencies_parsed_at":"2024-01-14T21:16:05.652Z","dependency_job_id":"363f823a-0b63-4b93-9487-069e49f2ce70","html_url":"https://github.com/JiteshShelke/CODSOFT","commit_stats":null,"previous_names":["jtxmaster/codsoft","jiteshshelke/codsoft"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/JiteshShelke/CODSOFT","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JiteshShelke%2FCODSOFT","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JiteshShelke%2FCODSOFT/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JiteshShelke%2FCODSOFT/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JiteshShelke%2FCODSOFT/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JiteshShelke","download_url":"https://codeload.github.com/JiteshShelke/CODSOFT/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JiteshShelke%2FCODSOFT/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270135076,"owners_count":24533203,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-12T02:00:09.011Z","response_time":80,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["codsoft","codsoftinternship","data-analysis","data-science","linear-regression","logistic-regression","machine-learning","machine-learning-algorithms","python"],"created_at":"2025-04-07T18:57:32.533Z","updated_at":"2025-08-12T21:08:30.988Z","avatar_url":"https://github.com/JiteshShelke.png","language":"Jupyter Notebook","readme":"# 🚀 CodSoft Data Science Projects 🧑‍💻\n\n## ✨ Author: Jitesh Santosh Shelke  \n**📌 Batch:** JAN BATCH A26  \n**📌 Domain:** Data Science  \n\nThis repository contains three exciting machine learning projects completed as part of CodSoft's Data Science Internship. Each project leverages real-world datasets and showcases various ML techniques. 🌟\n\n---\n\n## 🏆 **TASK-1: Titanic Survival Prediction**\n\n### 🎯 **Objective**\nPredict whether a passenger survived the Titanic disaster using machine learning models. 🛳️⚓\n\n### 📂 **Dataset** ([Download Here](https://www.kaggle.com/c/titanic/data))\n- `PassengerId`, `Survived`, `Pclass`, `Name`, `Sex`, `Age`, `SibSp`, `Parch`, `Ticket`, `Fare`, `Cabin`, `Embarked`\n\n### 🛠 **Technologies Used**\n- 📦 **Libraries:** NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn\n- 🤖 **Model:** Logistic Regression\n- 📊 **Evaluation Metrics:** Accuracy Score, Confusion Matrix, Classification Report\n\n### 📌 **Implementation**\n✅ Data Loading and Preprocessing  \n✅ Exploratory Data Analysis (EDA)  \n✅ Model Training and Evaluation  \n\n---\n\n## 🎬 **TASK-2: Movie Rating Prediction**\n\n### 🎯 **Objective**\nPredict movie ratings based on features such as genre, director, and actors. 🍿🎥\n\n### 📂 **Dataset** ([Download Here](https://www.kaggle.com/datasets/adrianmcmahon/imdb-india-movies))\n- `Name`, `Year`, `Duration`, `Genre`, `Rating`, `Votes`, `Director`, `Actor 1`, `Actor 2`, `Actor 3`\n\n### 🛠 **Technologies Used**\n- 📦 **Libraries:** Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn\n- 🤖 **Models:** Linear Regression, Ridge Regression, Random Forest Regressor, Decision Tree Regressor\n- 📊 **Evaluation Metrics:** Mean Squared Error, Mean Absolute Error, R-squared Score\n\n### 📌 **Implementation**\n✅ Data Preprocessing and Cleaning  \n✅ Feature Engineering  \n✅ Model Training and Hyperparameter Tuning  \n✅ Model Evaluation  \n\n---\n\n## 🌺 **TASK-3: Iris Flower Classification**\n\n### 🎯 **Objective**\nClassify Iris flowers into their respective species using machine learning models. 🌿🌸\n\n### 📂 **Dataset** ([Download Here](https://www.kaggle.com/uciml/iris))\n- `sepal_length`, `sepal_width`, `petal_length`, `petal_width`, `species`\n\n### 🛠 **Technologies Used**\n- 📦 **Libraries:** NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn\n- 🤖 **Models:** K-Means Clustering, Gaussian Naïve Bayes\n- 📊 **Evaluation Metrics:** Accuracy Score, Confusion Matrix, Classification Report\n\n### 📌 **Implementation**\n✅ Data Visualization and Preprocessing  \n✅ Model Training and Classification  \n✅ Model Evaluation and Performance Analysis  \n\n---\n\n## 💻 **Installation and Usage**\n1️⃣ Clone the repository:\n   ```bash\n   git clone https://github.com/yourusername/codsoft-datascience.git\n   ```\n2️⃣ Navigate to the project directory:\n   ```bash\n   cd codsoft-datascience\n   ```\n3️⃣ Install required dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n4️⃣ Run the Jupyter Notebook or Python scripts for each task.\n\n---\n\n## 🤝 **Contact**\nFor any queries or collaborations, feel free to connect with me:\n- 🏆 **GitHub:** (https://github.com/JiteshShelke/Jtxmaster)\n- 💼 **LinkedIn:** (https://www.linkedin.com/in/jitesh-shelke-702745286/)\n\n---\n\n**🚀 Made with ❤️ by Jitesh Santosh Shelke 🎯**\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjiteshshelke%2Fcodsoft","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjiteshshelke%2Fcodsoft","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjiteshshelke%2Fcodsoft/lists"}