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.

• Initial Exploration
• Introduction to Seaborn
• Univariate Analysis
• Multi-variate Analysis

• 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.

• 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