This project aims at building a robust Machine Learning Classification model in order to accurately predict Mobile price range using Python libraries like 'Pandas', 'NumPy' for EDA, libraries, 'Matplotlib', 'Seaborn' for visualization and 'Sci-kit learn' for model building.
In the competitive mobile phone market companies want to understand sales data of mobile phones and factors which drive the prices. The objective is to find out some relation between features of a mobile phone(eg:- RAM, Internal Memory, etc) and its selling price. In this problem, we do not have to predict the actual price but a price range indicating how high the price is.
The central goal of this project is to develop a comprehensive understanding of the mobile phone market dynamics, with a specific focus on the relationship between key features of mobile devices and their associated price ranges. By achieving this objective, we aim to assist mobile phone manufacturers, retailers, and stakeholders in making informed decisions that can positively impact their business strategies.
We will be learning and using key components like: Exploratory Data Analysis, Data Preprocessing, Feature engineering, making predictions with Classification model (Machine Learning) and Evaluation. Ultimately, our project will offer a clear understanding of the dataset, efficient data preparation, a well-selected machine learning algorithm, and a comprehensive evaluation process.
The core of our project involves selecting the most suitable machine learning algorithm for the given dataset. We will evaluate various algorithms and choose the one that best fits our data, all while considering the impact of class imbalance on model performance. To ensure the robustness of our model, we will implement an evaluation strategy that takes class imbalance into account. The outcome of this project will be invaluable to various stakeholders in the mobile phone industry. Manufacturers can make data-driven decisions about product features and pricing strategies. Retailers can optimize their inventory and pricing models. Consumers can benefit from a better understanding of how different features influence prices, aiding in their purchasing decisions.
In this project, we undertook a comprehensive analysis and prediction task to determine the price range of mobile phones using various classification machine learning algorithms. We utilized Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-Learn to perform data exploration, analysis, wrangling, manipulation and visualization, while implementing a range of classification models including Logistic Regression, Random Forest Classifier, Decision Tree, K-Nearest Neighbor, Naive Bayes Classifier, Support Vector Classifier, XGBoost Classifier for prediction on the training and testig sets.
The results emphasize the importance of data preprocessing, model selection, and hyperparameter tuning in achieving robust predictions. The insights gained from this project can assist both consumers and manufacturers in making informed decisions regarding mobile phone pricing. This project serves as a valuable example of how machine learning can be applied to real-world problems in the mobile phone industry, and it opens doors to further research and improvements in mobile price range prediction. In conclusion, this project demonstrates the feasibility of predicting mobile phone price ranges with a high degree of accuracy using various classification machine learning models.