https://github.com/rushilsharma1/iphonesales_eda

An exploratory data analysis of an Iphone sales in jupyter notebook with the help of python and its libraries
https://github.com/rushilsharma1/iphonesales_eda

data-science exploratory-data-analysis jupyter-notebook numpy pandas python seaborn

Last synced: 19 days ago
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An exploratory data analysis of an Iphone sales in jupyter notebook with the help of python and its libraries

• Host: GitHub
• URL: https://github.com/rushilsharma1/iphonesales_eda
• Owner: rushilsharma1
• Created: 2024-09-12T09:26:35.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-14T18:26:51.000Z (2 months ago)
• Last Synced: 2024-10-16T19:58:48.732Z (about 1 month ago)
• Topics: data-science, exploratory-data-analysis, jupyter-notebook, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 197 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
 # IphoneSales_EDA

To create a project for iPhone sales exploratory data analysis using Jupyter Notebook, follow these steps:

Step 1: Import necessary libraries Import essential libraries such as Pandas, NumPy, Seaborn, and Matplotlib to read and manipulate the data.

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

Step 2: Load the dataset Load the iPhone sales dataset into a Pandas DataFrame.

diab_df = pd.read_csv(Iphone_data.csv)

Step 3: Data Analysis Perform exploratory data analysis on the dataset, including:

Head and Tail: View the first and last few rows of the dataset.

Shape: Check the dimensions of the dataset.

Dtypes: Verify the data types of each column.

Describe: Generate summary statistics for the dataset.

Step 4: Data Visualization Create visualizations to understand the distribution of variables and relationships between them, including:

Bar chart: Visualize the distribution of the "Outcome" variable.

Boxplots: Compare the distribution of each independent variable for patients with and without diabetes.

Pairplot: Visualize the relationships between all numerical variables in the dataset.

Heatmap: Represent the correlation matrix of the dataset.

Step 5: Modeling Building Build a machine learning model to predict iPhone sales based on the analyzed data.