How can you identify outliers and missing values in your data during exploratory data analysis?

Outliers are data in a dataset which are significantly different from others. They are unusual or abnormal datas when compared to others in the dataset . Missing value is the absence of data in specific field of a dataset

In my experience as a data analyst, spotting unusual data points, called outliers, and dealing with missing information are key in making sure our data analysis is reliable. Outliers are like the odd ones out that can mess up our understanding, while missing values are pieces of information that are simply not there. By using techniques to find these outliers and filling in the missing parts, I make sure my data analysis is trustworthy, giving me results that are accurate and make sense in a way that everyone can understand.

Outliers are data points significantly different from others in a dataset, potentially influencing statistical analysis. Missing values occur when data is absent for certain observations, requiring attention during analysis to ensure accurate and reliable results. Both outliers and missing values can impact the integrity of your data analysis and machine learning models.

There are different ways to identify outliers. Below are 2 ways 1. Via visualization- scatter plots. Outliers on scatter plot are far away from majority points 2. Via Statistical method - Interquartile range. Outliers are the points below or beyond range

From my experience in working with data, finding outliers is like spotting the unusual or standout values in your data set. Here's a simpler way to identify them: Visual Check: Picture your data on a graph, like a scatter plot. Look for points that seem really different or far away from the rest—they might be outliers. Numbers Game: Use some math tricks, like the Z-score or Interquartile Range (IQR). These methods help you figure out which data points are way different from the others. Tech Help: Sometimes, we get a little help from computer programs, using special tools that are good at finding these odd-one-out values. Using these, you can easily pick out the unusual bits in your data and make sure your analysis is on the right track.

To identify outliers during Exploratory Data Analysis (EDA), use visualizations like box plots or scatter plots. For missing data, employ techniques like heatmap visualization or summary statistics to pinpoint null values in your dataset. Addressing outliers and missing data is crucial for robust analysis and model performance.

In my data experience, spotting missing values is like finding gaps in your information. You can find identify missing values in Blank Spaces: Look through your data, and if you see empty spaces or cells without any information, those are likely missing values. Another way is to check summary statistics, like counts or averages. If some variables have fewer values than others, there might be missing data. Sometimes, missing values are labeled as "NA" or "NaN" in your dataset. Keep an eye out for these codes. You can as well use some licensed software to highlight the missing values in your dataset, and ensure a more complete and reliable analysis

To identify missing values in your data, you can use techniques such as: Summary Statistics: Check for count and percentage of missing values in each column. Visualization: Use tools like heatmaps to visualize missing values in the dataset. Programming: Utilize programming languages like Python or R to programmatically detect and count missing values in each column. Library Functions: In Python, libraries like Pandas provide functions like isnull() or info() to identify missing values in a DataFrame. By employing these methods, you can efficiently identify and address missing values in your dataset during Exploratory Data Analysis (EDA).

Dealing with outliers in your data is part of ensuring accurate analysis. First you need to Understand the Context: Investigate the outliers to grasp their significance. Sometimes, they might be valid and essential data points. Secondly you need to Visualize the Data: Replot your data without outliers to see if they significantly impact the overall pattern. if possible apply Statistical Methods Data Transformation: If needed, transform the data using techniques like log transformation to mitigate the impact of extreme values.

Addressing missing values in your data is crucial for a comprehensive analysis. To start, you have to Identify Missing Values: Locate where data is missing. Ask yourself this question Is there how missing values might affect your analysis? Consider the importance of the missing data in the context of your study. You can Input the missing values, using methods like mean, median, or mode imputation. Be cautious not to distort the overall patterns. In some cases, removing rows or columns with missing values might be necessary. provided this won't compromise the integrity of your analysis. And finally, Clearly document how you handled missing values in your analysis to maintain transparency.

Outliers can distort conclusions, while missing values can compromise the integrity of results. To address outliers, we can use statistical techniques such as the IQR (Interquartile Range) method, which establishes a threshold to identify points outside the normal range of distribution. Additionally, graphical visualizations, such as boxplots, can help highlight discrepancies. Regarding missing values, it is possible to employ data imputation, either by replacing null values with means, medians, or using more advanced techniques, such as imputation with predictive models.

Another advanced way of showing outliers in your dataset is using Isolation forest which is an unsupervised anomaly detection algorithm that can detect outliers in both univariate and multivariate data with incredible speed. It works by randomly partitioning the data into smaller subsets until each data point is isolated.