Exploratory Data Analysis
GitHub: - SEE CODE
What is Exploratory data analysis (EDA) ?
Exploratory data analysis (EDA) is an approach to analyzing and summarizing data sets in order to gain insights and understanding about the data. EDA involves using a variety of techniques, including data visualization, descriptive statistics, and data exploration, to identify patterns, relationships, and anomalies in the data. The goal of EDA is to develop an initial understanding of the data and to inform further statistical modeling or analysis. EDA is often used in fields such as data science, statistics, and business analytics.
How does it works?
Exploratory data analysis (EDA) is the process of analyzing and summarizing data sets in order to gain insights and understanding about the data. The goal of EDA is to understand the patterns, relationships, and anomalies in the data, and to identify any data quality issues or missing values. EDA is typically performed on a data set before any formal statistical modeling is done.
The process of EDA involves several steps, including: -
Data Collection: - Gathering data from various sources.
Data Cleaning: - Identifying and dealing with missing values, outliers, and inconsistencies in the data.
Data Visualization: - Using graphs, charts, and other visual tools to represent the data and identify patterns and trends.
Descriptive Statistics: - Calculating summary statistics such as mean, median, standard deviation, and correlation coefficients to describe the data.
Data Exploration: - Examining the relationships between variables and identifying any patterns or anomalies in the data.
Hypothesis Testing: - Formulating and testing hypotheses about the data based on the insights gained from the exploratory analysis.
The results of EDA can help researchers identify important variables, relationships, and patterns in the data, and inform the development of more complex statistical models. EDA can be performed using a variety of statistical software packages and programming languages, such as Python, R, and SAS.
Ways of Exploratory data analysis
Exploratory data analysis (EDA) is an important step in any data analysis project for several reasons: -
Identify Data Quality Issues: - EDA helps to identify any issues or anomalies in the data, such as missing values, outliers, or inconsistencies. Addressing these issues early on ensures that the data used for subsequent analysis is reliable and accurate.
Develop Initial Understanding: - EDA helps to develop an initial understanding of the data and its characteristics, such as the range, distribution, and patterns. This understanding is important in determining the appropriate statistical techniques and models to use in subsequent analysis.
Explore Relationships: - EDA helps to explore the relationships between variables and identify any patterns or trends that may exist in the data. This information can be used to develop hypotheses or to inform further analysis.
Communicate Findings: - EDA provides a way to communicate the findings of the analysis to relevant stakeholders in a clear and concise manner. This is particularly important in business analytics or data science projects where the insights gained from the analysis may be used to make important decisions.
Save Time and Resources: - EDA can help to save time and resources by identifying issues or anomalies early on in the analysis process. This allows for prompt corrective action and can prevent the need for time-consuming and costly re-analysis.
EDA is an important step in any data analysis project as it helps to ensure that the data used for subsequent analysis is reliable and accurate, and that the appropriate statistical techniques and models are used to extract insights from the data.
Ways of Exploratory data analysis
Python has several packages for exploratory data analysis (EDA), each with its own set of functions and capabilities. Here are some commonly used packages and their functions in EDA with Python: -
NumPy: - NumPy is a fundamental package for scientific computing in Python. It provides tools for working with arrays, mathematical functions, linear algebra, and random number generation. It is often used in EDA for performing basic statistical analysis and data manipulation.
Pandas: - Pandas is a powerful package for data manipulation and analysis. It provides tools for reading in and writing data, cleaning and preprocessing data, and performing basic and advanced statistical analysis. It is commonly used in EDA for summarizing data, detecting missing values and outliers, and creating visualizations.
Matplotlib: - Matplotlib is a plotting library in Python for creating static, animated, and interactive visualizations. It provides a wide range of plots, including scatterplots, line plots, bar plots, histograms, and heatmaps. It is commonly used in EDA for visualizing data distributions, relationships between variables, and trends over time.
Seaborn: - Seaborn is a visualization library in Python that provides a higher-level interface for creating statistical graphics. It provides a wide range of plots, including scatterplots, line plots, bar plots, histograms, heatmaps, and regression plots. It is commonly used in EDA for visualizing complex relationships between variables and exploring data distributions.
Plotly: - Plotly is a web-based visualization library in Python that provides interactive visualizations for the web. It provides a wide range of plots, including scatterplots, line plots, bar plots, histograms, heatmaps, and 3D plots. It is commonly used in EDA for creating interactive visualizations for exploring complex relationships between variables.
Scikit-learn: - Scikit-learn is a machine learning library in Python that provides tools for data preprocessing, feature selection, and predictive modeling. It is commonly used in EDA for performing advanced statistical analysis, including regression analysis, clustering, and dimensionality reduction.
These are just some of the packages commonly used in EDA with Python. Each package has its own set of functions and capabilities, and the choice of package depends on the specific requirements of the analysis.
In Our Project
Our project, which is available on GitHub, involves conducting an exploratory analysis of data using Python in Jupyter Notebook. The primary objective of this analysis is to gain a better understanding of the data. To achieve this, we are utilizing various libraries, including Pandas, Numpy, Seaborn, and Matplotlib.
Pandas is a popular library for data manipulation and analysis, which allows us to import, clean, and transform data. Numpy is a library for numerical computing, which provides efficient data structures and algorithms for handling arrays and matrices. Seaborn is a data visualization library that provides a high-level interface for creating informative and attractive statistical graphics. Matplotlib is a plotting library that allows us to create a wide range of visualizations, from simple bar charts to complex 3D plots.
By utilizing these libraries, we aim to gain insights into the data and identify any patterns or trends that may exist. This exploratory analysis will serve as a foundation for further analysis and modeling, as well as providing valuable insights for decision-making.
Objective of Scrapping
The primary aim of conducting an exploratory analysis is to gain a better understanding of the white wine quality data, which is contained in the white_wine_quality.csv file, as well as the shoppers data, which is available in the shoppers.csv file. By performing this analysis, we hope to identify any notable characteristics or trends present in the data, which will provide a foundation for more detailed analysis and modeling.
Before conducting any advanced analysis, it is essential to undertake several fundamental steps. These steps include importing the data into our Python environment, ensuring that it is properly formatted and structured, and checking for any missing or erroneous values. We will also explore basic statistical properties of the data, such as its distribution, central tendency, and variability. This process will provide us with a better understanding of the data's characteristics and enable us to identify any issues that may require attention before proceeding further.
By undertaking these preliminary steps, we will establish a strong foundation for future analyses and ensure that our results are reliable and accurate. This exploratory analysis will provide valuable insights into the data, which will guide our subsequent analyses and aid in making informed decisions.
