This project focuses on the analysis of a dataset containing information about 80 different cereals. The primary goal is to perform data cleaning, exploratory data analysis (EDA), and data modeling using Python and Jupyter Notebook. The dataset was acquired from Kaggle.com and is used to gain insights into various aspects of the cereals.
- Jupyter Notebook
- Python
The datasets used in this project were obtained from Kaggle.com. They contain comprehensive information about 80 different cereals, providing details such as nutritional content, manufacturer information, and customer ratings.
Pandas, a powerful data manipulation library in Python, was employed to clean and preprocess the dataset. This involved handling missing values, removing duplicates, and ensuring data consistency for further analysis.
Data modeling was performed to extract meaningful patterns and relationships within the dataset. Various statistical and machine learning techniques were applied to uncover insights and trends related to cereal attributes.
NumPy, a fundamental library for scientific computing in Python, was utilized for exploratory data analysis (EDA). This step involved statistical analysis and summarization of the dataset to reveal key characteristics and trends.
Matplotlib and Seaborn, popular data visualization libraries in Python, were employed to create visual representations of the dataset. Plots, charts, and graphs were generated to provide a clearer understanding of the data and highlight important patterns.
- Clone the repository:
git clone https://github.com/your_username/80_cereals.git
- Install the required dependencies:
pip install -r requirements.txt
- Open the Jupyter Notebook:
Navigate to the notebook file (80_cereals.ipynb) and run the cells to reproduce the analysis.
jupyter notebook