50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
import pandas as pd
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import numpy as np
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data = pd.read_csv('./googleplaystore.csv')
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data.dropna(subset=['Rating', 'Type','Content Rating','Current Ver','Android Ver'], inplace=True)
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data.reset_index(drop=True, inplace=True)
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data = data.drop(columns=["Size", "Android Ver", "Current Ver", "Last Updated"])
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# normalizing text
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to_lowercase = ['App', 'Category', 'Type', 'Content Rating', 'Genres']
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for column in to_lowercase:
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data[column] = data[column].apply(str.lower)
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data["Installs"] = data["Installs"].replace({'\+': ''}, regex=True)
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data["Installs"] = data["Installs"].replace({',': ''}, regex=True)
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data["Price"] = data["Price"].replace({'\$': ''}, regex=True)
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data["Genres"] = data["Genres"].astype('category')
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data["Genres_numeric_value"] = (data["Genres"].cat.codes).astype('float32')
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# normalizing numbers
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data["Reviews"] = pd.to_numeric(data["Reviews"], errors='coerce')
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max_value = data["Reviews"].max()
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min_value = data["Reviews"].min()
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data["Reviews"] = (data["Reviews"] - min_value) / (max_value - min_value)
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data["Installs"] = pd.to_numeric(data["Installs"], errors='coerce')
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max_value = data["Installs"].max()
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min_value = data["Installs"].min()
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data["Installs"] = (data["Installs"] - min_value) / (max_value - min_value)
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data["Rating"] = np.asarray(data["Rating"]).astype('float32')
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data["Reviews"] = np.asarray(data["Reviews"]).astype('float32')
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data["Installs"] = np.asarray(data["Installs"]).astype('float32')
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data["Price"] = np.asarray(data["Price"]).astype('float32')
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print(data)
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# splitting into sets
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np.random.seed(123)
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train, validate, test = np.split(data.sample(frac=1, random_state=42), [int(.6*len(data)), int(.8*len(data))])
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print(f"Data shape: {data.shape}\nTrain shape: {train.shape}\nTest shape: {test.shape}\nValidation shape:{validate.shape}")
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train.to_csv('apps_train.csv')
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test.to_csv('apps_test.csv')
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validate.to_csv('apps_validate.csv')
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