{"id":18541506,"url":"https://github.com/allanotieno254/codsoft","last_synced_at":"2025-05-15T04:08:55.678Z","repository":{"id":245546222,"uuid":"818574673","full_name":"AllanOtieno254/CODSOFT","owner":"AllanOtieno254","description":"This repository showcases a series of data science projects completed during an internship with CODESOFT. 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Each project utilizes Python and various machine learning techniques to solve specific problems in data analysis, classification, regression, and predictive modeling.\n# Projects\n\n## 1.Titanic Survival Prediction\nPredicts survival of passengers aboard the Titanic using machine learning classifiers. Analyzes factors such as age, gender, ticket class, and more.\nclick to get the dataset:https://www.kaggle.com/datasets/yasserh/titanic-dataset  \n\n## 2.Movie Rating Prediction\nEstimates the rating of movies based on genre, directors, and actors using regression techniques. Explores features that influence movie ratings.\nclick to get the dataset:https://www.kaggle.com/datasets/adrianmcmahon/imdb-india-movies\n\n## 3.Iris Flower Classification\nClassifies Iris flowers into species (setosa, versicolor, virginica) based on sepal and petal measurements. Demonstrates introductory classification techniques.\nclick to get the dataset:https://www.kaggle.com/datasets/arshid/iris-flower-dataset\n\n## 4.Sales Prediction using Python\nForecasts product sales considering factors like advertising expenditure and target audience segmentation. Utilizes simple linear regression for prediction.\nclick to get the dataset:https://www.kaggle.com/code/ashydv/sales-prediction-simple-linear-regression/input\n\n## 5.Credit Card Fraud Detection\nIdentifies fraudulent credit card transactions using classification algorithms. Addresses data preprocessing, class imbalance, and model evaluation.\nclick to get the dataset:https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud\n\n# Topics Covered\n## Data Science\n## Machine Learning\n## Predictive Modeling\n## Python Programming\n## Data Analysis\n## Classification\n## Regression\n## Model Evaluation\n## Feature Engineering\n\n# Technologies Used\n## Python\n## NumPy\n## pandas\n## scikit-learn\n\n# How to Use\nEach project directory contains detailed instructions on setting up and running the code locally. Clone the repository, navigate to the project of interest, and follow the README.md file in that directory for specific guidance.\n\n# License\nThis repository is licensed under the MIT License. See the LICENSE file for more information.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fallanotieno254%2Fcodsoft","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fallanotieno254%2Fcodsoft","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fallanotieno254%2Fcodsoft/lists"}