This project aims to analyze a dataset of apple quality attributes to develop predictive models for classifying apples based on their features. Additionally, the study identifies key factors influencing apple quality and provides recommendations for improving apple production and marketing strategies.
The dataset contains various attributes of apples, such as Size, Weight, Sweetness, Crunchiness, Juiciness, Ripeness, Acidity, and Quality (classified as "good" or "bad"). The data was cleaned and preprocessed before analysis.
The initial exploration involved using histograms to visualize the distribution of attributes. These histograms provided insights into the frequency distribution, underlying patterns, and variability within each attribute. Additionally, the count of good and bad apples was calculated to understand the prevalence of each quality label.
A predictive model was developed to classify apples based on their attributes. The class probabilities were as follows:
- Class 'bad': Probability 49.90%
- Class 'good': Probability 50.10%
These probabilities indicate a balanced dataset, which is beneficial for model performance.
- Data Collection Method: The reliability and validity of the data depend on the data collection method, which may not be provided.
- Inaccurate Long-Term Forecasting: The dataset may not account for changes over time, such as seasonal variations, limiting its applicability for long-term analysis or forecasting.
- Temporal Factors: Collecting data at different times of the year to understand how apple quality varies across seasons. This can help identify seasonal trends, market changes, and long-term shifts in apple quality, improving forecasting and decision-making capabilities.