How to Build a Deep Neural Network (DNN) Model for Weather Forecast Classification Using Weather Data
Share
Condition for Building an Deep Neural Network (DNN) Model for Weather Forecast Classification Using Weather Data
Description: This code performs classification on a weather forecast dataset, predicting whether it will rain based on various weather features. It preprocesses the data by encoding categorical variables, scaling the features, and splitting the dataset for training and testing. A deep neural network (DNN) model is built, trained, and evaluated using performance metrics such as accuracy, F1-score, and confusion matrix.
Step-by-Step Process
Import Necessary Libraries: Import essential libraries like Pandas, Matplotlib, Seaborn, and Scikit-learn for data manipulation, visualization, and model evaluation. TensorFlow and Keras are imported for building and training the deep neural network model.
Load Dataset: Load the weather forecast dataset using pd.read_csv() and display the first five rows to preview the data.
Split Dataset: Split the dataset into independent variables (x) and the target variable (y). The Rain column is the dependent variable.
Check Class Imbalance: Plot the distribution of the target variable using a pie chart to check for class imbalance.
Data Preprocessing: Encode the target variable and scale the feature variables using LabelEncoder and StandardScaler, respectively.
Train-Test Split: Split the dataset into training and testing sets using train_test_split().
Build and Train the Model: Define the DNN model with one input layer, two hidden layers, and an output layer, and train it using the training data.
Evaluate and Visualize: Evaluate the model using performance metrics like classification report, confusion matrix, accuracy, F1-score, recall, and precision. Display the confusion matrix using a heatmap.
Sample Source Code
# Import Necessary Libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense
from sklearn.metrics import (classification_report, confusion_matrix, accuracy_score, f1_score, recall_score, precision_score)
# Build the model
lstm_model = Model(inputs=inputs, outputs=output_layer)
# Compile the model with Adam optimizer and binary crossentropy loss function
lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return lstm_model
model = DNN_model((X_train.shape[1],))
# Summary of the model
model.summary()
# Train the model
history = model.fit(X_train, y_train, batch_size=32, epochs=10, validation_data=(X_test, y_test), verbose=1)
y_pred = model.predict(X_test)
y_pred = [1 if i > 0.5 else 0 for i in y_pred]
Research Breakthrough Possible @S-Logix
