The price of a phone become a necessity in our daily lives and the price of that depends on its specifications. In this notebook, we will explore the factors that affect cell phone prices and predict new samples based on the best model. The classification goal is to predict the price range of a mobile phone by building a model that takes into account various features provided in the dataset.
The dataset used in this project is available on my Kaggle page:
👉 Mobile Price Range.
- Exploratory Data Analysis (EDA) to understand the dataset.
- Implements Decision Tree, Random Forest, and SVM for classification.
- Ensemlbe Learning, Feature selection and engineering to improve model accuracy.
- Visualization using Matplotlib, Seaborn, and Plotly.