Model training, Evaluation, and Prediction
Once analysis and vectorization is done. We can now explore any machine learning model to train the data. But before that perform the train-test split.
Python3
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, data['label'], test_size=0.33, stratify=data['label'], random_state = 42) |
Now we can train any model, Let’s explore the Decision Tree for the prediction.
Python3
from sklearn.tree import DecisionTreeClassifier model = DecisionTreeClassifier(random_state=0) model.fit(X_train,y_train) #testing the model pred = model.predict(X_train) print(accuracy_score(y_train,pred)) |
Output :
0.9244351339218914
Let’s see the confusion matrix for the results.
Python3
from sklearn import metrics cm = confusion_matrix(y_train,pred) cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = [False, True]) cm_display.plot() plt.show() |
Output :
Flipkart Reviews Sentiment Analysis using Python
This article is based on the analysis of the reviews and ratings user gives on Flipkart to make others aware of their experience and moreover about the quality of the product and brand. So, by analyzing that data we can tell the users a lot about the products and also the ways to enhance the quality of the product.
Today we will be using Machine Learning to analyze that data and make it more efficient to understand and prediction ready.
Our task is to predict whether the review given is positive or negative.
Before starting the code, download the dataset by clicking this link.
Contact Us