# Crop Prediction Model Using Machine Learning Algorithms
## 5 Methodology
In practice, the Naive Bayes algorithm performs well, especially when the data are sparse, and the number of features is extensive. Below is the pseudocode for Naive Bayes Classifier:  
Input: [X: Training set; m: Number of observations; n: Number of features; Labels y for the training set]. Output: [New data point x to be classified; predicted class for x]. * For each unique class value c in y, calculate the prior probability for class c  
\[\text{P(c)}=\text{count(c)/m}\]  
where count (c) is the count of observations with class c.
* For each feature i in X and each unique class value c in y, calculate the conditional probability of feature i given class c as follows:  
\[\text{P(i\,c)}=\text{(count (i, c))/(count(c))}\]  
where count (i, c) is the count observations with the feature i and class c.
* Calculate the posterior probability for each class given the new data point x:  
\[\circ\] Initialize the posterior probability P(c\(\,\mid\,\)x) to P(c)  
\[\circ\] For each feature i in x: =, multiply P(c\(\,\mid\,\)x) by P(i\(\,\mid\,\)c) if x[i] is observed in the training data, otherwise ignore the term. * Choose the class with the highest posterior probability as the predicted class for x. * Random Forest is a type of supervised machine learning algorithm that is used in regression and classification problems. It is an ensemble learning algorithm that uses Decision Trees to make a prediction. Random Forest creates many Decision Trees and combines their predictions to make a final prediction. More trees created yield high accuracy and robust results. Below is the pseudocode for the Random Forest algorithm:  
Inputs: {(x, y): Training data set; T: Number of trees; d: Maximum depth of each tree; f: Number of features to consider at each split}  
Outputs: {Learned trees for classification}  
* For each tree in the Random Forest:  
* Select a bootstrap sample from the training data set. * Create a Decision Tree T_t with a maximum depth of d.
* Randomly select f features to consider at each split of T_t. * Use the selected features to find the best split at each node of T_t. * Create the list of Decision Trees T_1, T_2,..., T_T. * For each input data:  
* For each decision, find the prediction. * Obtain predictions of all the trees. * Calculate the final predicted class. * Multilayer Neural Network is a machine learning algorithm consisting of multiple layers of interconnected nodes between the input and output layers. To minimize the error between the predicted output and the actual output, the neural network involves adjusting each neuron's weight and biases by using an optimization algorithm such as backpropagation. Below is the pseudocode for the Multilayer Neural Network algorithm:  
* Define the number of layers and the number of neurons per layer. * Initialize the weights and biases for each neuron in the network randomly. For each input data:  
* Forward propagate the input through the neural network to obtain the predicted output. * Calculate the error rate between the predicted output and the actual output. * Backward propagate the error through the neural network to adjust the weights and biases using the optimization algorithm. * Repeat until the error converges to a satisfactory level. By integrating these methodologies, we aimed to provide farmers with efficient and effective crop recommendations, ensuring they receive tailored advice based on the most pertinent features while minimizing the required effort and time investment.