Exploratory data analysis is one of the most important stage when beginning any data project.
It involves examining data, identifying characteristics, identifying anomalies and possible errors. This is made easier by used of visualization tools.
It also helps in identifying the appropriate data analysis techniques that can be used for a particular project.
Benefits Of Exploratory Data Analysis Process
-It helps you prepare a particular dataset for analysis.
-It helps one identify errors before the analysis process begins.
-It prepares your dataset better for machine learning.
Major Steps in EDA
-Data collection-This is a very essential step. It involves finding the actual data from a source. Some of the data can be from a database or online platforms such as kaggle and github.
-Data cleaning-This involves making sure your data is well organized. This involves removing unwanted data. By identifying null values and by dropping them from our dataset. It also involves removal of duplicate records
-Statistical Analysis-This helps in creating a statistical summaries of the various measures in our dataset. Some of the measures include
mean, median, standard deviation. In this stage outliers are also identified which are values that deviate further from the normal measures in our dataset. This help further our understanding the patterns of our dataset
-Data Visualization-This is an important step since it helps uncover hidden trends and patterns. Some of the possible tools that can be used here are histograms, scatterplots, correlation matrices and heatmaps, pie charts and box plots. Time series help identify trends over time.
Correlation matrices and scatterplots help in identifying relationships between various variables of our dataset.
-Feature Engineering-This involves creating new features from existing data to improve the performance of machine learning models. New features make the understanding of the dataset easier and simpler.
-Hypothesis Testing-EDA also involves formulating hypotheses about the data and testing them. This can help in understanding the underlying patterns and relationships within the data.
Exploratory Data Analysis Tools
The most common tools for EDA are R, Python, and SAS. The following are some of the libraries used in Python.
Python Libraries
Pandas-is used for data analysis in python. It has various tools for handing data such as csv files. It provides functionalities such as grouping and ordering of data which make data manipulation and handling easier.
NumPy-Is used for array manipulation and performing other arithmetical operations.
Matplotlib-Is used for creating interactive visualization of ones dataset. Some of the features that it can provide are scatterplots and box plots.
Seaborn-provides a higher level interface for creating interactive graphics. It can be used to create heatmaps.
In conclusion, EDA is an essential step in any data analysis process. It transforms raw data into easily understandable form, paving the way for deeper analysis and effective decision-making.
Plotly-Is simply a graphing library used for creating interactive visualizations of data.
Scipy-Is used for scientific computing.
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