https://github.com/najmaelboutaheri/codsoft

This repository contains all the tasks to be carried out during the @CodSoft internship. For more information visit https://www.codsoft.in/
https://github.com/najmaelboutaheri/codsoft

Last synced: 17 days ago
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This repository contains all the tasks to be carried out during the @CodSoft internship. For more information visit https://www.codsoft.in/

• Host: GitHub
• URL: https://github.com/najmaelboutaheri/codsoft
• Owner: najmaelboutaheri
• Created: 2024-02-10T08:26:22.000Z (12 months ago)
• Default Branch: master
• Last Pushed: 2024-03-07T16:33:24.000Z (11 months ago)
• Last Synced: 2024-03-07T17:49:05.308Z (11 months ago)
• Language: Jupyter Notebook
• Size: 2.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Internship CodSoft Project

Welcome to the Internship CodSoft project! This repository contains solutions to four tasks in the field of data science and machine learning. Each task addresses a specific problem and utilizes different datasets and techniques. Below, you'll find descriptions of each task along with relevant details.

## Tasks Overview:

### 1. Titanic Survival Prediction

The task involves building a model to predict whether a passenger on the Titanic survived or not. The Titanic dataset provides information about individual passengers, including age, gender, ticket class, fare, cabin, and survival status.

**Check Task1.**

### 2. Movie Rating Prediction with Python

In this task, the goal is to predict the rating of a movie based on features like genre, director, and actors. Regression techniques are used to tackle this problem. The dataset consists of historical movie data and associated ratings.

**Check Task2.**

### 3. Iris Flower Classification

The task focuses on classifying Iris flowers into different species based on their sepal and petal measurements. The Iris dataset contains measurements for three species: setosa, versicolor, and virginica.

**Check Task3.**

### 4. Sales Prediction using Python

Sales prediction involves forecasting the amount of a product that customers will purchase, considering factors such as advertising expenditure and target audience segmentation. Machine learning techniques in Python are employed to analyze and interpret sales data.

**Check Task4.**

## Getting Started

To explore each task, navigate to the respective directories in this repository. You'll find detailed instructions, code implementations, and dataset links within each directory.

## Requirements

- Python 3

- Jupyter Notebook

- Scikit-learn

- Pandas

- NumPy

- Matplotlib

- Seaborn

## Contributors

- Najma El boutaheri

- Email: [email protected]

Feel free to contribute to this project by providing enhancements, fixing bugs, or adding new features. If you have any questions or suggestions, please open an issue or reach out to the project maintainers.

Happy coding!