A SUMMARY OF EXPLATORY DATA ANALYSIS
Published: 2023-10-12 07:43:10

**<u>The Significance of EDA</u>**

The EDA is an approach to analyzing datasets and involves using various tools and techniques to examine and understand data. EDA helps analysts gain insights into the data, identify relationships, detect outliers and prepare the data for further analysis or modelling. Process of visually and statistically summarizing data to discover it's underlying, structure, distribution and relationships between variables.

**1.Data Collection-** gather relevant data to the study or problem. This could be collected through various resources databases, spreadsheets, APIs and web scraping.

**2.Data Cleaning-** this a step that involves removing and handling missing values, as the incorrect insights. Deal with the outliers that might skew the analysis and address duplicates and inconsistent values.

**3. Data Wrangling-**this is converting raw data into a usable form . Involves merging multiple data sources into a single dataset for analysis. 

**4.Statistics Summary-**calculates and visualize basic summary statistics like mean, median, standard deviation and quartiles for numerical variables.

**5.Data Visualization-**graphical representation of data to enhance understanding of the patterns, trends and insights within the data. They include: 
        -*<b>Bar Charts</b>* :To show frequency of categorical variables.
        -*<b>Scatter plots</b>*: To explore relationships between numerical variables.
        -*<b>Histograms</b>*: For visualizing the distribution of single variables.
        -*<b>Heatmaps</b>*: To visualize correlations between variables.
        -*<b>Box Plots</b>*: displays the distribution of a dataset, including the 
          median, quartiles and potential outliers.
**6.Correlation Analysis-**calculate correlation coefficients to understand the relationships between numerical variables e.g. Pearson, Spearman and then visualize correlations using matrices or heatmaps.

**7.Feature Engineering-**EDA can lead to the selection or creation of relevant features for predictive modeling. By exploring relationships between features and the target variable, data scientists can identify the most informative variables.

**8.Model Assumptions-** Understanding data distributions and relationships helps in selecting appropriate modeling techniques and verifying model assumptions. For instance, linear regression assumes a linear relationship between variables.

**<u>Methods of EDA</u>**
**1.Time Analysis-**EDA involves examining the temporal aspects of data, including trends, seasonality, and autocorrelation, through techniques like time series plots and autocorrelation functions.

**2.Univariate Analysis-** Focuses on a single variable at a time, summarizing its central tendencies, spread, and distribution using measures like mean, median, standard deviation, and visualizations such as histograms and box plots.

**3.Bivariate Analysis-**Explores the relationships between two variables. Scatter plots, correlation coefficients, and contingency tables are useful tools in this context.

**4.Multivariate Analysis-** Involves studying the relationships between multiple variables simultaneously. Techniques like principal component analysis (PCA) or clustering methods can be employed for dimensionality reduction and pattern discovery.