Advanced Mini-Project - DE with Python and Pandas - Google Play Store App Performance Predictor

[akash-coded]

The final part of the Google Play Store Data Analysis. Continuation of #43

This is a focused, real-time scenario-based project as a continuation of the Google Play Store data mini-project.

Advanced Mini-Project: Google Play Store App Performance Predictor

Scenario:
You're a data engineer at a mobile app development company. The product team is planning their next app and wants to use data-driven insights to maximize its chances of success. They've asked you to develop a model that predicts an app's potential performance based on various factors.

Dataset: https://github.com/schlende/practical-pandas-projects/blob/master/datasets/google-play-store-11-2018.csv

Challenge:
Develop a data pipeline that processes the Google Play Store dataset and creates a predictive model for app performance. Your solution should address the following requirements:

1. Data Preprocessing:

   • Handle missing values and outliers
   • Convert categorical variables into numerical format
   • Create a 'success_metric' column (you decide how to define success, e.g., installs * rating)

2. Feature Engineering:

   • Create a 'days_since_release' column
   • Develop a 'complexity_score' based on app size and whether it offers IAP
   • Generate a 'category_competitiveness' score based on the number of apps and average rating in each category

3. Correlation Analysis:

   • Identify the top 5 features most correlated with the 'success_metric'

4. Predictive Model:

   • Use the top correlated features to build a simple linear regression model predicting the 'success_metric'
   • Split the data into training and testing sets
   • Evaluate the model's performance using appropriate metrics

5. Insights Generation:

   • Based on your model, what are the key factors that contribute to an app's success?
   • Generate recommendations for the product team on how to position their new app for success

Hints and Approach:

1. Data Preprocessing:

   # Handle missing values
   df = df.dropna()
   
   # Convert categorical variables
   df = pd.get_dummies(df, columns=['category', 'content_rating'])
   
   # Create success_metric
   df['success_metric'] = df['installs'] * df['rating']

2. Feature Engineering:

   # Create days_since_release
   df['days_since_release'] = (pd.Timestamp.now() - pd.to_datetime(df['released'])).dt.days
   
   # Create complexity_score
   df['complexity_score'] = df['size'] + (df['in_app_purchases'] * 10)
   
   # Create category_competitiveness
   category_stats = df.groupby('category').agg({'app_id': 'count', 'rating': 'mean'})
   category_stats['competitiveness'] = category_stats['app_id'] * category_stats['rating']
   df = df.merge(category_stats[['competitiveness']], on='category')

3. Correlation Analysis:

   correlation = df.corr()['success_metric'].sort_values(ascending=False)
   top_features = correlation[1:6]  # Exclude success_metric itself

4. Predictive Model:

   from sklearn.model_selection import train_test_split
   from sklearn.linear_regression import LinearRegression
   from sklearn.metrics import mean_squared_error, r2_score
   
   X = df[top_features.index]
   y = df['success_metric']
   
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
   
   model = LinearRegression()
   model.fit(X_train, y_train)
   
   y_pred = model.predict(X_test)
   mse = mean_squared_error(y_test, y_pred)
   r2 = r2_score(y_test, y_pred)

5. Insights Generation:

   • Examine the coefficients of your linear regression model to understand feature importance
   • Use these insights to formulate recommendations for the product team

This project will test your ability to:

• Perform advanced data preprocessing and feature engineering
• Conduct correlation analysis to identify important features
• Build and evaluate a simple predictive model
• Translate data insights into actionable business recommendations

Additional Tasks:

6. Price Sensitivity Analysis:

   • Investigate how app price affects the success metric across different categories
   • Recommend an optimal pricing strategy for the new app

   Hint:

   def price_sensitivity(category):
       category_data = df[df['category'] == category]
       return category_data.groupby('price')['success_metric'].mean()
   
   price_sensitivity_by_category = {cat: price_sensitivity(cat) for cat in df['category'].unique()}

7. Competitor Analysis:

   • For each category, identify the top 5 apps by success metric
   • Analyze common characteristics of these top apps

   Hint:

   top_apps = df.groupby('category').apply(lambda x: x.nlargest(5, 'success_metric'))
   top_apps_features = top_apps[['category', 'app', 'rating', 'installs', 'price', 'size']]

8. User Sentiment Analysis:

   • Assuming the 'reviews' column contains text reviews, perform a simple sentiment analysis
   • Correlate sentiment scores with the success metric

   Hint:

   from textblob import TextBlob
   
   def get_sentiment(text):
       return TextBlob(text).sentiment.polarity
   
   df['sentiment_score'] = df['reviews'].apply(get_sentiment)
   sentiment_correlation = df['sentiment_score'].corr(df['success_metric'])

9. Launch Timing Analysis:

   • Investigate if there's an optimal time of year to launch apps in different categories
   • Provide recommendations on when to launch the new app

   Hint:

   df['launch_month'] = pd.to_datetime(df['released']).dt.month
   monthly_success = df.groupby(['category', 'launch_month'])['success_metric'].mean().unstack()

10. Final Recommendations:

    • Based on all your analyses, compile a list of top 5 recommendations for the product team
    • Include suggestions on app category, features, pricing, and launch timing

    Hint: This will be a text-based output summarizing your key findings and recommendations.

Final Output:
Prepare a concise report (you can use a pandas DataFrame for this) that includes:

• Predicted success metric for the proposed app
• Top 5 features contributing to app success
• Recommended category and price point
• Optimal launch timing
• Key characteristics to incorporate based on top-performing apps

recommendations = pd.DataFrame({
    'Aspect': ['Predicted Success', 'Top Feature 1', 'Top Feature 2', 'Recommended Category', 'Recommended Price', 'Launch Month'],
    'Recommendation': [predicted_success, feature1, feature2, rec_category, rec_price, rec_month]
})

This advanced mini-project challenges you to apply various pandas techniques to derive meaningful business insights. It covers data preprocessing, feature engineering, statistical analysis, and basic machine learning, all in the context of solving a real-world business problem. The project is designed to give you a sense of accomplishment in applying pandas to complex data analysis tasks and translating those analyses into actionable business recommendations.

[akash-coded]

Google Play Store App Performance Predictor: A Beginner's Guide

This documentation provides a step-by-step explanation of the Google Play Store App Performance Predictor project, designed for freshers and beginners in data science and machine learning.

Table of Contents

1. Project Overview
2. Data Preprocessing
3. Feature Engineering
4. Correlation Analysis
5. Predictive Modeling
6. Insights Generation
7. Best Practices and Coding Conventions

Project Overview

This project aims to predict app performance in the Google Play Store using various features of the apps. We'll go through data preprocessing, feature engineering, correlation analysis, building a predictive model, and generating insights.

Data Preprocessing

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Load the dataset
url = 'https://raw.githubusercontent.com/schlende/practical-pandas-projects/master/datasets/google-play-store-11-2018.csv'
df = pd.read_csv(url)
print(f"Initial shape: {df.shape}")

# Handle missing values
df = df.dropna()
print(f"Shape after dropping missing values: {df.shape}")

# Convert categorical variables
df = pd.get_dummies(df, columns=['genre'])

# Create success_metric
df['success_metric'] = df['min_installs'] * df['score']
print("Success metric created")

Explanation:

1. We start by importing necessary libraries: pandas for data manipulation, numpy for numerical operations, and scikit-learn for machine learning tasks.
2. The dataset is loaded from a URL using pd.read_csv().
3. Missing values are removed using dropna() to ensure data quality.
4. Categorical variables (in this case, 'genre') are converted to numerical format using one-hot encoding (pd.get_dummies()).
5. A 'success_metric' is created by multiplying 'min_installs' and 'score'. This metric assumes that successful apps have both high installations and high ratings.

Feature Engineering

# Create days_since_release
df['released'] = pd.to_datetime(df['released'])
df['days_since_release'] = (pd.Timestamp.now() - df['released']).dt.days

# Create complexity_score
df['complexity_score'] = df['offers_iap'].astype(int) * 5 + df['ad_supported'].astype(int) * 3

# Create genre_competitiveness
genre_stats = df.groupby('genre_id').agg({'app_id': 'count', 'score': 'mean'})
genre_stats['competitiveness'] = genre_stats['app_id'] * genre_stats['score']
df = df.merge(genre_stats[['competitiveness']], on='genre_id')

Explanation:

1. 'days_since_release' is calculated to capture the app's age, which might influence its performance.
2. 'complexity_score' is created based on whether the app offers in-app purchases (IAP) and is ad-supported. This assumes that apps with IAP and ads are more complex.
3. 'genre_competitiveness' is calculated by multiplying the number of apps in a genre with the average score of that genre. This metric aims to capture how difficult it might be for an app to stand out in its genre.

Correlation Analysis

correlation = df.corr()['success_metric'].sort_values(ascending=False)
top_features = correlation[1:6]  # Exclude success_metric itself
print("Top 5 correlated features:")
print(top_features)

Explanation:

We calculate the correlation between all features and our 'success_metric', then select the top 5 most correlated features. This helps us identify which features are most strongly related to an app's success.

Predictive Modeling

X = df[top_features.index]
y = df['success_metric']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: {mse}")
print(f"R-squared Score: {r2}")

Explanation:

1. We split our data into features (X) and target variable (y).
2. The data is then split into training and testing sets using train_test_split(). We use 80% of the data for training and 20% for testing.
3. We create a Linear Regression model and fit it to our training data.
4. The model is used to make predictions on the test set.
5. We evaluate the model using two metrics:
   • Mean Squared Error (MSE): Measures the average squared difference between predicted and actual values. Lower values indicate better performance.
   • R-squared (R²) Score: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. Values range from 0 to 1, with 1 indicating perfect prediction.

Mathematical Reasoning:

Linear Regression models the relationship between features and the target variable as a linear equation:

y = β₀ + β₁x₁ + β₂x₂ + ... + βₙxₙ + ε

Where:

• y is the target variable (success_metric)
• x₁, x₂, ..., xₙ are the features
• β₀, β₁, β₂, ..., βₙ are the coefficients (weights) the model learns
• ε is the error term

The model aims to find the coefficients that minimize the sum of squared residuals (differences between predicted and actual values).

Insights Generation

# Feature Importance
feature_importance = pd.Series(model.coef_, index=X.columns).sort_values(ascending=False)
print("Feature Importance:")
print(feature_importance)

# Identify top apps in each genre
top_apps_in_genre = df.groupby('genre_id')['success_metric'].idxmax()
top_apps_data = df.loc[top_apps_in_genre, ['genre_id', 'title', 'success_metric']]
print("\nTop Apps in Each Genre:")
print(top_apps_data)

# Exploring market saturation
market_saturation = df.groupby('genre_id')['success_metric'].sum() / df['success_metric'].sum()
print("\nMarket Saturation by Genre:")
print(market_saturation)

# Export processed data
df.to_csv('processed_play_store_data.csv', index=False)
print("\nProcessed data saved to processed_play_store_data.csv")

Explanation:

1. Feature Importance: We extract the coefficients from our linear regression model to understand which features have the most impact on the success metric.
2. Top Apps in Each Genre: We identify the app with the highest success metric in each genre.
3. Market Saturation: We calculate the proportion of total success metric each genre accounts for, giving an idea of which genres are most saturated or successful.
4. Finally, we export our processed data for future use or analysis.

Best Practices and Coding Conventions

1. Imports: All necessary libraries are imported at the beginning of the script.

2. Naming Conventions:

   • Variables and functions use snake_case (e.g., feature_importance, market_saturation).
   • Constants (if any) would use UPPER_CASE.

3. Comments: Comments are used to explain complex operations or the purpose of code blocks.

4. Code Structure: The code follows a logical flow: data loading, preprocessing, feature engineering, analysis, modeling, and insights generation.

5. Error Handling: While not explicitly shown in this script, in a production environment, you would want to include try-except blocks to handle

[akash-coded]

Let's reframe our explanation to focus on the data engineering perspective and relate the activities to the stages of the Data Engineering lifecycle. This approach will help students think more like data engineers.

Google Play Store App Analysis: A Data Engineering Perspective

Data Engineering Lifecycle Overview

The Data Engineering lifecycle typically consists of the following stages:

1. Generation
2. Storage
3. Ingestion
4. Transformation
5. Serving

Let's break down our project according to these stages:

1. Generation and 2. Storage

In our case, the data generation and storage stages have already been handled by the source (Google Play Store). The data is stored and made available as a CSV file at the given URL.

url = 'https://raw.githubusercontent.com/schlende/practical-pandas-projects/master/datasets/google-play-store-11-2018.csv'

3. Ingestion

The ingestion stage involves bringing the data into our system for processing.

df = pd.read_csv(url)
print(f"Initial shape: {df.shape}")

This step represents a simple form of batch ingestion, where we're pulling in the entire dataset at once. In a more complex data engineering scenario, this could involve streaming data ingestion or incremental loads from various sources.

4. Transformation

The transformation stage is where most of our data engineering work happens in this project. It involves cleaning, structuring, and preparing the data for analysis.

Data Cleaning

df = df.dropna()
print(f"Shape after dropping missing values: {df.shape}")

This step removes any rows with missing values, ensuring data quality for downstream processes.

Data Structuring

df = pd.get_dummies(df, columns=['genre'])

Here, we're transforming categorical data into a numerical format that can be used in machine learning models. This is a common ETL (Extract, Transform, Load) operation in data engineering.

Feature Engineering

df['success_metric'] = df['min_installs'] * df['score']

df['released'] = pd.to_datetime(df['released'])
df['days_since_release'] = (pd.Timestamp.now() - df['released']).dt.days

df['complexity_score'] = df['offers_iap'].astype(int) * 5 + df['ad_supported'].astype(int) * 3

genre_stats = df.groupby('genre_id').agg({'app_id': 'count', 'score': 'mean'})
genre_stats['competitiveness'] = genre_stats['app_id'] * genre_stats['score']
df = df.merge(genre_stats[['competitiveness']], on='genre_id')

These transformations create new features from existing data, enriching our dataset. In data engineering, this process often involves complex business logic and can be computationally intensive for large datasets.

5. Serving

The serving stage involves making the processed data available for downstream consumers, which in this case are our analysis and machine learning processes.

X = df[top_features.index]
y = df['success_metric']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Here, we're preparing the data for a machine learning model. In a larger data engineering context, this could involve loading data into a data warehouse, creating views for business intelligence tools, or setting up APIs for other applications to access the data.

df.to_csv('processed_play_store_data.csv', index=False)
print("\nProcessed data saved to processed_play_store_data.csv")

This step represents another aspect of the serving stage, where we're making the processed data available for future use by saving it to a CSV file.

ETL Activities

Throughout this project, we've performed several ETL activities:

1. Extract: We extracted data from a CSV file hosted on GitHub.
2. Transform: We performed several transformations including:
   • Cleaning (removing null values)
   • Structuring (one-hot encoding of categorical variables)
   • Feature engineering (creating new columns based on existing data)
3. Load: We loaded the transformed data into memory for analysis and also saved it to a new CSV file