EDA(Exploratory Data Analysis) Implementation using Python
How to ensure you are ready to use machine learning algorithms in a project? How to choose the most suitable algorithms for your data set? How to define the feature variables that can potentially be used for machine learning?
Exploratory Data Analysis (EDA) helps to answer all these questions, ensuring the best outcomes for the project.
This project concerns the preliminary analysis and illustration of data related to House Prices of 1400 houses in New York.
What we have learnt so far?
- Initial Exploration
- Introduction to Seaborn
- Univariate Analysis
- Multi-variate Analysis
What are we going to do in this project :
- We will be using House Pricing dataset for Exploratory Data Analysis(EDA).
- You will Learn to use Matplotlib and Seaborn to Plot the one or more than one Features.
- We will be using visualization techniques on dataset and get some meaningful insights.
How will completing this project help you
- EDA helps in first level of analysis of data.
- We will analyse the data before making any intuition or assumptions, by plotting it.
- To sharpen your analytics skills before going into feature selection.
Also, EDA helps use to analyse the following types of features:
- Categorical Features
- Numerical Features
You can revise and refer to the EDA from the slides.
For the assignment we will be using the below packages :
- matplotlib
- seaborn
We will be working with the following features below:
- YearBuilt
- TotalBsmtSF
- GrLivArea
- SalePrice
If you want to have a detail description look at the data description given in data folder click here.
By completing this project you have an opportunity to win 300 points
Let's get started!
Note :- include the line plt.switch_backend('agg') in every build.py