vinothkumar2 / Mobile-Price-Range-Prediction

Supervised ML- Built a Multi-Class classification model to find the relation between features of a mobile phone(RAM, Internal Memory etc) and its selling price. Model will predict the price range indicating how high the price is.

Mobile Price Range Prediction

AlmaBetter Verfied Project - AlmaBetter School

Built a Multi-Class classification model to find the relation between features of a mobile phone(RAM, Internal Memory etc) and its selling price. Model will predict the price range indicating how high the price is.

💾 Project Files Description

This Project includes 2 executable files, 1 text files ,1 h5 file as well as 1 directories as follows:

Executable Files:

• mobile_price_range_prediction_Apoorva_KR.ipynb - Includes all functions required for classification operations and generates the model.h5 file after execution.
• final_notebook_mobile_price_range_prediction_Apoorva_KR.ipynb - after execution, evaluation is done on the unseen data as in confusion_matrix.txt.

Output Files:

• model.h5 - Model contains information about the predictions of the train set, such as 0(low),high(1),very high(2).
• confusion_matrix.txt - Contains information about the classified emotions of the test set.

Source Directories:

• Dataset - Includes all dataset for the training phase and testing phase of the model in the csv format.

📖 Random Forest Classification

Random forest classifier creates a set of decision trees from randomly selected subset of training set. It then aggregates the votes from different decision trees to decide the final class of the test object.Ensembled algorithms are those which combines more than one algorithms of same or different kind for classifying objects. For example, running prediction over Naive Bayes, SVM and Decision Tree and then taking vote for final consideration of class for test object.Basic parameters to Random Forest Classifier can be total number of trees to be generated and decision tree related parameters like minimum split, split criteria etc.

📖 XGboost

XGBoost stands for “Extreme Gradient Boosting”. XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements Machine Learning algorithms under the Gradient Boosting framework. It provides a parallel tree boosting to solve many data science problems in a fast and accurate way.

📋 Execution Instruction

The order of execution of the program files is as follows:

1) final_notebook_mobile_price_range_prediction_Apoorva_KR.ipynb

This file must be executed, to define all the functions and variables required for classification operations which leads to the production of the model.h5 file. and to evaluate the model performance on unseen data

📜 Credits

< Apoorva KR > | Avid Learner | Data Scientist | Machine Learning Engineer | Deep Learning enthusiast

Contact me for Data Science Project Collaborations

📚 References

• 'Mobile Price Prediction Using Machine Learning Blog' . [Online].

  Available: https://medium.com/@Nivitus./mobile-price-prediction-using-machine-learning-fa9cab6fb242

• 'Mobile Price Prediction Through Four Classification Algorithms '. [Online].

  Available: https://www.analyticsvidhya.com/blog/2022/02/learn-mobile-price-prediction-through-four-classification-algorithms/ /

• Youtube.com,'Mobile Price Classification and Prediction using 3 Machine Learning Algorithms'. [Online].

  Available: https://www.youtube.com/watch?v=rROyz_hZjKQ

• Youtube.com, 'Mobile Price Prediction Kaggle Dataset Analysis | Data Science | ML | Kaggle'. [Online].

  Available: https://www.youtube.com/watch?v=880YXMnTeu4

• Manisha-sirsat.blogspot.com, 'What is Confusion Matrix and Advanced Classification Metrics?'. [Online].

  Available: https://manisha-sirsat.blogspot.com/2019/04/confusion-matrix.html