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An analysis on Google Play Store Apps

In this project, I will try to explore, visualize and answer some of the basic questions from the Google Play Store Dataset. I will be applying some of the analysis techniques to analyze the dataaset with help of tools like:- Python, Pandas, Matplotlib, Numpy, Seaborn, and Jovian. This project is being done as a final course project while pursuing the course Data Analysis with Python: Zero to Pandas provided by Jovian.ml in collaboration with freecodecamp. While doing this project I have learned many techniques and gained various skills in Data Analysis Domain.

Dataset :- The dataset is being taken from Kaggle and it can be downloaded from hereDataset.

Downloading the Dataset

The dataset is being downloaded from here:- Link

project_name = "GooglePlayStoreAnalysis"

Data Preparation and Cleaning

Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is an important step prior to processing and often involves reformatting data, making corrections to data and the combining of data sets to enrich data. Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.

# Imports
import pandas as pd
import numpy as np
import jovian

import warnings
warnings.filterwarnings('ignore')