https://github.com/faris771/investigate_a_dataset
This repository contains a Jupyter Notebook that investigates a dataset using data analysis techniques.
https://github.com/faris771/investigate_a_dataset
data-analysis
Last synced: 8 months ago
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This repository contains a Jupyter Notebook that investigates a dataset using data analysis techniques.
- Host: GitHub
- URL: https://github.com/faris771/investigate_a_dataset
- Owner: faris771
- Created: 2025-03-10T23:30:38.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2025-03-12T01:55:13.000Z (11 months ago)
- Last Synced: 2025-03-12T02:28:32.752Z (11 months ago)
- Topics: data-analysis
- Language: HTML
- Homepage:
- Size: 2.54 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Investigate a Dataset
This repository contains a Jupyter Notebook that investigates a dataset using data analysis techniques.
## Project Overview
In this project, we explore and analyze a dataset, performing various data cleaning, visualization, and statistical analysis tasks. The goal is to extract meaningful insights from the data.
## Prerequisites
To run this project, you need the following:
- Python 3.x
- Jupyter Notebook
- Required Python libraries (install using the instructions below)
## Installation
1. Clone this repository:
```bash
git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Open Jupyter Notebook:
```bash
jupyter notebook
```
4. Open `Investigate_a_Dataset.ipynb` and run the cells.
## Dataset
The dataset used here is MDB 5000 Movie Dataset which includes data about different moveies with their names, budget, rating, and other features. The dataset was not included in this repository due to its size. If you wish to use the notebook, please download the dataset separately and update the file path accordingly: [dataset](https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata).
## Features
- Data Cleaning & Preprocessing
- Exploratory Data Analysis (EDA)
- Data Visualization
- Statistical Insights
## Contributing
Feel free to fork this repository and submit pull requests with improvements.