Avocado Avg Price prediction

Keywords: Avocado Avg

Background

Avocado is a popular fruit with a fluctuating average price influenced by various factors such as supply, demand, and market conditions. In this study, the author tries to predict the average price of avocados using the model of ANN. The target variable is the average price, while the input features include variables such as date, total volume, total bags, small bags, large bags, and type of avocado. The dataset is preprocessed by standardizing the numerical variables to ensure consistent scaling across features. Later, few of the categorical variables were label encoded too. The ANN model is then constructed with four hidden layers, along with an an input layer and output layer, hidden layers with activation functions such as ReLU, and an output layer with a linear activation function was constructed. During training, the model optimizes its parameters using various optimization algorithms, here the author applied Adam optimizer, to minimize the difference between the predicted and actual average prices. The loss function used for this regression task is typically mean squared error (MSE), which measures the average squared difference between predicted and actual values. To prevent overfitting and enhance generalization, techniques such as dropouts and batch normalization can be applied. The metrics used for calculating the prediction involves R2 score and it came to be 57 percent.

Aim & Objectives

Aim
To Predict the avocado Avg Price Using DL Techniques
Objective

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

— Preview truncated. The complete document (full chapters, references, diagrams, and appendices) is shared with clients as part of project delivery. —