Visualization Techniques ,Exploratory Data Analysis(EDA), Histogram
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Exploratory Data Analysis (EDA) is crucial for understanding data features, identifying patterns, and detecting anomalies, guiding further analysis and model selection. Visual tools such as histograms, box plots, and scatter plots assist in assessing data quality and relationships between variables. EDA improves data analysis reliability by addressing issues like missing values and helping validate assumptions.
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. Please check the description box for the link to Machine Learning videos.
- 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! Please check the description box for the link to Machine Learning videos.