google_playapps_python_project.py

#!/usr/bin/env python
# coding: utf-8

# ## 1. Import required libraries and read the dataset.

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import pandas as pd
import numpy as np


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play = pd.read_csv(r"E:\GLCA_AUG_2023\Python_Files\python project\project_4\Apps_data+(1).csv")


# ## 2. Check the first few samples, shape, info of the data and try to familiarize yourself with different features.

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play.head(5)


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play.shape


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play.info()


# ## 3. Check summary statistics of the dataset. List out the columns that need to be worked upon for model building.

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play.describe(include="all")


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play.nunique()


# ## 4. Check if there are any duplicate records in the dataset? if any drop them.

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play[play.duplicated()]


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play.drop_duplicates(inplace=True)


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play[play.duplicated()]


# ## 5. Check the unique categories of the column 'Category', Is there any invalid category? If yes, drop them.
# 

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play.drop(play.query("Category == '1.9'").index , inplace= True)


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play.Category.unique()


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play[play.Category == '1.9']


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# ## 6. Check if there are missing values present in the column Rating, If any? drop them and and create a new column as 'Rating_category' by converting ratings to high and low categories(>3.5 is high rest low).

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play.isna().sum()


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play.replace(" ", np.nan, inplace = True)


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play.dropna(inplace=True)


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play[play.Rating == ' ']


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play["Rating_Category"] = play.Rating.apply(lambda x: "High" if x > 3.5 else "LOW")


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play.head(10)


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# ## 7. Check the distribution of the newly created column 'Rating_category' and comment on the distribution.

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play.Rating_Category.unique()


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play.Rating_Category.value_counts()


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#There are 8007 ratings which are greater than 3.5 ratings given by custemors 
#and 879 low rating which are below 3.5 given by customers 


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# ## 8. Convert the column "Reviews'' to numeric data type and check the presence of outliers in the column and handle the outliers using a transformation approach.(Hint: Use log transformation)
# 

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play["Reviews"] = play["Reviews"].astype(int)


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play.Reviews.dtype


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play.Reviews.plot(kind="box")


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play["Reviews"] = np.log10(play["Reviews"])


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play.head(10)


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play.Reviews.plot(kind="box")


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# ## 9. The column 'Size' contains alphanumeric values, treat the non numeric data and convert the column into suitable data type. (hint: Replace M with 1 million and K with 1 thousand, and drop the entries where size='Varies with device')
# 

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play.Size.dtype


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play["Size"] = play["Size"].str.replace("M",'000000')


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play["Size"] = play["Size"].str.replace("k",'000')


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play


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play.drop(play.query("Size == 'Varies with device'").index , inplace= True)


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play["Size"] = play["Size"].astype(float)


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play.Size.dtype


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play.head(10)


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# ## 10. Check the column 'Installs', treat the unwanted characters and convert the column into a suitable data type.

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play["Installs"] = play.Installs.str.replace(",",'')
play["Installs"] = play.Installs.str.replace("+",'')


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play.head(10)


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play["Installs"] = play["Installs"].astype(int)


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play.Installs.dtype


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# ## 11. Check the column 'Price' , remove the unwanted characters and convert the column into a suitable data type.

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play.Price.unique()


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play["Price"] = play["Price"].str.replace("$",'')


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play["Price"] = play["Price"].astype(float)


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# ## 12. Drop the columns which you think redundant for the analysis.(suggestion: drop column 'rating', since we created a new feature from it (i.e. rating_category) and the columns 'App', 'Rating' ,'Genres','Last Updated', 'Current Ver','Android Ver' columns since which are redundant for our analysis)
# 

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play.head()


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play.drop(['App','Rating' ,'Genres','Last Updated', 'Current Ver','Android Ver'], axis=1,inplace=True)


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play.head()


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play.nunique()


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# ## 13. Encode the categorical columns.
# 

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play.head()


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play1 = pd.get_dummies(play,columns=["Category", "Content Rating"])


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play1.head()


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from sklearn.preprocessing import LabelEncoder


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LEncoder = LabelEncoder()


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play1["Type"] = LEncoder.fit_transform(play1["Type"])


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play1["Rating_Category"] = LEncoder.fit_transform(play1["Rating_Category"])


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play1.head()


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# ## 14. Segregate the target and independent features (Hint: Use Rating_category as the target)
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from sklearn.model_selection import train_test_split as tts


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x = play1.drop(["Rating_Category"], axis=1)


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y = play1[["Rating_Category"]]


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# ## 15. Split the dataset into train and test.
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x_train, x_test, y_train, y_test = tts(x,y, random_state=1, test_size=0.20)


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# ## 16. Standardize the data, so that the values are within a particular range.
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from sklearn.preprocessing import StandardScaler

scale = StandardScaler()


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data = scale.fit_transform(play1)


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standard = pd.DataFrame(data, columns=play1.columns)


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X =standard.drop(['Rating_Category'], axis=1)


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Y = play1[['Rating_Category']]


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X_train, X_test, Y_train, Y_test = tts(X,Y, random_state=1, test_size=0.20)


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from sklearn.linear_model import LinearRegression

regression_model = LinearRegression()


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regression_model.fit(X_train, Y_train)


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