This repository contains the projects I completed during my Data Science internship at CodSoft. Each project focuses on a specific task and utilizes Python for analysis, modeling, and prediction.
In this project, I developed a predictive model using machine learning algorithms to predict the survival of passengers aboard the Titanic. The dataset includes various features such as age, gender, fare, etc. I explored the data, performed data preprocessing, and trained a model to predict survival.
In this project, I built a movie rating prediction system using Python. I utilized a dataset containing information about movies, including genres, actors, directors, and user ratings. By analyzing this data, I developed a model to predict movie ratings based on the provided features.
This project involved the classification of Iris flower species based on features such as petal length, petal width, sepal length, and sepal width. I used machine learning algorithms to train a model that can accurately classify different Iris flower types.
In this project, I developed a sales prediction model using Python. The dataset consisted of historical sales data, including information about products, regions, and time periods. I utilized various data analysis techniques to predict future sales and identify trends and patterns in the data.
The Credit Card Fraud Detection project focuses on detecting fraudulent credit card transactions. I employed machine learning techniques to analyze a dataset containing transaction information, including features like transaction amount, time, and various anonymized variables. The goal was to build a model that can effectively identify fraudulent transactions.
To get started with these projects, you can follow the steps below:
- Clone the repository:
git clone https://github.com/subdas374/CODSOFT.git - Install the necessary dependencies (see Dependencies section for details).
- Follow the instructions provided in the individual project folders to run and explore each project.
The projects have certain dependencies that need to be installed. Ensure you have the following:
- Python 3.x 🐍
- Jupyter Notebook 📓
- pandas 🐼
- numpy 🧮
- scikit-learn 🤖 🧠
- matplotlib 📈
- seaborn 🌊
- Install Python 3.x from the official Python website: https://www.python.org/downloads/
- Install Jupyter Notebook by running the following command:
pip install jupyter notebook - Install the required Python libraries by running the following command:
pip install pandas numpy scikit-learn matplotlib seaborn
Detailed usage instructions for each project can be found in their respective folders. Generally, you will find Jupyter Notebook files (.ipynb) that contain the code, analysis, and results for each project. Open these files in Jupyter Notebook to explore and execute the code.
I appreciate any contributions or suggestions for improving these projects. If you wish to contribute, please follow the standard GitHub workflow: Fork the repository, make your changes, and submit a pull request.
The projects in this repository are licensed under the MIT License.