https://github.com/nick-peter-marcus/chocolate-bar-analysis
Analyzing Chocolate Bar Features and Ratings - Data Visualization, Decision Trees, Random Forest
https://github.com/nick-peter-marcus/chocolate-bar-analysis
data-analysis data-visualization decision-trees python random-forest seaborn sklearn
Last synced: about 2 months ago
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Analyzing Chocolate Bar Features and Ratings - Data Visualization, Decision Trees, Random Forest
- Host: GitHub
- URL: https://github.com/nick-peter-marcus/chocolate-bar-analysis
- Owner: nick-peter-marcus
- License: mit
- Created: 2023-06-16T01:13:09.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-01T18:56:38.000Z (over 2 years ago)
- Last Synced: 2025-02-09T06:33:27.666Z (over 1 year ago)
- Topics: data-analysis, data-visualization, decision-trees, python, random-forest, seaborn, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Chocolate Bar Analysis
In this project, I am analyzing a dataset taken from kaggle containing ratings and characteristics of over 2500 chocolate bars. The dataset can be found here:
https://www.kaggle.com/datasets/nyagami/chocolate-bar-ratings-2022
## 1. Exploratory Analyses and Data Visualization
In the first step, I perform some explorative analyses, observing frequencies and distributions of the variables both statistically and graphically. For this, NumPy, pandas, Matplotlib, and Seaborn are utilized.
In this process, some variables were recoded and a copy of the dataset has been saved for the next step of the analysis.
## 2. Using Random Forest and Decision Tree to predict Chocolate Bar Ratings
In the next step, I build models to predict the ratings of the chocolate bars given their characteristics. Here, Random Forest and Decision Tree Regressors where used. Both methods were observed with default parameters, as well as optimized parameters derived from Hyper Parameter Tuning.