mobile price classification python code file
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title: tutorial: mobile price classification using python
introduction:
in this tutorial, we'll explore how to build a mobile price classification model using python. we'll use a dataset containing various features of mobile phones such as battery power, camera quality, ram, etc., and classify them into different price ranges. this tutorial will cover data preprocessing, model building, and evaluation using popular python libraries such as pandas, scikit-learn, and matplotlib.
dataset:
for this tutorial, we'll use the mobile price classification dataset available on kaggle. you can download it here.
step 1: data preprocessing
let's start by loading the dataset and performing some preprocessing steps:
step 2: feature scaling
next, we'll scale the features to bring them to the same scale:
step 3: splitting data
now, let's split the dataset into training and testing sets:
step 4: model building (random forest classifier)
we'll use the random forest classifier for our classification task:
step 5: evaluation
finally, let's evaluate the model using accuracy:
step 6: conclusion
in this tutorial, we learned how to build a mobile price classification model using python. we covered data preprocessing, model building using the random forest classifier, and evaluated the model's performance. you can further experiment with different classification algorithms and fine-tune hyperparameters to improve the model's accuracy. happy coding!
note: don't forget to install necessary libraries using pip if you haven't already:
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