Google Play Store App Rating Prediction Using Machine Learning

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Google Play Store App Rating Prediction Using Machine Learning

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1. Abstract

This project focuses on predicting the ratings of mobile applications available on the Google Play Store using machine learning techniques. The ratings of apps depend on several factors such as category, number of installs, reviews, size, and other characteristics of the applications.

In this project, a dataset scraped from the Google Play Store is used for analysis and prediction. The dataset contains detailed information about different mobile applications and their features. Since the dataset was originally created for analytical purposes rather than prediction, extensive data preprocessing and feature engineering are required before training the machine learning model.

A Random Forest Regressor model is used to predict the ratings of apps based on the processed dataset. The model learns patterns from the features and estimates the rating of an app.

After building the prediction model, a web application is developed using the Django framework. The trained machine learning model is integrated into the web application so users can input app-related details and receive predicted ratings.

Finally, the Django web application is deployed on the Heroku cloud platform using GitHub integration, making the system accessible online. This project demonstrates how machine learning, data preprocessing, and web technologies can be combined to build real-world predictive applications.

2. Objectives

The main objectives of this project are:

To understand the concept of rating prediction using machine learning.
To analyze the Google Play Store dataset and its features.
To perform data preprocessing and feature engineering.
To extract useful features from the dataset.
To build a machine learning model using the Random Forest Regressor algorithm.
To train and evaluate the model for predicting app ratings.
To develop a web application using the Django framework.
To deploy the application on Heroku using GitHub integration.

3. Existing System

In the existing system, app ratings are given by users on the Google Play Store based on their personal experience with the application. Although ratings provide feedback about the app, predicting app ratings beforehand is not typically automated.

The traditional system has several limitations:

App popularity analysis is mainly based on manual observation.
It is difficult to predict ratings before the app is released or widely used.
The dataset available is mainly prepared for analysis rather than prediction.
Manual analysis of large datasets is time-consuming.
There is no automated system to estimate app ratings based on app features.

Due to these limitations, developers cannot easily predict how well their apps might perform in terms of ratings.

4. Proposed System

The proposed system uses machine learning techniques to predict app ratings based on different characteristics of the apps.

In this system:

A dataset scraped from the Google Play Store is used.
Data preprocessing is performed to clean and prepare the dataset.
Feature engineering and extraction are applied to create meaningful input variables.
A Random Forest Regressor model is trained using the dataset.
A Django-based web application is developed to provide an interactive user interface.
Users can enter app details to receive predicted ratings.
The application is deployed on Heroku using GitHub for online access.

This system provides a fast, automated, and data-driven way to estimate app ratings.

5. Implementation Procedure

The implementation of this project is carried out in the following steps:

Step 1: Data Collection

Obtain the Google Play Store dataset containing information about various mobile applications.

Step 2: Data Preprocessing

Clean the dataset.
Handle missing values.
Remove unnecessary or inconsistent data.
Prepare the dataset for machine learning.

Step 3: Feature Engineering

Extract useful features from the dataset.
Create new features if necessary.
Convert categorical data into numerical format.

Step 4: Model Development

Select the Random Forest Regressor algorithm.
Train the machine learning model using the prepared dataset.

Step 5: Model Evaluation

Test the model using test data.
Evaluate the performance using regression metrics.

Step 6: Web Application Development

Develop a web application using the Django framework.
Integrate the trained machine learning model into the application.

Step 7: Deployment

Upload the project to GitHub.
Deploy the Django application on Heroku.
Make the web application accessible online.

6. Software Requirements

The software used in this project includes:

Operating System: Windows / Linux / macOS
Programming Language: Python 3.x
Framework: Django
IDE: Jupyter Notebook / VS Code / PyCharm

Libraries:

NumPy
Pandas
Matplotlib
Seaborn
Scikit-learn

Deployment Tools:

GitHub
Heroku

Web Browser: Chrome / Firefox

7. Hardware Requirements

The hardware required for this project includes:

Processor: Intel i3 / i5 or higher
RAM: Minimum 4 GB (8 GB recommended)
Storage: Minimum 128 GB free space
System: Laptop / Desktop Computer
Internet Connection

8. Advantages of the Project

Predicts app ratings automatically using machine learning.
Helps developers understand factors affecting app ratings.
Handles large datasets efficiently.
Uses feature engineering for better prediction accuracy.
Provides a user-friendly web interface.
Allows real-time rating prediction.
Can be deployed online for easy access.
Helps developers improve app quality before release.

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