https://github.com/farscent/oemoem2024-dsai-challenge

My submission for Omah-Ti's DSAI Data Analysis Challenge.
https://github.com/farscent/oemoem2024-dsai-challenge

exploratory-data-analysis jupyter-notebook linear-regression machine-learning python random-forest

Last synced: 23 days ago
JSON representation

My submission for Omah-Ti's DSAI Data Analysis Challenge.

• Host: GitHub
• URL: https://github.com/farscent/oemoem2024-dsai-challenge
• Owner: Farscent
• Created: 2024-09-10T09:44:49.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-12-07T08:03:16.000Z (about 1 month ago)
• Last Synced: 2024-12-07T09:17:20.998Z (about 1 month ago)
• Topics: exploratory-data-analysis, jupyter-notebook, linear-regression, machine-learning, python, random-forest
• Language: Jupyter Notebook
• Homepage:
• Size: 913 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Oemoem2024-DSAI-Challenge

My submission for Omah-Ti UGM's DSAI Data Analysis Challenge.

The dataset given were about Clams and their physical characteristics. I made a predictive model that can predict their age. 

With my submission, I obtained the Best Project winner in the Data Science and Artificial Intelligence Class of OemOem.

## Project Overview

This notebook explores a dataset containing physical measurements of clams, such as length, diameter, height, and weight. The primary objective is to predict the age of the clams using these variables through **Exploratory Data Analysis (EDA)** and **machine learning models**.

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## Key Features

1. **Exploratory Data Analysis (EDA)**:

   - Examines the relationships between physical measurements and age.

   - Visualizes distributions and correlations between features.

2. **Feature Engineering**:

   - Combines dimensions (e.g., length, diameter, height) to calculate volume.

   - Encodes categorical variables such as gender for model training.

3. **Data Preprocessing**:

   - Handles missing values and outliers.

   - Scales numerical features for model compatibility.

4. **Modeling**:

   - Builds machine learning models to predict clam age.

   - Evaluates model performance using metrics like accuracy and F1-score.

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