Visualization Techniques, Exploratory Data Analysis(EDA), Histogram.

1. Data Science
Exploratory Data Analysis (EDA)
(Histogram)
Part-I

2. Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is the process of examining and
visualizing data to understand its main features, uncover patterns, and
identify relationships between variables.
The main goal of Exploratory Data Analysis (EDA) is to gain insights into
the data, understand its underlying structure, and identify patterns, trends,
and anomalies. It helps in formulating hypotheses, guiding further analysis,
and making informed decisions about data preprocessing and modeling
strategies.
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3. Aim (Importance) of EDA
Data Understanding: EDA helps in getting familiar with the data,
including its structure, distributions, and characteristics. This
understanding is essential for determining the appropriate analytical
approach and interpreting the results accurately.
Identifying Patterns and Relationships: EDA allows analysts to uncover
patterns, trends, and relationships between variables in the dataset. This
helps in generating hypotheses and guiding further analysis.
Detecting Anomalies and Outliers: EDA helps in identifying anomalies,
outliers, and errors in the data. Detecting and addressing these issues
early on can improve the quality and reliability of the analysis results.

4. Aim (Importance) of EDA
Guiding Feature Selection: In machine learning and predictive modeling
tasks, EDA helps in selecting relevant features and understanding their
importance in predicting the target variable.
Improving Data Quality: Through visualization and summary statistics,
EDA highlights data quality issues such as missing values,
inconsistencies, or data entry errors. Addressing these issues early on can
lead to more reliable analysis results.
Assessing Assumptions: By examining the data visually, analysts can
validate whether the data meets the assumptions required for specific
analyses.

5. Data Visualization Techniques in EDA
Histogram
distribution of a continuous
numerical data
Box Plots
distribution of a numerical
data
Scatter Plots
Relationship between two
continuous numerical
variables
Bar Plots
categorical or discrete data
Line Plots
visualize changes in one
continuous numerical
variable over time

6. Histogram
• A histogram is a graphical representation of the frequency distribution of
continuous series using rectangles. The x-axis of the graph represents the
class interval, and the y-axis shows the various frequencies corresponding
to different class intervals.
• A histogram is a two-dimensional diagram in which the width of the
rectangles shows the width of the class intervals, and the length of the
rectangles depicts the corresponding frequency. They provide insights
into the central tendency, spread, and shape of the data.
• The hist() function in Matplotlib is used to create histogram.

8. Interpreting the Histogram:
• A symmetric histogram has a prominent mound in the center and similar
tapering to the left and right.If the histogram is symmetric, it suggests a
relatively even distribution of ages.
• Skewed histograms indicate that ages are more concentrated towards one
end of the spectrum. A distribution said to be positively skewed when the
tail on the right side of the histogram is longer than the left side (very few
higher score).
• For example, a histogram skewed to the right (positive skew) suggests a
larger proportion of younger individuals.
• Outliers may represent unusual cases, such as very young or very old
individuals, or data entry errors.

9. Skewness

10. Thanks for Watching!
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Machine Learning videos.